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Socio-Economic Impact Analysis of Smart Metering of Natural Gas End-ConsumersFinal ReportDecember 13 2017

Elering AS Socio-Economic Impact Analysis of Smart Metering of Natural Gas End-Consumers 1 p.

Content

Executive Summary................................................................................................................21 Introduction...................................................................................................................62 Project’s Background.....................................................................................................7

2.1 Smart metering systems..........................................................................................72.1.1 Smart metering system in Estonia........................................................................72.1.2 Legal framework...................................................................................................8

2.2 Overview of Estonia’s gas market............................................................................93 Project methodology....................................................................................................11

3.1 Overall methodology..............................................................................................113.2 Baseline data collection.........................................................................................113.3 Financial modelling and socio-economic analysis...................................................11

3.3.1 Analysis of alternatives......................................................................................123.3.2 Socio-economic impact assessment...................................................................12

3.4 Risk analysis...........................................................................................................143.5 Sensitivity analysis.................................................................................................15

4 Socio-economic impact of smart-metering for natural gas end-customers..................174.1 Analysis of alternatives..........................................................................................174.2 Main assumptions..................................................................................................214.3 Quantitative socioeconomic analysis.....................................................................24

4.3.1 Financial modelling results.................................................................................244.3.2 Financial modelling result assessment...............................................................284.3.3 Social analysis....................................................................................................294.3.4 Social analysis results.........................................................................................29

4.4 Socio-economic impact analysis results.................................................................304.4.1 Impact on economy from tariff change...............................................................304.4.2 Impact on economy from project investment.....................................................34

4.5 Qualitative socioeconomic analysis........................................................................374.6 Social benefits from smart gas metering deployment............................................384.7 The most suitable scenario selection.....................................................................39

5 Risk analysis................................................................................................................426 Sensitivity analysis......................................................................................................49

6.1 Analysis on the effect of different variables changes towards socio-economic impact assessment’s results..............................................................................................496.2 Analysis on the effect of reinvestment into remote or smart gas meters under AS-IS situation.............................................................................................................................546.3 Analysis on the effect on investments and operational expenses of introducing different consumption benchmarks for household customers under AS-IS situation..........566.4 Optimal scenarios derived from sensitivity analysis...............................................586.5 Smart metering deployment for all customers.......................................................58

7 Recommendations.......................................................................................................597.1 Smart gas metering deployment based on the most suitable alternative..............597.2 Recommendations on reducing the negative impacts and increasing the effect of positive impacts.................................................................................................................597.3 Other relevant recommendations...........................................................................60


Executive SummaryThe main objective of this report is to present the findings of the analysis on socio-economic impact of the transition to smart metering of natural gas for Estonian society. The analysis of different alternatives of smart gas metering deployment takes into account various costumer categories, possible synergies via data communication infrastructure of other utilities as well as different technical requirements for data communication systems and different volumes, based on natural gas consumption level taking into account necessary investments for gas and electricity distribution system operators (hereafter – DSOs) and Elering. The socio-economic analysis of smart gas metering deployment takes into account that almost 85% of Estonian gas consumption is already measured with smart meters since large customers are equipped with gas metering devices having necessary smart meter features. However, the total percentage of meters which have smart meter functionalities are only 3.6%, thus smart gas metering deployment project will mostly affect other 96.7% natural gas customers, using on average considerably lower amount of natural gas. Therefore, the objective of this socio-economic analysis is to show whether it is beneficial for small customers to have smart gas meter or not.To fulfil the aforementioned task, the methodology of this study relies on the following steps:

1. Qualitative review of different elements’ impact on smart metering system development (incl. analysis of legal implications) is conducted taking into account the background information on the situation of gas metering in Estonia and other countries, which led to come up with different smart gas metering system implementation alternatives for further analysis and potential implementation.

2. Taking into account the findings of the qualitative review, financial model is developed in order to estimate required infrastructure, investments and operational costs for each of the defined alternative. Financial modelling is conducted by using EY developed modelling tool for cost-benefit analyses, which, for the study purposes is adjusted to the case of Estonian gas sector, its metering system and pre-defined alternatives. It assumes that investments related to new gas metering system (remote/smart gas meters, IT systems, other equipment) are borne by gas DSOs, while Elering, which already has enough capacity for their Datahub, does not have any investments related to the smart gas metering deployment. In the financial model, DSOs investments to smart gas metering system and costs for operations of gas metering system are assumed to be covered by the variable part of distribution tariff. Thus, the emphasis is on the amount of investment necessary to install the smart gas meters which might have more or less effect on natural gas end-consumers.

3. Socio-economic modelling is conducted taking into consideration the financial analysis and it estimates direct and indirect effects of each of the defined alternatives. The socio-economic modelling relies on the principles of a widely acknowledged Leontief model which enables to use Estonian economy’s input-output tables to derive multipliers for indirect and / or induced impacts that stem from changes in employment (induced effects are not evaluated due to lack of Type II multipliers for Estonian economy). AS-IS situation is the reference point in order to evaluate the effects of smart gas metering deployment project. Direct impact is estimated from the perspective of customers using natural gas while indirect impact is assessed with regard to the indirectly related companies in Estonian market.

4. Risk analysis for possible alternatives is conducted in order to indicate and evaluate possible risks related with smart gas metering deployment project and identify possible measures of mitigation and avoidance.

5. Sensitivity analysis is conducted for several independent variables in order to indicate the most sensitive variables for analysis results and support recommendations.

All aforementioned tasks are conducted taking into account the difference between AS-IS situation and analyzed alternatives, thus, the results of two analyzed project alternatives are based on their difference from AS-IS situation. All alternatives in the analysis, including AS-IS situation, consider current changes in Natural Gas Act which indicates the smart gas metering deployment for customers using more than 750 m3 of natural gas per year. However, differences of the alternatives are based on the different data communication technologies and IT system responsibilities: in AS-IS and Alternative I


it is assumed that gas metering infrastructure would be separate from electricity metering system, while in Alternative II, possible synergies between electricity and gas metering systems are considered. The main difference between AS-IS situation and project alternatives is that for Alternatives I and II customers using less than 750 m3 of natural gas per year are equipped with meters which only have a remote metering functionality. In the table below a short description of each alternative is presented.Table 1 Project’s alternatives

Project’s alternatives

Parameter AS-IS Alternative I Alternative IIProject calculation period Project calculation period for all alternatives is 2018 – 2030.

Project implementation period

Project implementation period is based on the deployment of smart/remote gas meters which is assumed to be 5 years in Alternatives I and II, thus for all alternatives the period of 2018 – 2022 is accounted as project implementation period.

Deployment volume

► For all customers who use more than 750 m3

per year, meter is changed into smart gas meter

► For others who use less than 750 m3 per year standard gas meter remains

► For all customers who use more than 750 m3 per year, meter is changed into smart gas meter

► For others who use less than 750 m3 per year, remote gas meters is installed

► For all customers who use more than 750 m3 per year, meter is changed into smart gas meter

► For others who use less than 750 m3 per year, remote gas meter is installed

Duration of deployment

2 years for smart gas meters deployment (2018-2020)

2 years for smart gas meters deployment (2018-2020)5 years for remote gas meters deployment (2018-2022)

2 years for smart gas meters deployment (2018-2020)5 years for remote gas meters deployment (2018-2022)

Intensity of deployment of smart meters

50% of metering points per year of deployment



Intensity of deployment of remote meters

No remote meters are installed for this alternative

10%

20%

30%

30%

10%

10%

20%

30%

30%

10%

Communication technology

GPRS – 70%LPWAN – 30%

GPRS – 70%LPWAN – 30%

Electricity communication technologies are used for data collectionFor multi-metering – wireless M-bus (100%)

Responsibility of IT systems

Data collection system – gas DSOsMeter data management – gas DSOs

Data collection system – gas DSOsMeter data management – gas DSOs

Data collection system – electricity DSOsMeter data management – gas DSOs

Analysis has shown that smart gas metering deployment for all customers using more than 750 m 3 of natural gas per year and remote gas metering deployment for customers using less than 750 m3 of natural gas per year might result in a negative impact towards natural gas customers due to the higher tariff caused by additional costs of meters, their installation as well as maintenance. However, the project’s investment perspective related to the meters and other equipment installation shows a positive effect for possible smart metering installation companies (gas DSOs and subcontractors) which might have a positive impact on Estonian economy as a whole. These effects might happen during different periods – the positive effect from project investment occurs during the smart metering deployment (2018-2022), while negative effect from tariff change remains during all calculation period (2018-2030). However, calculations of project investment impact are based on generalized assumptions, which might not reflect the real situation about different companies included in smart metering deployment, thus, when assessing the impact of smart meters roll-out, more emphasis should be put on the impact of tariff change. Therefore, it is necessary to highlight that smart gas metering also will have the benefits that are not quantifiable but are very important for Estonian society: such as


more accurate billing, more accessible information, more flexible supplier change process as well as possibility to get more advanced analytical services which would help to monitor their energy consumption.From the analysis of different aspects, Alternative II has shown the least negative effect towards Estonian society: financial analysis indicated smaller incremental financial costs, which resulted in less negative financial net present value (-4.3 m EUR); social analysis indicator – economic net present value also shows less negative result of Alternative II (-3.6 m EUR) than Alternative I (-6.2 m EUR). Socio-economic impact assessment results taking into account tariff change and project investment also shows better results for Alternative II: total impact on GDP from tariff change is (-0.19 m EUR) and total impact on GDP from project investment is 2.8 m EUR. In the table below the main indicators of the analysis are presented.Table 2 Project’s alternatives results


Result indicator Alternative I Alternative IIFinancial analysis

Financial net present value -7,104,337 -4,296,431Social analysis

Economic net present value -6,172,008 -3,610,659Socio-economic analysis (macroeconomic impact):

Impact of tariff changeTotal impact on GDP (direct and indirect) -273,926 -185,432Total impact on employment (direct and indirect) -51 -34Total impact on household disposable income (direct and indirect) -1,583,919 -1,072,261

Total impact on government taxes and revenues (direct and indirect) 242,697 164,295

Impact of project investmentTotal impact on GDP (direct and indirect) 2,087,510 2,777,629Total impact on employment (direct and indirect) 69 93Total impact on government taxes and revenues (direct and indirect) 313,445 417,411

Risk analysis has been prepared to evaluate the impact of various external and internal factors which could affect successful smart metering deployment. In risk analysis it has been assumed that the identified risks for all with-project alternatives are the same; however, for Alternative II some additional risks are identified related to key stakeholders’ involvement. Even though most risks can be associated with all project alternatives, for some risks the impact and probability can still slightly differ between the alternatives.During risk analysis 34 risks were identified: 17.6% of the risks were classified as “low”, 67.6% as “medium” and 14.7% as “high”. The main risks associated with the project implementation are the following:

► Lack of engagement from key stakeholders (i.e. regulating authorities, government, gas DSOs) and other interested parties which results in delays of project implementation.

► Electricity DSOs’ objection to use their electricity smart meters for sending gas data.► Significant number of repeated visits to meter points in order to install smart gas meters.► Changes in consumer preferences, which means that consumers start to prefer alternative

energy sources more than natural gas.► Changes in consumer gas consumption preferences due to higher gas distribution tariff.

In order to mitigate the above mentioned risks it is required to: (i) maintain contact with all key stakeholders and interested parties and try to involve them in project development from its initiation and first discussions; (ii) develop an action plan in case of project technical failure; (iii) constantly make


an in-depth gas market analysis in order to understand consumer preferences of new services and overall gas consumption.The main risks that can differ between the alternatives are risks related with lack of engagement and acceptance of the project development by key stakeholders and unexpected technical limitations between gas and electricity metering systems. In Alternative II it is assumed that gas DSOs will be using electricity meters which will collect gas consumption data and will send it to data collection system of electricity DSO, while data management will be done by a separate gas meter data management system. Total reliability on electricity metering infrastructure might cause project delays due to more complicated project management process between electricity and gas DSOs as well as more complex technical requirements. Sensitivity analysis was performed for the chosen optimal alternative using such independent variables:

► Meter prices;► Other equipment (gas/electricity modules; data loggers) prices;► Installation prices of the meters and other equipment;► Operational costs of IT systems;► Data communication costs;► Distribution of communication technologies used (GPRS and LPWAN);► IT investment redistribution if other utilities (water supply and heating) are involved;► Distribution of installation costs between DSOs and suppliers;► Natural gas consumption (only from the perspective of distribution tariff);► Different consumption benchmarks for household customers using specific amount of natural

gas per year.

From the results of sensitivity analysis it was found that the main variables which mostly influence the results of the financial modelling (FNPV) and socio-economic impact assessment (impact on GDP) are: meter prices, natural gas consumption benchmark (i.e., the threshold above which smart meters are installed) and installation prices of meters and other equipment. However, it is not possible to change separate variables in a way that causes ENPV value to be neutral (0). While it might be possible to get neither negative nor positive ENPV (when financial costs of the project are equal to economic benefits derived from project implementation) using certain favorable combination of meter and equipment prices, installation costs and different consumption benchmark for household customers.After conduction of financial modelling, socio-economic impact assessment, risk and sensitivity analyses of smart/remote gas metering deployment in Estonia, it could be concluded that smart gas metering deployment for customers using more than 750 m3 of natural gas per year and remote gas metering deployment for customers using less than 750 m3 of natural gas per year might be associated with certain costs to the gas sector and society, but it also brings benefits to the customers in quantifiable and qualitative form. While comparing the two project alternatives it is seen that Alternative II results in less negative result towards society than Alternative I. Thus, in addition to this, the social analysis and qualitative analysis of smart metering as such was performed which shows that smart meters will bring more qualitative benefits for the society such as more accurate billing and more accessible information. Further investment into analytics and network management will also bring additional benefits for project implementer which will allow to get full scope of possible services from the consumption data gathered by smart gas metering systems and also will improve the performance of the network.


1 IntroductionThe European Union has set ambitious energy policy objectives that need to be achieved by 2020. Smart metering is directly linked to the visions of the Energy Union for Europe and is indispensable in achieving the European Union’s goals of a secure, competitive and sustainable energy supply system. According to European Commission, it is expected that by 2020 approximately 40%1 of consumers in Europe will have a smart meter for gas. To date smart metering is used only for 3.6% of the gas consumption points in Estonia. In general, utilization time of a gas meter is approximately 12 years, thus by 2024 majority of gas meters will need to be changed. It is necessary to assess whether it is feasible to implement smart metering system for small-volume gas consumers and whether metering system will create significant socio-economic benefits that would outweigh the investment costs.Two main objectives of the project are as follows:

► To determine the socio-economic impact of transition from manual to smart metering and reading system of natural gas to different end-consumer categories;

► To assess the optimal technical solution for gas metering installation.In order to achieve these objectives of the project, several different alternatives have been investigated and socio-economic impact analysis model has been built. Financial modelling and socio-economic impact assessment is made to the maximum extent in a quantitative manner from financial perspective of the smart/remote gas metering deployment (calculations on necessary infrastructure, investments, operational expenses which might drive the changes in gas distribution tariff) and from socio-economic analysis perspective of different changes in distribution tariff caused by smart/remote gas metering deployment (changes in employment, labor productivity, natural gas TSO and DSO revenues and expenses, etc.). Analysis also includes a qualitative assessment of factors that could not be measured according to their monetary values, as for example, more accurate billing and more accessible information. For different consumer categories (large consumers, other commercial consumers and households) and market participants (Elering AS, DSOs, public sector etc.), direct and indirect effect from implementation of smart/remote metering has been analyzed in a socio-economic impact assessment. The socio-economic impact assessment has been made for a time period from 2018 to 2030 while project implementation period is from 2018 to 2022 (it is assumed that smart/remote metering deployment will start in 2018 and the full scope of meters will be installed at the end of 2022). In the following chapters the project’s background, overall methodology and the results from socio-economic impact assessment, risk and sensitivity analysis are described.

1 Retrieved from: https://ec.europa.eu/energy/en/topics/markets-and-consumers/smart-grids-and-meters

https://ec.europa.eu/energy/en/topics/markets-and-consumers/smart-grids-and-meters


2 Project’s Background

2.1 Smart metering systemsInternet of Things (IoT) technologies continues to grow in 2017, with the utility sector being responsible for the highest adoption of IoT applications. IoT creates opportunities for more direct integration of the physical world into computer-based systems resulting in improved efficiency, accuracy and economic benefits in addition to reduced human intervention. Smart meters are becoming the top IoT device among utilities, allowing easy monitoring and management of the energy usage on a daily basis. Smart grids are energy networks that can automatically monitor energy flows and that adjusts to changes in energy supply and demand accordingly. When coupled with smart metering systems, smart grids reach consumers and suppliers by providing information on real-time consumption. With smart meters, consumers can adapt – in time and volume their energy usage to different energy prices throughout the day, saving money on their energy bills by consuming more energy in lower price periods.2

A smart metering infrastructure consists of three elements: (1) metering device and associated devices; (2) communication and data processing infrastructure that is needed to connect the metering device to the customer and (3) in-home energy use display (optional). A smart gas meter is a digital electronic meter that records consumption of gas in intervals of an hour or less and communicates that information at least daily back to the utility company for monitoring and billing3. Smart meters provide more information than conventional meters. These meters have a communication system that is connected with the network company/meter data collector and these meters provide the operator with automated, up-to-date information on the amounts of electricity/gas used. Smart metering systems allow the end consumers to be energy efficient users by providing them with accurate and more frequent information on their own consumption. In EU automated meter reading (AMR) for gas metering systems is also introduced which allows customers and operators avoid manual readings. However, remote meters can only read remotely gas consumption data and send it to the operator but they are not able to provide the up-to-date information for the customer, which is why they are not considered to be the same as smart meters and they cannot produce the same benefits as smart meters, especially ability for customers to coordinate their energy consumption.Benefits of smart metering are recognized internationally and there are a number of key EU legislative instruments promoting smart metering to ensure that customers are properly informed of the actual energy consumption and costs to enable them to regulate their energy consumption. Smart metering systems can also provide benefits to suppliers through two-way communication and automated remote meter readings. Benefits for suppliers can rise from reduced operating costs through savings in manual meter readings and from theft protection. Benefits could also span from improved processes, better scheduling and from consumer engagement opportunities. Additionally, nonmonetary benefits, such as greater transparency for customers and the potential to reduce carbon emissions by influencing consumers to improve energy efficiency, can occur.4

However, there are still many concerns regarding data protection in complex smart grid systems, especially protection of personal details of customers and information about their patterns of energy use. More about EU regulations regarding data protection is described in sub-section 2.2.1.3.2.1.1 Smart metering system in EstoniaSmart meters play an important role in monitoring performance, energy usage, characteristics of the load on the grid, and smart metering systems have a potential to empower consumers to take a more active part in the energy system.5

2 Retrieved from: http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32012H0148&from=EN 3 Retrieved from: http://www.wpsdlocal6.com/story/35419369/smart-gas-metering-market-global-industry-analysis-size-share-growth-trends-and-forecast-2016-2025-the-insight-partners 4 Retrieved from: https://www.metering.com/smart-gas-metering-europe-an-untapped-opportunity/ 5 Retrieved from: https://www.parent-project.eu/wp-content/uploads/D1.4_Smart-grid-roll-out-and-access-to-metering-data.pdf

https://www.parent-project.eu/wp-content/uploads/D1.4_Smart-grid-roll-out-and-access-to-metering-data.pdf

https://www.metering.com/smart-gas-metering-europe-an-untapped-opportunity/

http://www.wpsdlocal6.com/story/35419369/smart-gas-metering-market-global-industry-analysis-size-share-growth-trends-and-forecast-2016-2025-the-insight-partners

http://www.wpsdlocal6.com/story/35419369/smart-gas-metering-market-global-industry-analysis-size-share-growth-trends-and-forecast-2016-2025-the-insight-partners

http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32012H0148&from=EN


The Grid code and Estonia’s legislation states that until 2017 all electricity meters should have been gradually replaced with smart meters. Estonia‘s largest electricity distribution network operator, Elektrilevi AS, has already installed smart metering devices for almost all electricity consumers. Regarding gas market, gas smart metering devices have been installed only for large natural gas consumers6; however it is intended to install metering devices with remote reading and other smart metering capabilities for most of gas customers.For electricity, two communication technologies for the smart metering operations through the wide area network are used: (1) power line communications (PLC) - 90% and (2) GPRS – 10%7. The difference when using AMR systems compared to regular consumption metering comes from the cost of data communication module, which also allows customers to avoid manual meter data reading, however does not have all smart metering functionalities. In addition to purchasing the module, a data connection between the service provider and customer must be established. Commonly used solutions include using PLC, GSM/GPRS, LPWAN8, etc.

2.1.2 Legal framework

2.1.2.1 EU legislation regarding smart metersThe European Union (EU) has set ambitious energy policy objectives that need to be achieved by 2020. These objectives are related to reduction of carbon footprint, increase of energy efficiency, high penetration of renewable energy sources, etc. Smart metering is directly linked to the visions of the Energy Union and is indispensable in achieving the European Union’s goals of a secure, competitive and sustainable energy supply system. Its successful deployment will empower consumers to measure and thus manage consumption patterns and market-based price signals, assist in optimizing networks and set the base for further services addressed to consumers.Smart metering technology has been introduced and supported in Europe mainly by the Third Energy Package, which focuses on the use of intelligent metering systems in electricity and gas to the benefit of the consumer while taking into account the high level of consumer protection. It is also complemented by the Energy Efficiency Directive and directives regarding security of supply. At the end of 2016, the European Commission presented “Winter Package” that is designed to strengthen and standardize the EU energy markets. One of the key goals for “Winter Package” is to promote energy efficiency, cleanliness and performance. EU’s “Winter Package” can also be seen as a boost to intelligent technology investments9.In further sub-chapters main EU regulations regarding smart metering in electricity and gas sectors are described. It is important to emphasize that the electricity sector regulations align with the gas sector regulations, hence there is a possibility to derive synergies between the sectors in terms of regulations.

2.1.2.1.1 EU regulations regarding smart meters in electricity sectorAccording to EC, it is expected that by 2020 almost 72% of consumers in Europe will have a smart meter for electricity.

► According to EU Directive 2009/72/EC of the European Parliament and of the Council of 13 July 2009 concerning common rules for the internal market in electricity10, „Member States shall ensure the implementation of intelligent metering systems that shall assist the active participation of consumers in the electricity supply market. The implementation of those metering systems may be subject to an economic assessment of all the long-term costs and benefits to the market and the individual consumer or which form of intelligent metering is economically reasonable and cost-effective and which timeframe is feasible for their distribution“.

6 Information provided by Elering7 Retrieved from: https://ec.europa.eu/eurostat/cros/system/files/essnet_pilot_smart_meters_p.pdf 8 LPWAN is a type of wireless telecommunication network that provides real-time consumption data and a detailed usage overview. With decreased power requirements, longer range and a lower cost than a mobile network, LPWAN is targeted to enable a wide range of IoT applications. Retrieved from: http://noranet.ee/en/ 9 Linklaters, European Commission presents Energy Winter Package 201610 Retrieved from: http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32009L0072&from=EN

http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32009L0072&from=EN

http://noranet.ee/en/

https://ec.europa.eu/eurostat/cros/system/files/essnet_pilot_smart_meters_p.pdf


► Directive 2005/89/EC of the European Parliament and of the Council of 18 January 2006 concerning measures to safeguard security of electricity supply and infrastructure investment11 specifies that member states may encourage „the adoption of real-time demand management technologies, such as advanced metering systems“ to maintain balance between electricity demand and supply.

2.1.2.1.2 EU regulations regarding smart meters in gas sectorAccording to EC, it is expected that by 2020 approximately 40%12 of consumers in Europe will have a smart meter for gas.

► According to EU Directive 2009/73/EC of the European Parliament and of the Council of 13 July 2009 concerning common rules for the internal market in natural gas13 „Member States shall ensure the implementation of intelligent metering systems that shall assist the active participation of consumers in the gas supply market . The implementation of those metering systems may be subject to an economic assessment of all the long-term costs and benefits to the market and the individual consumer or which form of intelligent metering is economically reasonable and cost-effective and which timeframe is feasible for their distribution“.

► Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 on energy efficiency establishes a set of binding measures to help the EU reach its 20% energy efficiency target by 2020. Under the Directive, all EU countries are required to use energy more efficiently at all stages of the energy chain, from production to final consumption.

► On 30 November 2016, the Commission proposed an update to the Energy Efficiency Directive, including a new 30% energy efficiency target for 2030, and made measures to update the Directive to make sure the new target is met14.

2.1.2.1.3 EU regulations regarding data protectionTo protect consumers' personal data, when it comes to smart meters and smart grids, the European Commission recommends various data protection and privacy provisions. EU addressed the area of data protection in 1995 with the release of the data protection directive 95/46/EC and provided useful and stable concepts and principles to form legal framework in Member States. However, with the fragmented application of the data protection directive 95/46/EC and combined with new challenges posed by technological developments in computing, internet, mobile communications, social media, etc., EU adopted new General Data Protection Regulation (GDPR) to ensure that regulatory framework applied in Member States is consistent and efficient. Consumer personal data is protected by the new directive on data protection (Data Protection Directive)15 and a corresponding regulation (Data Protection Regulation)16. These regulations determine rules on who can access personal data and under what circumstances. The objective of this new set of rules is to give citizens back control over of their personal data and to simplify the regulatory environment for businesses.The Commission has also implemented a guidance on data protection and privacy for data controllers and investors in smart grids. 2014/724/EU Commission recommendations of 10 October 2014 on Data Protection Impact Assessment Template for Smart Grid and Smart Metering systems provide guidance to safeguard personal data and guarantee data security when data are processed in smart metering systems and smart grids. Additionally, these Commission’s recommendations support data controllers in the smart grid sector to comply with a future legal obligations under the Data Protection Regulation.

11 Retrieved from: http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32005L0089&from=EN 12 Retrieved from: https://ec.europa.eu/energy/en/topics/markets-and-consumers/smart-grids-and-meters 13 Retrieved from: http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32009L0073&from=EN 14 Retrieved from: https://ec.europa.eu/energy/en/topics/energy-efficiency/energy-efficiency-directive 15 Directive (EU) 2016/680 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, and on the free movement of such data, and repealing Council Framework Decision 2008/977/JHA.16 Directive (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)

https://ec.europa.eu/energy/en/topics/energy-efficiency/energy-efficiency-directive


https://ec.europa.eu/energy/en/topics/markets-and-consumers/smart-grids-and-meters



2.1.2.2 Estonian legislation regarding gas smart metering systemAccording to Estonian Natural Gas act17, a network operator shall ensure the metering of all quantities of gas consumed from the network, collection and processing of meter readings and shall keep relevant records. If the existing metering system of a customer does not comply with the technical requirements established, the network operator shall replace the metering system at its own expense, unless otherwise stipulated in the contract.According to the amendments of the Estonian Natural Gas act18, network operator should guarantee that all measurement points, through which gas consumption is at least 750 m3/year must be supplied with measurement system that allows to take into account the gas temperature in the measurement system and allows the remote reading functionality of measurement data. In case gas is consumed at pressure above 20 mbar, the measurement system should take into account the pressure, temperature and allow the remote reading functionality of measurement data. According to information provided by Elering, mostly at every point where gas is consumed above 20 mbar, the volume is also more than 750 m3/year. Nevertheless, all these metering points have recently been converted into smart gas meters.

2.2 Overview of Estonia’s gas marketIn the graph below historical natural gas consumption from the year of 2012 and forecasted gas consumption till 2026 is presented, which indicates a downward sloping trend of natural gas consumption in Estonia. The decreasing natural gas consumption is a result of shifting towards more environmentally friendly energy sources as well as more efficient energy consumption.

Figure 1 Natural gas consumption in Estonia (2012-2026, GWh) (Source: Information provided by Elering).

As it is seen from the graph above for the last years natural gas consumption in Estonia has been fluctuating around 5 thousand GWh. The largest natural gas consumers has been energy companies (39%) and industrial companies (25%). The gas consumption distribution in 2015 can be seen in Graph 2 below19.

17 Estonian natural gas act, retrieved from: https://www.riigiteataja.ee/en/eli/508112013011/consolide 18 Estonian natural gas act, retrieved from: https://www.riigiteataja.ee/akt/130062017002 19 Retrieved from: https://www.stat.ee/database

https://www.stat.ee/database

https://www.riigiteataja.ee/akt/130062017002

https://www.riigiteataja.ee/en/eli/508112013011/consolide


25%

39%2%

1%

20%

13%Industrial sector

Energy sector

Agriculture and fisheries

Transport sector

The public and private sector

Households

Figure 2 Natural gas consumption distribution in 2015 (Source: Estonian Statistics Database, “Consumption of fuels by branch of economy and type of fuel”).

There are several factors that have influenced the overall consumption. These factors are as follows20: ► The changed structure of consumption:

o Decline in industrial consumption; o Diminished use of gas in electricity and heat production due to implementation of new

biomass cogeneration stations;o Price of wood chips decreased compared to the price of natural gas; o The future reduction in electricity market prices in the Baltic zone because of Nord Pool

and LitPol link, which influence the use of natural gas for electricity generation; o Extreme temperature fluctuations. Most consumers use gas for generating heat, and

changes in the ambient temperature will impact changes in demand;o An increase in energy efficiency (both for industrial consumers and households).

► Inadequate infrastructure:o In order to improve Estonia‘s gas market, several industry development projects are

required. The electricity and gas transmission system operator in Estonia is Elering AS. Elering AS owns the Estonia’s gas transmission network of 885 km, including 36 gas distribution stations and 3 gas metering stations21.The distribution market leader in Estonia is AS Gaasivõrgud, which uses 1,465 km long distribution network, owned by AS Eesti Gaas, under the commercial lease contract. Besides AS Gaasivõrgud there are 24 other natural gas distribution enterprises, which possess at total of 650 km of distribution network.According to information provided by Elering, the natural gas consumption is measured by approximately 55,321 and-user natural gas consumption metering points, out of which around 50,317 natural gas measuring points are used for household consumers.Almost 85% of Estonian gas consumption is already read remotely since large customers are equipped with gas metering devices having all necessary smart meter features. However, the total percentage of meters which have smart meter functionalities is only 3.6%, thus smart / remote gas metering deployment project will mostly affect other 96.4% natural gas customers, using considerably lower amount of natural gas. Therefore, it is important to analyze whether it is beneficial for small customers to have smart/remote gas meter or not.

20 Retrieved from: https://prelive.elering.ee/sites/default/files/attachments/Gaasi%C3%BClekandev%C3%B5rgu-arengukava-2016-2025.pdf 21 Retrieved from: https://prelive.elering.ee/sites/default/files/attachments/Gaasi%C3%BClekandev%C3%B5rgu-arengukava-2016-2025.pdf

https://prelive.elering.ee/sites/default/files/attachments/Gaasi%C3%BClekandev%C3%B5rgu-arengukava-2016-2025.pdf





3 Project methodology

3.1 Overall methodologyIn order to determine the socio-economic impact of the transition from manual to smart/remote metering and reading of natural gas to different end-consumer categories and to assess the optimal technical solution for data communication, the following steps have been executed:

1. Baseline data collection and qualitative review of impact of different elements on smart metering system’s possible developments in order to come up with smart/remote gas metering system implementation set-ups for further analysis and potential implementation.

2. Socio-economic impact assessment of smart / remote metering for natural gas end-consumers: 2.1. Quantitative socio-economic analysis:

o Analysis and definition of alternatives which will be analyzed and compared with each other;

o Financial modelling based on cost-benefit analyses and guidelines for smart metering deployment;

o Socio-economic modelling using Leontief’s input-output model methodology: evaluation of foreign and local investments impact on different customer groups via distribution tariff change (direct and indirect impacts are evaluated) and evaluation of local project investments as a local project (evaluation of impact from project revenues of local companies working on smart/remote metering deployment – direct and indirect impact is evaluated).

2.2. Qualitative socio-economic analysis:o Qualitative assessment of smart gas metering benefits for customers, project

implementers and energy sector.3. Risk analysis has been carried out in order to identify the project related risks and define

mitigation measures. 4. Sensitivity analysis is carried out for several main variables in order to better support the

recommendations.

Figure 3 Project’s methodology

3.2 Baseline data collectionIn order to determine the socio-economic impact on the transition from manual to remote / smart metering of natural gas to different end-consumer categories, it is necessary to collect all input data required for the socio-economic benefit analysis. It includes data regarding natural gas consumption, prices (different tariffs for customer groups) of natural gas in Estonia, data on current smart and non-smart gas metering infrastructure and necessary information on initial investments of smart gas metering deployment and operational expenses for related companies. The required information has been gathered from the following sources:

► Elering AS; ► Natural gas DSO’s (most of information comes from largest gas DSO - AS Gaasivõrgud);► Suppliers of gas meters, other equipment and data communications;► Publicly available data sources such as Estonian Statistics Database, Eurostat and OECD.

More detailed baseline data collection is described in Appendices.


3.3 Financial modelling and socio-economic analysisFinancial modelling and socio-economic analysis is made to the maximum extent in a quantitative manner from financial perspective of the smart gas metering deployment (calculations on necessary infrastructure, investments, operational expenses which might drive the changes in gas distribution tariff) and from socio-economic analysis perspective of different changes in distribution tariff and in payables for distribution services caused by smart / remote gas metering deployment (changes in employment, labor productivity, natural gas TSO and DSO revenues and expenses, etc.). Analysis also includes a qualitative assessment of factors that cannot be measured according to their monetary values. Such factors are, for example, more accurate billing and more accessible information on natural gas consumption for customers. In addition to this, social analysis of smart metering as such is conducted in order to show the possible benefits coming from smart metering introduction for end-customers.The socio-economic analysis determines which alternative from the analyzed alternatives is the best alternative of the project by combining financial and socio-economic indicators. In order to measure changes in values, it is necessary to define the base case (AS-IS scenario) and two with-project alternatives.

3.3.1 Analysis of alternativesThe main stages in the analysis of alternatives are as follows:

► Definition of AS-IS alternative;► Definition and analysis of project’s alternatives;

3.3.1.1 Definition of AS-IS scenarioAS-IS scenario (current number of meters per customer group, natural gas consumption per customer group, legal environment, employment, value added, taxes paid, etc.) is the reference point against which to assess the impact of implementing the smart gas metering system. AS-IS scenario also assumes smart metering deployment for customers using more than 750 m3 of gas per year (based on Natural Gas Act). Each of the defined smart gas metering system alternative will have a different impact compared to AS-IS situation; however, it is important to mention that possible variations of different implementation parameters and outputs will be measured during the sensitivity analysis.

3.3.1.2 Definition of project’s alternativesIn the socio-economic analysis two alternatives have been analyzed:

► Alternative 1: Separate smart / remote gas metering system, for customers using less than 750 m3 of natural gas metering point is changed into remote meter with only remote reading functionality;

► Alternative 2: Assumed synergies with currently implemented smart electricity metering system, for customers using less than 750 m3 of natural gas metering point is changed into remote meter with only remote reading functionality.

In each of the alternatives different infrastructure, investments and operational costs have been calculated for each of the smart gas metering system developer: Elering AS and DSOs. In all alternatives it is assumed that Elering AS will not have any additional investments into IT systems, only maintenance costs related to it, while all gas DSOs will be treated as one logical unit and costs related to smart and remote gas meters, internal IT systems, etc. will be borne by them.The main parameters that define alternatives are the following:

► Deployment volume;► Duration of deployment;► Intensity of deployment;► Communication technology;► Responsibility of gas metering data collection system;► Responsibility of gas metering data management system;


► Basic functionalities for smart gas meters.More detailed analysis of alternatives is described under section 5.1.

3.3.2 Socio-economic impact assessment

3.3.2.1 Quantitative analysisIn order to identify the socio-economic impact of different alternatives of the smart gas metering implementation, the impact should be assessed for different market participants, as the split of the costs between participants will have an effect on the payables for natural gas distribution services for different customer group.At first, financial modelling was carried out. Financial modelling is based on calculations of cost-benefit analyses and guidelines of smart metering deployment22. Infrastructure, investments, operational expenses, tariff calculation and total payables by each customer group were calculated in the financial modelling (detailed financial modelling plan is presented in the figure below). Further, the socio-economic impact assessment was done. In order to identify the socio-economic impact of different alternatives of smart gas metering implementation on different market participants, Leontief model (also referred to as Input-Output model) has been used. Leontief model is often used to assess the impact of various business objects or business sectors in an economy. Socio-economic impact has been assessed for the following market participants:

► Large (industrial) consumers; ► Other commercial consumers; ► Household consumers; ► Electricity producers (both large and small);► Elering AS;► Distribution Network Operators;► Public sector.

Leontief’s model allows to determine direct, indirect and induced impact of the identified action - project on the overall state’s national economy. The general steps of suggested methodology model are as follows:

► Step 1. Estimation of the direct impact (capital and operating expenditures of smart and remote natural gas metering). Direct impact is estimated only from the perspective of households, public sector and commercial enterprises which consume natural gas (the impact on different customer groups and different commercial enterprises by their economic activity is estimated by using the ratio of their natural gas consumption from total natural gas consumption in Estonia);

► Step 2. Estimation of the indirect impact of the smart and remote natural gas metering. Type I coefficients of Leontief model are used to estimate the indirect effects in terms of GDP by multiplying these coefficients with the direct impact. It is important to mention that Leontief’s model allows to exclude import/export effects and therefore allows to calculate indirect impact to the country’s economy. This effect is related to OPEX and CAPEX of domestic purchases (i.e. there is no impact in case of imports) from supporting industries such as:

o Wholesale and retail supply (including primary inputs e.g. biomass, coal/oil shale, natural gas, etc.);

o Transportation; o Machinery and equipment, and their maintenance; o Construction, etc.

22 References: JRC „Guidelines for Cost Benefit Analysis of Smart Metering Deployment“, retrieved from: https://ses.jrc.ec.europa.eu/sites/ses.jrc.ec.europa.eu/files/publications/guidelines_for_cost_benefit_analysis_of_smart_metering_deployment.pdf and European Commission recommendation on preparation for the roll-out of smart-metering systems (2012/148/EU), retrieved from: http://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A32012H0148

http://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A32012H0148

https://ses.jrc.ec.europa.eu/sites/ses.jrc.ec.europa.eu/files/publications/guidelines_for_cost_benefit_analysis_of_smart_metering_deployment.pdf

https://ses.jrc.ec.europa.eu/sites/ses.jrc.ec.europa.eu/files/publications/guidelines_for_cost_benefit_analysis_of_smart_metering_deployment.pdf


► Step 3. Estimation of the induced impact of the smart and remote natural gas metering. Type II Leontief’s model coefficients can be used to estimate the induced effects in terms of GDP by multiplying these coefficients with change in household expenses; however, specific Type II coefficients are not derived for Estonian economy, thus induced impacts cannot be calculated according to Leontief’s model methodology. Therefore, in the analysis this impact is not measured;

► Step 4. Calculation of all aggregated and relative indicators. The proposed methodology and developed model enables to calculate all the indicators (including aggregated indicators such as impact on GDP, employment, disposable income and government taxes and revenues), which will allow to perform sensitivity analysis in order to check how assumptions made influence the socio-economic impact of each case.

In the figure below a logical assessment scheme to perform the calculations of financial modelling and socio-economic impact, where Leontief’s model is applied, is presented.

Figure 4 Financial and socio-economic model overview

The indirect impact’s multipliers for GVA and for employment are derived based on the most recent available data on Estonia’s economy’s input-output tables from the database of Organization for Economic Cooperation and Development (OECD). Input-output tables and derived coefficients allow to evaluate country’s economy based on the performance of its economic sectors:

► Input-output tables are used for reporting flows of goods and services between industries, for imports and exports, capital and employment contributions to economic activities. Thus, by calculating input coefficients, input-output tables can be used to map industry sector supply chain. These coefficients reflect which part of the production value is used as an input in other industrial sectors.

► Input coefficients are also used to calculate output multipliers that include additional demand in each sector when production is increased by a certain unit in an identified industry sector. Mathematical process by which the multipliers are obtained is known as Leontief’s inversed value matrix.

► Input-output tables divide an economy into broad sectors and detail the intermediate consumption of industries from other industries, as well as imports and gross value added for a given industry. From the input-output tables a number of multipliers, such as GVA and employment indirect multipliers, are calculated.


Main indicators used are income and expenses. Other indicators, e.g. taxes paid, value added, number of employees have been estimated as a percentage of income/expenses based on sector average value where actual data is not available. Quantitative socio-economic impact assessment of smart metering for natural gas end-consumers is described in section 4.3 “Quantitative socioeconomic analysis”.

3.3.2.2 Qualitative analysisNot all socioeconomic benefits can be assessed in monetary terms. In addition to quantitative socio-economic impact analysis, benefits that arise from the project‘s implementation are described qualitatively. Qualitative benefits are divided into three categories:

► Consumer benefits;► Energy companies’ benefits;► Other benefits.

The main consumer benefits arise from more accessible information and more accurate billing. After implementation of smart meters, energy companies will be able to improve customer service, have better information about gas consumption, peak periods and be able to manage consumer and supplier balance more efficiently. Additionally, implementation of the smart gas meters could help to reduce CO2

emission amount. More about qualitative socioeconomic benefits is described in section 4.5 “Qualitativesocioeconomic analysis”.

3.4 Risk analysisRisk analysis is made to assess the impact of various external and internal factors to the successful project implementation. Risk analysis of the project is done from the project implementers’ perspective. Implementation of the project is a large-scale and technically complicated process that contains various implementation-related risks. The aim of risk analysis is to describe the level of likelihood and impact of potential risks as well as the measures for mitigation and avoidance of the detected risks. Project risks are subdivided into five groups of risks, including risks related to planning and design, procurement, implementation process, infrastructure testing and risks related to carrying out the project. For all project’s alternatives the identified risks are the same, thus are shown in the same risk analysis. Each identified risk is classified according to its potential impact on the project as defined in the table below23.Table 3 Risk severity classification

Classification Relevance

I No relevant effect on social welfare, even without remedial actions.II Minor. Minor loss of social welfare generated by the project, minimally affecting the

project’s long run effects; however, remedial or corrective actions are needed.III Moderate. Loss in social welfare generated by the project, mostly financial damage, even

in the medium – long run. Remedial actions may correct the problems.

IVCritical. High social welfare loss generated by the project. Occurrence of the risk causes a loss of the primary function(s) of the Project. Remedial actions, even large in scope, are not enough to avoid serious damage.

V Catastrophic. Failure in the project that may result in serious or even total loss of the project’s functions. Main project’s effects in the medium-long term do not materialize.

Table 4 Risk probability classification

Classification Probability Possibility

A Very unlikely 0-10%

23 European Commission Guide to Cost-Benefit Analysis of Investment Projects for 2014 - 2020.24 European Commission Guide to Cost-Benefit Analysis of Investment Projects for 2014 - 2020.


Classification Probability Possibility

B Unlikely 10-33%C About as likely as not 33-66%D Likely 66-90%E Very likely 90-100%

Table 5 Risk matrix

Risk level Color Severity/ Probability I II III IV V

Low A Low Low Low Low Moderate

Moderate B Low Low Moderate Moderate High

High C Low Moderate Moderate High High

Unacceptable D Low Moderate High Unacceptable Unacceptable

E Moderate High Unacceptable Unacceptable Unacceptable

Qualitative risk evaluation includes a description and causes of risks, as well as risk mitigation measures. More detailed risk analysis is described in the section 5 “Risk analysis”.

3.5 Sensitivity analysisSensitivity analysis is conducted to assess the impact of various factors. Sensitivity analysis allows determining to which particular variables the resulting outcome is the most sensitive to. Sensitivity analysis of the project is done from the smart metering project implementer’s perspective in the upcoming project stage.Sensitivity analysis is undertaken to determine how different values of the model‘s input data affect the results of the socio-economic impact analysis.Sensitive variables are variables that affect the result of the analysis the most (usually ENPV, ERR, B/C are evaluated; in socio-economic impact assessment – FNPV and impact on GDP are evaluated). Also, sensitive variables are those for which a 1% change leads to more than 1% change in the parameters of the project. Sensitivity analysis is made by changing independent variables and comparing the obtained financial and socioeconomic factors’ values to their initial value. The main variables that are tested are as follows:

► Meter prices;► Other equipment (gas/electricity modules; data loggers) prices;► Installation prices of the meters and other equipment;► Operational costs of IT systems;► Data communication costs;► Distribution of communication technologies used (GPRS and LPWAN);► IT investment redistribution if other utilities (water supply and heating) are involved;► Distribution of installation costs between DSOs and suppliers;► Natural gas consumption (only from the perspective of distribution tariff);► Different consumption benchmarks for household customers using specific amount of natural

gas per year.

Results from the sensitivity analysis are used to indicate critical aspects and assumptions of the analysis, as well as to point out variables, which need to be carefully monitored during the project’s implementation process.


4 Socio-economic impact of smart-metering for natural gas end-customers

This section presents derived alternatives, main assumptions and results for financial modelling and socio-economic analysis of different smart gas metering deployment solutions in Estonia. Financial modelling and socio-economic impact is calculated based on the alternatives and assumptions aligned with Elering and using other countries’ smart gas metering deployment cost-benefit analyses (e.g. UK, Ireland and Lithuania) as examples for this analysis. Financial modelling is conducted using main parameters of costs and benefit analyses for smart metering deployment and it includes the necessary calculation for changes in infrastructure, capital investments and operational costs. It is indicated that all alternatives of smart gas metering deployment are investments which has negative financial result due to the fact that there are no significant reduction in operational expenses compared to AS-IS situation. However, Alternative I is less expensive due to smaller number of additional equipment which is necessary to install (wireless M-bus communication modules); however it has higher communication and operational costs. On the other hand, Alternative II, which includes synergies with electricity metering system shows higher investments due to additional investments into wireless M-bus communication modules, while lower operational costs. Financial modelling description in more detail is described in chapters 4.3.1 “Financial modelling results” and 4.3.2 “Financial modelling result assessment”.Socio-economic impact assessment calculations are based on input-output calculations of Leontief model, which indicates the impact of tariff change on customers’ payables and revenues for Elering and DSOs, and also shows the impact of smart metering investment which is observed locally and includes only installation costs of meters and other equipment. In addition to this, both impact calculations indicated that smart metering deployment for gas customers in Estonia is a small investment which will not have a significant impact on its economy. However, it shows that separate gas smart metering system would have higher negative impact on the economy through tariff change, than a joint electricity and gas metering system which was evaluated under Alternative II. While looking only from investments perspective, investments in Alternative II have higher positive effect on the companies installing the infrastructure than in Alternative I which can also effect the economy as a whole. However, impact assessment of the project investment is based on generalized assumption about the companies working in smart metering related industries, thus it is not advisable to lean the socio-economic impact analysis conclusions on impact of investment oneself. Therefore, in order to indicate the optimal solution it is necessary also to take into account all possible social benefits which cannot be measured quantitatively.In the following chapters the analysis of alternatives, main assumptions, financial modelling and socio-economic impact assessment is described highlighting the main parameters and findings of the calculations.

4.1 Analysis of alternativesThe parameters of alternatives are derived from examples of cost-benefit analyses of smart metering deployment for electricity and/or gas in other countries (e.g. Lithuania, Ireland and UK) and gas metering regulation amendments for Natural Gas Act. Main parameters consist of:

► Deployment volume;► Duration of deployment;► Intensity of deployment;► Communication technology;► Responsibility of gas metering data collection system;► Responsibility of gas metering data management system;► Basic functionalities for smart gas meters.

According to the amendments to Natural Gas Act, AS-IS alternative is also considering a change of conventional gas meters into smart gas meters. AS-IS alternative takes into account new Natural Gas Act amendments as a base case and it is assumed that for all customers consuming more than 750 m3


of natural gas per year a measurement point is changed into smart meter with all basic functionalities (see the table below).While two “with-project” alternatives assume that all measurement points are changed; however, for customers who consume less than 750m3 of natural gas per year, measurement point is changed into remote gas meter which has only remote reading functionality. The difference between two “with-project” alternatives comes from different distribution of responsibilities of IT systems and different equipment installed. Alternative I assumes that gas DSOs create separate gas meter data collection and data management systems and are responsible for both of these systems. While Alternative II assumes that gas DSOs are using electricity meters and their data collection system for gas meter data collection, while data management is done by separate gas meter data management system which validates the data and sends it to other responsible parties such as Elering. In the table below each alternative is represented by each parameter and graphic representation of possible IT structure.


Table 6 Alternatives for socio-economic analysis

Parameter AS-IS (only gas)1 Alternative I (only gas)2 Alternative II (gas+electricity)3 Comments

Deployment volume

► For customers that consume at least 750 m3 per year and for customers that have gas pressure in measurement point above or up to 20 mbar, meter is changed into smart meter with all basic functionalities

► For customers that consume less than 750 m3 per year, measurement point is not changed

► For customers that consume at least 750 m3

per year and for customers that have gas pressure in measurement point above or up to 20 mbar, meter is changed into smart meter with all basic functionalities

► For customers that consume less than 750 m3

per year, measurement point is changed into remote meter with only remote reading functionality

► For customers that consume at least 750 m3 per year and for customers that have gas pressure in measurement point above or up to 20 mbar, meter is changed into smart meter with all basic functionalities

► For customers that consume less than 750 m3 per year, measurement point is changed into remote meter with only remote reading functionality

1 - For AS-IS standard gas meters for customers using less than 750m3 per year will not be changed. Separate gas metering system (see Figure 1).2 – I alternative replacement of old meters might happen based on geographical plan or customer groups which are the most beneficial. Separate gas metering system (see Figure 2).3 – II alternative replacement of old meters might happen based on geographical plan or customer groups which are the most beneficial. One data collection system for gas and electricity meters, while separate electricity and gas metering data management systems (see Figure 3).

Duration of deployment

2 years for customers using more than 750 m3 of gas per year(start – 2018 January, end – 2020 January)

2 years for customers using more than 750 m3 of gas per year(start – 2018 January, end – 2020 January)5 years for customers using less than 750 m3 gas per year(start – 2018 January, end – 2023 January)

2 years for customers using more than 750 m3 of gas per year(start – 2018 January, end – 2020 January)5 years for customers using less than 750 m3 gas per year(start – 2018 January, end – 2023 January)

According to the amendments of Natural Gas Act, it is compulsory to have gas meters with remote reading and other smart meters functionalities for customers that uses at least 750 m3 of gas per year till January 2020.



Intensity of deployment of smart meters (meters deployed per year, based on deployment volume)

1st year – 50% 2nd year – 50% 1st year – 50% 2nd year – 50% 1st year – 50% 2nd year – 50%

It means that for 50% of customers using more than 750 m3 of gas per year smart meters are installed during the year.

Intensity of deployment of remote meters (meters deployed per year, based on deployment volume)

No remote meters are deployed for AS-IS alternative 10% 20% 30% 30% 10% 10% 20% 30% 30% 10%

Indicated percentage represents the share of customers using less than 750 m3 of gas per year for which the remote meters are installed during the year.

Communication technology GPRS – 70%, LPWAN – 30% GPRS – 70%, LPWAN – 30%

For communication with data collection system it is assumed to use electricity communication system installed – PLC – 90 %; GPRS – 10%.

Daily frequency of data transfer from smart / remote gas meter to data collection system for all alternativesFor communication between electricity and

gas meter –Wireless M-bus - 100%

Part responsible of gas consumption data collection

Gas DSOs (as Figure 1) Gas DSOs (as Figure 2) Electricity DSOs (as Figure 3)

Part responsible of validation of gas consumption data and its provision to Elering Data Hub

Gas DSOs (as Figure 1) Gas DSOs (as Figure 2) Gas DSOs (as Figure 3)

Basic functionalities of smart meters

► Remote reading of metrological register(s) and provision to designated market organization(s)► Two-way communication between the metering system and designated market organization(s)► Temperature measurement in the system► Pressure conversion functionality (for customers that have gas pressure in measurement point above

or up to 20 mbar)



► Support of advanced tariffing systems

Figures

Figure 1. AS-IS (gas utilizing separate data communication system)

HEAD-END SYSTEM FOR DATA COLLECTION

(OWNED BY GAS DSO)

Sm Sr

Gas meters

ELERING DATA HUB

Gas consumption

data

METER DATA MANAGEMENT SYSTEM OF GAS DSO

RmCmDL

SmP

Gas consumption

data

Figure 2. I ALTERNATIVE (gas utilizing separate data communication system)


(OWNED BY GAS DSO)

Rm Sm Sr

Gas meters

ELERING DATA HUB

Gas consumption

data

METER DATA MANAGEMENT SYSTEM OF GAS DSO

DL

Gas consumption

data

SmP

Figure 3. II ALTERNATIVE (gas meters using electricity head-end system for raw meter data collection)

METER DATA MANAGEMENT OF GAS DSO

M-bus

E1 E2 E3

Rm Sm Sr

Gas meters

ELERING DATA HUB

Electricity meters


(OWNED BY ELECTRICITY DSO)

METER DATA MANAGEMENT OF ELECTRICITY DSO

Electricity consumption data

Gas consumption

data

Gas consumption

data

SmP

E4

LEGEND- Raw consumption data- Validated consumption data

Sm - Smart diaphragm gas meter

Sr - Smart rotary/turbine gas meter

Rm - Remote diaphragm gas meter

Cm - Standard (conventional) diaphragm gas meter

DL - Data logger

SmP - Smart diaphragm gas meter with pressure conversion

- Electricity/remote gas meterËs communication module


4.2 Main assumptionsIn order to calculate project’s socioeconomic costs and benefits the following assumptions of socioeconomic impact model have been assumed. Table 7 Assumptions for socio-economic impact assessment

No Assumptions

General assumptions

1 Cost calculation All calculations are made in 2016 prices and all expenses and inputs are in the real terms.

2 Project life cycle and implementation period

It is assumed that project‘s operational (calculation) period is until 2030 and project’s implementation period is 2018-2022.

3 Economic discount rate In the analysis, 5% economic discount rate is used according to European Commission recommendations.

4 Financial discount rate In the analysis, 4% financial discount rate is used according to European Commission recommendations.

Assumptions on gas meters and metering points

5 Standard diaphragm gas meters

Conventional meter which is not read remotely, diaphragm meter diameter varies from G1.6 to G25.

6 Standard rotary/turbine gas meter

Conventional meter which is not read remotely, rotary/turbine meter diameter varies from G40 to G6500, it is assumed it has pressure conversion function.

7 Remote diaphragm meter

Standard diaphragm gas meter which allows to add communication module and can provide remote reading functionality, diaphragm meter diameter varies from G1.6 to G25.

8 Remote rotary/turbine meter

Standard rotary/turbine gas meter which allows to add communication module and can provide remote reading functionality, rotary/turbine meter diameter varies from G40 to G6500.

9 Smart diaphragm gas meter

Diaphragm gas meter with diameter from G1.6 to G25 with these basic functionalities: 1. Remote reading of metrological register(s) and provision to designated market organization(s);2. Two-way communication between the metering system and designated market organization(s);3. Temperature measurement in the system;4. Support of advanced tariffing systems.

10Smart diaphragm gas meter with pressure conversion

Diaphragm gas meter with pressure conversion function and diameter from G1.6 to G25 with these basic functionalities: 1. Remote reading of metrological register(s) and provision to designated market organization(s);2. Two-way communication between the metering system and designated market organization(s);3. Temperature measurement in the system;4. Pressure conversion functionality;5. Support of advanced tariffing systems.

11 Smart rotary/turbine gas meter

Rotary/turbine gas meter with diameter from G40 to G6500 with these basic functionalities: 1. Remote reading of metrological register(s) and provision to designated market organization(s);2. Two-way communication between the metering system and designated market organization(s);3. Temperature measurement in the system;4. Support of advanced tariffing systems.

12 Diaphragm meters Meter types which diameter varies from G1.6 to G25 are assumed to be diaphragm meters.

13 Rotary and turbine meters

Meter types which diameter varies from G40 to G6500 are assumed to be rotary/turbine meters.

14 Distribution of the meters among different

► Distribution of meter types (meter diameters) is based on the largest DSO distribution of meter types per customer group.


No Assumptionscustomer groups and meter types

► Distribution of all meters among customer groups is based on Elering datahub information.

15 Distribution of first time installed meters

The number of newly installed meters is based on the largest DSO installation of first time installed meters and is enlarged proportionally to meet the proportion of total number of meters based on Elering information.

Assumptions on customer groups and gas consumption

16Distribution of gas consumption among different customer groups

Distribution of gas consumption in Estonia among different customer groups is based on Elering's datahub information on distribution of different customers' consumption in 2017. Information for the months of the year 2017 which are still unknown is distributed based on gas consumption forecast for 2017 and actual gas consumption of the year of 2016.

17 Large (industrial) commercial customers

Customer group which consists of commercial customers consuming 20,000 m3 per year or more (including electricity producers).

18 Other commercial customers

Customer group which consists of commercial customers consuming less than 20,000 m3 per year.

19 Household customersCustomer group which consists of separate household customers and apartment buildings' residents (the number of meters in the apartment buildings is included in this group).

20 Public sector customers Customer group which consists of public customers such as governmental institutions, municipalities, diplomacies or publicly owned buildings.

21 Change in gas consumption

Change in gas consumption for the years from 2016 till 2026 is based on data provided by Elering.

22Reduction in gas consumption after smart metering deployment

Calculations on reduction in gas consumption after smart metering deployment is based on European Commission's recommendation on Cost-benefit analysis for Smart metering deployment25 and gas consumption of currently installed smart gas meters. It is assumed that for currently installed smart gas meters there will be no additional reduction in gas consumption. The reduction in gas consumption after smart metering deployment is 0.38%.

23Reduction in technical gas losses after smart metering deployment

Reduction in technical losses after smart metering deployment is based on EC recommendation on Cost-benefit analysis for Smart metering deployment26.

Assumptions on infrastructure

24 Number of data loggers

According to the largest DSO information, data loggers are installed only for medium and large consumption points. In the analysis it is assumed that medium and large consumption points have rotary/turbine meters and one logger is able to collect and transfer information from one gas meter and send the data to data collection system. Data loggers are installed only under AS-IS situation and Alternative I.

25Number of wireless M-bus or GPRS/LPWAN communication modules for gas meters

► Wireless M-bus communication module for gas meter is installed with each remote gas meter under Alternative II.

► GPRS/LPWAN communication modules for gas meter is installed with each remote meter under Alternative I.

26Number of wireless M-bus communication modules for electricity meters

Wireless M-bus communication module for electricity meter is installed with each remote diaphragm gas meter and all smart gas meter types under Alternative II.

27 Number of changed gas meters

Change in gas meters is based on the example of one country’s smart metering CBA calculations which is calculated as a percentage of failure for standard, remote and smart meters. Failure rate for standard gas meters is 0.8%, for remote and smart meters – 1%.

25 European Commission, 2012, Guidelines for Cost-Benefit analysis of smart metering deployment; Retrieved from: https://ses.jrc.ec.europa.eu/sites/ses/files/documents/guidelines_for_cost_benefit_analysis_of_smart_metering_deployment.pdf 26 Retrieved from: http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32012H0148&from=EN


https://ses.jrc.ec.europa.eu/sites/ses/files/documents/guidelines_for_cost_benefit_analysis_of_smart_metering_deployment.pdf


No Assumptions

28 Number of manual readings

For all standard meters (diaphragm and rotary/turbine) regular inspection and checking of the reading value occurs once a year.

29Number of changes in batteries for pressure conversion devices

For pressure conversion devices batteries are changed once in 3-5 years - the average of 4 years is taken.

30Number of changes in batteries for smart and remote meters

For smart and remote (diaphragm and rotary/turbine) meters batteries are changed once during 4 year period.

31Number of oil changes for rotary/turbine meters

For rotary/turbine meters the oil is changed 1-5 times per year - the average of 3 times per year is taken.

32 Number of checks of alarms

For rotary/turbine meters the check of alarms is once a year and repeating verification once during 8 year period.

Assumptions regarding necessary investments

33 Prices for metersPrices for meters are based on the information from the largest DSO, Elering, potential suppliers and examples from other countries. The average for different type of meters is taken.

34 Prices for other equipment

Prices for other equipment are based on the information from the largest DSO, potential suppliers and available information from analyses in other countries. The average for different type of other equipment (modules and data loggers) is taken.

35 Investments in IT systems (DSO part)

► Under Alternatives 0 and I, investments into data collection system and meter data management system are based on the examples from Elering (the average investments per meter are taken).

► Under Alternative II, investments into data collection system are based under other country’s CBA example where for the electricity DSO collection system, gas DSOs pay 1 EUR per meter.

► Distribution between different IT systems’ investment is based on the CBA examples from other countries. 38% of total investment into IT systems is for Data collection systems while 62% of total investment into IT systems is for Meter data management system.

36Installation costs of meters and other equipment for DSO

Installation costs of meters and other equipment for DSO are based on the information from largest gas DSO.

37Installation costs of meters and other equipment for subcontractor

Installation costs of meters and other equipment for subcontractor are based on other countries information gathered by EY.

38Costs of project management and publicity

Project management and publicity costs are based on the example of other countries - information gathered by EY. Percentage of project management costs derived from total investment is 2%, publicity – 1%.

39 Investments in Datahub (Elering)

It is assumed that there are no investments in Datahub based on information provided by Elering.

Assumptions regarding operations and expenses

40 IT operational costs for gas DSOs

► IT total OPEX is based on the internal information provided by Elering. The OPEX division between Data collection system and Meter data management system is based on internal EY sources and other countries examples (Ireland, UK, Slovenia). 46% of total OPEX is for Data collection system and 54% - for Meter data management system.

► Under Alternative II, it is assumed that gas DSOs do not have any operational costs related to Data collection system because it is included for electricity DSO.

41 Technical maintenance of the meters

► For all standard meters regular inspection and checking of the reading value occurs once a year.

► For remote and smart meters batteries are changed once in 3-5 years - the average of 4 years is taken.

► For remote, smart meters and smart meters with pressure conversion


No Assumptionsbatteries are changed once during 4 year period.

► For rotary and turbine meters the oil is changed 1-5 times per year - the average of 3 per year is taken.

► For meters with pressure conversion device, the check of alarms is once a year and repeating verification once during 8 year period.

► Repaired connections of gas and electricity meters is based on other countries’ CBA examples, and it is assumed that 1% of all connected gas meters will have to be reconnected to electricity meter.

► Other costs for operations and maintenance for all remote and smart meters is based on the examples of other countries’ CBA calculation and it is assumed that additional maintenance costs will be 2.5% of initial meter price.

42 Costs of technical maintenance Costs for these operations are taken from other countries CBA examples.

43 Data communication costs

► Communication costs from remote / smart gas meter / data logger to Data collection system using LPWAN are based on the information provided by one of LPWAN technology provider (0.2 per month for one message per week).

► Communication costs from remote / smart gas meter / data logger to Data collection system using GPRS are based on Elering’s provided information (0.7 for SIM card per meter/ per month).

44 Operational costs for Datahub

Operational costs for Datahub are based on the information provided by Elering and statistical data on average labor costs for 2 IT specialists in energy sector.

45 Depreciation / amortization

Years of depreciation of different meters, equipment and amortization of IT systems are based on information from the largest gas DSO.Assumptions regarding tariff forecasts

46 Distribution tariff calculations

► In the analysis it is assumed that only the scope of project investments, their depreciation/amortization and related operational expenses are included in distribution tariff calculations. Tariff calculations are based on the methodology for Gas network tariff calculations27.

► The average distribution tariff for all DSOs is taken.► Change in DSO tariff is incremental – as a difference from tariff in AS-IS

situation.Assumptions on impact of tariff change calculations

47Direct change in revenues for gas DSOs and Elering

Direct change in revenues for gas DSOs and Elering is based on the differences in operations for different activities. It is assumed that higher revenues are due to higher tariff for all customers.

48

Direct change in revenues/disposable income for different customer groups or economic sectors

Direct change in household's disposable income, commercial customers and public sector revenues is based on change in payables for natural gas paid by different customer groups (calculated using their consumption in natural gas distributed by different economic sectors).

49 Direct and indirect change in GDP

Direct and indirect change in GDP is calculated using value added multipliers for different economic sectors for commercial customers (value added multiplier is based on data from Estonian statistics database). For households and public sector direct and indirect change in GDP is assumed to be the same as in disposable income.

50 Direct and indirect change in employment

Direct and indirect change in employment is calculated dividing change in GDP by labor productivity in different sectors (for commercial customers). For household customers, direct and indirect change in employment is also calculated dividing change in GDP by average labor productivity in Estonia.

51 Direct and indirect Direct and indirect change in labor productivity is calculated dividing total 27 Gaasi võrguteenuste hindade arvutamise ühtne metoodika, 2013,retrieved from: https://energiatalgud.ee/img_auth.php/8/85/Konkurentsiamet._Gaasi_v%C3%B5rguteenuste_hindade_arvutamise_%C3%BChtne_metoodika._2013.pdf

https://energiatalgud.ee/img_auth.php/8/85/Konkurentsiamet._Gaasi_v%C3%B5rguteenuste_hindade_arvutamise_%C3%BChtne_metoodika._2013.pdf

https://energiatalgud.ee/img_auth.php/8/85/Konkurentsiamet._Gaasi_v%C3%B5rguteenuste_hindade_arvutamise_%C3%BChtne_metoodika._2013.pdf


No Assumptions

change in labor productivity

GDP (the sum of current GDP and calculated change for different alternative) by total employment (the sum of current labor force and change in employment for different alternative).

52Direct and indirect change in government taxes and revenues

Direct and indirect change in government taxes and revenues is calculated assessing how much of taxes are paid by households in different sectors (assumption on household spending is based on data on household spending from Estonian statistics database) and how the change in disposable income will have effect on change in government taxes and revenues.

53Indirect change in disposable income and revenues

Indirect change in disposable income and revenues is calculated by multiplying direct change by backward linkage output multiplier for different sectors from OECD excluding the direct change.

Assumptions on impact of project investment calculations

54 Locally observed investments

Calculations are based on locally observed investments - thus only installation costs of meters and other equipment for DSOs and subcontractors are included.

55Local investments’ distribution among different sectors

It is assumed that installation costs include work of employees’ in gas supply (DSO) and construction (subcontractor) sectors and respective transportation costs in transportation sector for DSO and subcontractors.

56 Percentage distribution of different investments

Distribution of investments between the sectors is based on the example of one country’s CBA calculations of new gas meter connections, where almost 80% of total costs is salary for a worker together with other expenses and 20% of total cost of gas meter connection is transportation.

57 Direct and indirect impact calculations

Other calculations for changes in disposable income/revenues, GDP, labor productivity, employment and government taxes and revenues are the same as in tariff change calculations.

4.3 Quantitative socioeconomic analysisThe model described above is used to assess the impact of transitioning from traditional gas metering to smart/remote gas metering for gas end-consumers and predict the behavior after the changes in gas metering system. In order to evaluate the impact of alterations, AS-IS situation is used as a reference point. In financial modelling current situation of investments and operations is used also taking into account forecasted future investments and operational costs related to different activities and specific parameters (i.e. meter type affecting cost of its maintenance), while in socio-economic impact assessment current GDP, employment and taxes are used. Thus, in this chapter financial modelling and socio-economic impact analysis will be presented comparing all project alternatives to AS-IS situation.

4.3.1 Financial modelling resultsSince different alternatives covers different investment solutions, financial modelling assess the possible financial outcome for project implementers. It consists of investments for gas DSOs listed here:

► Investments into gas meters;► Investments into other metering equipment (data loggers and communication modules for gas

and/or electricity meters);► Investments into IT systems;► Installation costs of meters and other equipment;► Project management and publicity costs.

Since Elering as a transmission system operator does not have any investments regarding smart gas metering deployment, they are not included in the analysis. In the following table total investments for gas DSOs are listed during all calculation period (2018 – 2030) as well as project implementation period (2018-2022).


Table 8 Total project investments for DSOs during AS-IS situation and different alternatives, EUR

Component of CAPEX

Alternative AS-IS Alternative I Alternative IIDuring all calculation

period(2018-2030)

During project implementation (2018-2022)

During all calculation

period (2018-2030)



period (2018-2030)


Gas meters 11,934,262 5,346,702 11,932,512 9,753,882 11,932,512 9,753,882Data loggers 358,200 225,600 358,200 225,600 0 0Communication modules 0 0 1,500,496 1,374,423 3,445,593 3,009,134IT systems (Data collection and MDM) 225,396 109,541 413,881 201,160 378,269 183,851Installation costs 6,553,849 2,290,129 5,861,851 4,756,731 6,577,941 5,373,848Project management 0 0 326,236 326,236 366,414 366,414Project publicity 0 0 163,118 163,118 183,207 183,207Total: 19,071,707 7,971,972 20,556,294 16,801,150 22,883,936 18,870,336

In the figures below, comparison of different composition of CAPEX in different alternatives is presented compared to AS-IS situation.Figure 5 Total CAPEX composition in Alternative I compared to AS-IS, 2018-2030, thousand EUR

AS-IS Gas meters Data loggers

Communi-cation

modules (Wireless M-bus)

IT systems (Data col-lection and

MDM)

Installation costs

Project man-

agement

Project pub-licity

Alternative I

19,072

1,500 188 326 1632 0

692

20,556

Decrease

Figure 6 Total CAPEX composition in Alternative II compared to AS-IS, 2018-2030, thousand EUR

AS-IS Gas meters Data loggers

Communi-cation

modules (Wireless M-bus)

IT systems (Data col-lection and

MDM)

Installation costs

Project man-

agement

Project pub-licity

Alternative II

19,072

3,446153 24 366 183

2 358

22,884

Decrease

In the figure below, yearly distribution of investments for gas DSOs is presented for each Alternative and AS-IS scenario.


Figure 7 Distribution of investments for gas DSOs, 2018-2030, thousand EUR

2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 20300

1,000

2,000

3,000

4,000

5,000

6,000

3,382

3,426

386

1,952 1,961

4,492

4,883

3,109

4,9685,443

3,550

488

AS-IS Alternative I Alternative II

Thousa

nd E

UR

From the graph above differences in the trends of investment for all alternatives can be seen. For instance, AS-IS situation assumes only smart gas metering deployment till the beginning of 2020, thus the sharp reduction of investments appears in 2020 compared to Alternatives I and II, which assume remote gas metering deployment for household customers using less than 750 m3 of natural gas per year. Another difference in trends of investments between AS-IS situation and Alternatives can be seen in 2024: increases in investment for AS-IS situation from the year 2024 indicates reinvestment into new standard gas meters for customers using less than 750 m3 of natural gas per year, while Alternative I and II does not have reinvestment into meters accounted in the calculation period but includes reinvestment into IT systems in 2027.Other part of financial modelling consists of different operational activities and their expenses for project implementers. The list of components for operational expenses for gas DSOs are listed here:

► Operational costs for IT systems;► Data communication costs;► Costs of operations and maintenance of remote and smart meters;► Costs of change in meters;► Costs of manual readings;► Costs of technical maintenance (change in batteries; change in oil; check of alarms of

conversion devices);► Costs of verification of gas meters with pressure conversion;► Costs of repaired connections of gas and electricity meters.

In the table below, total operational expenses for gas DSOs are listed during all calculation period (2018 – 2030) and project implementation period (2018-2022).Table 9 Total operational expenses for DSOs during AS-IS situation and different alternatives, EUR

Component of OPEX



period (2018-2030)

During project

implementation

(2018-2022)


period (2018-2030)

During project

implementation (2018-

2022)


period (2018-2030)

During project


2022)Operational costs for IT systems 2,275,000 875,000 2,275,000 875,000 1,237,286 475,879

Data communication costs 2,562,225 896,064 5,480,840 1,641,300 427,062 127,866Costs of operations and maintenance of remote and smart meters

2,382,988 848,254 3,808,069 1,212,115 3,808,069 1,212,115

Costs of change in meters 172,678 63,109 176,309 52,784 176,309 52,781


Costs of manual readings 912,900 346,916 152,843 152,843 152,843 152,843Costs of technical maintenance 1,549,614 392,124 2,703,841 486,745 2,703,841 486,745Costs of verification of gas meters with pressure conversion

4,275 0 4,275 0 4,275 0

Costs of repaired connections of gas and electricity meters 0 0 0 0 35,689 10,692

Total: 9,859,679 3,421,466 14,601,176 4,420,787 8,545,375 2,518,922

In the figures below, comparison of different composition of OPEX in different alternatives is presented compared to AS-IS situation.Figure 8 Total OPEX composition in Alternative I compared to AS-IS, 2018-2030, thousand EUR

AS-IS Opera-tional

costs for IT systems

Data communi-

cation costs

Costs of operations and main-tenance of

SM

Costs of change in

meters

Costs of manual readings

Costs of technical mainte-nance

Costs of verifica-tion of

pressure conversion

devices

Costs of repaired connec-

tions with electricity

meters

Alternative I

9,860

02,919

1,4254 1,154 0 0760

14,601

Decrease

Figure 9 Total OPEX composition in Alternative II compared to AS-IS, 2018-2030, thousand EUR

AS-IS Opera-tional

costs for IT systems

Data commu-nication

costs

Costs of operations and main-tenance of

SM

Costs of change in

meters

Costs of manual readings

Costs of technical mainte-nance

Costs of verifica-tion of

pressure conversion

devices

Costs of repaired connec-

tions with electricity

meters

Alterna-tive II

9,860

1,4254 1,154

0 36

1,0382,135

760 0

8,545

Decrease

In the figure below yearly operational expenses for gas DSOs are presented for each Alternative and AS-IS situation.


Figure 10 Distribution of operational expenses for gas DSOs, 2018-2030, million EUR

2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 20300

200

400

600

800

1,000

1,200

1,400

1,600

547

716

849 876 904

578

931

1,2311,340

1,202

362

535 733823

666


Thousa

nd E

UR

As it is seen from the figure above, all alternatives generate different operational expenses during the project calculation period. The main aspects which indicate the differences between these alternatives are different number of smart and remote meters in AS-IS situation and Alternatives, and different data communication costs for AS-IS situation and Alternative I compared to Alternative II. For instance, Alternative II generates even lower operational costs than AS-IS due to lower data communication costs via electricity metering system. While Alternative I is the most expensive because it uses GPRS and LPWAN communication technologies and has more remote gas meters compared to AS-IS situation. Total investments and operational costs for gas DSOs under each alternative for calculation and project implementation period are presented in the table below.Table 10 Total investments and operational costs for gas DSOs, EUR

Component of costs

AS-IS Alternative I Alternative IIDuring all calculation

period (2018-2030)

During project


2022)


period (2018-2030)

During project


2022)


period (2018-2030)

During project


2022)CAPEX 19,071,707 7,971,972 20,556,294 16,801,150 22,883,936 18,870,336OPEX 9,859,679 3,421,466 14,601,176 4,420,787 8,545,375 2,518,922Total: 28,931,386 11,393,438 35,157,470 21,221,937 31,429,311 21,389,259

For Elering operational expenses related to smart gas metering includes only 2 FTE for IT system’s (DataHub’s) maintenance and since it does not depend on changes in gas meters quantity, these costs remain the same for Elering considering all alternatives (see the table below). Operational costs are constant throughout the years and stays at 30.450 EUR per year for each Alternative and AS-IS situation.Table 11 Total operational costs for Elering, EUR

Component of OPEX

AS-IS Alternative I Alternative IIDuring all calculation

period (2018-2030)

During project


2022)


period (2018-2030)

During project


2022)


period (2018-2030)

During project


2022)Operational costs for IT systems 395,852 152,251 395,852 152,251 395,852 152,251

4.3.2 Financial modelling result assessmentFinancial modelling result is calculated using financial flows value and adjusting it to the financial discount rate of 4%. Financial net present value shows which one of the alternatives will have less negative impact towards project implementers’ return. After financial calculations which included all investments and operational costs it can be seen that neither of alternatives indicate positive returns


for project implementers, thus the least negative alternative should be chosen looking from the financial results perspective. In the table below calculated financial net present value is presented for different customer groups under two different alternatives. It is necessary to mention that these calculations are based on cash flow differences between alternatives with project and AS-IS scenario, thus it shows negative signs of the larger financial flows.Table 12 Financial net present value of different alternatives compared to AS-IS situation, EUR

Customer group Alternative I Alternative IILarge (industrial) commercial customers 36,148 303,361Other commercial customers 86,809 399,792Household customers -7,254,715 -5,082,632Public sector customers 27,422 83,048Total financial net present value -7,104,337 -4,296,431

From the table above it is seen that Alternative II should be implemented since it has smaller financial costs (capital and operational costs) for project implementers and it shows positive, even though very small, financial net present value for large (industrial) commercial customers and other commercial customers. On the other hand, Alternative I shows larger financial costs which arise from higher operational costs caused by higher data communication.From financial modelling the impact on possible project implementers is derived. Since for Elering there are no investments related to smart metering deployment and looking from operational expenses there is no difference between alternatives and AS-IS situation, there is no impact on Elering from transitioning to smart gas metering. However, according to Estonian legislation DSOs are main metering infrastructure providers, thus they bear the costs of investment into metering developments. In smart/remote gas metering deployment DSOs would also bear the related costs of new smart/remote metering system which includes procurement of new meters, equipment, enlargement of IT systems and installation. Since all alternatives (including AS-IS) assume smart metering deployment and thus, operations and maintenance for smart/remote meters are incorporated for all alternatives, the main differences in manual readings and data communication costs occur. Having all project investments and operational expenses accounted, the impact on DSOs is mainly driven through financial costs which increase due to investments and higher number of operations on smart and remote meters. However, since DSOs operate in the regulated market, higher investments and operational expenses are calculated as an increase in distribution tariff, which respectively increases the revenues of project implementers – in this case DSOs.

4.3.3 Social analysisSince input-output model focuses on broad macroeconomic effect on economy, in order to include every aspect which might affect natural gas customers when smart gas metering would be installed, social analysis of customer benefits is conducted. Comparing project alternatives and AS-IS situation, only one social benefit which could be quantitatively calculated is indicated - possible reduction in time spent reading a meter value manually.The table below contains monetary value of time indicators that represent the value of persons working and not working time saved having ability to access electronic content without physical travel to measurement location.Table 13 Values of time spent reading meter value, EUR/h

Value of time28 2018 - 2019 2020 - 2024 2025 - 2028 2029 - 2030Ability to access electronic content instead of physical travel to measurement location (not working value of time)

2.98 3.48 4.25 4.84

28 Retrieved from Lithuanian central project management agency from the file “Conversion coefficients and values of components of socio-economic benefits (costs)”, link: http://www.ppplietuva.lt/teisine-metodine-informacija/metodiniai-dokumentai/

http://www.ppplietuva.lt/teisine-metodine-informacija/metodiniai-dokumentai/


Value of time 2018 - 2019 2020 - 2024 2025 - 2028 2029 - 2030Ability to access electronic content instead of physical travel to measurement location (working value of time)

7.43 8.71 10.61 12.10

In the analysis it is assumed that customer spends around 4 minutes per month to read and record gas metering value (based on one of EU country’s example), so for each Alternative and AS-IS situation the distribution of value of time spent reading meter values for standard gas meters are presented in the figure below.Figure 11 Distribution of value of time spent reading metering value in each Alternative and AS-IS situation, thousand EUR

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 20300

50

100

150

200

250

300

350

400

98 118 100 79 84 89 94 99 105 111 117 123 130 138 145

98118

9155 33 9 0 0 0 0 0 0 0 0 0

98

118

91

5533 9

0 0 0 0 0 0 0 0 0


Thousa

nd E

UR

For both alternatives, there is a quite significant decrease in value of time spent reading gas metering value while AS-IS situation shows that leaving some standard meters for household customers which consume less than 750 m3 per year results in higher time spend on reading these meter values. The higher the reduction in the value of time spent reading meter value for customers, the more customers have time to spend on other activities.

4.3.4 Social analysis resultsAfter calculation of social benefits, project’s financial and social performance is measured together by the economic net present value which takes into account the financial costs borne by the project and social benefits which are then discounted using economic discount factor of 5%. For all alternatives this performance measurement can be seen in the table below.Table 14 Socioeconomic indicators


Indicator Measurement unit Alternative I Alternative II

Economic net present value EUR -6,172,008 -3,610,659

Economic net present value (ENPV) is the sum of discounted project's financial and socioeconomic cash flows and it shows the ability of financial and socioeconomic benefits to cover financial and socioeconomic costs related to a project. ENPV determines whether a project is beneficial to a society. Positive measure means that a project is beneficial because gains from a project are larger than costs. From the result presented in the table above it is seen that both alternatives result in negative ENPV, which means that financial costs of the project are higher than predicted social benefits. Independent social benefits from smart metering introduction as such are presented in the section 4.6 “Socialbenefits from smart gas metering deployment”.


4.4 Socio-economic impact analysis resultsSocio-economic impact analysis is prepared in order to evaluate the effect of introducing smart/remote gas metering to end-customers in Estonia to the economy as a whole, different customer groups and possible project. Socio-economic impact is calculated using Input-Output model which is known for assessing direct, indirect and induced impacts. However, due to the small impact on the whole economy and lack of reliable sources for induced impacts calculations (specific Type II multipliers for induced impacts), the assessment of induced impacts is not calculated for this socio-economic analysis. In the following chapter impacts on economy and different customer groups are analyzed.

4.4.1 Impact on economy from tariff changeIn order to evaluate the socio-economic impact of transitioning to smart gas metering, direct and indirect effects on the economy are evaluated.

► Direct impact is considered to be value added, people employed, taxes and fees paid to the government as a direct result of smart/remote gas metering deployment. These effects stem from direct changes in the payables for gas network services of commercial consumers and subsequently households, public institutions, etc. Since there is no additional injection of funds / increase in demand in the country, all value added and related effects are caused by increase in DSO tariffs (due to increased investments into infrastructure and higher operational costs).

► Indirect impact is considered to be value added, people employed, taxes and fees paid to the government by the companies that constitute the supply chain of different sectors. Indirect impact is calculated using OECD output multipliers for different sectors.

Direct changes in disposable income/revenues are calculated as a change in payables for gas by different customers. The following table presents how payables are distributed between different customer groups in each alternative as net present value (hereinafter - NPV) compared to AS-IS scenario.Table 15 Change in payables compared to AS-IS scenario, NPV of 2018-2030, EUR


Customer group Alternative I Alternative II

Large (industrial) commercial customers -6,235,095 -4,220,793Other commercial customers -226,582 -153,382Household customers -948,946 -642,406Public sector customers -167,186 -113,175Electricity producers -148,661 -100,635Total: - 7,726,471 - 5,230,391

Sum of changes in payables for large (industrial) and other commercial customers is distributed among the economic sectors multiplying the sum of changes in payables for commercial customers by the percentage of gas consumed in the sector compared to total gas consumption by commercial customers. Direct changes in disposable income/revenues (changes in payables for gas for different economic sectors) for one sector are spread among different intermediate consumption categories acceding to the relative share of consumption by specific sector. The spread of payables by sectors is done assuming that changes in gas consumption (changes in total gas payables for different sectors) are fully absorbed by changes in intermediate consumption. Distribution of changes in disposable income/revenues of main sectors under Alternative I is listed in the table below.


Table 16 Distribution of disposable income/revenues according their spending in different industries under Alternative I, EUR

Industry

Food products, beverages

and tobacco

Textiles, textile

products, leather

and footwear

Electricity, gas and water supply

Construction

Transport and

storage

Post and telecommunications

Real estate

activities

Computer and

related activities

EducationHealth

and social work

Total change in disposable income

Agriculture -16,626 -372 -4,612 -2,173 -17,089 -1,263 -2,047 -384 -63 -947 -279,010Mining and quarrying -207 -1,156 -10,834 -1,286 -13,773 -464 -1,452 -502 -109 -245 1,847,840Manufacturing -90,464 -56,265 -63,809 -15,681 -103,803 -30,688 -28,293 -7,883 -4,399 -1,515 -543,611Electricity, gas, steam and water supply 6,161 1,909,764 726,434 131,695 121,328 82,974 66,681 17,643 15,452 1,536 603,026Construction -358 -1,353 -707 -7,660 -5,639 -1,088 -2,343 -469 -125 -104 -42,530Wholesale and retail trade; repair of motor vehicles and motorcycles

-1,453 -116 -4,324 -3,246 -20,688 -6,129 -20,748 -1,559 -413 -112 -2,850

Transporting and storage -1,270 -436 -5,606 -3,138 -279,669 -7,789 -10,587 -2,861 -552 -221 -338,657Accommodation and food service activities -19,369 -74 -3,215 -895 -1,699 -529 -9,534 -236 -194 -57 -12,905Information and communication -3 -1 -57 -49 -102 -3,130 -122 -34 -7 -1 8,857Financial and insurance activities -2 0 -18 -18 -26 -26 -126 -69 -9 -3 -6,809Real estate activities -2,031 -1,766 -15,854 -132,065 -5,616 -13,716 -90,228 -2,561 -1,281 -453 -109,960Public administration and defense; compulsory social security

-102 -36 -569 -762 -635 -577 -646 -334 -282 -15 6,864

Education -2,338 -84 -7,175 -4,144 -1,448 -1,512 -2,848 -907 -3,201 -119 3,871Human health and social work activities -858 -36 -2,090 -1,028 -548 -465 -652 -341 -355 -2,353 -4,972Total change in spending for a product group -128,922 1,848,069 607,565 -40,449 -329,406 15,597 -102,945 -496 4,464 -4,608 1,129,153

Different changes in disposable income comes from Alternative II, which assumes lower financial costs, thus, distribution tariff changes differently compared to the Alternative I. In the table below, distribution of changes in disposable income/revenues among different industries is listed for Alternative II.

Product


Table 17 Distribution of disposable income/revenues according their spending in different industries under Alternative II, EUR

Industry

Food products, beverage

s and tobacco

Textiles, textile

products, leather

and footwear

Electricity, gas and

water supply

Construction

Transport and

storage


Real estate

activities

Computer and

related activities

EducationHealth

and social work

Total change in disposable income

Agriculture -11,255 -252 -3,122 -1,471 -11,568 -855 -1,385 -260 -43 -641 -188,873Mining and quarrying -140 -783 -7,334 -870 -9,324 -314 -983 -340 -74 -166 1,250,889Manufacturing -61,239 -38,088 -43,195 -10,615 -70,269 -20,774 -19,152 -5,336 -2,978 -1,026 -367,988Electricity, gas, steam and water supply 4,171 1,292,808 491,757 89,150 82,133 56,169 45,139 11,943 10,461 1,040 408,217

Construction -242 -916 -478 -5,185 -3,817 -736 -1,586 -318 -84 -70 -28,789Wholesale and retail trade; repair of motor vehicles and motorcycles

-984 -78 -2,927 -2,197 -14,004 -4,149 -14,045 -1,055 -279 -76 -1,928

Transporting and storage -860 -295 -3,795 -2,124 -189,320 -5,273 -7,167 -1,936 -373 -150 -338,657Accommodation and food service activities -13,112 -50 -2,176 -606 -1,150 -358 -6,454 -160 -131 -39 -8,736

Information and communication -2 0 -38 -33 -69 -2,119 -82 -23 -5 -1 5,996

Financial and insurance activities -1 0 -12 -12 -18 -18 -85 -47 -6 -2 -4,609

Real estate activities -1,375 -1,196 -10,732 -89,401 -3,801 -9,285 -61,079 -1,733 -867 -307 -74,436Public administration and defense; compulsory social security

-69 -24 -385 -516 -430 -391 -437 -226 -191 -10 4,646

Education -1,583 -57 -4,857 -2,805 -980 -1,024 -1,928 -614 -2,167 -80 2,620Human health and social work activities -581 -24 -1,415 -696 -371 -314 -441 -231 -240 -1,593 -3,366

Total change in spending for a product group -87,272 1,251,044 411,289 -27,381 -222,988 10,559 -69,687 -336 3,022 -3,120 654,987

Product


The following figure presents changes in Estonian GDP which is a result of the increase in payables for gas distribution services.Figure 12 Change in GDP, NPV of 2018-2030, thousand EUR

Alternative I Alternative II

-500

-400

-300

-200

-100

0

100

200

-407.8

-276.0

135.4 91.6

Direct impact Indirect impact

Thousa

nd E

UR

Due to high investments and operational expenses on smart/remote metering system and thus, increase in distribution tariff, most of the sectors, together with the labor force employed, shrink. The cumulative change in employment in all sectors (whole economy) is presented in the figure below.Figure 13 Change in employment, cumulative of 2018-2030, FTEs


-60

-50

-40

-30

-20

-10

0

-40.5

-27.4

-10.3

-7.0


FTEs

As employment is assumed to be changed respective to GDP, model does not reflect any significant change in labor productivity. In reality, the micro level effect to particular companies due to changes in payables for gas network services is insignificant, thus, there is no reason to assume significant changes in productivity.The following figure represents changes in household disposable income. The direct effects result from the increased payables for gas or gas bill (due to higher DSO tariff), since it is assumed that DSO will pass the higher costs of investments and operational expenses on their customers as higher tariff for distribution services. This will result in higher prices for all customer groups (commercials, households and public). The indirect effect on household disposable income is born from unemployed FTEs in the economy.


Figure 14 Change in household disposable income, cumulative of 2018-2030, thousand EUR


-1,800

-1,600

-1,400

-1,200

-1,000

-800

-600

-400

-200

0

-948.9-642.4

-635.0

-429.9


Thousa

nd E

UR

As consumption of other products decreases due to higher distribution tariffs and lower disposable income, governmental taxes collected increase due to higher collection of taxes from DSOs. Impact on government revenues is presented in a figure below.Figure 15 Change in government taxes and revenues, cumulative of 2018-2030, EUR

Alternative I Alternative II0

50

100

150

200

250

300

165.4112.0

77.8

52.7


Thousa

nd E

UR

The results of socio-economic evaluation of transitioning to smart gas metering system in Estonia reflects negative socio-economic results regardless of the alternative. Each analyzed alternative shows negative impact on economy, employment and household disposable income; however, government revenue is positive due to higher distribution tariff.

4.4.2 Impact on economy from project investmentThe same as in the socio-economic impact analysis of tariff change for customers of smart metering introduction, the evaluation of project investment impact on local economy sectors which might be involved in installation of the meters and equipment is conducted. Socio-economic impact of smart gas metering investment is assessed using direct and indirect effects on the economy.It is necessary to highlight that only locally (inside the country) observed investment is measured for this analysis, which means only installation costs of meters and equipment are accounted.Direct changes in revenues are calculated as a positive change of having this investment. The following table presents how revenues are redistributed between different project implementers in each alternative as net present value compared to AS-IS scenario.


Table 18 Change in distribution of investments compared to AS-IS scenario, NPV of 2018-2030, EUR


Customer group Alternative I Alternative II

DSO (Electricity, gas and water supply sector) 2,281,830 2,963,246Subcontractors (transportation and construction sectors) 280,120 444,351Total: 2,561,950 3,407,597

Direct changes in revenues of the companies for gas supply, construction and transportation sectors are spread among different intermediate consumption categories reflecting the relative share of consumption by specific sector.Distribution of change in disposable income/revenues of main sectors is listed in the table below. Table 19 Distribution of disposable income/revenues according their spending in different industries under Alternative I, EUR

Industry Food products, beverages

and tobacco

Textiles, textile

products, leather and

footwear


Construction

Transport and storage


Real estate activities

Computer and related activities

Education Health and social work

Total change in disposable

incomeElectricity, gas, steam and water supply 2,420 2,362 285,308 51,723 47,652 32,588 26,189 6,929 6,069 603 292,387Construction 643 788 1,269 13,756 10,127 1,954 4,208 843 224 187 68,732Transporting and storage 1,317 823 5,810 3,252 289,860 8,073 10,973 2,965 572 229 347,640Total change in spending for a product group

4,379 3,973 292,387 68,732 347,640 42,615 41,371 10,737 6,865 1,019 708,759

Different changes in revenues compared to Alternative I comes from Alternative II, which assumes higher investments, thus, impact on economy from project investments are also higher. In the table below, distribution of changes in revenues among different industries is listed for Alternative II.Table 20 Distribution of disposable income/revenues according their spending in different industries under Alternative II, EUR

Industry


and tobacco

Textiles, textile


footwear


Construction







incomeElectricity, gas, steam and water supply 3,142 3,067 370,509 67,169 61,882 42,320 34,010 8,998 7,881 783 380,250Construction 1,020 1,250 2,013 21,821 16,065 3,099 6,676 1,337 355 297 93,317Transporting and storage 1,751 1,095 7,728 4,326 385,537 10,738 14,595 3,944 761 304 463,484Total change in spending for a product

14,672 13,358 965,024 230,781 1,158,763 141,386 138,022 35,753 22,726 3,422 2,354,569

Product

Product


Industry


and tobacco

Textiles, textile


footwear


Construction







incomegroup

ProductProduct


The following figure presents changes in Estonian GDP which is a result of the investments in different Alternatives.Figure 16 Change in GDP, NPV of 2018-2030, thousand EUR


500

1,000

1,500

2,000

2,500

3,000

1,914.6

2,549.1

170.7

225.6


Thousa

nd E

UR

Due to high investments most of the sectors, together with the labor force employed, increases. The cumulative change in employment in all sectors (whole economy) is presented in the figure below.Figure 17 Change in employment, cumulative of 2018-2030, FTEs


102030

405060

708090

100

64.6

86.1

4.8

6.4


FTEs

The same as in analysis of tariff change, these calculations do not reflect any significant change in labor productivity. In reality, thus, there is no reason to assume significant changes in labor productivity.Since in the investments part there is no directly included households (they do not participate as contractors in this case), change in disposable income is not analyzed.As consumption of these sectors increase, governmental taxes collected also increase. Impact on government revenues is presented in a figure below.


Figure 18 Change in government taxes and revenues, cumulative of 2018-2030, thousand EUR


50

100

150

200

250

300

350

400

450

292.0

388.9

20.5

27.2


Thousa

nd E

UR

The results of socio-economic evaluation of transitioning to smart gas metering system in Estonia reflects positive socio-economic results from project investment regardless of the alternative. Each analyzed alternative shows positive impacts on analyzed sectors, their employment and government revenue.

4.5 Qualitative socioeconomic analysisNot all socioeconomic benefits can be assessed in monetary terms. Qualitative benefits can be divided into three categories: consumer benefits, energy companies’ benefits, and other benefits. The table below shows qualitative assessment of project’s benefits.Table 21 Non-monetized socioeconomic benefits

No Indicator Description

Consumer benefits

1 More accessible information► Consumers will benefit from more accessible information. Better

information on costs and use of gas might lead to changes in consumer behavioral and to more efficient usage.

2 More accurate billing

► Smart/remote gas meters will improve accuracy of gas bills. There are times when gas bills are based on inaccurate usage data. Inaccuracy might occur due to missed or inaccurate readings.

► After implementation of the remote metering, consumers will pay only for the actually used gas, as suppliers will be able to access accurate data for billing.

3 Time savings for consumers► When the smart/remote meters will be installed, consumers will be able

to save time, as the readings will be done automatically and will be precise. More accurate bills will decrease consumer complaints and save time, which they would use to solve billing related issues.Energy companies’ benefits

4 Debt management ► After installation of the smart meters, it will be possible to manage consumer and supplier balance more efficiently.

► For consumers, information about gas consumption can be communicated alongside cost information, which can help to raise awareness of consumption and its costs.

► For energy suppliers, more frequent and accurate consumption data for billing purposes will enable suppliers to identify customers at risk of building up debt, discuss and agree to reactive measures with the customer at risk. A supplier might, for example, provide energy efficiency advice to customers to reduce energy expenditure or might


No Indicator Descriptionoffer a different payment arrangement, or develop with a consumer a debt repayment plan.

5 Improved customer service ► Energy suppliers can improve their customer service by offering different tariffs for specific consumption patterns.

6More information regarding actual consumption and consumer usage patterns

► Energy companies will have better information about gas consumption during different usage periods (daily, monthly, quarterly usage) as well as times of the year. This will help to make more accurate consumption forecast, to improve their hedging ability and to reduce unnecessary costs.

► More detailed information could help to understand consumers’ energy usage patterns, and further create a possibility to offer consumers different gas tariffs regarding their usage patterns.

► Additionally, more detailed information could help to better manage energy companies’ activities and investments.

7 Avoided site visits

► After implementation of the smart meters, energy company’s technical staff will be able to receive and analyze most of the necessary information electronically, and for them it will not be necessary to visit consumers without any important reason.

► Reduction of site visits will result in lower costs for energy companies.

8 Easier switching between suppliers

► Introduction of the remote metering will smooth the process of changing a supplier. Trouble shooting teams employed to resolve exceptions or to investigate data issues will no longer be needed. Suppliers will be able to take accurate readings on the day of a change of a supplier, resolving the need to follow up any readings that do not match and reducing instances of miss-billing.

9 Better gas balancing and reduced balancing losses

► Due to smart and remote metering, companies responsible for balancing will be able to make more accurate forecasts for gas consumption which consequently reduce imbalances in gas networks and associated imbalance costs. Reduction in costs of imbalance losses might also reduce gas network tariffs for customers.

► This is also very relevant when customers change a gas supplier – new supplier will have historical data of the customer, which would allow to make better consumption forecasts for a new customer of this supplier.

10Reducing call center traffic, with fewer queries about estimated bills

► The smart/remote meters will improve accuracy of gas bills, and it is expected to result in a lower demand on call centers for billing enquiries and complaints.

11Compatibility with Network Code on Gas Balancing of Transmission Networks29

► Daily gas consumption data reporting to transmission operators (TSO), in Estonian case to Elering, will allow to be compatible with EC regulations of gas balancing of transmission networks.

► Information available for gas TSO will allow better predict necessary amounts in the network and also will prevent major incidents to happen which may cause gas leakages or even explosions due to better network monitoring.

Other benefits

12 Climate change

► Implementation of the smart/remote gas meters could possibly reduce CO2 emission amount.

► CO2 reduction can result from more efficient energy usage, decreased amount of site visits (decreased transport costs) and due to sooner identification of possible gas leakage.

13 Digitalization ► Implementation of the smart gas metering system might be a step towards wider digitalization in Estonia. Smart gas metering will introduce the possibility to access metering data online (through Elering’s Datahub), which also might be used to introduce other

29 Retrieved from: EC Regulation No 312/2014 of 26 March 2014 establishing a Network Code on Gas Balancing of Transmission Networks, http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32014R0312&from=EN

http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32014R0312&from=EN


No Indicator Descriptiondigitally based services, such as websites to explore different supplier options, which will be able to provide personalized offers and services based on gas consumption data.

4.6 Social benefits from smart gas metering deploymentSince all alternatives in the analysis assume similar scale of smart metering deployment, it is not possible to measure all possible smart metering benefits by comparing similar alternatives. However, there are several social benefits that can be measured by comparing against the current situation (where only 3.6% of meters are read remotely and have the necessary functionalities of a smart meter). In this case, it is possible to capture the benefits such as higher savings due to reduction in value of time spent reading a meter value manually, reduction in costs spent on gas consumption and reduction in value of CO2 emissions due to less gas burnt.In the figure below, please find the total accumulated benefits for the period of 2018-2030 which are derived comparing all alternatives with current situation.Figure 19 Total accumulated social benefits of smart metering deployment for the period of 2018-2030, million EUR

AS-IS Alternative I Alternative II0.24

1.46 1.461.43

1.43 1.431.49

1.49 1.49Total: 3.16

Total: 4.39 Total: 4.39

Saved value of time spent reading meter valueSaved gas consumption costsSaved value of CO2 emissions due to gas consumption

Figure above indicates that only value of time spent reading meter value differs among the alternatives: it shows that smart meter deployment only for customers, which consume more than 750 m3 per year, will reduce the benefits that can be captured from smart gas metering deployment for all customers – the differences between AS-IS situation and Alternatives I and II come from the benefits captured by the customers for whom remote gas meters will be installed under these two alternatives (household customers, using less than 750 m3 of natural gas per year). However, it also shows that smart metering will generate positive impact on Estonian society.

4.7 The most suitable scenario selectionThe most appropriate scenario is chosen considering the results and insights from different analysis: financial, social, socio-economic (macroeconomic impact) and qualitative. In addition to this, the most suitable scenario should:

► Take into consideration possibilities to combine both natural gas and electricity smart and remote metering capabilities.

► Have an optimal technical solution for data communication. ► Have a most beneficial solution for the economy and different customer groups.


Table 22 Comparison of Alternatives based on the most suitable scenario indicators

Most suitable scenario indicator Alternative I Alternative II Selected

alternative

Financial analysis

Financial net present value -7,104,337 -4,296,431 Alternative II

Social analysis

Economic net present value -6,172,008 -3,610,659 Alternative II

Socio-economic analysis (macroeconomic impact):

Impact of tariff changeTotal impact on GDP (direct and indirect)

-273,926 -185,432 Alternative II

Total impact on employment (direct and indirect)

-51 -34 Alternative II

Total impact on household disposable income (direct and indirect)

-1,583,919 -1,072,261 Alternative II

Total impact on government taxes and revenues (direct and indirect)

242,697 164,295 Alternative I

Impact of project investmentTotal impact on GDP (direct and indirect)

2,087,510 2,777,629 Alternative II

Total impact on employment (direct and indirect)

69 93 Alternative II

Total impact on government taxes and revenues (direct and indirect)

313,445 417,411 Alternative II

Other indicators

Possibility to combine natural gas and electricity metering systems

► This alternative does not include multi-metering solution, because multi-metering devices (wireless M-bus communication modules) are not included into investments.

► Different functionalities of smart gas meters might have an interface which would allow to add communication between meters later.

► This alternative includes synergy between electricity and gas metering systems. Installation of wireless M-bus communication modules will allow to connect electricity and gas meter through wireless connection (in the analysis it is assumed that installed smart electricity meters and remote gas meters will be equipped with wireless M-bus communication module, while smart gas meters already has a wireless M-bus communication module

Alternative II assumes synergy between electricity and gas metering systems as an optimal technical solution.


installed in the meter).► Synergy between electricity

and gas metering system also comes from combined IT systems: it is assumed that gas meters will be able to use electricity data collection system for their consumption and meter data (the data will be encrypted for the privacy issues).

► Meter data management systems for electricity and gas DSOs are assumed to be separate in order not to have gas meter data available for the third parties.

Optimal technical solution for data communication

► Optimal technical solution for this alternative is assumed to be GPRS (70%) and LPWAN (30%) communication technology mix. In other countries practice GPRS is the first option to choose; however it is more costly (monthly fee for SIM card is 0.7 Eur). While LPWAN technology is new and not widely tested; however, it is less expensive: monthly fee is assumed to be around 0.2 Eur.

► For this alternative, data communication technologies are not explored separately for gas because it is assumed that electricity metering system will be used for data transfer from meters to the system. It is assumed that annual fee for data communication for electricity DSOs would be 0.6 Eur per gas meter.

► The suggested communication technology for meter-to-meter connection is wireless M-bus, which is widely used in Europe due to its EU licensing and efficient way of connection – there is no need for cables between the meters, and the connection between them meets EU standards.

Alternative II assumes cheaper technical solution for data communication using electricity metering system, which is approved by EU regulation.

Customer benefits ► Even though socio-economic impact analysis have shown negative results for the economy, customer benefits should be measured taken into social analysis and qualitative study. This would encounter the non-quantitative benefits such as more accessible and accurate information, time savings for reduction in time spent reading the value of the meter manually. Additional benefits should also come from improved customer services of DSOs which will be able to identify gas leakages or others faulty

► The same benefits as in Alternative I might be encountered through this alternative, even though the higher investments and lower operational expenses result in lower negative impact towards customers.

► Additional benefits of combining electricity and gas metering systems, might result in higher customer satisfaction of combined services and possibility to get all necessary information as a one message or bill (i.e. using Elering’s Datahub information).

Both alternatives should generate similar customer benefits; however, Alternative II might include customer satisfaction of provided services related to the combined electricity and gas metering systems (i.e. one bill, one source of information related to energy consumption).


deterioration and work immediately.

Taking into account all of the results from different analyses, it is assumed that Alternative II would be more appropriate solution compared to Alternative I, as it would result in slightly lower tariff for customers even though the investment is assumed to be slightly higher than in Alternative I. The other benefits which will be created through smart gas metering introduction do not differ between the alternatives; however in the Alternative II the possible synergy between electricity and gas metering system might include an indirect customer satisfaction of combined electricity and gas metering system’s solution (e.g. one bill and one source of information, such as Elering Datahub).


5 Risk analysisFor all project’s Alternatives, in each of the project's critical stages (i.e. planning & design, procurement, implementation, etc.) there are risks identified that can substantially affect the successful completion of the project, as well as risks associated with the newly implemented smart metering system and operations of the infrastructure after the project is completed. For all with project alternatives it can be assumed that the identified risks are the same except that for some risks the impact and probability can slightly differ between the alternatives. In the table below a qualitative risk analysis is presented, including identified project’s risks, their description, likelihood, impact, total risk level as well as mitigation activities.Table 23 Risk analysis

No Risk Description Likelihood

Impact

Total risk

Measures for mitigation

Planning & design

1Actual investments into chosen technological solution do not meet the costs of planned investments

Actual investments into smart gas metering are significantly higher or lower than expected (planned) and could not meet estimated project's budget on infrastructure. Discrepancy occurs due to the limited information from different suppliers during period of the analysis, thus investments in real time might differ from calculated costs.

B III Moderate

The market research of the proposed technological solution needs to be updated in a timely manner. For final investment decision the sensitivity analysis should be conducted. During the estimation of the project's costs, it is necessary to involve independent experts with knowledge of the market prices of relevant cost items. If the estimated costs increase, cheaper analogue solutions must be sought or other costs must be reviewed within the scope of the budget. In case of necessity to attract extra funding to cover additional costs, financial institutions should be approached to negotiate a possible extension of loan or the incurred excessive costs should be covered from the budget of the applicant.

2 Unexpected changes in legislation.

During period of project implementation changes occur in legislation, which affect the project’s implementation.

B II Low It is necessary to follow up with the current/planned future trends in national Estonian and EU legislation.

3Estonian government is not fully involved in decision making process for smart gas deployment project

The decision making of Estonian government does not allow to develop smart gas metering project and services related to smart meters.

B III Moderate

It is necessary to get involved in communication with Estonian government representatives in order to keep them introduced with upcoming project development.

4 Need for additional The need for adjustments of C III Moderat It is necessary to have an experienced project



Impact

Total risk


adjustments of smart gas metering deployment plan

smart gas metering deployment might include additional work for financial and deployment planning due to inaccurately prepared deployment plan.

emanagement and planning team and keep contact with the team on a timely manner in order to understand the status and upcoming tasks.

5

Discoordination between project stakeholders and other interested parties such as other utilities (electricity DSOs, district heating suppliers, etc.)

Differences in interests and regulation for various suppliers of utility services. Too little engagement into project development of other stakeholders.

C III Moderate

Project managers should maintain consistent communication with all stakeholders in order to meet their expectations and needs from the project.

6

Lack of engagement from key stakeholders (i.e. regulating authorities, government, gas DSOs) and other interested parties (i.e. electricity DSOs) which results in delays of project implementation

Lack of engagement of key stakeholders might restrict further project development or even stop project's development in case of electricity DSOs refusal to engage into project's implementation in Alternative II.

C IV High

It is necessary to keep contact with all key stakeholders and interested parties and try to involve them in project development from the starting point. In case of Alternative II, it is necessary to clearly define the responsibilities of gas and electricity DSOs regarding the required communication modules installation on electricity meters, and take into account the regulation of electricity metering system before starting the deployment.

7No acceptance of electricity DSO’s to use their electricity smart meters for sending gas data

Lack of collaboration from electricity DSOs which might result in inability to use their metering infrastructure to transfer gas metering data.

C IV High

It is necessary to understand electricity DSOs main concerns about the synergy of gas and electricity metering infrastructure by keeping constant contact with their representatives, clearly define responsibilities between different parties.

8 Inadequate project management structure

Project’s implementation will require staff with experience in project management, finance, procurement and legal matters. There is a possibility that the skillset of the project’s management team could be insufficient.

B III Moderate

In order to carry out works and procurement procedure and to ensure successful implementation of the project, it is recommended to establish a project team involving both local and international experts before the start of the project's implementation.

Procurement



Impact

Total risk


9Increase in duration of the public procurement procedure

The actual public procurement procedure takes more time than planned due to inability to find suitable contractor or unexpected changes in procurement requirements.

B III Moderate

It is necessary to start the public procurement procedure with a contingency time allowance because of possible appeals.

10The defined contract conditions do not appropriately transfer risks to the contractor

In the contract the responsibilities of the contractor are not clearly defined and includes only a small scale of responsibilities for contractor.

B II LowIt is necessary to involve legal experts with relevant experience and knowledge in tender dossier preparation and agreement formulation.

11Unclear qualification and selection criteria of participants

In the public tender the necessary qualifications and selection criteria are not clearly defined due to lack of experience in similar projects procurements.

B III Moderate

During development of selection criteria for the public tender, it is necessary to involve experts with relevant experience and knowledge in tender dossier preparation and selection requirement (criteria) formulation.

Implementation

12The stakeholders are dissatisfied with the project’s implementation

The way the project is being implemented does not meet the expectations of the stakeholders, causing dissatisfaction.

B II LowIt is necessary to inform the stakeholders about each project’s stage results and to make regular status meetings.

13Lack of detailed information in the technical solution design

The technical solution design is not comprehensive enough to provide all the necessary details due to lack of experience in similar projects.

B III Moderate

It is necessary to involve qualified experts and to ensure information and documentation exchange using both electronic means of information exchange and regular meetings with the project's implementers. In order to come up with optimal technical solution, different possible technical solutions should be analyzed taking into account the most advanced solutions.

14 Unforeseen issues not addressed in the technical solution design

There is a possibility that the technical solution design does not foresee or address some issues, i.e. inability to access

B III Moderate

It is necessary to involve qualified experts that will be able to solve all unforeseen issues.



Impact

Total risk


the meters or deploy them that occur during implementation.

15

Unforeseen issues with technical requirements for electricity and gas metering synergy which were not addressed in the technical solution design

Additional configuration and costs for technical updates for electricity meters arise which might cause delays and additional investments into gas and electricity synergy metering solution.

B III Moderate

It is necessary to involve experts with experience in similar project before smart metering deployment.Technical parameters of both metering system should be discussed in detailed with particular experts from gas and electricity DSOs.Optimal solution should include both parties (electricity and gas DSOs) in project development in order to have both parties accounted for unexpected problems and their solving.

16 Delayed implementation

Implementation is not done in a timely manner due to lack of resources and experience in similar projects or delayed required legislation.

B III Moderate

It is necessary to foreseen an additional time for implementation of the smart meters.

17Implementation of the smart meters is done in poor quality

The contractors set up the gas smart meters in poor quality due to lack of experience in similar projects.

B III Moderate

It is necessary to clearly define in the construction contracts what happens if there is a failure to comply with the conditions of the contract.

18 Need for additional investments

Additional investments into smart gas metering system (i.e. additional IT systems for network management, etc.) may be required to fully exploit the benefits of the smart meters’ implementation.

B III Moderate

If the required investment amount is higher than the expected, it is necessary to attract additional funding.

19Significant number of repeated visits in order to install gas smart meters

Repeated visits due to inability to access metering point or unexpected technical issues are costly for the project's implementer.

D III High

In the agreement with smart gas metering deployment implementer (possible subcontractor) there should be a clause about purchased ready to use solution installation services which would mean that there are no additional costs for gas DSO of revisiting the same customer.

20 General sub-contractors cannot fulfil the contract

Contractors cannot set up the gas smart meters on time or

B III Moderat It is necessary to clearly define in the construction contracts what happens if there is a failure to comply



Impact

Total risk


obligations the work is done in unacceptable quality. e

with the conditions of the contract. Contract have to include penalties for contractor if not all conditions (or not to the full extent) of the agreement are met.

Infrastructure testing and entry into operation

21 Delayed smart gas metering entry into service

Various issues (i.e. inability to access metering points, unexpected technical issues) may arise while implementing the new smart meters, thus causing delays in its enforcement.

B III Moderate

It is necessary to identify possible obstacles of implementing smart meters in advance and to solve them as soon as possible. Additionally, it is necessary to prepare and to regularly monitor the detailed implementation plan.

22 Failures found during testing phase

While performing infrastructure testing, it is found that some part of the system is not functioning properly (i.e. failures of data transfer, data collection system).

B III Moderate

During the implementation process it is necessary to involve qualified construction experts and IT specialists, who in a timely manner can identify and prevent flaws.

23 Underestimated testing costs

Due unexpected issues with the system, testing can result in higher costs than expected (i.e. it might require retesting after some of the issues are solved which also will require additional workers on this matter).

B III Moderate

In order not to have increases in testing costs, it is necessary to include the condition of project implementation of 100%, meaning that contractor have to complete the deployment of smart metering system which works without any issues, and required testing does not increase the price of the system implementation.

Operating activities

24 The smart meters are defected

There is a possibility that all errors or defects have not been identified during the infrastructure testing. There could be defects in the smart meters, e.g. inaccurate measures or technical errors.

B III Moderate

It is necessary to perform tests before the meters installation, to clearly define quality measures in the contract with the supplier and to define what further actions are in case smart meters are defected.

25 The quality of services related with smart gas

The quality of the services could be less than expected

B III Moderate

In the project’s implementation stage, it is necessary to involve technical experts in each project’s



Impact

Total risk


metering system are below the expected

before the implementation due to lack of experience in similar project or inability to implement technical solution to the full extent.

implementation stage and to regularly perform quality checks on smart gas metering system and equipment deployment.

26 Insufficient equipment for maintenance works

The company does not have the required equipment to perform maintenance works of the newly implemented smart metering system.

B II LowThe company needs to understand in a timely manner what kind of maintenance works will be required and what equipment will be needed in order to perform them.

27Operational and maintenance costs are higher than planned

Costs incurred during operation or maintenance of the smart meters could be higher than expected due to unpredicted issues.

B III Moderate

If the actual costs are higher than expected, it is necessary to search for a cheaper analogue solution or other costs must be reviewed within the scope of the budget.

28The stakeholders are dissatisfied with the results of the implemented project

It is possible that the expectations of the stakeholders regarding the project and its work are not met, therefore causing dissatisfaction.

B II LowIt is necessary to inform the stakeholders about each phase of the project and to report periodically regarding the project’s results.

29 Shortages of skilled labor for operation and maintenance

There are not enough skilled workers to operate and maintain the smart metering system.

B II LowIt is necessary to perform training for all technical specialists so that they will be able to maintain and employ the new smart metering system.

30

Smart gas metering system failure can cause disruptions in daily activities of related parties (i.e. gas DSOs and suppliers)

In case the system fails, it can take a while until it is fixed, causing disruptions in operating activities and billing processes of gas DSOs and/or natural gas suppliers.

B IV Moderate

It is necessary to perform regular system’s inspections and to develop an action plan in case of system failure.

31Consumers refuses to accept implementation of natural gas smart meters

Natural gas consumers do not accept the implementation of natural gas smart meters due to lack of information on

B III Moderate

It is necessary to inform consumers about implementation of smart meters and potential benefits from the project’s implementation.



Impact

Total risk


potential benefits of smart gas metering.

32 Changes in consumer preferences

Consumers start to prefer alternative energy sources more than natural gas what stimulates decreasing gas consumption and reducing the amount of smart meters below the projected amount.

C IV High

It is necessary to make an in-depth market research regarding trends in the gas market that would help to determine the necessary amount of smart meters to be implemented and would assist in determining the potential benefits from the project’s implementation.Additionally, it is necessary to ensure satisfactory customer service and transparent pricing strategy in order to maintain the existing customer base.

33 Changes in consumer gas consumption preferences

After the increase or reduction of gas tariff, customers change their natural gas consumption preferences which might mean the lower consumption due to higher distribution service tariff.

C IV High

It is necessary to understand customer preferences for gas consumption which might lead to adjusted tariff system for different customer segment.Additionally, it is necessary to ensure satisfactory customer service and transparent pricing strategy in order to maintain the existing customer base.

34

Installed new smart meters and related infrastructure (equipment and IT systems) are more sensitive to cyber security attacks

New measuring devices are more sensitive to cyber security attacks which might result in the need for additional security programing (i.e. IT systems which helps to secure customer's data from cyber-attacks).

B III Moderate

In order to secure new devices from cyber security attacks, it is necessary to have experienced IT specialists working with IT systems development, programming and updating which will help to avoid a possibility of cyber-attacks. In addition, based on the recommendations on preparation for the roll-out of smart metering systems30, data protection and information security features should be built into smart metering systems before they are rolled out and used extensively. Such features can effectively improve consumers’ control over the processing of personal data.

The main risks associated with the implementation of the Project are as follows:► Lack of engagement from key stakeholders (i.e. regulating authorities, government, gas DSOs) and other interested parties which results in

delays of Project implementation. 30 Retrieved from: http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32012H0148&from=EN



► No acceptance of electricity DSO’s to use their electricity smart meters for sending gas data.► Significant number of repeated visits in order to install gas smart meters.► Changes in consumer preferences, which means that consumers start to prefer alternative energy sources more than natural gas, decreasing gas

consumption and reducing the amount of smart meters below the projected amount. ► Changes in consumer gas consumption preferences due to higher gas distribution tariff.


In order to mitigate the abovementioned risks it is required to keep contact with all key stakeholders and interested parties and try to involve them in Project development from the starting point, develop an action plan in case of project failure, make an in-depth gas market analysis in order to understand consumer preferences of new services and overall gas consumption.The main risks that can differ between the alternatives are risks related with lack of engagement and acceptance of the Project development by key stakeholders and unexpected technical limitations between gas and electricity metering systems. In Alternative II it is assumed that gas DSOs will be using electricity meters which will collect gas consumption data and will send it to data collection system of electricity DSO, while data management will be done by a separate gas meter data management system. Total reliability on electricity metering infrastructure might cause project delays due to more complicated project management process between electricity and gas DSOs and more complex technical requirements. Due to these assumptions, the key risks that differ between the alternatives are as follows:

► No acceptance of electricity DSO’s to use their electricity smart meters for sending gas data.► Unforeseen issues with technical requirements for electricity and gas metering synergy which

were not addressed in the technical solution design.


6 Sensitivity analysisThe following section presents the sensitivity analysis for the most suitable project alternative – Alternative II – based on the results of financial analysis and socio-economic impact assessment from tariff change. Sensitivity analysis is not presented from the socio-economic impact assessment of project investment side as it only affects installation costs and therefore has less significant effect towards Estonian economy. Sensitivity analysis is presented as an effect towards socio-economic impact assessment’s results: from the project implementer’s side – effect on financial net present value (FNPV) and from the society side – effect on cumulative impact on GDP calculated from tariff change.As smart gas metering project implementers - gas DSOs operate in a regulated business environment, it is unreasonable to assess the impact of the changes in price of natural gas as it highly correlates with analysis calculated outcome – distribution tariff. Regarding the different scenarios of gas consumption in Estonia (based on the data provided by Elering) it is also seen that this parameter is tightly interrelated with distribution tariff in the analysis as it affects the changes in distribution tariff. Sensitivity analysis has been conducted for three variables which are not specifically related to Alternative II, thus they are not discussed in detail. These variables are:

► Redistribution of IT costs when different utilities are involved;► Distribution between communication technologies (GPRS and LPWAN).► Data communication costs for Alternative I (the same costs are assumed for AS-IS situation).

IT redistribution was tested for Alternative I, where gas DSOs will have their separate system and thus, other utilities could use gas DSOs metering infrastructure, shows that inclusion of all currently known other utility meters (based on Elering information 35,772 district heating and water supply meters) will have very small positive effect towards analysis results – 0.05% increase in FNPV and 0.04% increase in cumulative impact on GDP calculated from tariff change for Alternative I. While sensitivity tested for other variable (distribution of communication technologies – GPRS and LPWAN), showed that for Alternative I it has significant impact: if LPWAN is used for 100% of gas smart / remote meters, it increases FNPV by 41.16% and impact on GDP by 33.83%. Thus, sensitivity analysis for Alternative I shows that distribution of communication technologies is a key parameter for Alternative I. In addition to this, data communication costs is a sensitive variable for results of Alternative I – reduction of data communication costs (costs for GPRS and LPWAN communication used in Alternative I) for this alternative results in higher FNPV by 7.84% and cumulative GDP by 6.44%.

6.1 Analysis on the effect of different variables changes towards socio-economic impact assessment’s results

The sensitivity analysis for Alternative II is performed on these variables:► Meter prices;► Other equipment prices;► Installation prices of the meters and equipment;► Operational costs of IT systems;► Data communication costs for Alternative II;► Distribution of installation costs between DSOs and suppliers;► Natural gas consumption (only from the perspective of distribution tariff);► Different consumption benchmarks31 for household customers using specific amount of natural

gas per year.The outcomes of changes in one of the variable are derived holding other variables constant. As a result, the sensitivity analysis (see figures below) shows how changes in the above mentioned variables affect the estimations of FNPV and impact on GDP in comparison to the AS-IS situation for the period of 2018-2030.

31 Consumption benchmark is a specific amount of consumption of natural gas per year based on which:► Under AS-IS situation – customers using less than specific consumption benchmark will not have a smart gas meter

installed. For all using more – smart gas meters are installed;► Under Alternatives I and II – customers using less than specific consumption benchmark will have a remote gas meter

installed. For all using more –smart gas meters are installed.


For variables such as meter prices, other equipment prices, installation costs and operational costs of IT systems five distinct cases of changes are presented in the table below.Table 24 Sensitivity cases for variables such as meter prices, other equipment prices, installation costs, operational costs of IT systems and data communication costs

Pessimistic Less pessimistic Base case Less optimistic Optimistic+25% +10% 0% (no change) -10% -25%

For other variables (distribution of installation costs, different consumption benchmarks for household customers using specific amount of natural gas per year and gas consumption scenarios) more specific sensitivity cases are tested which are presented in the table below.Table 25 Sensitivity cases for variables such as distribution of installation costs, different consumption benchmarks and natural gas consumption amounts

For distribution of installation costs such cases are tested:100% DSO – 0%

supplier70% DSO – 30 %

supplier (base case)50% DSO – 50%

supplier30% DSO - 70%

supplier0% DSO – 100%

supplierFor different consumption benchmarks32 such cases are tested:

1100 m3 900 m3 750 m3 (base case) 500 m3 300 m3

For different natural gas consumption amounts such cases are tested:Pessimistic natural gas consumption forecast

Base natural gas consumption forecast

Optimistic natural gas consumption forecast

In the figure below, effects on FNPV from changes in values of meter/other equipment prices, installation prices and operational costs of IT systems are presented.Figure 20 Changes in FNPV due to changes in values of meter prices, other equipment prices, installation prices, operational costs of IT systems and data communication costs

-25% -10% 0% 10% 25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%16.00%

-16.00%

-4.82%

4.82%

Smart / remote meter pricesInstallation costs of meters and other equipmentOperational costs of IT systemsOther equipment pricesData communication costs for Alternative II

Change in variable, %Ch

an

ge

in F

NP

V, %

The effect on GDP (calculated from tariff) due to changes in the same variables are presented below.

32 Consumption benchmark is a specific amount of consumption of natural gas per year based on which:► Under AS-IS situation – customers using less than specific consumption benchmark will not have a smart gas meter

installed. For all using more – smart gas meters are installed;► Under Alternatives I and II – customers using less than specific consumption benchmark will have a remote gas meter

installed. For all using more –smart gas meters are installed.


Figure 21 Changes in impact on GDP (calculated from tariff) due to changes in values of meter prices, other equipment prices, installation prices, operational costs of IT systems and data communication costs

-25% -10% 0% 10% 25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%15.35%

-15.35%

-3.58%

3.58%

Smart / remote meter pricesInstallation costs of meters and other equipmentOperational costs of IT systemsOther equipment pricesData communication costs for Alternative II

Change in variable, %

Ch

an

ge

in im

pa

ct o

n G

DP

, %

Both cases show almost similar results: increases in meter, equipment and installation prices has a negative effect towards FNPV and socio-economic impact assessment result (impact on GDP from tariff change): increasing meter prices, other equipment prices and installation costs by 25% decreases FNPV by 16.00%, 14.48% and 6.78% respectively and socio-economic assessment result reduces by 15.35%, 12.62% and 6.11% respectively, having otherwise effect on FNPV and cumulative GDP if meter prices and installation costs are decreased by 25%. The similar effect is seen from changes in data communication costs for Alternative II: increasing data communication costs by 25% reduces FNPV by 1.93% and cumulative GDP by 1.42%, having otherwise effect if communication costs are decreased. However, operational costs of IT systems show a different trend which is the result of changes in AS-IS situation – decreasing the operational costs in AS-IS scenario makes it more positive compared to changes in operational costs of IT systems in Alternative II.In the figures below, changes in FNPV and cumulative GDP (calculated from tariff impact) are presented due to changes in the distribution of installation costs of meters and other equipment between DSOs and subcontractors.Figure 22 Changes in FNPV due to changes in the distribution of installation costs between DSOs and subcontractors

100% - 0% 70% - 30% 50% - 50% 30% - 70% 0% - 100%

-20%

-10%

0%

10%

20%

30%

40%

50%

-16.77%

0.00% 11.18%

22.36%

39.12%

Installation costs (distribution between DSO - Subcontractor)

Change in distribution, %

Ch

an

ge

in

FN

PV

, %


Figure 23 Changes in impact on GDP (calculated from tariff) due to changes in the distribution of installation costs between DSOs and subcontractors

100% - 0% 70% - 30% 50% - 50% 30% - 70% 0% - 100%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

-11.73%

0.00%

7.76%

15.56%

27.25%

Installation costs (distribution between DSO - Subcontractor)

Changes in distribution, %Ch

an

ge

s in

im

pa

ct

on

GD

P, %

Similar trend is seen in distribution of installation costs between DSO and subcontractor: having installation of subcontractor of 100% shows very positive effect on FNPV (39.12%) and on socio-economic impact on GDP (27.28%) due to lower installation costs for meters and other equipment installed by subcontractors.In the figures below, different consumption benchmarks for household customers are analyzed. In the base case scenario (750 m3 of natural gas consumption per year benchmark) it is assumed that households consuming less than 750 m3 of natural gas per year will not be equipped with new smart meter in AS-IS situation, while in Alternative II they will have a remote meter installed. In all other cases, the specific consumption benchmark means that all household customers using less than the specific amount of gas per year will not be equipped with smart gas meter under AS-IS situation and will have a remote gas meter installed under Alternative II.Figure 24 Changes in FNPV due to changes in consumption benchmarks for household customers

300m3 500m3 750m3 900m3 1100m3

-30%

-20%

-10%

0%

10%

20%

30%

40%

50% 43.93%

20.60%

0.00%

-10.00%

-21.79%

Consumption benchmark

Consumption benchmark, m3

Ch

an

ge

in

FN

PV

, %


Figure 25 Changes in impact on GDP (calculated from tariff) due to changes in consumption benchmarks for household customers

300m3 500m3 750m3 900m3 1100m3

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30% 26.67%

11.94%

0.00%

-5.48%

-11.70%

Consumption benchmark

Consumption benchmark, m3Ch

an

ge

s in

im

pa

ct

on

GD

P, %

In both cases, lowering the consumption benchmark for household customers generates very positive effect towards analysis results (lowering consumption benchmark to 300m3 increases FNPV by 43.93% and cumulative impact on GDP by 26.67%), which shows that possibility to have more customers equipped with meters which has all smart metering functionalities can shift project outcome towards more positive result. The positive effect of lower benchmark is due to several aspects – lowering consumption benchmark for AS-IS situation mean that later there will be less meters which will need reinstallation (starting from 2024), while for Alternative II it means that it is more beneficial to have more smart meters due to only one communication module installation on smart electricity meter instead of two: on remote gas meter and smart electricity meter.The last case which is analyzed in the sensitivity analysis is different gas consumption forecasts provided by Elering. In the table below three distinct consumption scenarios are presented which are later analyzed in distribution tariff calculations.Table 26 Different natural gas consumption in Estonia forecasts33

Year Base case gas consumption forecast (m3)

Optimistic gas consumption forecast (m3)

Pessimistic gas consumption forecast (m3)

2016 519,575,059 519,575,059 519,575,0592017 476,107,335 525,317,455 460,096,3472018 471,521,544 535,363,916 447,262,1642019 466,935,752 545,530,477 434,566,9032020 462,349,961 555,764,447 422,003,6182021 461,483,119 567,938,818 413,177,6272022 460,521,551 580,252,222 404,470,7442023 459,465,256 592,711,609 395,877,0152024 458,314,235 605,324,280 387,390,7812025 457,068,487 618,097,898 379,006,6672026 455,870,102 631,040,510 370,719,5692027 450,083,475 643,428,234 358,565,7792028 448,023,670 656,059,137 346,810,4432029 445,973,293 668,937,992 335,440,4982030 443,932,298 682,069,667 324,443,308

Changes in distribution tariff based on different gas consumption forecasts are presented in the tables below.

33 Source: Elering AS


Table 27 Average changes for the period in distribution tariff (average of all gas DSOs in Estonia, not a particular companu) due to different gas consumption forecasts

Base case gas consumption forecast

Optimistic gas consumption forecast

Pessimistic case gas consumption forecast

Period AS-IS, EUR/m3

Alt II, EUR/m3

Change in DSO tariff,

EUR/m3

AS-IS, EUR/m3

Alt II, EUR/m3


EUR/m3

AS-IS, EUR/m3

Alt II, EUR/m3


EUR/m3

2018-2019 0.0767 0.0765 (0.0002) 0.0762 0.0760 (0.0002) 0.0769 0.0767 (0.0002)2020-2024 0.0772 0.0789 0.0017 0.0763 0.0776 0.0013 0.0778 0.0797 0.00192025-2030 0.0775 0.0790 0.0015 0.0760 0.0771 0.0011 0.0788 0.0807 0.0019

From the table above it is seen that higher gas consumption in the future could lead to lower increase in distribution tariff compared to base case or pessimistic forecast, while pessimistic forecast might have quite negative effect towards AS-IS situation too.


6.2 Analysis on the effect of reinvestment into remote or smart gas meters under AS-IS situation

Since AS-IS situation assumes the reinvestment into standard gas meters for customers using less than 750 m3 of natural gas per year in the period of 2024-2029, in order to check how the reinstallation into more advanced meters during the same period of time would affect the investments for AS-IS situation, the sensitivity of reinvestment into different metering solutions is tested. Both additional scenarios also assume that no standard gas meters are installed from 2018 (starting of smart/remote metering deployment), which means that defected standard gas meters are changed into smart or remote meter and new customers consuming less than 750 m3 of gas per year are already equipped with remote or smart gas meter, depending on the scenario (AS-IS with Remote or AS-IS with Smart).In the table below, reinvestments into standard, remote and smart gas meters for customers using less than 750 m 3 of gas per year are presented for AS-IS situation, separating the costs for period of project implementation (2018-2022) and project calculation (2018-2030). Table 28 Reinvestments into standard, remote and smart gas meters for AS-IS situation for the period of project implementation and calculation, EUR

Component of CAPEX

AS-IS with standard meters AS-IS with remote meters Difference from AS-IS with standard meters AS-IS with smart meters Difference from AS-IS

with standard meters


period (2018-2030)

During Project


2022)


period (2018-2030)

During Project


2022)

Difference in total

Difference in implementati

on period


period (2018-2030)

During Project

implementation

(2018-2022)

Difference in total

Difference in implementat

ion period

Gas meters 11,934,262 5,346,702 12,708,762 5,378,982 774,500 32,280 13,286,058 5,582,118 1,351,796 235,416Data loggers 358,200 225,600 358,200 225,600 0 0 358,200 225,600 0 0Communication modules (Wireless M-bus)

0 0 1,500,496 129,790 1,500,496 129,790 0 0 0 0

IT systems (Data collection and MDM) 225,396 109,541 353,837 114,462 128,440 4,920 364,829 114,462 139,432 4,920Installation costs 6,553,849 2,290,129 7,154,995 2,290,975 601,146 846 6,358,565 2,315,345 -195,284 25,216Project management 0 0 0 0 0 0 0 0 0 0Project publicity 0 0 0 0 0 0 0 0 0 0Total: 19,071,707 7,971,972 22,076,290 8,139,808 3,004,583 167,836 20,367,652 8,237,525 1,295,945 265,553

From the results of these calculations it can be observed that alternative to install more advanced gas meters for customers using less than 750 m 3 of gas per year when the expected lifetime of their old standard meters will end, will result in higher investments; however these investments are only 1.2 m EUR (with smart meters) and 3.0 m EUR (with remote meters) higher then reinvestments into standard meters. These differences show that either way reinvestment will require additional funds which would be more efficient to spent on more advanced technology and make every gas measurement point compatible with installed metering system infrastructure (i.e. IT systems).


In the table below, analysis results for both project alternatives are presented, highlighting the difference in the results when AS-IS scenario assumes reinvestments for household customers using less than 750 m3 of natural gas per year into remote or smart gas meters.Table 29 Effect of reinvestments into standard, remote and smart gas meters for AS-IS situation on analysis results, EUR

Analysis result

Alternative I (compared to

AS-IS with standard meters)

Alternative II (compared to



AS-IS with remote meters)

Difference from



Alternative II (compared to AS-IS with

remote meters)

Difference from


standard meters)


AS-IS with smart meters)

Difference from




smart meters)

Difference from


standard meters)

FNPV -7,104,337 -4,296,431 -4,509,169 2,595,168 -1,701,263 2,595,168 -5,879,966 1,224,371 -3,072,060 2,595,168ENPV -6,172,008 -3,610,659 -4,161,533 2,010,474 -1,600,185 2,010,474 -5,416,520 755,488 -2,855,171 2,010,474Impact on GDP -273,859 -185,365 -208,567 65,292 -99,844 85,521 -188,397 85,462 -79,820 85,521

Both reinvestment situations result in better analysis results (FNPV, ENPV and impact on GDP) for project’s alternatives due to the fact that reinvestments into remote or smart gas meters are more expensive than reinvestments into standard gas meters, thus compared AS-IS and project alternatives become more similar in terms of investments.


6.3 Analysis on the effect on investments and operational expenses of introducing different consumption benchmarks for household customers under AS-IS situation

In order to test whether consumption benchmark of 750 m3 of natural gas per year defined in Natural Gas Act should be changed to lower or higher benchmark, an analysis on generated investments and operational expenses introducing different consumption benchmarks for household customers is conducted. It shows how investments and operational expenses change when a lower or higher consumption benchmark for household customers is introduced under AS-IS situation. In the table below, different investments and operational expenses for consumption benchmarks of 300 m3, 500 m3, 750 m3 (base case), 900 m3 and 1100 m3 under AS-IS situation are presented.Table 30 Investments and operational expenses for different consumption benchmark under AS-IS situation, EUR

Component of CAPEX

AS-IS with 300 m3

benchmarkAS-IS with 500 m3


benchmark (base)AS-IS with 900 m3


benchmarkDuring all calculation

period (2018-2030)

During Project

implementation

(2018-2022)


period (2018-2030)

During Project

implementation

(2018-2022)


period (2018-2030)

During Project

implementation

(2018-2022)


period (2018-2030)

During Project

implementation

(2018-2022)


period (2018-2030)

During Project

implementation

(2018-2022)Gas meters 12,336,372 6,610,022 12,118,332 5,922,312 11,934,262 5,346,702 11,850,402 5,077,322 11,752,282 4,766,622Data loggers 358,200 225,600 358,200 225,600 358,200 225,600 358,200 225,600 358,200 225,600Communication modules 0 0 0 0 0 0 0 0 0 0IT systems (Data collection and MDM) 285,527 138,770 252,781 122,853 225,396 109,541 212,586 103,317 197,774 96,115Installation costs 6,472,628 2,860,608 6,517,404 2,550,084 6,553,849 2,290,129 6,572,216 2,168,496 6,592,724 2,028,204Project management 0 0 0 0 0 0 0 0 0 0Project publicity 0 0 0 0 0 0 0 0 0 0Total CAPEX: 19,452,727 9,834,999 19,246,717 8,820,849 19,071,707 7,971,972 18,993,404 7,574,734 18,900,980 7,116,541

Component of OPEXOperational costs for IT systems 2,275,000 875,000 2,275,000 875,000 2,275,000 875,000 2,275,000 875,000 2,275,000 875,000Data communication costs 3,239,562 1,128,937 2,870,661 1,002,117 2,562,225 896,064 2,417,841 846,428 2,251,046 789,066Costs of operations and maintenance of remote and smart meters

2,784,248 986,210 2,565,706 911,080 2,382,988 848,254 2,297,453 818,848 2,198,642 784,867

Costs of change in meters 177,000 64,541 174,654 63,775 172,678 63,109 171,690 62,713 170,702 62,392Costs of manual readings 736,522 286,274 832,580 319,300 912,900 346,916 950,502 359,846 993,940 374,780Costs of technical maintenance 1,858,170 447,186 1,690,140 417,216 1,549,614 392,124 1,483,842 380,388 1,407,864 366,852Costs of verification of gas meters with pressure conversion

4,275 0 4,275 0 4,275 0 4,275 0 4,275 0


Component of CAPEX

AS-IS with 300 m3



benchmark (base)AS-IS with 900 m3


benchmarkDuring all calculation

period (2018-2030)

During Project

implementation

(2018-2022)


period (2018-2030)

During Project

implementation

(2018-2022)


period (2018-2030)

During Project

implementation

(2018-2022)


period (2018-2030)

During Project

implementation

(2018-2022)


period (2018-2030)

During Project

implementation

(2018-2022)Costs of repaired connections of gas and electricity meters

0 0 0 0 0 0 0 0 0 0

Total OPEX: 11,074,777 3,788,148 10,413,016 3,588,488 9,859,679 3,421,466 9,600,603 3,343,223 9,301,469 3,252,957CAPEX+OPEX

CAPEX 19,452,727 9,834,999 19,246,717 8,820,849 19,071,707 7,971,972 18,993,404 7,574,734 18,900,980 7,116,541OPEX 11,074,777 3,788,148 10,413,016 3,588,488 9,859,679 3,421,466 9,600,603 3,343,223 9,301,469 3,252,957Total: 30,527,504 13,623,148 29,659,733 12,409,338 28,931,386 11,393,438 28,594,006 10,917,958 28,202,449 10,369,498Difference from AS-IS with 750 m3 consumption benchmark

1,596,118 2,229,709 728,346 1,015,899 0 0 -337,380 -475,481 -728,937 -1,023,941

Calculations of investments and operational expenses for different consumption benchmarks under AS-IS situation shows that lowering consumption benchmark will result in higher costs for project’s implementer compared to base case (comparing the total costs for all calculation period), while higher consumption benchmark will not result in significantly lower costs for project’s implementer compared to the base case (comparing the total costs for all calculation period). However, this analysis only shows the variations in AS-IS situation considering the amendments to Natural Gas Act related with consumption level of compulsory smart metering deployment and in order to assess most relevant consumption level for Estonian economy, AS-IS situation should assume no smart metering deployment. In addition to this, before any decision (change in consumption level for compulsory smart metering deployment) is made the possibility to capture more social benefits from introducing smart meters to more household customers should be taken into account – when more customers have smart meters, more customers save their time on reading meter value manually as well as more customers have a possibility to get more accurate bills based on the remotely read meter values.


6.4 Optimal scenarios derived from sensitivity analysisConducted sensitivity analysis for Alternative II has shown that the most sensitive parameters are meter/other equipment prices, installation costs and different consumption benchmarks. However, changing only one of them and leaving the other variables constant is not sufficient to achieve break-even point (i.e., the point of neutral result) of the Alternative II with reasonable fluctuation range. In the table below, break-even points (changing percentages) for aforementioned variables is shown (changing only one variable keeping others constant).Table 31 Breakeven points of changes for meters/other equipment prices, installation costs and consumption benchmark which will generate 0 ENPV

Breakeven point for meter prices

Breakeven point for other

equipment pricesBreakeven point for

installation costsBreakeven point for gas consumption benchmark ENPV

-130% -146% -294% 70 m3 0

A possibility to make smart metering deployment without negative effect towards the society would be trying to make economic net present value positive. It would mean that economic benefits generated from the project deployment equals financial costs which causes society neither positive nor negative impact. In the table below necessary changes for all critical variables at once are identified in order to get 0 economic net present value (ENPV) – meaning that project will not have negative effect for the society.Table 32 Changes meters/other equipment prices, installation costs and consumption benchmark which will generate 0 ENPV

Change in meter prices

Change in other

equipment prices

Change in installation

costs

Consumption

benchmark

Minutes spent to read meter

valueFNPV, EUR

ENPV, EUR

Impact on GDP, EUR

-34% -34% -43% 300m3 4 -480.230 0 -53.483

However, this ENPV still generates negative impact on GDP calculated from tariff change – it reflects the negative value of FNPV since change in tariff takes into account increased investment and operational costs for smart metering deployment project and does not take into account possible economic benefits for the society. Negative impact on GDP shows that increased tariff leaves overall economy with less disposable income to spend in other economy sectors.

6.5 Smart metering deployment for all customersSensitivity analysis also allows to look for results of smart gas metering deployment for all customers regardless households’ consumption of natural gas per year. Changing the variable of consumption benchmark for household customers to 0 indicates that it has a very positive impact towards all analysis results. Analysis results for smart metering deployment for all customers under Alternative II are presented in the table below.Table 33 Analysis results if smart gas metering deployment will be done for all customers

Result indicator Alternative IIFinancial net present value 2.363.021

Economic net present value 2.087.622

Total impact on GDP (direct and indirect) 64.853


7 RecommendationsThe following chapter consists of recommendations for selecting an alternative for smart gas metering deployment for end-gas customers in Estonia. Current and future suggestions for smart gas metering deployment are followed by their compatibility with the Estonian legal system as well as technical implementation possibilities.

7.1 Smart gas metering deployment based on the most suitable alternative

After conducting financial modelling and socio-economic impact assessment, it is found that analyzed alternatives might not create specific direct benefits for the Estonian society; however, it will create the additional qualitative benefits which cannot be reasonably expressed in quantitative terms. In the analysis different customer profiles such as large (industrial) commercial, other commercial, household and public sector are analyzed which show that main benefits might be derived from large (industrial) and other commercials, while most negative impact is towards households. In addition to this, smart gas metering will be deployed for all larger customers based on Natural Gas Act amendments, which indicates that socio-economic benefits from introducing smart gas meters for larger customers (large (industrial) commercial and other commercial customers) calculated in this analysis will be captured.In the suggested alternative (Alternative II), all larger customers will be equipped with smart gas meters and communication devices for their electricity meters which would result in positive ENPV for these customers – when financial costs for smart metering introduction for this customer group is lower than their economic benefits. Household customers due to their small gas consumption amounts will not be a beneficial customer group for the project implementers – this group consumes only 12% of all gas consumption in Estonia; however, the number of meters for household customers amounts to almost 91% of all meters. From this point of view, household customers should be the last ones in the smart/remote gas metering deployment cycle, which would allow to capture the main benefits from the commercial customers group at the beginning of deployment and also will allow to train smart/remote metering deployment staff in order to deploy the meters for other customers more efficiently in the later stages of deployment.From the examples of other countries, smart metering deployment for electricity and/or gas usually is driven by installation costs optimization and thus, geographical approach is more popular. Which means that having multiple meters in one location can eliminate travel costs which usually makes around 20% of total installation costs. Therefore, optimal smart metering implementation plan should highly depend on the location of necessary to install gas meters.The suggested alternative combines gas and electricity metering systems which from the technical perspective is possible and there are some examples from other countries which have tried to combine it (i.e. UK); however, it might increase the costs of deployment due to unexpected failures of the meters, unexpected necessary corrections or other related issues with different technologies used for electricity and gas metering systems. On the other hand, key issues may arise from the legal point of view which might include customers’ consumption data privacy and asset ownership issues (i.e. necessary data communication modules for electricity meters). Also, synergy between electricity and gas metering systems will have additional risks involved – as gas DSOs will be main implementers of smart gas metering deployment they will need to acquire approval and considerable involvement of electricity DSOs. Considering the fact that electricity DSOs have already installed smart electricity meters, this project might not be the priority project for the electricity DSOs. Therefore, it is necessary to keep contact with all key stakeholders, especially electricity DSOs, in order to keep them involved from the beginning of the project’s discussions.

7.2 Recommendations on reducing the negative impacts and increasing the effect of positive impacts

In order to increase the positive outcome of smart gas metering, it is necessary to understand that the project result is based on the amount of necessary investment which means that the more it is necessary to invest, the more negative project outcome might be financially. Since the main project


investment comes from the gas meters and other equipment, in reality the costs of meters and equipment might be lower due to more favorable procurement terms if larger amount of meters and necessary equipment are purchased at once as well as constant decrease of smart metering equipment prices (since 2010 to 2017 the average meter price decreased by 32%). Thus, buying larger amount of meters can significantly decrease the costs calculated in the analysis. Also, keeping the analysis of the communication technologies up-to-date can have a positive effect towards more efficient and cheaper communication technology, which could encourage gas DSOs to create their own gas metering infrastructure and would allow to avoid possible coordination risks of important other party involvement (i.e. electricity DSOs).Under Alternative II remote gas meters for household customer consuming less than 750 m3 of natural gas per year are assumed to be installed during 5 year period; however, deployment for these customers might be prolonged and then it would become as a natural change of old meters which might not increase the tariff as much as in the analysis calculations. In addition to this, sensitivity analysis has shown that introducing smart meters for more customers (for customers which consume less than 750 m3 of natural gas per year) might generate the least negative impact to the society compared to the calculated analysis results. Smart diaphragm gas meters which are usually installed for smaller gas consumption points are not as expensive as meters for larger customers, thus, installation of smart gas meters for household customers might be more beneficial than installation of AMR devices (standard gas meters which have communication interface). It would also include larger volume of more advanced meters for more customers, which might generate more benefits for project implementer such as more visibility of the gas distribution network and possibility to provide smart gas metering related services for more customers in the future. This might require additional amendments to Natural Gas Act in order to include customers which will generate these benefits. Based on the sensitivity analysis, the consumption benchmark should be lowered to around 70 m3 of gas per year (the other variables remains stable) in order to achieve break-even point.

7.3 Other relevant recommendationsThe socio-economic impact assessment results show that smart metering itself will not generate significant improvements in the energy market because it is only an infrastructure for gathering gas consumption data. However, additional investments into network management systems, data analytics, etc. which would allow to get additional value from the data obtained, might be the key drivers of improvements into the energy market, especially in gas distribution network and it is necessary to plan these additional investment in the beginning of smart metering deployment project. For instance, introduction of other services such as analytics of energy consumption, recommendations on more efficient energy consumption based on the gathered smart meter data which might increase customer energy efficiency by showing them their preferences and possible ways to improve their consumption. From the customer perspective the possibility to get full scope of their energy consumption analysis might involve other sensors in their environment (i.e. climate sensors or devices that inform about energy price) which also need additional analytical tools to get a finalized output from the gathered data. In addition to this, if the optimal alternative would be Alternative I (due to changes in costs of different components), there would be a possibility for gas DSOs to get additional revenues from the services provided for other utilities. From the increased amount of customer data, all utilities might be able to provide services for customers which will be more adjusted to their individual consumption and preferences. The proposed solution of integrated utility meter systems might be a first step for the future provision of multi-energy services facilitating service providers’ deployment, and creating a consumer-friendly mechanism that supports smart services.However, the possibilities to combine other utilities’ metering systems depend on the technical abilities of the gas and other utilities metering infrastructure (i.e. data load, data communication technologies) and most importantly on the ability to combine it based on legal system in Estonia. Since utilities are highly regulated and their metering systems are usually the key infrastructure, before implementing any project related to metering infrastructure it is necessary to understand the legal requirements for the different utility providers and how it can be combined without causing inconveniences for the regulators. The main emphasis should be given for personal data privacy and security questions, which


have to be resolved using specific data encryption or coding methods for gas consumption data in order for it to be transferred using third party’s infrastructure (using electricity DSO’s data collection system). Even though, there are some actions taking towards more combined electricity and gas system as Elering datahub and other initiatives, before inclusion of additional utilities in this picture it is necessary to conduct an additional Estonian regulatory framework analysis.

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