ANALYSIS OF GRID-CONNECTED BATTERY ENERGY STORAGE …... · This thesis aims to design different modelings in order to dimension and analyze the behavior ... Photovoltaic-battery

Master of Science Thesis KTH School of Industrial Engineering and Management

Energy Technology EGI-2016-088 MSC EKV1167 Division of Heat and Power Technology

SE-100 44 STOCKHOLM

ANALYSIS OF GRID-CONNECTED BATTERY ENERGY STORAGE AND PHOTOVOLTAIC

SYSTEMS FOR BEHIND-THE-METER APPLICATIONS

Case Study for a commercial building in Sweden

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Abstract Lithium-ion battery enables major changes to current electricity consumption patterns and can finally transform renewable and local, but intermittent, energy production into systems for secure and stable energy supply. However, battery brings several challenges. Notably regarding how it should be utilized to maximize its value generation and lifetime. Batteries can be used for different purposes like photovoltaic surplus utilization, peak-shaving, price arbitrage and other services for the electric grid. The utilization management, i.e. when and how to charge and discharge them in various situations, needs to be optimized. This thesis aims to design different modelings in order to dimension and analyze the behavior of lithium-ion batteries for different strategies from self-consumption, peak-shaving to price arbitrage management. The developed models are implemented in Matlab and simulations run on real data from a Swedish commercial center. Simulation results based on data from 2015 implies that current market price for batteries is too high to allow the investigated revenue streams to make battery investments economically feasible. However, the available data does not reflect all interesting dynamics and characteristics for a commercial center which might influence the obtained economic result of the analysis. This thesis was thus focused on the modeling processes in order to dimension and analyze the behavior of any battery-photovoltaic system for a wide range of loads such as: residential buildings, commercial buildings and industries.

Keywords

Modeling, Photovoltaic-battery system, Grid storage system, Electricity price, Self-consumption, Peak-shaving, Price arbitrage

Master of Science Thesis EGI-2016-088 MSC EKV1167

ANALYSIS OF GRID-CONNECTED BATTERY ENERGY STORAGE AND PHOTOVOLTAIC

SYSTEMS FOR BEHIND-THE-METER APPLICATIONS

Case Study for a commercial building in Sweden

Addis Moiteaux

Approved

2016-10-10

Examiner

Miroslav Petrov - KTH/ITM/EGI

Supervisor

Sara Ghaem Sigarchian Commissioner

KIC InnoEnergy

Contact person

Arshad Saleem

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Sammanfattning

Litium-ion batterier möjliggör stora förändringar mot nuvarande elförbrukningsmönster och kan på sikt erbjuda transformation av variabel förnybar och lokal energiproduktion till system för säker och stabil energitillförsel. Men batterier medför också en mängd utmaningar, inte minst gällande hur de ska användas för att maximera både dess värdeskapande och livslängd. Batterier kan användas för olika ändamål så som för; att utnyttja överskott från solceller, kapning av effekttoppar, prisarbitrage samt en rad andra tjänster för elnätet. Deras metodik för användning, alltså när och hur de ska laddas i och ur i olika situationer, behöver optimeras för att möjliggöra multipel tjänsteleverans med en rimlig trade-off mellan värdeskapande och livslängd. Denna avhandling syftar till att utforma olika modeller för att dimensionera och analysera beteendet hos litium-ion batterier för olika strategier för att utnyttja överskott från solceller, kapning av effekttoppar, prisarbitrage. De utvecklade modellerna är implementerade i Matlab och simuleringar genomförs med verklig data från ett svenskt köpcentrum. Simuleringsresultat för data från 2015 visar att det aktuella marknadspriset för batteri är för högt för att de möjliga inkomsterna ska göra en batteriinvestering finansiellt gångbar. Den tillgängliga datan speglar dock inte alla intressanta dynamiker och egenskaper för ett köpcentrum och kan därmed påverka det erhållna ekonomiska resultatet från analysen. Denna avhandling är dock inriktad på modelleringsprocessen vilken kan användas för att dimensionera och analysera beteendet hos ett batteri-system kombinerat med solceller för flertalet typer av laster som exempelvis: bostäder, kommersiella fastigheter eller industrier.

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To my family,

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Acknowledgements

The thesis has been jointly conducted with the market creator division at KIC InnoEnergy, Sweden and the Sustainable energy engineering department of KTH Royal Institute of Technology.

At the end of this master thesis, I would like to firstly express my gratitude to Dr. Arshad Saleem and Fredrik Billing who offered me the possibility to define and write my master thesis within KIC InnoEnergy. Moreover, they have constantly been available for any request and for this I am more than grateful. Thank you for their help, advices and confidence placed in me throughout these six months.

I would also like to thank my supervisor and examiner at KTH, Sara Ghaem Sigarchian and Miroslav Petrov, who kindly accepted to follow this master thesis work. Thank you for their advices and feedbacks during the whole project which brought me continuous knowledge. Sara Ghaem has done everything to set the best conditions for this master thesis, inviting me to events and conferences, and so for this I am very thankful.

A special thanks to Albin Engholm and Eric Jensen, who have introduced and helped me to understand better the subject through their previous works and simulations, sharing their knowledge and ideas for improvement. I have really appreciated their guidance throughout the master thesis. Always enthusiastic to answer questions and make the thesis as rewarding as possible, it was a real pleasure working with them.

Moreover, I am grateful to all the persons I have met through the incubator of KIC InnoEnergy. This open-space environment has allowed me to meet great people leading amazing start-ups. This diversity of truly motivating and inspiring people has brought me a lot, professionally and personally. It was an amazing experience to being able to work in such environment.

Last but not least, I especially thank all the people I have met at KIC InnoEnergy and notably all KIC employees, master thesis and intern students I have met during this thesis. It was really nice to work with them, sharing experience and motivating each other. All these people have contributed, through their help but also thanks to their joy and happiness, to make this master thesis motivating and very rewarding.

KIC InnoEnergy was definitely a great and stimulating environment; close to KTH and the research environment but also to the business world thanks to its numerous projects with notorious companies and starts-ups. And so for this, I am really thankful in having had the possibility to write my master thesis in such environment.

Everyone has been a great source of inspiration! Thank you!

Addis Moiteaux Stockholm, August 2016

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NOMENCLATURE

Abbreviations

Abbreviation Significate AC Alternate Current ASCII American Standard Code for Information Interchange CAP Capacity CAPEX Capital Expenditure DC Direct Current DESS Distributed Energy Storage System DOD Death Of Discharge DNI Direct Normal Irradiation DSM Demand Side Management DSO Distribution System Operator EIA Energy Information Administration EPA Environmental Protection Agency ESS Energy Storage Systems EV Electric Vehicle GHG Greenhouse Gases GHI Global Horizontal Irradiance IEA International Energy Agency IRENA International Renewable Energy Agency IRR Internal Rate of Return KIC Knowledge and Innovation Communities KTH Kungliga Tekniska Höskolan LCOE Levelized Cost of Electricity LI-ION Lithium-ion LSO Local System Operator NPV Net Present Value NREL National Renewable Energy Laboratory OPEX Operational Expenditure PV Photovoltaic RES Renewable Energy Sources SAM System Advisor Model SE4All Sustainable Energy for All (UN program) SEIA Solar Energy Industries Association SEK Swedish Krona SOC State of Charge SVK Svenska Kraftnät, Swedish TSO TOU Time-of-use tariff TSO Transmission System Operator UN United Nations USD United States Dollar

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Symbols

Latin symbols Unit Significate A [𝑚2] Area d % Self-sufficiency rate p % Percentage peak reduction (decision factor 3) f % Factor of photovoltaic surplus to store s % Self-consumption rate

Greek symbols Unit Significate η [-] Efficiency

Subscripts

Subscripts Description battDiff Energy from the grid to fully charged the battery batteryEnergy Battery energy level status bE Size of the battery bPIn Maximum power the battery can charge bPOut Maximum power the battery can discharge BTU British thermal unit, energy unit consumptionCost Consumption cost at the current time step d1 Maximum daily consumption cost accepted (decision factor 1) d2 Maximum daily electricity price accepted (decision factor 2)

diff Energy which can be taken from the grid before reaching the limit wanted

EBC Solar energy consumed for battery charging

EBD Total discharged energy during the whole year on the dc-side of the battery system

EDU Solar energy consumed for direct use EL Load, total energy consumption EUB Usable battery capacity EPV Total solar energy consumption electricityPrice Electricity price at the current time step GtoHB Net hourly consumption from the grid to house and battery kcharge Time step to charge load Hourly load reduced by the production of PV localMaxPeak Threshold defined for the next future peak to shave the peak Ms Millisecond nc Annual number of storage cycles nmax Maximum number of storage cycles NaS Sodium-sulfur PVoverproduction Photovoltaic energy surplus (excess) pExcess Excess power over the threshold pMaxWanted Grid consumption limitation Tax Taxation tc Cycle lifetime

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Table of Contents 1 Introduction .......................................................................................................................................................... 1

1.1 Background and rationale .......................................................................................................................... 1

1.1.1 Context ................................................................................................................................................ 1

1.1.2 KIC InnoEnergy ................................................................................................................................ 3

1.1.3 Master thesis purpose ........................................................................................................................ 4

1.2 Previous work on the topic ....................................................................................................................... 5

1.3 Objectives ..................................................................................................................................................... 8

1.4 Methodology ................................................................................................................................................ 8

2 Theoretical framework ........................................................................................................................................ 9

2.1 Introduction ................................................................................................................................................. 9

2.2 Technology overview ................................................................................................................................. 9

2.2.1 Photovoltaics ...................................................................................................................................... 9

2.2.2 Batteries .............................................................................................................................................12

2.3 Electricity market in Sweden ...................................................................................................................15

2.3.1 Actors in the electricity market in Sweden...................................................................................15

2.3.2 Actors in the electricity pricing market for the consumer .........................................................16

2.3.3 Energy, financial and information flow within the electricity market .....................................16

2.4 Electricity consumer types .......................................................................................................................17

2.5 Grid-Connected Battery Energy Storage Services ...............................................................................18

2.5.1 Main possible services offered by a battery energy system .......................................................18

2.5.2 Photovoltaic surplus management ................................................................................................18

2.5.3 Peak reduction: Peak-shaving and peak-shifting .........................................................................19

2.5.4 Price electricity arbitrage .................................................................................................................20

2.6 Criteria parameters for energetic analysis ..............................................................................................20

2.6.1 Self-consumption .............................................................................................................................20

2.6.2 Self-sufficiency..................................................................................................................................21

2.6.3 Annual number of storage cycles ..................................................................................................21

2.6.4 Cycle life time ...................................................................................................................................21

3 Data and current case analysis ..........................................................................................................................22

3.1 Haga centrum ............................................................................................................................................22

3.2 Load analysis ..............................................................................................................................................23

3.3 Solar data ....................................................................................................................................................24

3.3.1 Solar radiation data ..........................................................................................................................24

3.3.2 Solar production calculation ...........................................................................................................25

3.4 Electricity data and analysis .....................................................................................................................25

3.4.1 DSO ...................................................................................................................................................25

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3.4.2 Retailer ...............................................................................................................................................26

3.4.3 Taxes and VAT ................................................................................................................................26

3.4.4 Electricity Consumption cost.........................................................................................................26

3.4.5 Photovoltaic electricity sellback to the grid .................................................................................26

3.4.6 Electricity Nordpool price variation and arbitrage potential in Sweden .................................27

4 Performance Model ...........................................................................................................................................28

4.1 General description in the battery sizing modeling .............................................................................28

4.1.1 General model description .............................................................................................................28

4.1.2 Iterative process ...............................................................................................................................29

4.1.3 Power to energy ratio of the battery .............................................................................................29

4.1.4 Battery losses assumption ...............................................................................................................29

4.2 Model 1: Photovoltaic surplus management ........................................................................................30

4.2.1 Model description ............................................................................................................................30

4.2.2 Model implementation ....................................................................................................................30

4.2.3 Model design process ......................................................................................................................30

4.2.4 Model possible improvement ........................................................................................................33

4.3 Model 2: Peak shaving management ......................................................................................................33





4.4 Model 3: Price arbitrage management ...................................................................................................39





4.5 Techno-economic performance evaluation ..........................................................................................45

5 Results ..................................................................................................................................................................46

5.1 Photovoltaic surplus management .........................................................................................................46

5.2 Peak shaving management .......................................................................................................................48

5.3 Price arbitrage management ....................................................................................................................51

6 Discussion ...........................................................................................................................................................56

7 Model limitations and future works ................................................................................................................58

8 Conclusion ...........................................................................................................................................................59

References .....................................................................................................................................................................60

APPENDIX A .............................................................................................................................................................64

APPENDIX B .............................................................................................................................................................69

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Index of tables

Table 1: Lithium-ion parameters summary: ................................................................................................................................ 15 Table 2: Main possible services offered by a battery energy system [50] ............................................................................... 18 Table 3: E.ON DSO price ............................................................................................................................................................. 25 Table 4: E.ON retailer price........................................................................................................................................................... 26 Table 5: Taxes and VAT ................................................................................................................................................................. 26 Table 6: Energy usage per activities in Haga centrum .............................................................................................................. 66

Index of figures

Figure 1: KIC InnoEnergy and formal partners .......................................................................................................................... 3 Figure 2: Power diagram without (a) and with storage contribution (b) .................................................................................. 6 Figure 3: Solar irradiation over Europe (kWh/m2/y) [19]......................................................................................................... 9 Figure 4: The Solar Settlement, a sustainable housing community project in Freiburg, Germany ................................... 10 Figure 5: I-V and P-V photovoltaic characteristic depending on temperature [22]............................................................. 10 Figure 6: Cumulative and yearly installed PV capacity in Sweden [25] .................................................................................. 11 Figure 7: Photovoltaic module cost ($/MWh) per cumulative model shipment (MWp) ................................................... 12 Figure 8: Tesla Powerwall [36] ....................................................................................................................................................... 13 Figure 9: Tesla Powerwall [36] ....................................................................................................................................................... 15 Figure 10: Electricity market overview in Sweden [44] ............................................................................................................. 17 Figure 11: Photovoltaic surplus concept representation[53] .................................................................................................... 19 Figure 12: Peak-shifting and peak-shaving concept representation [53] ................................................................................ 19 Figure 13: Price arbitrage concept representation [53].............................................................................................................. 20 Figure 14: Haga centrum entrance [57] ........................................................................................................................................ 22 Figure 15: Haga centrum map [57] ............................................................................................................................................... 22 Figure 16: Daily average energy consumption of the facility for the whole year ................................................................. 23 Figure 17: Daily average energy consumption of the facility for winter season (December-February) ........................... 24 Figure 18: Sorted graph of the electricity spot price from Nordpool 2015 [62] .................................................................. 27 Figure 19: Modeling flow chart ..................................................................................................................................................... 28 Figure 20: Logical flow chart for battery design ......................................................................................................................... 29 Figure 21: Battery charge and discharge representation ........................................................................................................... 29 Figure 22: Electricity consumption cost [SEK], Combined decision factors ....................................................................... 40 Figure 23: Decision factors: electricity consumption cost [SEK], electricity price [SEK/kWh] ....................................... 40 Figure 24: Daily maximum consumption cost and electricity price decision values ............................................................ 40 Figure 25: System under PV surplus management, year overview ......................................................................................... 46 Figure 26: System under PV surplus management, PV charging and peak shaving on the next peak ............................. 47 Figure 27: Share of consumption to meet the load ................................................................................................................... 47 Figure 28: Monthly peak loads (a) and grid electricity cost distributions for each system (b) ........................................... 48 Figure 29: Battery size dependent on the percentage peak shaving reduction simulated ................................................... 48 Figure 30: System under peak shaving management, year overview ...................................................................................... 49 Figure 31: System under peak shaving management, charging from from PV surplus and the grid ................................ 50 Figure 32: Monthly peak loads (a) and grid electricity cost distributions for each system (b) ........................................... 50 Figure 33: System under price arbitrage management during the whole year (a) and during five days (2) ..................... 51 Figure 34: Saving proportion after different management and technology investment ..................................................... 56

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1 Introduction

1.1 Background and rationale

1.1.1 Context

Energy is a broad topic which brings more and more interest all around the world in order to face current challenges, related to the access of electricity, climate change and shift to renewable energy solutions. Indeed, energy is often described as the golden thread to ensure social equity, education, and economic growth, hence its stability importance. As pointed it out by the United Nations in the frame of the “Sustainable energy for all (SE4All)” initiative, a sustainable world is based on a stable sustainable energy system [1].

However, the current energy system has been blamed for being unsustainable, polluting and dangerous for the society. It is in fact mainly based on a centralized, conventional and traditional energy production, which mostly comes from fossil and nuclear sources [2]. Yet, raw materials are getting scarcer and fossil energy production has been the main reason of the planet’s degradation and current climate change. Moreover, current massive centralized production plants even though sustainable, like hydro and nuclear, have already led to major human and environmental disaster [3]. Thus, a global interest to leapfrog existing systems based on large centralized facilities by local and decentralized renewable energy systems has merged.

The world has realized the dangerous path taken along further utilization of fossil fuels and momentum has been recently gathered for developing cleaner and more efficient energy solutions. More and more worldwide conferences are summoned through influential organizations like the United Nations (UN) or the International Renewable Energy Agency (IRENA), setting ambitious key goals to develop a sustainable low-carbon economy, at an international but also local level. In Sweden for example, the government has outlined three main action plans to be improved by 2030: the energy efficiency, the promotion of renewable energies, and fuel independence in the transport sector [4].

Furthermore, more and more end-users have started to invest in renewable sources. In Germany, more than half of the renewable energy installed is owned by private individuals [5]. However, the current system has not succeeded in any fruitful way to bring the consumer to have a major impact on the system transformation. Indeed, the current energy system is not built in favor of local renewable energy insertion, like wind and solar. These energies are indeed intermittent and can easily disrupt the balance of demand and supply if not correctly handled. Nowadays, local energy production has to be fed back to the grid, which adds lots of strain on the grid and limits the wider deployment of renewable energy.

The growing electricity demand leads to higher load peaks, which need to be covered. To face it, system operators’ main solution is usually based on grid reinforcement and peaking plants, which implies huge investments [6], whereas smart demand-side management for peak-shaving at the user end could be a better choice. This business model is unsustainable as it bogs down the energy system to a static, inflexible, oversized and mainly fossil/nuclear based system. Therefore, currently the utility companies do not seem ready, neither willing to propose business models and roles that could bring end-users and their assets to have a core role in the energy transformation; hence the importance of new actors, the introduction of innovative technical infrastructures and business models able to approach a more sustainable energy system through flexible, efficient and dynamic models based on renewable energy.

The transformation of the world’s energy system is a reality; the question now is how the changes can be applied in the most cost-effective way and how fast this could happen [1]. Indeed, current regulations and established routines can be a hindrance to this development; hence, the importance to speed up this transition through governments’ and companies’ decisions. KIC InnoEnergy, which is funded by the European Union, is one of these structures aiming to foster the development of innovative solutions and

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business models to support the successful transition to a new energy system, cleaner and closer focused on the consumer. Thanks to locally installed energy devices such as batteries and PV panels, consumers have now the opportunity to become proactive both in energy consumption and production, and to claim a much deeper involvement in the system transformation process.

The world is finally aiming for a complete energy transformation through a revolution in the energy sector, a revolution often compared to the way mobile technology revolutionized telecommunication [1]. The world is literally on the way to change its way of producing and consuming, adapting itself to the new energy world which aims to be greener and closer to the consumer; hence, this master thesis aims to analyze how the consumer can be proactive in his consumption and production through the ownership and smart operation of distributed energy items such as batteries and photovoltaics.

Two of the renewable energy sources that are most promising in terms of sustainability are wind and solar. Their main advantage is the fact that they are renewable and present virtually everywhere, allowing for a decentralization of energy production. An ideal energy system would be a system where the energy is produced and consumed locally. Indeed, this will cut unsustainable and costly energy transport of scarce raw material. This will also reduce the risk of major accidents which can occur during massive centralized production of raw materials, such as for example the recent Bento Rodrigues dam disaster in 2015 [7].

However, the major challenge of wind and solar energy is their intermittent nature. They cannot deliver a constant and stable energy to meet the demand, causing frequency drops in the grid and blackouts [8]; hence the long struggle they had in the previous years to compete with conventional energies. Indeed, conventional energy has the advantage to rely on an intrinsic storage capacity that is released exactly when needed, for instance all types of fossil fuels are naturally occurring forms of chemical energy storage. But the energy sector is moving fast, notably in terms of storage development for renewable energy, allowing photovoltaics and wind to be able to compete with traditional energy sources. The ongoing photovoltaic and battery development are certainly among the most interesting technology revolutions which are currently happening in the energy sector. Their combination is promising both on the utility and consumer side in order to allow self-consumption and a stable production at all times.

Besides the advantage to smoothen the intermittency of renewable energy, batteries can deliver other services. They can provide power quality support, frequency regulation and manage peak demand to reduce customer’s electricity bill. Indeed, a battery could allow for the universal application of energy storage that is charged when electricity is cheap and discharged to re-use that energy during peak hours, i.e. when the price of electricity is most expensive. Therefore, end users, distribution system operators (DSO) and transmission system operators (TSO) are more and more interested in the development of batteries, without counting the evolution of servicing companies which see an enormous business potential in providing demand-side management services between customers and grid owners.

During the last several years, both photovoltaic and battery technology have seen their costs falling steadily, thanks to technological development and increasing market competitiveness, allowing an ever increasing number of residential and business customers to become energy producers. Moreover, the price reduction of PV panels and batteries is poised to continue due to ongoing research in this field and ever greater mass production. PV and battery system pricing are expected to fall 40% [9] and 50% [10] respectively by 2020, from the 2015 levels. And as it is known that the current world’s development is driven by financial and economic growth, it bodes well for the future of PVs and batteries where a large number of small-scale users see the potential to create a business of it, especially since it proposes a sustainable and green solution to a looming challenge.

The project work presented in this report was performed in collaboration with KIC InnoEnergy, which has launched similar projects concerning the introduction and optimization of battery and photovoltaic systems at the consumer end.

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1.1.2 KIC InnoEnergy

KIC InnoEnergy [11] is a young for-profit organization founded in 2010, fostered by the EU-funded European Institute for Innovation and Technology (EIT). They promote education through different Master and PhD programs but also support primarily start-up business and entrepreneurship. With 27 shareholders that include top ranking industries, research centers and universities, KIC InnoEnergy is dedicated to promote innovation, entrepreneurship and education in the field of sustainable energy; with the name KIC standing indeed for Knowledge and Innovation Communities. In total, more than 150 additional partners contribute to KIC InnoEnergy activities.

KIC InnoEnergy has the aim to create and bring to market applied innovation, new technologies and services in the field of sustainable energy in order to achieve a competitive and climate neutral Europe. Their vision is to become the leading engine of innovation and entrepreneurship in the field of sustainable energy. This will be achieved by connecting education, research, innovation and entrepreneurship in order to facilitate the transitions from idea to product, from lab to market and from student to entrepreneur.

Headquartered in Eindhoven, the Netherlands, the company manages its activities through a Europe wide network of local offices, based in Belgium, France, Germany, the Netherlands, Poland, Portugal, Spain, and Sweden. Its activities deal with different thematic related to sustainable energy: energy from chemical fuels, sustainable nuclear and renewable energy convergence, renewable energy, clean coal technologies, intelligent energy-efficient buildings and cities, and smart electric grid and energy storage.

Figure 1: KIC InnoEnergy and formal partners

In Sweden, KIC InnoEnergy is a partnership between KTH Royal Institute of Technology, Uppsala University, ABB and Vattenfall. Its activities are focused on smart electric grid and energy storage. Located in Stockholm, it focuses on 3 main topics like most KIC InnoEnergy centers: education, research and industry. KIC InnoEnergy is also involved in education and research through the development of MSc and PhD programs, designed to provide a challenging and innovative combination of engineering, research and entrepreneurship training.

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Regarding the industry activity, three poles have been developed: Business start-ups, Innovation projects and Market creator:

- KIC InnoEnergy can help entrepreneurs and startups to spin off their product or service ideas. This is the Business creation role which acts as an incubator in helping companies to develop themselves. It provides the essential networking opportunities, human resource base, market expertise and financial support.

- Concerning the Innovation projects division, it deals with projects developed both by new companies and in collaboration with established industrial partners.

- The Market creator division aims to foster the creation of new business opportunities for the startups already incubated and for KIC InnoEnergy itself.

This master thesis has been performed in the Market creator environment.

The Market creator is developing an innovative idea of a Local System Operator (LSO), which would be responsible of the smart management of electricity consumption and production at a local level. Indeed, the LSO can help the consumer to reduce locally its electricity consumption through smart metering and the use of storage through well-planned charge/discharge strategies, and at the same time help the consumer with self-consumption through local energy devices like rooftop based photovoltaic panels. The idea is to help the startups incubated in the KIC organization to reach the market at a much faster speed. Indeed, most of the startups are technically ready and need a way to integrate the market at a faster rate. LSO is aiming to do so in becoming the operator that consumers would need to become prosumers. The startups will intervene then in the technologies or services delivery. LSO would be thus a way to connect these startups together and open new markets for them.

Many startups incubated under KIC InnoEnergy, such as for example Greenely, Ferroamp and Solelia Greentech, have indeed developed interesting concepts, services and technologies which could be developed further in the LSO concept. Greenely has developed a friendly-user interface through a mobile application to help tenants get an overview of their electricity consumption and a full control over their everyday life energy behavior. Ferroamp provides innovative power electronics for smart grid applications related to solar power, energy storage, and electric-vehicle (EV) charging. Ferroamp has for example developed an interesting single multi-port inverter with reduced power losses that can be used both for batteries and PV systems. Finally, Solelia Greentech has developed an interesting concept of a so called “Solar Bank” to allow electric vehicles charging infrastructures to be connected to networks of PV plants, contributing in matching the solar production to the consumption [12].

1.1.3 Master thesis purpose

The management of electricity through energy storage on both consumer and producer side is challenging but still very promising. Smart innovative solutions like batteries can indeed bring important added-value regarding the empowerment of local renewable energy production, the energy cost savings of consumers and the balancing of the electricity production and consumption across a territory (within a TSO region), a limited district (within a DSO) or a local point such as a building (in a LSO, local system operator).

On the production side, the producer announces every day an energy production profile for the next day, aiming to balance the supply and demand. However, a difference from this profile could beget penalties on the repurchase of electricity, hence the importance to transform intermittent energy, like the one from solar units, into a guaranteed and stable energy flow with the help of energy storage, besides the technical need for stable production.

On the consumer side, energy storage would allow consumers to store the energy from intermittent local production sources during off-peak hours in order to discharge it during peak-hours where prices are much higher. This corresponds to a demand side management (DSM) concept based on the demand response method which aims to flatten the consumer load profile through technical and financial incentives. The DSM will help save money in the reinforcement of networks and the need of new power

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plants usually necessary to meet the peak load demand. DSM is thus an important management tool for grid operators who wish to increase their share of renewable energy.

Nowadays, the current management of the electricity is owned by a monopoly of DSO and TSO which does not really encourage smart regulation of consumer behavior. A new energy operator named Local System Operator (LSO) is introduced and analyzed in this study. The idea is to locally replace the DSO by the LSO in order to help consumers manage their load profile thanks to smart innovative solutions. LSO would be responsible for the smart electricity consumption management in industries, commercial or residential buildings. However, by law, the management of electricity of a district cannot be hold by private companies. Only DSO has the right to, hence the DSO would be responsible of the electricity distribution between buildings and the LSO of the local consumption. This strategy would encourage local energy production like integrated PV solar roofing. Local energy production with storage charge and discharge and electricity consumption within the building would be entirely managed by the LSO.

This master thesis has its main focus on the role of electric batteries from a consumer perspective. Three main potentials areas have been identified: (1) Peak-shaving by energy storage without local production; (2) Price Arbitrage strategy; and (3) Self-consumption through integrated PV + battery electricity production and storage system. This study aims to model different designs for the proposed system in order to properly size and understand the behavior of lithium-ion batteries for the different application strategies. The developed models are built under the Matlab software and simulations were performed based on historical load data provided by a commercial shopping center in the city of Örebro, Sweden. The resulting models can be used to size and analyze the behavior of any battery-photovoltaic system, in any residential, commercial or industrial installation.

1.2 Previous work on the topic Several papers have been published on similar subject areas. Here is a brief summary of some relevant studies focusing on behind-the-meter battery and photovoltaic systems’ integration.

The National Renewable Energy Laboratory of the U.S. Department of Energy, NREL, has published a paper [13] which deals with the role of batteries for demand charge reduction in a behind the meter application. A peak-shaving control algorithm has been developed under the BLAST (Battery Lifetime Analysis and Simulation Tool) to identify cost-optimal battery configurations and their influence on the load. In order to achieve quick payback, it was found that optimal batteries were small with short cycle duration and sized for short load spikes reduction applications. Peak loads were reduced in the order of 2.5% of peak demand. As for PV, it was found that PV has little to no influence on the optimal battery design because of its little influence on the aggregate load. It was also pointed out that added battery to a facility with no storage is more likely to be profitable than a facility including also PV. PV installations are interesting when solar power generation occurs at the same time as demand charge time periods for batteries. The simulation was carried out on 98 facilities with 35 different battery types.

A study [14] performed at the University of Palermo, presents a price arbitrage strategy for battery use in a customer-centered energy system. A model has been developed to find the optimal charge/discharge scheduling method to maximize the arbitrage benefit of the storage system. The simulation has been run over only a week on an academic building load profile. The design of the battery was only done through the hourly price profile regardless of the energy consumption of the facility. This is a major simplification which has to be taken into account in the understanding of the sizing and operation of the battery. It is indeed worth to be noticed that the aim was not to flatten the power profile by shifting the peak loads and that the price arbitrage strategy can instead lead to an increase of gap between peak and off-peak loads, as can be seen in Figure 2, but also provides flexibility for peak reduction, as the price evolution is usually related to peak hours. Thus, the battery is designed just to capture the price differential and no consumption analysis is done though consumption and price differentials are not always correlated.

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Figure 2: Power diagram without (a) and with (b) energy storage contribution in price arbitrage [14]

The model can be used for all kinds of batteries. However, the simulations [14] have been run for three types of battery: Lead-acid, lithium-ion (Li-ion) and sodium-sulfur (NaS) batteries as they are the most suitable to be used in residential, commercial or industrial buildings for load shifting applications. It was concluded that none of the batteries were profitable due to the high upfront investment cost. However, if subsidies are taken into account to lower the initial investment cost, NaS battery proved to be the most profitable contrary to lead-acid batteries being the less attractive solution. NaS has the advantage to have the greatest number of cycles to failure and an upfront investment cost lower than Li-ion batteries. However, the situation could rapidly change due to the promise of Li-ion batteries in terms of cost reduction and cycling performance. The most critical parameters are the cost of the battery bank and the number of cycles to failure. Another important conclusion is that it can be very interesting to operate a battery at low depth-of-discharge (DOD), thus to have a flexible DOD, especially when the gap between max/min electricity prices is limited.

NREL proposes also two other case studies [15] on peak-shaving under the in-house developed software SAM (System Advisory Model). Published in November 2015, it analyses the integration of batteries for behind-the-meter applications like demand charge mitigation for commercial facilities with or without solar PV integration. A store market in Los Angeles and a primary school in Knoxville were studied. Different dispatch strategies were analyzed both through manual schedule as the 2015 version of SAM suggests, and automatic peak-shaving (released on SAM in 2016). The objective is to find ideal ways to use the storage so it can be the most profitable to efficiently mitigate demand charge. Indeed, by reducing demand charge through peak-shaving, it can reduce commercial customer bills. Data such as incentives, complex electricity tariffs, and site-specific load and PV data were used to perform the techno-economic analysis. The financial value considered as metric are the NPV (Net Present Value) and the payback

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period. SAM links a PV-coupled battery energy storage performance model to detailed financial models to predict the economic benefit of a system as well as the technical performance. The manual dispatch strategy allows the user to hourly choose how to charge and discharge the battery each month of the year. This strategy is developed manually after analyzing the load profile along the year. The automatic dispatch strategy is set to reduce peak grid purchases as much as possible knowing the load and PV production 24 hours in advance. First, it computes the net grid power required to meet the load if no battery exists. It determines then highest grid power required. The battery is then programmed to discharge throughout the day to reduce the peak load as much as possible before reaching the minimum state of charge. It was found that the manual dispatch controller is not enough to reach a profitable strategy for peak shaving due to the high variable nature of load demand. None of the scenarios with a manual dispatch-control brought a positive NPV, contrary to the automatic dispatch strategy.

A paper [16] on peak-shaving and frequency regulation analyses more deeply the technical advantage grid storage systems could bring to the electricity network while combining services. With the foreseen investment in renewable energy and an increasing tendency for a horizontal grid, storage has a huge role to play in order to smarten the grid. Yet, a lot of progress still needs to be done regarding the regulatory and electricity market to increase the insertion of storage services into the grid and avoid conflict between the current grid actors like DSOs and new local storage actors. This study focusses only on one type of battery, the vanadium redox flow battery (VRFB). Vanadium redox flow batteries have high cycling tolerance, high depth of discharge but low energy density. Their price is positioned between Na-S and Li-ion batteries. Their main advantage is their fast time to respond. They can deliver the requested power at a battery response time of 2 ms. The model was developed on Matlab/Simulink and the controller implemented in a way to perform both strategies simultaneously. However, simulations were carried out for very short period of time (30 seconds to 2 minutes) due to the complexity of the modeling. The storage system is designed for a medium voltage substation and a residential load. To do so, a micro distribution grid was developed. It was found that the vanadium redox flow battery was fast enough in their demand-response to offer frequency regulation effectively while acting as a peak-shaving performer. Thus, contrary to most studies, this paper describes the viability of a double storage service. Batteries can offer lots of services and all its full potential should be considered during its design in order to increase the financial assessment. All in all, the authors clearly advocate the use of batteries under both a regulatory and competitive frame in order to optimize their function and enable maximum benefit for investors.

Another paper from the University of Aachen [17] deals also about PV and battery systems design for commercial facilities, evaluating their economic advantages. A case study on a supermarket in Aachen, Germany, with a yearly consumption of 238 MWh and a peak load of 50 kW, was carried out. It studies both the PV systems with batteries and without batteries. However, the only battery case was not carried out. Indeed, they focus more here on the photovoltaic surplus utilization and PV costs and benefits and how its design can be optimized notably through battery. Thus, batteries are limited to only store PV energy and no grid electricity. No arbitrage, not peak-shaving were considered and thus the battery is not optimized. They concluded that the optimal PV systems that should be installed on roof should be 2 times the peak demand to be profitable from a self-sufficiency and self-consumption point of view (see fig. 11.). Concerning the integration of PV-battery systems, they were found to be non-economically advantageous compared to a single-PV system. Indeed, PV production matches demand loads of most commercial buildings, as their main activities occur during the day, and thus a very small amount of PV production can be stored.

Thus, lots of research have been done and are still going on in this field. However, few are investigating the potential of multiple-objective PV-battery system regarding arbitrage, peak-shaving and self-consumption; hence the purpose of this master thesis, which aims to develop modelings regarding such implementation with a particular interest in Sweden and for commercial buildings.

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1.3 Objectives The motivation behind this research is that storage can bring several services like increasing grid flexibility, helping consumers to lower their electricity bills and fostering the integration of renewable sources into the power system. Thus, the main goal of this master thesis is to respond on how batteries and photovoltaic systems can be inserted into the energy mix on the consumer side thanks to smart innovated solutions. Indeed, the constraints imposed to manage the consumption from the grid or intermittent energies raise some questions on how to size and optimize the storage and the strategy to follow for the charging and discharging management of the battery.

The expected main deliverable of the master thesis will be a final project report comprising a techno-economic analysis, dynamic models of PV with storage systems, and a guide for the different modelings.

1.4 Methodology This present section is about the methodology chosen to do the work in the time frame of the master thesis. A following plan has been followed:

1) Literature review on batteries, behind-the-meter strategies and implementation like peak-shaving, arbitrage and PV self-consumption have been carried out. Some research regarding grid regulation, incentives and electricity prices were analyzed too

2) Acquaintance with the previous modeling done by the Market creator team like peak-shaving modeling under Matlab, understand the main requirements for the modeling

3) Data collections 4) Techno-economic analysis 5) Discussion on the results

Concerning the informatics tools, the modeling will be performed under MATLAB. System Advisory Model (SAM), a NREL tool (National laboratory of the U.S. Department of Energy) could also be used.

Concerning the data, load profiles will be obtained from a Swedish housing company based in Örebro which has different meters in some malls, schools, swimming pools, etc. Grid pricing and electricity retail pricing data can be collected from DSO and Nord pool spot price. Finally, climate data were taken from the Swedish website STRÅNG. Solar data are also available in the SAM software.

The methodology involved a modeling of the system aiming to develop an energy storage dispatch model in different scenarios and a techno-economic analysis of the system. The task will consist in modeling battery and PV panels in the energy system of a commercial building to ensure their consumption and peak-shaving. Arbitrage and self-consumption strategies will also be studied. Finally, cost analysis will be performed to evaluate the profitability of such investment.

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2 Theoretical framework

2.1 Introduction The current developed technologies like batteries and photovoltaic systems are now on the market and are more and more affordable, allowing a large use that could facilitate the change to a new energy system more sustainable and close to communities. These technologies can finally help the consumer to become prosumer and active in the management of his energy demand. This new approach of the energy consumption will save costs and avoid over-sized distribution grids, allowing flexibility and improved economic situation both for DSOs and consumers. However, what is missing is a path and method to bring these technologies in the right direction, a management tool to optimize the use of these new technologies. Indeed, different strategies can be developed, bringing different technical and financial benefits. The corresponding terms and technologies will be explained in this section as the notions and tools used to evaluate and precede the simulations.

“The worldwide transition from fossil fuels to renewable sources of energy is under way”

Earth Policy Institute’s new book, The Great Transition

2.2 Technologies

2.2.1 Photovoltaics

“Even on our latitudes and with a summer like this one, solar cells can still be a powerful tool with present technological developments”, Swedish Minister for Energy, Ibrahim Baylan.

The energy from the sun is an extraordinary free source of energy which can be consumed locally and everywhere in the world, even in Sweden where this master thesis will take as main focus. Indeed, the average solar radiation in Sweden is around 1000 kWh/m² per year, which not much lower than in Germany, which is one of the world’s leaders in solar energy with 40 GWh of photovoltaic capacity [18].

Figure 3: Solar irradiation over Europe (kWh/m2/y) [19]

A scientific paper from Norut Northern Research Institute [20] has notably highlighted the fact that certain sunny Nordic regions can produce as much solar energy as Freiburg in south Germany [21].

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Figure 4: The Solar Settlement, a sustainable housing community project in Freiburg, Germany

Moreover, being in a cold environment increase the efficiency of solar panels which rises with lower temperatures as can be seen in the figure 5.

Figure 5: I-V and P-V photovoltaic characteristic depending on temperature [22]

It is true that compared to southern countries which receive solar energy during the whole year, Sweden is very limited due to the northern location and long winters. However, the long sunny hours of summer are an interesting potential to increase the share of renewable energy in Sweden. Thus, since 2013, the Swedish Energy Agency, “Energimyndigheten”, has decided to emphasize its support in the Swedish solar market by investing SEK 123 million in research into photovoltaics, thermal solar power and solar fuels for the next 3 years. As a consequence, with the aid of government funding, the Swedish solar-cell market has begun to grow. Government involvement through tax reduction and increasing awareness among the public about solar energy, has encouraged more and more people to take the step to generate their own electricity. The largest increase has been noticed in the residential and single family house [23]. 13.9 megawatts (MW) newly installed in 2015 in photovoltaic systems for homes, compared with 9.6 MW in 2014. One of the reasons for this increase is the declining system prices but also thanks to direct capital subsidy system, a tax system introduced in 2015. Solar owners, who can produce excess power back to the grid, can receive 60 cents extra per kilowatt hour in addition to the compensation they receive from their

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utility company when selling their solar electricity [23]. Moreover, the photovoltaic technology is very popular among Swedish citizens driven by sustainability [24].

Figure 6: Cumulative and yearly installed PV capacity in Sweden [25]

Solar capacity has thus been doubling nearly each year, since the beginning of government support. Starting with only 8 MW in 2012, 19 MW new solar installations have been deployed in 2013 and 36 MW in 2014. Although a small slowdown during 2015 with still an increased by 31 % of the total installed PV power in Sweden, the Swedish PV market is still continuing growing [24]. Thus, Sweden is finally following up German model which has succeeded in becoming world’s superpower when it comes to solar energy, notably thanks to generous government subsidies.

At the end of the year 2015, 126.8 MW solar cells have been installed in Sweden. 115.8 MW were connected to the grid and 11 MW were independent installations [24]. This is sufficient to cover around 0.1 percent of the annual gross electricity demand with PV systems. In 2014, solar electricity in Sweden accounted for less than 0.6 percent of total electricity consumption, according to Swedish Minister for Energy, Ibrahim Baylan [26]. Thus, a significant increase has been noticed. However, there is still some room for improvement to increase the proportion of solar electricity in Sweden compared to Germany where solar electricity accounts for 7% [27]. A new solar energy proposal will thus be announced for end 2016 with an autumn budget that will contain an investment of SEK 450 million in support of such installations [26].

Overall, in 2015, 125 GWh are produced annually from solar installations, enough to power over 5500 households [28].

Moreover, globally photovoltaic systems prices have seen a tremendous drop in the recent years. As was noticed in an article of Greentech Media, some years ago, the idea that a module cost could decrease less than 99 cents per watt would be barely thinkable [29]. In 2015, module cost less than 60 cents per watt [30]. The fall in module costs has been dramatic. The Bloomberg New Energy Finance (BNEF) Report, published in June 2016, shows the rapidly declining costs of solar energy. This drop in prices is all the more impressive that the chart is on a logarithmic scale.

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Figure 7: Photovoltaic module cost ($/MWh) per cumulative model shipment (MWp)

For every doubling solar capacity, costs fall by 26%. This cost is known as the solar ‘learning rate’, also known under the name Swanson's Law. Compared to coal and oil, solar is an unlimited resource, which makes solar prices only dependent on the technology and as for every technology, the further the technology advances, the cheaper it gets. Prices are thus led to decrease through research, better efficiency and massive production. Greentech Media forecast a price decrease of photovoltaic system by 40% by 2020 [9]. According to BNEF, solar will be the cheapest form of producing electricity with wind in most of the world by the 2030s [31].

“Solar is a technology, not a fuel”, Bloomberg New Energy Finance

As for Sweden, prices of larger PV systems have fallen by 6-10 percent in 2015. The price trend for residential systems including labor is now about 15 SEK / watt excluding VAT in 2015.

2.2.2 Batteries

“Batteries can be made to perform as an energy cell that stores a large amount of energy, or a power cell that is capable to deliver high load currents. […] An analogy is a water flask that is designed to hold a large volume of liquid while offering a wide opening to permit quick pouring [32]”, Battery University.

Batteries for consumer electronics have been part of people’s daily life for a long time already, but their potential to be fully integrated into the mainstream power system is very recent. The benefit to match rechargeable battery with renewable energy production to allow intermittent renewable energy to compete in the energy market has finally come to the fore.

Batteries store the electricity into chemical energy and convert it back into electricity when needed. It can perform all along the electricity grid and deliver direct services to the producer, the grid operator or the consumer.

For the producer, batteries can contribute in upgrading renewable energy source from intermittent to a stable, allowing to compete with other resources. Indeed, the producer announces every day an energy production profile for the next day, aiming to balance the supply and demand. However, a difference from this profile could beget penalties on the repurchase of electricity, hence the importance to transform intermittent energy into a guarantee and stable energy thanks to energy storage. It brings moreover technical advantage of a stable production. It can indeed smooth out the variability of flow and store excess energy when demand is low to restore it when it is high. Currently, this fluctuation is handled by drawing power from natural gas, nuclear or coal-fired power plants. However, the problem with such plants, besides their sustainability questioning, is their long time to ramp up. Contrary to such plants which take usually long time to ramp up, batteries respond quickly, in the order of 20 milliseconds and have very low CO2 emission [33].

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For grid operators, batteries can ensure a healthy grid in charging or discharging when needed in order not to saturate the grid. This will help to reduce the need of new networks and power plants investments usually necessary to meet peak-hours energy demand.

For the consumer, batteries would allow different services, from storing the energy from intermittent energy during off-peak hours or surplus in order to discharge it during peak-hours where prices are much higher, reduce the power consumption to reduce the energy bill or provide grid services for the grid like frequency regulation. Batteries can now change the consumer energy demand regarding peak-hours in acting as a flexible reserve through technical and financial initiatives. In this report, batteries will be analyzed for such purposes only; batteries management will be analyzed to determine interesting strategies for consumers.

Lithium ion (Li-ion) battery [33] is nowadays leading the electrical energy storage devices market thanks to its maturity, superior electrochemical properties and combination of high energy and power density [34]. In this thesis which focus on grid connected energy storage system, it has been decided to focus only with Li-ion battery and notably with a power to energy ratio of 1:2 similar to the Tesla battery. Indeed, the main task of this thesis is to see the implementation through different strategy of PV-battery system for the near future, and the most interesting battery technology for the next coming years are the lithium-ion battery.

The most common and matured behind-the-meter battery in electrical energy storage devices market is the lithium-ion battery. It has indeed the best quality price ratio, superior electrochemical properties and a combination of high energy and power density [34]. Though more expensive than lead-acid batteries which have been in use for many years, lithium-ion batteries are more suitable [35] for behind the meter application thanks to a better lifetime cycling properties, better energy densities, no memory effect and a slow loss of charge when not in use. They can provide a wide range of services to foster the utilization of cleaner energy like solar and wind, and for different actors from end-users to producers through grid operators. Indeed, battery response is faster and more accurate than any peaker plants with a time response in milliseconds and their aggregation can allow renewable energies to enter the ancillary services power market. Moreover, modular and transportable, multiple distributed batteries can be aggregated, deployed and operated locally and everywhere along the grid, contrary to generation-based peakers. Concerning the memory effect, which is common with batteries, lithium-ion batteries do not have this problem. Memory effect can be described by the fact that after a while, the battery does not charge to its maximum value anymore though it still has the capacity for. Its maximum state-of-charge decreases with time. Lithium-ion batteries have this advantage to have no memory effect and can charge to their maximum value as long as their capacity allows it. This report and the following modeling will mainly focus on the lithium-ion battery of Tesla commercialized for end-user, also known under the name Powerwall.

Figure 8: Tesla Powerwall [36]

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2.2.2.1 Li-ion batteries characteristics

In this following section, the characteristics [37] of the battery Tesla [36] will be presented.

Energy densities of lithium-ion batteries

This corresponds to the maximum amount of electricity the battery can store at one point in time. It is the amount of energy that the battery can store. The Powerwall is a 7kWh lithium-ion battery storage system. However, it has an efficiency of 92.5%. Thus, the Tesla Powerwall has a working capacity of 6.4 kWh. It has been conceived cycle on daily basis, such as for load shifting. Thus, the battery is designed to charge and discharge each day.

Power densities of lithium-ion batteries

It indicates the loading capability, i.e. how fast a battery can charge or discharge. Batteries for power system are made for high specific power in order to have a quick time response.

The Tesla Powerwall has 3.3 kW and is one of the highest compared to its energy density.

Power/Energy ratio of lithium-ion batteries

The power/energy ratio is a really interesting tool for the understanding of the management of the battery. Indeed, a battery can have a greater role in terms of energy, i.e. the need to store or provide a great quantity of energy, no matter how long it can take to charge or discharge the battery, or in terms of power, i.e. being able to provide or store a high amount of energy rapidly.

𝑝𝑝𝑝𝑝𝑝 𝑝𝑟𝑟𝑟𝑝 = 𝑝𝑝𝑝𝑝𝑝𝑝𝑒𝑝𝑝𝑒𝑒

The Tesla Powerwall has a power ratio of 1/2, making this battery one of the most interesting in term of high power ratio.

Efficiency

The Tesla Powerwall has a 92.5 percent round-trip DC efficiency.

Depth of discharge

Lithium-ion battery has a depth of discharge (DOD) between 80 percent and 100 percent [38]. 100 percent is obtained thanks to advanced battery management and sensors. The Tesla Powerwall and Sonnenbatterie have a depth of discharge (DOD) of 100 percent. Indeed, contrary to lead acid batteries which has a DOD of 50 percent [39], lithium-ion batteries can handle much more without decreasing their life time. However, it is usually recommended to maintain a DOD of 80 percent to increase the battery life time. It is wise to allow discharge no lower than no lower than 20 percent. Full discharging inducing a lot of strain on the battery and decreasing its overall useful lifespan. For a daily use, it is thus recommended to pay attention to charge the battery regularly in order to avoid full discharge [40].

Cycles

Lithium batteries on the current market have a number of cycles per year between 5000 [41] and 10000 [38] cycles. The Tesla battery is set to deliver 5000 cycles per year. The cycles counting of a battery is evaluated during the discharge of the battery. It is indeed based on a percentage discharge that is summed each time the battery is discharge even if it is charge in between. For instance, if the battery is discharge 50% one day, then charge fully and discharge again 50% the next day, one battery cycle has been used [42].

𝐶𝑒𝐶𝐶𝑝𝐶 = 𝐶𝑒𝐶𝐶𝑝𝐶 + 𝐷𝑟𝐶𝐶ℎ𝑟𝑝𝑒𝑝𝑎 𝑝𝑒𝑝𝑝𝑒𝑒

𝐵𝑟𝑟𝑟𝑝𝑝𝑒 𝐶𝑟𝑠𝑝

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2.2.2.2 Battery cost and prediction

Li-ion battery costs are expected to fall by 50 percent in next 5 years, mainly driven by increase of renewable generation and massive production [10]. In the following table, the current prices for different battery type are presented besides the predicted future cost in 5 years.

Current lithium-ion battery price is around 2600 SEK/kWh. On top of this, it should be also added the cost of installation and a DC-AC inverter to convert the direct current into alternating current. This surplus of investment could easily reach 13000 SEK for inverter, installations and taxes cost. The price of the entire system can then go up to 60000 SEK [43].

Currently, prices of batteries are still high though lots of research going on and a sharp decrease in prices in the recent years. Tesla has even set their goals to commercialize battery costs for less than 900 SEK per kilowatt-hour. Tesla is ahead in the research on reducing battery costs and continues to have a significant lead on competing electric-vehicle [29].

Table 1: Lithium-ion parameters summary:

Energy storage subsystem cost (SEK/kWh) 2600

Power subsystem cost (SEK/kW) 5200

Cycles 5000

Self-discharge (%/month) at 20°C 2-10

In the following modeling, self-discharge losses of the battery are disregarded. Though the slow loss during charging and discharging and self-discharge, it would be interesting to implement it in the modeling.

Figure 9: Tesla Powerwall [36]

2.3 Electricity market in Sweden To know how much the end-user can reduce his electricity bill, an understanding of the Swedish electricity market and the type of possible electricity contract possible in Sweden is necessary.

2.3.1 Actors in the electricity market in Sweden

The main actors in the electricity market are given as follow, from the production to the consumption

• Power plant operator • Transmission system operator (TSO) • Balance responsible party (BRP) • Distribution system operator (DSO) • Retailer (electricity provider) • End-user (consumer and prosumers)

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There is one TSO in Sweden, SvK (Svenska Kraftnät), and it is responsible of the grid at a national level, ensuring that the load match the production. They are the one responsible to ensure frequency stability around 50 Hz in the grid. Other actors take part in the energy balancing between production and consumption like BRPs who are approved by the TSO to provide such service [44]. On the contrary, DSOs intervene on a smaller scale, on the low, medium and high voltage whereas TSOs intervenes on the high and very high voltage. DSOs are responsible to operate the grid to distribute the electricity to the end-users. They are thus responsible in the collecting and distributing metering data besides grid maintenance and reinforcement. There are around 160 DSO in Sweden [45]. Concerning retailers, there were 123 electricity suppliers registered on the price comparison site Elpriskollen.se in 2013 [46]. They are the one trading and supplying the electricity to the end-users. The three main electricity retailers are E.ON, Fortum and Vattenfall who have a share of 42% [47].

2.3.2 Actors in the electricity pricing market for the consumer

All consumers have two different contracts [48]: one with the electricity network operator, also named distribution system operator (DSO) that owns the power transmission and distribution and one for the electricity supplier, retailer, which buy the electricity for the consumer. Hence, the main stakeholders that intervene in the price of the electricity are mainly the DSO, the retailer and the State who applies taxes and incentives for certain type of electricity production and consumption.

The DSO is responsible to provide and distribute the electricity directly to the consumer, i.e. to the meter of the household it should provide in electricity. Thus, the DSO charges for the operation and maintenance and upgrade of power lines which are necessary to ensure high quality electricity distribution. Thus, the DSO is responsible for the infrastructure to provide the grid and physical meters. This includes meter operation and reading, data collection, data storage, meter data validation and distribution of data to other market participants [45].

However, the responsibility to supply the consumer in electricity is given to another actor: the electricity supplier also called retailer. The electricity suppliers charge for retailing electricity on the Nordpool spot market. The Nordpool price is the wholesale market in the Nordic countries of (Sweden, Norway, Finland, Denmark, Estonia, Latvia and Lithuania) and gives the electricity spot price a day ahead. Sweden is split into 4 price areas. Stockholm is in the third one, also named SE3.

The costs are both based on the energy consumption and the contract with the DSO has in addition, a fee for the power consumption, also called demand charges depending on the maximum power required. The last contract is usually applied for big consumers like in the commercial or industrial sector and is called power tariff. The contract only based on the energy consumption with no additional fee on power delivery is called real-time pricing or time-of-use (TOU) as it depends on the time the energy is purchased. Contrary to real-time pricing which are set according to the Nordpool spot market, two or more tariffs are used to differentiate price between high-peak and low-peak times in the TOU tariff with prices varying over hours but remaining the same day after day.

2.3.3 Energy, financial and information flow within the electricity market

The energy flow goes from power plants to end-users, through the grid, which is operated by the TSO (national level) and the DSO (local level). On a financial level, commercial entities like retailers and BRP are the interface between power plants, end-users and grid operators to ensure that the load matches the production through load prediction and commercial transactions.

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Figure 10: Electricity market overview in Sweden [44]

2.4 Electricity consumer types PV-battery strategy investigation is different for each type of load and consumer; hence the interest to analyze one particular kind of load and consumer and thus the importance to define the type of building this thesis should deal with.

First, three broad categories of buildings can be identified: residential, commercial and industrial. The energy consumption from each type differs, which bring to different grid and energy tariff and furthermore, their loads can have different profiles which lead to complete different strategy for the sizing of the battery and its energy management.

Residential loads usually present two peaks, in the morning and at night, whereas commercial loads will have a peak to cover mostly in the end of the afternoon and for a longer time and industry can have one peak during the day or even more depending of the type of industry and its work strategy. Thus, loads can still be completely different within one category, hence the need to choose one type and study all kind of possible loads.

Commercial buildings are interesting consumers compared to residential buildings as they usually dispose of more roof-space for high installed PV capacities. Moreover, the peak hours of the load are mostly closed to the solar energy generation, which occur in the middle of the day. Thus, schools and work offices are interesting data to work with to evaluate the interest of PV installation on such roofs. Furthermore, peaks are usually very visible due to high consumption during rush-hours. Commercial buildings are classified according to their principal activity, type of business, type of commerce such as education, shopping center, swimming pool, sport center, food sales, health care, lodging, office, religious worship [49].

Finally, industrial consumers have the advantage to allow the possibility to develop microgrids within the industry. Industrial consumers are thus ideal to experiment future local distributed energy production, storage and consumption. Moreover, their size usually allows the possibility to install renewable energy production locally, contrary to commercial building which can be limited by their smaller space available. Moreover, energy consumption and peaks are more distinct and uniform through time which allow easier implementation of batteries for peak-shaving needing less management and control system to predict the load. Furthermore, grid services requirement, like frequency regulation, can be much easily met thanks to their high energy and power consumption which allow higher aggregated battery disposition than in the commercial and residential sector.

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2.5 Grid-Connected Battery Energy Storage Services In this part, the possible different strategies will be presented. This section will provide more in depth argumentation on the possible ways to use a grid-connected photovoltaic and battery system, with a focus on three type of battery management.

2.5.1 Main possible services offered by a battery energy system

Services offered by a battery energy system

For end-users For the grid

Peak-shaving, to lower the peak demand charges, especially if based on a power tariff.

Peak-shaving, to reduce demand during peak hours. Reduce investment in transmission and distribution lines and substations.

Price arbitrage in shifting peaks, to lower energy charges in buying cheap electricity from un-peak hours.

Peak-shifting or price arbitrage, to reduce demand during peak hours. Reduce investment in transmission and distribution lines and substations.

Photovoltaic surplus utilization, to optimize their solar assets.

Buffer grid imbalance, to help stabilize the grid, enabling more intermittent energy into the grid.

Increase the value of solar energy in smoothing photovoltaic fluctuation.

Frequency regulation, to use batteries as spinning reserve to regulate the grid frequency. Avoid investment in peaker plants or spinning reserves.

Reduce operational risks like power outages (back-up solution).

Provide other ancillary grid services to improve the quality of the grid like voltage support.

Table 2: Main possible services offered by a battery energy system [50]

In this master thesis, the main focus will be about battery energy system for end-user and more particularly commercial users like shopping centers. Among the services stated above, this master thesis will deal with:

• Photovoltaic surplus utilization, to optimize their solar assets, • Peak-shaving, to lower the peak demand charges, especially if based on a power tariff, • Price arbitrage in shifting peaks, to lower energy charges in buying cheap electricity from un-peak

hours.

2.5.2 Photovoltaic surplus management

Photovoltaic brings some notable challenges for end-users and grid operators due to their rapid output variation (ramping), their intermittency and usual mismatch between peak demand and photovoltaic production. Grid-connected battery provides then means to manage those issues and upgrade photovoltaic energy to a stable renewable source of energy

Battery can contribute in optimizing local photovoltaic production. More and more end-users see the potential of photovoltaic production for sustainability purposes or money wise issues. Battery would help to shift the electricity powered by photovoltaic panels to peak hours, which usually differ from solar peak production, although this challenge is less dramatic for commercial and industrial (C&I) energy end-use than in the residential sector [51].

Battery would also allow in the near future to oversized photovoltaic production in order to store the surplus of electricity for numerous number of services like EV charging, grid services like frequency regulation, besides the need to meet the load during peak hours. In this way, photovoltaic battery system

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can reduce the energy drawn from the grid and therefore reduce their cost and increase their self-sufficiency [52].

The figure 11 represents a type of photovoltaic surplus use when the energy is produced during the day and used later to meet the load:

Figure 11: Photovoltaic surplus concept representation [53]

2.5.3 Peak reduction: Peak-shaving and peak-shifting

Peak shaving or shifting strategy is to use the battery during times of peak demand. The advantages of such strategy are multiple. By using electricity stored during periods of lower demand, it avoids congestion in the grid and at the same time reduces demand charges as less power is demanded during usual peak hours [54].

Despite their similarities, a small difference can be noticed between the two notions. Both peak-shaving and peak-shifting are used during peak hours. However, peak-shaving consists in reducing peaks in shaving them. A peak is reduced at a lower power but spread on a longer period, whereas peak-shifting cuts a peak at a certain hour to consume it another hour. It reduces the shifted peak power but has no special interest in reducing the power of the new peak. In this thesis, the term peak-shifting is used within the arbitrage strategy to shifts peaks to consume cheaper electricity, whereas the term peak-shaving is used within the peak-shaving strategy to reduce demand charges.

(a) (b)

Figure 12: Peak-shaving (a) and Peak-shifting (b) concept representation [53]

Peak hours depend on the type of seasons and the type of user. Consumption is indeed much higher during winter and during evenings and early mornings for residential users. However, commercial center, offices and schools can have different peak hours and must be identified separately to be able to provide adequate services.

In Sweden, peak hours can be identified in two season periods. Hence most DSO, like Vattenfall [55], suggests different pricing schedules for winter or summer period (TOU tariff).

Through batteries, besides relieving constraints on the grid, end-users can save money in decreasing their peak demand cost. Indeed, end-users usually have to pay a monthly or annual fee depending on their maximum peak demand in the month or in the year.

Thus, the main advantages with peak-reduction are the possibility for end-user to save money on their electricity bills by reducing peak demand. For utilities, it reduces the need for peaking units, decreasing operational cost of generating power during peak periods. And finally, grid operators earn too in saving investment cost in grid infrastructures which is less strained thanks to flatter loads with smaller peaks.

20

Moreover, electricity prices are usually dependent on those peak hours. Therefore, if peak demand of the system considered matches these peak hour prices, peak shaving will join another service presented in the next section, and known under the name of price arbitrage.

2.5.4 Price electricity arbitrage

Price electricity arbitrage corresponds to a certain type of battery management. The battery charges when electricity price is cheap and discharges during peak prices hours. If peak price hours match peak demand, price arbitrage service would be the same as peak-shaving as the energy used during peak hours will be charged during low price hours which correspond to low demand either. Typically, peak demand for residential load occurs during peak price hours, times when both the electricity use and price are highest. Thus, price arbitrage strategy is an excellent strategy for money saving while reducing peak load when peak energy hours matches peak price hours. However, for other systems like commercial centers these hours might be different, hence the interest of this study.

The price arbitrage strategy can be summed up in the following graph:

Figure 13: Price arbitrage concept representation [53]

Through price arbitrage, the consumer can reduce its electricity bill at the end of the month buying cheaper electricity. The benefit is the difference in price between on-peak and off-peak hours. This income should also be decreased by the cost for energy losses during the storage charge-discharge cycle [56], which has not been taken into account in this study.

However, electricity suppliers (retailers) might not appreciate massive deployment of such concept. Prices are indeed set according to some consumption predictions. If consumption changes last minute when consumption was expected to be low, retailers would need to change their energy supply program which would bring penalties. Thus, such concept should be developed in close contact with retailers to fully expand to the electricity market.

2.6 Criteria parameters for energetic analysis To evaluate results from the simulation, some terms needs to be defined. It will be done in this following section [52].

2.6.1 Self-consumption

The self-consumption rate s, is defined as the ratio between the solar energy locally consumed (for direct use, EDU and battery charging, EBC) over the total solar energy consumption, EPV. Thereby a battery can be a mean to raise self-consumption in storing photovoltaic surplus and discharging it when solar electricity cannot cover the load demand anymore. This criterion is important to ensure PV production is consumed as much as possible, and especially that the photovoltaic surplus is consumed locally through storage [17].

𝐶 =E𝐷𝐷 + E𝐵𝐵

E𝑃𝑃 (1)

21

2.6.2 Self-sufficiency

The self-sufficiency rate d, describes how much of the load demand can be covered by the local solar generation. The self-sufficiency rate can be calculated as the ratio of solar energy consumed (for direct use, EDU and battery charging, EBC) over the total energy consumption, EL. This criterion is important to describe the share of solar production provided by the photovoltaic battery system to meet the total load demand, EL.

𝑎 =E𝐷𝐷 + E𝐵𝐵

E𝐿= 𝐶 ∗

E𝑃𝑃E𝐿

(2)

2.6.3 Annual number of storage cycles

Batteries have a limited number of cycles and are often a critical parameter for their use. It is thus important to be able to evaluate how frequent the battery is used each year; hence, nc, the annual numbers of storage cycles. nc is calculated as the ratio between the total discharged energy during the whole year on the DC-side of the battery system, EBD, over the usable battery capacity, EUB.

𝑒𝑐 =𝐸𝐵𝐷𝐸𝐷𝐵

(3)

2.6.4 Cycle life time

The cycle lifetime tc can then be calculated from the annual storage cycle by dividing the maximum number of storage cycles nmax, provided by the battery manufacturer, by the annual number of storage cycles nc.

𝑟𝑐 =𝑒𝑚𝑚𝑚

𝑒𝑐 (4)

22

3 Data and current case analysis To support decisions related to the design of such photovoltaic battery systems, the investments and strategies to follow in terms of battery management, modeling and simulation are essential. In this report, they were performed under the software Matlab and a special case study was analyzed.

The case study analyzed in this report is a middle-size commercial center named Haga centrum and situated in Örebro, Sweden.

Figure 14: Haga centrum entrance [57]

3.1 Haga centrum Haga centrum is a shopping center in Örebro [58] and is the property of Öbo, a municipal real-estate company which owns different residential and commercial buildings in the region of Örebro.

Figure 15: Haga centrum map [57]

Haga centrum includes approximately 22 locals spread over two floors. The estimated total surface is about 17404 m2 (local and common areas included). The total roof surface for possible photovoltaic installations is about 8702 m2 and the total parking surface is about 7415 m2. Calculations were provided from plans given by Öbo, which can be found in appendix.

The shopping center includes locals with various field of activity. To classify these locals in terms of energy activities, their yearly energy consumption was estimated by calculating their surface and assuming

23

a specific energy consumption given by the American governmental organization, EIA (Energy Information Administration) and Energy Star, an American governmental program developed by EPA (Environmental Protection Agency) and the department of Energy. They have identified for the American market the average energy use of different buildings classified by their type of activity [59]. As main interesting energetic consumers, one can find in Haga centrum a grocery store, a library, a restaurant, an indoor swimming pool and a café. A complete list of different owner, ranked from high energy user to lower, is given in appendix.

3.2 Load analysis The system studied consists of the facility of the shopping center Haga centrum.

The data available and which has been worked on during this master thesis are indeed hourly electricity load data for the year 2015 of the facility, which includes ventilation, public lighting and other electricity use in the common areas (elevators, pumps, etc.). Indeed, the data available are from Öbo which is only responsible of the common areas. Each shop has its own meter and pays its consumption directly to the DSO. A further future work would be to analyze a new system where sub-meters would be installed behind a principal DSO meter so that the load analyzed would be from the entire building.

From assumptions and calculations presented in appendix, the energy consumption of the common areas represents 5% of the total energy consumption of the whole shopping center, with an annual electricity consumption of 341000 kWh and an annual peak power of 96 kW in July.

The facility load is mainly stable and constant along the year, with no difference noticed during weekdays and weekends. However, one must know that the ventilation system has been improved during the study time of the system. The load has thus been reduced by half after the month of September as it can be seen in appendix. The energy consumption of an average day of the whole year is given as follow and does not include the month of September, which is the month of the ventilation upgrade.

Figure 16: Daily average energy consumption of the facility for the whole year

The peaks are more pronounced in winter (December-February), with high demand between 6-10 am and 16-24 pm as it can be seen on the figure 17. Thus, the management of such peak hours does not differ much from residential areas which have peak hours during early morning and evening.

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kWh

Hours

24

Figure 17: Daily average energy consumption of the facility for winter season (December-February)

For the simulations, the load has been manually modified in order to consider one system with no major system changes contrary to the case with real data. The load after September has thus been increased so that results represent a normal case scenario. The modified load profile can be found in appendix and has an increased annual electricity consumption of 420000 kWh and an annual peak power of 96 kW in July.

3.3 Solar data

3.3.1 Solar radiation data

The solar data are calculated from solar irradiation extracted from STRÅNG. STRÅNG data are from the Swedish Meteorological and Hydrological Institute (SMHI), and were produced with support from the Swedish Radiation Protection Authority and the Swedish Environmental Agency. The STRÅNG system was modelled to provide historical data of fields of global radiation and direct normal radiation in Scandinavian countries at a temporal resolution of one hour and a horizontal resolution of about 11 x 11 km. The results are given in W/m² and provided as time series under an ASCII file with five columns separated by white space containing the year, month, day, hour and the selected radiation quantity. Parameters to be chosen are the dates defining the beginning and end of the time series and the latitude and longitude.

The interesting solar data for solar power output of photovoltaic systems is the global radiation. It takes into account three components: the direct radiation, the diffuse radiation and the reflected radiation. The direct radiation is the component which is neither reflected nor scattered, and which directly reaches the surface; this is the component that produces shadows. The component that is scattered by the atmosphere, and which reaches the ground is called diffuse radiation. The small part of the radiation reflected by the surface and reaching an inclined plane is called the reflected radiation. These three components together create global radiation.

The global Horizontal Irradiation/Irradiance (GHI) is the sum of direct and diffuse radiation received on a horizontal plane. GHI is a reference radiation for the comparison of climatic zones; it is also an essential parameter for calculation of radiation on a tilted plane.

Case study (Haga centrum in Örebro, Sweden) geographical data needed for STRÅNG:

Latitude: 59.2758513, i.e. North 59° 51’ 16. 33.065’’

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

kWh

Hours

25

Longitude: 15.153029500000002, i.e. East 15° 9’ 10.906’’

3.3.2 Solar production calculation

To calculate the electricity generated in output of a photovoltaic system, the formula followed is used [60]:

𝑃 = 𝐴 . 𝑝 .𝐻 .𝑃𝑃

Where,

P = Power output (kW/m².)

A = Total solar panel Area (m²)

r = Solar panel yield (%). This is the ratio of the electrical power (in kWp) of one solar panel divided by the area of one panel. 0.15 is usually taken since a 1 kWp panels in general corresponds usually to a 6.5 m^2 solar panel [61]. This ratio can change depending on different factors the average radiation, the cell temperature and wind speed.

𝑝 = 𝑘𝑊𝑊𝑆𝑆𝑆𝑆𝑚𝑐𝑆

H = Solar radiation on panels (kW/m².)

PR = Performance ratio, coefficient for losses (range between 0.5 and 0.9, default value = 0.75). It evaluates the performance of the installation independently of the orientation and inclination of the panel in evaluating the quality of a photovoltaic installation. It sums up the quality of the installation and the different losses, which mainly depend on the site, the technology, and sizing of the system. It can include losses for the inverter (4% to 10 %), temperature (5% to 18%), DC cables (1 to 3 %), AC cables (1 to 3 %), shadings (0 % to 80% specific to each site), Losses at weak radiation (3% to 7%), dust or snow (2%).

We assumed the thumb rule [61] that 1 kWp photovoltaic panel can be generated by a 6.5 m2; hence a solar panel yield r of 0.15. Concerning the performance ratio, PR, we assumed a ratio of 0.9.

3.4 Electricity data and analysis The DSO and retailer for Öbo, who is responsible of such the facility load of Haga centrum, is E.ON. The type of tariff established between Öbo and E.ON is a power tariff contract (‘Effektariff’). The power tariff contract is the preferred contract for commercial buildings in order to take into account their peak consumption. The electricity consumption cost is the sum of both the DSO and retailer electricity fees.

3.4.1 DSO

The DSO yearly price includes a fixed grid subscription in order to have a connection point to the grid, a monthly power effect fee for the maximum power that is sent through the grid in a month and an electricity consumption fee for the amount of electricity that is sent through the DSO grid infrastructure. The values used in the modeling are taken from the Örebro local DSO, E.ON.

Table 3: E.ON DSO price

Fixed grid subscription 7200 SEK/year

Monthly power effect fee 76.3 SEK/kW/month

Electricity consumption fee 5.8 öre/kWh

One must remember for future sensitivity analysis that due to increasing renewable electricity production in decentralized installations, network charges are increasing, especially at the distribution grid level.

26

3.4.2 Retailer

Each end-user is free to choose its retailer. To provide price arbitrage, it is supposed that the end-user agreed for hourly real-time tariff with the retailer, defined on a daily basis. Prices are indeed set for the next day 24 hours in advance based on the Nordpool price market.

The hourly retailer price includes an electricity consumption fee based on the Nordpool spot price market for the amount of electricity the retailer is supplying. It varies every hour but it is in a range of 22 öre/kWh. It includes also electricity certificates which are defined for each month. They are fees to promote the expansion of electricity from environmentally friendly sources of energy. Certificate prices vary between electricity suppliers. Adjusting prices, which are defined for each month, and a retailer premium interest, are finally added in addition. The retail premium interest is fixed for the whole year and is based on the energy consumption. Prices from the retailer E.ON is given as follow.

Table 4: E.ON retailer price

Nord pool electricity consumption fee

~ 22 öre/kWh

Electricity certificates ~ 2.63 öre/kWh

Adjusting prices ~ 0.44 öre/kWh

Retail premium interest 3.5 öre/kWh

3.4.3 Taxes and VAT

In addition to your electricity network and electricity costs there are other fees to authorities (for electrical safety, network monitoring, and electrical preparation) and VAT (Value-Added Taxes). The energy tax is determined for one each year at a time.

Table 5: Taxes and VAT

Taxes 29.2 öre/kWh

VAT 25% of total price

3.4.4 Electricity Consumption cost

The total cost (SEK) the consumer has to pay can be summed up under this formula:

𝐸𝑐𝑐𝑐𝑐 = 𝐷𝐷𝐷𝑐𝑐𝑐𝑐 + 𝑃𝑝𝑟𝑟𝑟𝐶𝑝𝑝𝑐𝑐𝑐𝑐 (5)

𝐷𝐷𝐷𝑐𝑐𝑐𝑐 = (1 + 𝑉𝐴𝑉) .�𝑓𝑔𝑐 + � 𝑃𝑚𝑟𝑃𝑚𝑐𝑚𝑐ℎ

12

𝑚=1

.𝑓𝑊𝑆 + � 𝐶𝑝𝑟𝑎𝐻𝑝 . 𝑓𝑆𝑐

8760

ℎ=1

� (6)

𝑃𝑝𝑟𝑟𝑟𝐶𝑝𝑝𝑐𝑐𝑐𝑐 = (1 + 𝑉𝐴𝑉) . �� 𝐶𝑝𝑟𝑎𝐻𝑝 . (𝑓𝑚𝑊,𝑆𝑐 + 𝑓𝑐 + 𝑓𝑚 + 𝑓𝑆𝑟

8760

ℎ=1

+ 𝑓𝑐� (7)

3.4.5 Photovoltaic electricity sellback to the grid

There is no national electricity feed-in tariff in Sweden. On the contrary, it is set depending on a retailer who agreed in trading such excess of electricity for a monthly or yearly fixed compensation. This revenue is set by the retailer and can vary between Nordpool spot price to 1.35 SEK/kWh. The

27

VAT is included in the trading. In this master thesis, it is assumed during the simulations that the surplus of electricity can be sold back to the grid at the Nordpool price.

3.4.6 Electricity Nordpool price variation and arbitrage potential in Sweden

To analyze how prices fluctuate and see the potential to play with price variation through price arbitrage, electricity Nordpool price variations can be analyzed. It is indeed the main varying component of consumers’ cost where they can play on prices.

Figure 18: Sorted graph of the electricity spot price from Nordpool 2015 [62]

Most of the hours during the year have their hourly electricity price between 0.10 SEK/kWh and 0.30 SEK/kWh. The revenue of price arbitrage per hour based only on the Nordpool electricity price is less than 0.20 SEK/kWh for each hour. After analysis of the spot price, the average of maximum price differential which can made in a day is limited to 0.088 SEK/kWh. Thus, variation of grid electricity price is low in Sweden. Moreover, it is known that the electricity price in Sweden is already one of the lowest [63]. Nordpool spot price average electricity price is around 0.20 SEK/kWh in Sweden. However, it is expected that the distribution fee will get more and more expensive due to grid upgrade and expansion to meet the growing electricity demand. Retail price of electricity will thus increase, and thus increase the consumption electricity price. Arbitrage and peak-shaving strategies will be thus interesting strategies to decrease end-user bills and relieve grid strains [64].

0.00

100.00

200.00

300.00

400.00

500.00

600.00

700.00

131

462

794

012

5315

6618

7921

9225

0528

1831

3134

4437

5740

7043

8346

9650

0953

2256

3559

4862

6165

7468

8772

0075

1378

2681

3984

52

Nor

dpoo

l Pric

e [S

EK

/MW

h]

Cumulated hours price are higher

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4 Performance Model Modelings and simulations based on economic, energetic and operation prediction are essential tool to support decisions regarding investment, design and strategies of such technologies especially when numerous kinds of services can be offered. This modeling looks into how to predict the amount of energy and the right timing of charging and discharging in order to provide difference services.

Three services were analyzed under three modelings:

• Photovoltaic surplus management • Peak-shaving management • Price arbitrage management

In this section, the modelings are explained. The three presented modelings were built under the same structure presented as follow.

4.1 General description in the battery sizing modeling

4.1.1 General model description

The modeling is based on the initial load and the solar system size. The studied load is then the initial load reduced by the photovoltaic production. Thus, when solar panels produce electricity, it is consumed directly by the building, i.e. the initial load is reduced by the PV production, which brings the load to be negative when the PV production exceeds the load need. And it is what the modelings focus on; how big should be the storage to handle this excess of energy. In the “photovoltaic surplus management modeling”, the model determines how big the storage should be to store a certain amount of this surplus, whereas in the “peak-shaving management” and “price arbitrage modeling”, the modeling do not look into increasing the battery size for storing the surplus of photovoltaic but to optimize the charging from photovoltaic surplus rather than the grid.

System Design

Dynamic Simulation over a year and Battery Design Load Data Operating

Method

Meteorological Data

Price Data

Techno-economic Calculations

Performance Indicators (Savings, Payback time, etc.)

Figure 19: Modeling flow chart

29

4.1.2 Iterative process

The sizing of the battery is determined after a dynamic simulation where the designed battery is evaluated during a whole year. The battery should fulfill all the conditions defined by the according strategy. To size the battery, the modeling is processed iteratively. The size of the battery is saved under the variable bE, i.e. the maximum state of charge of the battery. An initial nominal battery size is set to 1 kWh at the beginning of the dynamic simulation test. During the iteration process, each time the battery size is not big enough to fulfill the strategy conditions defined for each modeling, the size of the battery is increased by 1 kWh so at the end the battery can provide the required service during the full year.

This logical flow chart explains the iterative process for the battery design:

Figure 20: Logical flow chart for battery design

4.1.3 Power to energy ratio of the battery

The power to energy ratio also known under the name charge and discharge rate, needs to be set at the beginning of the simulation. To take into account the power to energy ratio of the battery, two parameters were added, bPIn and bPOut, the power the battery charge and discharge. These two parameters are defined depending on the size of the battery and the power to energy ratio. In this modeling, a lithium-ion battery was assumed with a power ratio of 1/2 similar to the Tesla one. Hence, the power to energy ratio was evaluated in the modeling such as,

bE

bPIn = bE/2 bPOut = bE/2

Each hour, the battery can charge of bPIn and discharge of bPOut. The charging is modelled knowing the battery cannot charge more than bPIn, reciprocally for the discharge.

4.1.4 Battery losses assumption

In the modeling no losses were included as first main assumption. Battery capacity was assumed constant throughout the battery life and no losses were considered during the charging and discharge process.

Nominal battery design

Dynamic simulation test

Increase battery volume

End of design Strategy

conditions fulfilled?

YES

Figure 21: Battery charge and discharge representation

30

4.2 Model 1: Photovoltaic surplus management

4.2.1 Model description

“PV surplus battery peak shaving” is a Matlab modeling which looks into the size of battery if it should be sized to cover a certain amount of photovoltaic surplus produced in excess during the day. This surplus of energy stored in the battery is then used to cut the following next peak.

4.2.2 Model implementation

The modeling is evaluated in the Matlab file which can be found in the folder “PV surplus battery design”:

PV_surplus.m

It is where the initialization is processed and the graph plotted. One important factor to choose in this function is the factor f, which decides how much percentage of PV surplus the user is willing to store in his battery. This factor has been added to allow smaller battery size when the amount of PV surplus is too large. This file calls a function where the sizing of the battery is performed. This file is named:

BatteryPVsurplusSimFindBattSize.m

4.2.2.1 Battery charging management

The battery is modeled so that it can only charge from photovoltaic surplus. If the battery size is not enough to store the amount of expected surplus, the loop is reset is increasing the battery size. Depending on f, the battery will be sized to store only a certain amount of the surplus.

4.2.2.2 Battery discharging management

Concerning the discharging, which occurs when there is no surplus to cover and the battery is still charged is modeled to discharge as soon as possible. However, a forecasting loop has been created through the function findLocalMaxPeak.m to find the best appropriate time to discharge. This function looks indeed into how many hours the following peak is before the next photovoltaic surplus or at least in the next 24 hours. Knowing when to discharge, the battery waits to discharge to shave the peaks during the next 24 hours which follow the previous surplus. The whole amount of energy available in the battery is used to shave the peak.

4.2.3 Model design process

In this part, the iterative process, variables and logical flow of the design process are drawn and explained to understand how the modeling proceeds.

31

Input: • Load, Hourly load reduced by the production of PV • f, percentage of PV surplus aimed to be stored

Load

Terminology: • batteryEnergy, battery energy level status • bE, battery size (maximum energy the battery can

store) • bPOut, maximum power output the battery can

discharge • GtoHB, the net hourly consumption from the grid

to house and battery

f

Load < 0

Need to charge

No surplus to charge, peak to shave?

then

else

Charging strategy

Discharging strategy Load > localMaxPeak

Peak-shaving

then

Save the battery to shave the next peak

else

No change in the battery energy level for the next hour

• PVoverproduction, photovoltaic surplus • pExcess = load – pMaxWanted, excess power over the threshold • localMaxPeak, threshold defined for the next future peak (calculated in the sub-function findLocalMaxPeak.m)

PV self-consumption (& peak-shaving optimization)

Load

32

else

battDiff = bE – batteryEnergy

then

Charging strategy

f

Increase the battery size and reset the loop

Increase the battery size and reset the loop Load

pExcess = load – localMaxPeak

Load > LocalMaxPeak

Check if need for peak-shaving

Change the load GtoHB, the batteryEnergy with bPOut

Change the load GtoHB, the batteryEnergy and grid2battery with pExcess

Change the load GtoHB, the batteryEnergy with bPOut

Change the load GtoHB with batteryEnergy, and the batteryEnergy to 0

Battery is fully discharged


Discharging strategy

LocalMaxPeak = threshold defined for the next future peak

No need for peak-shaving yet

else

pExcess < bPOut then

else

batteryEnergy < bPOut

then

else

Battery charged enough to fully shave the peak

pExcess < batteryEnergy then

Battery energy level limits the energy flow out the battery

else

bE-batteryEnergy

> bPIn

then

Battery energy level limits the energy flow in the battery

Battery discharged enough to be charged at maximum power bPIn

Load < 0

PVoverproduction of PV PVoverproduction = - f . Load

else

Charging strategy from

PV

Discharging strategy for peak-shaving

Change the batteryEnergy and the load GtoHB with PVoverproduction

Change the batteryEnergy and the load GtoHB with PVoverproduction

Battery big enough to charge the whole surplus

Battery big enough to charge the whole surplus

battDiff > PVoverproduction

then

else

PVoverproduction < bPIn

then

else

33

4.2.4 Model possible improvement

Global solar irradiation should be calculated in taking into account specific inclination (slope, tilt) and orientation (azimuth), which has not been done in this research and could be improved. Here the global solar radiation has been calculated as the solar panels were posed horizontally on the ground for simplicity reasons.

4.3 Model 2: Peak shaving management


This model provides a battery size for peak shaving. The simulation is carried out in order to optimize photovoltaic self-consumption through a PV prediction method to know when to charge depending on forecast photovoltaic surplus production.

As input, the user specifies a percentage of peak-shaving, p, which will be apply to the maximum monthly peak to define the monthly threshold. The battery will then manage the load so that the consumption does not exceed the monthly power threshold.

𝑟ℎ𝑝𝑝𝐶ℎ𝑝𝐶𝑎𝑚𝑐𝑚𝑐ℎ𝑙𝑙 = 𝑝 ∗ 𝑝𝑝𝑟𝑝 𝐶𝑝𝑟𝑎𝑚𝑚𝑚,𝑚𝑐𝑚𝑐ℎ𝑙𝑙 (8)


The modeling is evaluated in the Matlab file which can be found in the folder “Peak shaving battery design”:

Peak_shaving.m

It is where the initialization is processed and the graph plotted. One important factor to choose in this function is the percentage of peak reduction p, which decides to which percentage of the monthly peak load the user is willing to reduce his load. This threshold is set for each month in order to allow peak shaving during seasons with low energy demand. This file calls a function where the sizing of the battery is performed. This file is named:

BatteryPeakShaveSimFindBattSize.m


Contrary to the “photovoltaic surplus management” modeling, the battery sized in the “peak-shaving management” modeling is based on the discharging part. Indeed, the battery is here sized to be able to discharge enough energy to provide peak-shaving, whereas in the “photovoltaic surplus management” modeling, the battery is sized to be able to charge a certain amount of photovoltaic surplus. Hence, reset loops are defined in the charging part, contrary to this case in the “peak-shaving management” modeling.

The battery is modelled to discharge each time the load is higher than the power threshold set for the month. If the battery is not charged enough or not big enough to discharge the needed amount of energy to shave the peak, the loop is reset and the battery size increased.


If no power peak is found at the current time step, the modeling checks if the battery needs to charge. The battery can only charge during time steps when discharge of the battery is not needed.

The charging can be done from the grid or from photovoltaic surplus. However, the charging should support the charging from photovoltaic surplus rather than from the grid. Indeed, the battery

34

management should pay attention during grid charging to keep some space for the PV surplus that might come later on. This is done through a prediction function PVoverproductionBeforePeak.m.

The prediction function determines the amount of energy the battery should charge from the estimated photovoltaic surplus within 24 hours and before the next peak. If the battery needs to charge, it will charge from the grid but will let some space for the forecast estimated photovoltaic surplus generation. Hence, knowing the future photovoltaic surplus, PVoverproduction, the maximal level energy of the battery, bE, for charging from grid is lowered to bE – PVoverproduction in order to save some storage for the future PVoverproduction. The battery is then fully charged when the photovoltaic surplus time has finally arrived. Thus, the battery charging prioritizes photovoltaic surplus consumption.

Nonetheless, the battery size is not increased if the battery is full. It is indeed not the aim of this modeling. To know the size of the battery necessary to charge all photovoltaic surpluses, the “photovoltaic surplus management” modeling I used.


In this section the modeling and logical decisions written under Matlab are explained. As previously explained, the modeling gives the size of the battery and his behavior to provide peak-shaving over an initial load while optimizing photovoltaic surplus utilization.

A global view of the modeling is first given, followed by a more detailed description of the modeling for the discharging and charging part. The charging part contains two description sections depending on whether the charging is operated from the grid or photovoltaic surplus.

35

Load

pMaxWanted

Load > pMaxWanted


Charging strategy batteryEnergy < bE

Terminology: • batteryEnergy gives the energy battery level status • bE is the battery size (maximum energy the battery can store)

Battery not fully charged

Battery fully charged


Need to discharge

No need to discharge, need for charging?

Input: • Load, Hourly load reduced by the production of PV • pMaxWanted, maximum peak power wanted

then

then

else

else


Peak-shaving management (& PV self-consumption optimization)

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batteryEnergy ≥ bPOut

pExcess > batteryEnergy

Battery big enough to discharge

then

then

else

else

Terminology: • batteryEnergy gives the energy battery level status • bPOut is the maximum power output the battery can discharge • GtoHB is the net hourly consumption from the grid to house and battery • pExcess = load – pMaxWanted, is the excess power over the threshold

pExcess ≥ bPOut then

else


Battery too small to cover the excess



Battery too small to cover the excess

Change GtoHB, batteryEnergy, the number of total cycles

DISCHARGE

Change GtoHB, batteryEnergy, the number of total cycles

DISCHARGE

Battery energy level limits the energy flow out from the battery

Battery charged enough to discharge at maximum power bPOut


37

• Charging from PV surplus

Charge to fill the battery at maximum energy level,

bE

• No PV over-production yet, charging from grid

Charge to fill the battery at a lowered energy level to save some

space for the PV future PV surplus,

bE – PVoverproduction

Charging strategy From PV surplus

Charging strategy From grid

• The charging strategy is based on a prediction method, which looks into the amount of PV surplus before the next peak to save some space for the future PV surplus charging

Charging strategy

43

bE-batteryEnergy

> bPIn

battDiff < PVoverproduction

then

then

else

else

PVoverproduction < bPIn

then

else

Change the load GtoHB, the batteryEnergy and PV2grid with PVoverproduction

Battery energy level limits the energy flow in from the battery


Change the load GtoHB and PV2grid with battDiff, and the batteryEnergy to bE

Change the load GtoHB, the batteryEnergy and PV2grid with bPIn

Change the load GtoHB, the batteryEnergy and PV2grid with PVoverproduction

Not enough margin to charge from the grid at maximum value, due to the pMaxWanted

Battery can be fully charged

Load < 0

PVoverproduction of PV PVoverproduction = – Load

battDiff = bE – batteryEnergy

(bE-PVoverproduction) -batteryEnergy > bPIn

battDiff < diff

then

then

else

else

diff < bPIn then

else

Change the load GtoHB, the batteryEnergy and grid2battery with diff


Change the load GtoHB and grid2battery with battDiff, and the batteryEnergy to (bE-

PVoverproduction)

Change the load GtoHB, the batteryEnergy and grid2battery with bPIn


Not enough margin to charge from the grid at maximum value, due to the pMaxWanted

Battery can be fully charged

battDiff > 0 then


else

then

else

battDiff = (bE – PVoverproduction) – batteryEnergy

Battery energy level limits the energy flow in from the battery


PV


grid Charging strategy

No need to charge from the grid The coming PV over-

production big enough

diff = pMaxWanted - Load

39


In charging part of the modeling, the PV surplus prediction function was developed to optimize solar consumption. However, the prediction period is done until the next peak, whereas it should be reduced to at most 24 hours if the peak arrives much later. Indeed, solar prediction barely exceeds 24 hours.

Moreover, the battery was sized only for discharging and not for PV charging. Thus not all PV surpluses are stored. A function to allow the battery to discharge just before a PV surplus should be added. Hence, a similar prediction function, as in the charging part, should be developed to let the battery discharge enough, so it can charge from forecast solar surplus in the next 24 hours.

Finally, it would be interesting to optimize the charging from the grid regarding the electricity price, by introducing spot price and narrow charging from grid during cheapest electricity price, i.e. price arbitrage management, strategy which has been developed in the third modeling and explained in the next section.

4.4 Model 3: Price arbitrage management


Different strategies are possible when one wants to size a battery-photovoltaic system and one of the main one is to look at the electricity prices to arbitrate the prices and choose the best time to consume and use the battery. This is the so called price arbitrage strategy. Hence, the importance to know each hour the electricity prices and to analyze them. In this part, the modeling of the decision for price arbitrage will be explain. The simulation has been carried out without any method to optimize photovoltaic self-consumption contrary to the previous models.

4.4.1.1 Price arbitrage method: decision factors

The choice of the battery size should be a compromise between gaining arbitrage savings and peak reduction in order not to use the battery when the load is low. Hence, besides the electricity price, the consumption cost is a factor to take into account in order not to use the battery when prices are maybe high but peak loads relatively low. Thus, a trade off should be found in saving money and peak load reduction. Use the battery when electricity prices and consumption are both high.

Moreover, in taking both electricity price and consumption cost in consideration for price arbitrage discharge and charge, peak hours are reduced to most important hours the batteries should work. If the consumption cost was not considered, the battery would be used more often and even when the electricity consumption is low, preventing from using the battery another time.

The analyzed hourly electricity price was calculated from the spot price market given by Nord pool. The electricity consumption cost was calculated from hourly electricity prices and hourly load. Only a part of the different fee presented in previous sections will need to be analyzed. Indeed, fixed fee like the fixed subscription fee, the fee for monthly peak or the taxes and retailer interest are fixed or do not vary enough. For such strategy, what is interesting are hourly varying prices.

Two kind of cost were analyzed: The consumption cost that the consumer has to pay for the electricity supply and the use of the grid; and the electricity cost that the retailer set for every hour depending on the spot price.

Electricity consumption cost = Hourly retailer price . Load + Hourly DSO price . Load [SEK] (9)

Electricity price = Nordpool pot prices + other retailer prices (certificates, adjusting prices)

[SEK] (10)

The decision factors are then daily defined based on the average of the electricity consumption cost and electricity cost. The user set a percentage to displace these decision factors around these averages and price arbitrage is done within these intervals. In the following graphs, the decision factors are represented on the figure 23. The battery should provide price arbitrage each time electricity price and consumption

40

costs are simultaneously higher than these thresholds. The hours the battery should provide price arbitrage are then represented by the blue areas which can be seen in figure 22.

Figure 22: Electricity consumption cost [SEK],

Combined decision factors

Figure 23: Decision factors: electricity consumption cost

[SEK], electricity price [SEK/kWh]

For these reasons, two decision factors were chosen to determine when to charge and discharge the battery in arbitrating electricity prices and consumption cost, and are named for the rest of the study d1 and d2. The two decision factors are defined daily based on the average of the electricity consumption cost and electricity cost. The user set a percentage to displace these decision factors around these averages and price arbitrage is done within these intervals. Price arbitrage is thus based on the maximum cost the consumer is willing to pay for the electricity consumption and electricity cost. There are two decision factors to determine when to do price arbitrage method. A third decision factor is added, named p. It is the reduction percentage of load when discharging during price arbitrage. This value is set at the beginning of the simulation.

Figure 24: Daily maximum consumption cost and electricity price decision values

41

This graph represents for the whole year the maximum decision values set each day which determined whether or not price arbitrage should be operated. If prices and cost exceed these decision values, the battery is used for arbitrage. The last parameter for the decision is the level of peak reduction allowed.

4.4.1.2 Peak-shifting method with arbitrating on prices

To provide price arbitrage, it is supposed that the end-user has an hourly tariff with the retailer, defined on a daily basis. Knowing the hourly electricity price 24 hour in advance, the load is shifted the next day in order to buy the electricity when it is cheap to use it when it is expensive. However, this shift should not transform low peak load into uncontrolled high peak load to charge the battery during low price electricity. Hence, a maximum value, pMaxWanted, was set for the charging. Charging increases the load no more than the current maximum monthly peak.


The modeling is evaluated in the Matlab file which can be found in the folder “Price arbitrage battery design”. Two functions need to be run to provide the simulations:

• DefaultParameters.m • Price_arbitrage.m

4.4.2.1 Default parameter function

To launch the modeling, DefaultParameters.m needs to be run first in order to set all the default parameters, extract excel data and launch the initialization. One important factor to choose in DefaultParameters.m is the percentage to define the decision factors for price arbitrage (rate compared to the mean value of consumption cost and electricity price) and the percentage reduction of the load allowed when using the battery instead of grid consumption.

To generate the load information, DefaultParameters.m run a sub-function excelExtractData.m, which extracts data from excel files. The extraction process is different for each excel data file. According to the name of the studied place, the load is extracted. Indeed, not all the data information has the same excel data shape, hence an own extraction process for each load data is needed. This extraction process gives as outputs two excel files which will be used later on in the simulation:

- dataHourlyLoad.xlsx; in this excel file, one can find the load data for each hour during the whole year of study.

- dataMonthlyPeaks.xlsx; in this excel file, one can find the maximum peak power for each month. These data are needed to calculate some DSO or retailer prices.

Thus, all the possible needed data are stored through this function and the excel files are created to shape the given data in a proper way for Matlab extraction.

4.4.2.2 Price arbitrage function

The process for price arbitrage is then carried out with Price_arbitrage.m. This file calls a function where the sizing of the battery is performed. This file is named:

BatteryArbitrageSimFindBattSize.m

According to the values defined in Price_arbitrage.m, parameters used for the simulations are set from the file DefaultParameters.m. Finally, the load to be taken into account for the study is generated according to the previous information given.

42


As in the “peak-saving management” modeling, the battery size is based on the discharging part. Indeed, the battery is here sized to be able to discharge enough energy to provide price-arbitrage. Hence, reset loops are defined in the discharging part.

The battery is modelled to discharge each time the load is higher than the thresholds set for the day and presented in the previous section 4.4.1.1. If the battery is not charged enough or not big enough to discharge the needed amount of energy to shift the peak to lower price hours, the loop is reset and the battery size increased.


If no costly peak is found at the current time step, the modeling checks if the battery needs to charge. The battery can only charge during time steps when discharge of the battery is not needed and not more than the monthly peak power in order not to increase tariffs based on the power.

The charging should be done when electricity prices are at their lowest. The charging is done during low price period, in a time interval of 24 hours after the previous peak. This is done through a prediction function findMinPriceForCharging.m.

The prediction function determines the time step to charge from the grid. It creates indeed a table of study between the previous discharge and the next costly peak and for less than a day prediction. Its min value corresponds then to the cheapest price possible to charge from the grid.

Regarding the amount of electricity, one can charge, it is true that price arbitrage has less restriction than peak-shaving strategy. However, if no restriction is done, the charging in an hour can be uncontrolled, hence the decision to limit the charging to at least the maximum load. It has been reduced to ¾ of the maximum load without changing the size of the battery.


In this section the modeling and logical decisions written under Matlab are explained. As previously explained, the modeling gives the size of the battery and his behavior to provide price arbitrage over an initial load while optimizing discharging/charging of the battery depending on electricity price and consumption cost.

A global view of the modeling is first given, followed by a more detailed description of the modeling for the discharging and charging part.

43

Input: • Load, Hourly load reduced by the production of PV • p, d1, d2, threshold for peak reduction and decision factors

Load

p, d1, d2

consumptionCost > d1 & electricityPrice > d2

Need to discharge

No need to discharge, time to charge?

then

else


Charging strategy

batteryEnergy < bE & k = kcharge

then

Save the battery to charge when cheaper

else


Terminology: • batteryEnergy, battery energy level status • bE, battery size (maximum energy the battery can

store) • bPIn/bPOut, maximum power input/output the

battery can charge/discharge • GtoHB, the net hourly consumption from the grid

to house and battery • d1 and d2, decision factors which fix each day the maximum cost and price allowed for grid consumption • consumptionCost and electricityPrice, consumption cost and electricity price at the current time step • kcharge time step when electricityPrice is at its lowest in the next 24 hours and before next discharge • pMaxWanted, Grid consumption limitation set at least at the current annual max grid power consumption. • pExcess = load – pMaxWanted, excess power over the threshold

Price arbitrage and Peak-shifting

44

else

consumptionCost > d1 &

electricityPrice > d2

Subfunction to have kcharge findMinPriceForCharging

then


p, d1, d2

Disharging strategy

Charging strategy


Change the batteryEnergy and the load GtoHB with pExcess

Increase the battery size and reset the loop Load

pMaxWanted (<= Loadmax) (1) Grid consumption limitation

batteryEnergy < bE & k = kcharge

Check if need for charging

Change the load GtoHB, the batteryEnergy with bPIn


Change the load GtoHB, the batteryEnergy with diff

Change the load GtoHB with battDiff, and the batteryEnergy to bE

Battery is fully charged


Charging strategy Conditions to charge not respected yet

else

diff < bPIn then

else

battDiff < diff then

else

Battery discharged enough to be charged as much as (1) allows

bE - batteryEnergy > bPIn then

Battery energy level limits the energy flow out the battery

else

diff = pMaxWanted - Load

battDiff = bE - batteryEnergy

batteryEnergy > bPOut

then

Battery energy level limits the energy flow in the battery

Battery charged enough to be charged at maximum power bPOut

Discharged allowed pExcess = f . Load

else

Change the batteryEnergy and the load GtoHB with pExcess



pExcess < batteryEnergy

then

else

pExcess < bPOut then

else

45


Photovoltaic surplus is here not taken into account. It would be interesting to add a final case for PV surplus utilization and price arbitrage allowing simultaneously peak-shifting.

Moreover, the grid consumption limitation in the charging part could also be improved in order to find an optimal grid limitation when charging.

4.5 Techno-economic performance evaluation The techno-economic analysis was evaluated under the Matlab code analyseCost.m where the capital cost for battery and photovoltaic system installations were estimated, financial savings calculated and technical gains regarding energy saving and peak reduction determined. It is important to have such indicators to ensure the feasibility and viability of photovoltaic battery systems projects

46

5 Results In this section, the results are presented for the three developed modelings. The simulations were processed from the data presented previously.

5.1 Photovoltaic surplus management In a paper presented in the literature review, it was suggested to size a photovoltaic system twice as much as the annual peak load to be profitable from a self-sufficiency and self-consumption point of view [17]. The peak load of the system is 96 kW. However, the simulation shows that to store the all surplus of a 180 kWp of photovoltaic system, it would need a huge battery system of 930 kWh. The photovoltaic system size was thus limited to 100 kW. The simulation for PV surplus utilization purpose designs then a battery system of 185 kWh. Assuming that Li-ion batteries of Tesla type are used, a 185 kWh battery system can be met by aggregating 29 batteries.

100 kWp photovoltaic system represents a total surface of 650 m² assuming that 1 kWp roof top photovoltaic panel needs 6.5 m² [61]. The installed photovoltaic surface represents a percentage of 7.5 % of the total 8702 m² roof space of the building. Thus, there is space for photovoltaic oversizing. This photovoltaic system will provide in total 83.5 MWh of which 10% exceeds the load. This surplus of energy can be sold back to the grid or be self-consumed through a storage investment.

As a reminder, in this modeling, the battery is designed to only store the surplus of PV and is used daily by discharging the whole energy stored in the evening to cut the peak. PV surplus is defined as the difference between the load and the PV production when the latter one is higher than the demand.

Figure 25: System under PV surplus management, year overview

In summer, the load is reduced by more than 10%. This is due to high PV production during summer, which leads to PV surplus which are stored to cut the following peak as it can be seen on the figure 26.

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Figure 26: System under PV surplus management, PV charging and peak shaving on the next peak

As the figure 26 clearly shows, the battery is used daily, charging the battery from the entire PV surplus and discharging it fully in the same day. As soon as there is some PV surplus, the battery starts charging. When there is no more PV surplus to store, the battery waits for the appropriate time to start discharging in shaving the peak and not discharging it fully at once.

During the whole year, the total battery system will be cycles 10030 times which corresponds for a total of 346 cycles per battery during the year. The usage of the battery is nearly daily as it has been design to store each photovoltaic surplus. Assuming an annual battery cycle of 5000 cycles [41], batteries are forecast to last at least 14 years. However, calculations show that 70% of the battery life time could be used for other purposes since the battery is used only during the day and early evening to cut the peak and is not used the rest of the time.

Simulation shows that a battery photovoltaic surplus to cover the study case load would decrease grid dependence by 25%.

Figure 27: Share of consumption to meet the load

Results shows that investing in a photovoltaic system helps to decrease summer peaks by 6% and at the same time the annual peak load as in the case study the peak demand of 96 kW occurred in July. This peak

75%

22%

3%

Grid consumption Direct PV consumption Indirect PV consumption (PV surplus, battery)

48

reduction is obtained thanks to optimal photovoltaic production and low consumption in winter. Adding a battery system improves this result by 5%, with a peak reduction by 11%. Moreover, the photovoltaic battery system extends the reduction of the monthly peaks to other months as it can be seen in the following graph.

(a) (b)

Figure 28: Monthly peak loads (a) and grid electricity cost distributions for each system (b)

Finally, calculation on revenue shows that using a battery would allow 5100 SEK more saving than selling the photovoltaic surplus to the grid, which brings a saving of 1030 SEK. However, saving engender by investing in a battery system to support a PV system are much less than just investing in a PV system, which would bring revenue of 59000 SEK compared to a non PV battery system.

Thus, not only a battery system allows self-consumed of the surplus of photovoltaic production, bringing six times more financial savings than selling it back to the grid, but it can also improve peak reduction. It decreases even more monthly peaks and applies the peak reduction to other months where photovoltaic productions are more limited.

5.2 Peak shaving management In these simulations, a system with 100 kWp was taken for the study. As a reminder, the aim of this modeling is to size the battery to shave peaks for a certain amount of percentage. The simulation was launched for several percentages to have an idea of the battery size to choose while finding a happy medium between high peak reduction and reasonable battery size.

Figure 29: Battery size dependent on the percentage peak shaving reduction simulated

49

As the figure 29 shows, the battery size progressively increases until reaching a critical percentage of 25%, where the battery size starts increasing exponentially. This is due to the fact that higher the percentage is, the more peaks need to be covered. The critical limit is reached when not only peaks but also base loads need to be covered and shaved to the demanded percentage. Indeed, the battery is used for longer times, hence increasing the battery size. Thus, the more the load is uniformed with no clearly peaks, the sooner this critical point will be met, limiting the peak-shaving percentage.

It was chosen for the rest of the study, a battery system size of 147 kWh allowing a peak-shaving of 25%. If a battery size of 185 kWh was chosen like in the previous case, the graph above shows that the system could expect a little bit more than 25% of total peak-shaving during the whole year as 147 kWh is already the critical point. A 147 kWh battery system can be met by aggregating 23 batteries of the lithium-ion Tesla type.

Figure 30: System under peak shaving management, year overview

The uniformity problem of the load can be noticed for the end of the year when the load is too uniform during the whole month increasing the battery size to 143 kWh so the battery can be fully discharge. Indeed, as a reminder the maximum peak load is set each month in applying the peak-shaving percentage to the monthly peak load. Thus, if the load is uniform around the monthly peak load, the battery will need to be operated much more than usual. It would be interesting to change the peak reduction method depending on the shape of the load and notably in case of uniform load.

The figure 31 describes the battery behavior in terms of charging from the grid or from photovoltaic surplus. It highlights a charging process that occur from the grid as there is no photovoltaic surplus in the next 24 hours, but also a charging from photovoltaic surplus. One can notice that the whole photovoltaic surplus is not fully charged. It would be interesting to ensure a battery sufficiently discharged in advance in order to charge the whole surplus.

50

Figure 31: System under peak shaving management, charging from PV surplus and the grid

During the whole year, the total battery system will be cycles 6092 times which correspond for a total of 265 cycles per battery during the year. Contrary to the previous modeling, the battery is not designed to be used daily. Thus, the battery state can stay fully charged if there is no need to shave peaks as it can be seen during summer, when the load is already low besides being reduced by the PV production. Assuming an annual battery cycle of 5000 cycles [41], batteries are forecast to last at least 19 years. Thus, the battery is not stress with few cycles per year allowing the possibility to offer other services than peak-shaving during its life time, like a combination of services with a full PV surplus consumption. Services should all the more be combined since the strategy uses the battery for only 30% of the total year.

Through this modeling, the load is decreased all year round by decreasing peaks by 25% of the monthly peak load. This strategy allows to decrease the monthly peak fee related to the monthly peak power. As it can be seen on the figure 32, the saving is here mainly done regarding the power cost of the DSO, and amounts up to 22600 SEK.

(a) (b)

Figure 32: Monthly peak loads (a) and grid electricity cost distributions for each system (b)

Thus, this management of battery based on the reduction of peaks allows savings by the DSO and notably concerning the power fee contrary to the first proposed method, photovoltaic surplus management, which allows saving mainly by the retailer side, for the energy fee. Combining these two methods, battery investment would thus bring even more revenue. Assuming that battery can fulfill both missions, revenue could go up to 28000 SEK.

51

In the peak-shaving modeling, batteries have been designed to keep space and wait to charge from future photovoltaic surplus. Nonetheless, when there is no future photovoltaic surplus in the next 24 hours or before the next peak, the battery charges from the grid right after the previous shaved peak. However, when it charges from the grid, it would be interesting to charge it when prices are at their lowest. This corresponds to the third strategy, which will be described in the following section.

5.3 Price arbitrage management In these simulations, the studied system does not have any photovoltaic system, as the developed modeling does not take into account the management of photovoltaic surplus. This could be improved in future work, charging photovoltaic surplus in batteries. Hence, the case studies just a battery system.

The design of the battery was only done through the hourly price profile taking into account both the energy consumption of the facility and electricity price differential, as they are not always correlated. This is a main simplification which has to be taken into account in the understanding of the sizing and operation of the battery. It is indeed worth to be noticed that the aim was not to flatten the power profile by shifting the peak loads and that the price arbitrage strategy can instead lead to an increase of gap between peak and off-peak loads, as can be seen on the figure 33.

(a)

(b)

Figure 33: System under price arbitrage management during the whole year (a) and during five days (b)

52

Results suggest a battery system size of 143 kWh. A 143 kWh battery system can be met by aggregating 22 batteries of the lithium-ion Tesla type. During the whole year, the total battery system will be cycles 6667 times which correspond for a total of 298 cycles per battery during the year. Thus, the battery is not used daily. Thus, the battery state can stay fully charged if there is no price arbitrage potential. Assuming an annual battery cycle of 5000 cycles [41], batteries are forecast to last at least 17 years. Thus, the battery is not under stress with few cycles per year allowing the possibility to provide other services than price arbitrage during its life time, like a combination of services with a full PV surplus consumption or peak-shaving. Services should all the more be combined since a price arbitrage strategy use the battery for only 50% of the total year.

As a reminder, the charging is based on low electricity price whereas the discharging is based on both the electricity price and consumption cost. The consumption cost is the load multiplied by the electricity price and represents what the consumer will actually pay at the end of the month. The battery is designed to discharge when these both factors are high enough compared to the threshold defined each day in regards to the daily mean value (decision factor 1 and 2). These thresholds are indeed set each day by the daily mean electricity price or consumption cost, adjusted by a percentage given as input. In this case, it was chosen a threshold set 10 % above each daily mean value. The amount of energy to discharge is defined by a percentage of the load to decrease. This percentage is given in the input too (decision factor 3). In this case, it is set at 20 %. On the opposite, the amount of energy to charge the battery can go up to the maximum monthly peak load in order not to increase the monthly peak load which influence on the power cost. Thus, there is no peak reduction as the charging can go up to the monthly peak load.

Although, the battery is here designed to capture only the price differential with no peak reduction and even more high power consumption from the grid due to charging during low price hours, one can notice that the load is reduced when the load is high, as peak price hours correspond to peak load hours. For example, on the previous graph, the load is reduced between 9 am and 7 pm and the load is then increased much more in the early morning, at 5 am, when consumption is at its lowest. The battery is then charged fully, consuming up to the maximum monthly peak power. This charging leads to more high power consumption, which occurs nonetheless when load is usually low. Thus, although there is no peak reduction, it can provide grid flexibility during peak price hours which can match with peak load hours, by decreasing peaks by 20%, set as input in the modeling. Grid is thus relieved by reducing load during those peak hours, as the price evolution is usually related to peak hours.

As for the revenue of such strategy, results show that the amount of saving does not exceed 1000SEK due to a low electricity price of around 0.20 SEK/kWh in Sweden and a small spot price differential of 0.088 SEK/kWh for an average day, bringing minimum average revenue of 2.6 SEK/day. It would be thus interesting to study the case with another country where electricity prices and price differentials are higher.

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6 Discussion

Figure 34: Saving proportion after different management and technology investment

The graph above presents the share of the savings for each of the strategy management presented previously. It is thus an investment in photovoltaic system only which brings highest revenue, followed by an investment in battery for peak-shaving. However, investments in photovoltaic and battery systems are still currently high and calculations of the payback time when no incentives are taken into account can easily reach more than 50 years. Thus, investing in a battery system to increase by 5% photovoltaic self-consumption is not interesting if no other services like peak-shaving are developed. As for price arbitrage, saving reminds small in Sweden due to low electricity price and small daily price differential. Thus, the price arbitrage should not be chosen as a battery design strategy but as a complementary service to other strategies like peak-shaving, in order to charge from the grid when prices are low.

Moreover, the price arbitrage modeling was done with no forecast on future electricity price. Prices are subject to changes and as a consequence the annual savings, all the more if customers begin adopting price arbitrage strategies to lower their electricity bills, changing their consumption behavior. Thus, price arbitrage is as an unsure source of revenue to pay back the costly invested battery and hardware. If such a strategy should be chosen, the retailer supplying the electricity should be informed and put on board in order not to mislead his consumption prediction.

Concerning the combining of modelings, results have shown that battery are not always used and can be used for different services like combining photovoltaic surplus management to peak shaving management strategy, or peak-shaving management to price arbitrage strategy, or even the three strategies together. However, in this study case, it has been seen that the load does not suit well for combining both price arbitrage and peak-shaving as peak price hours differs from peak load hours. If peak prices hours and peak load hours would have matched, price arbitrage would have been the same, which is not the case here. Hence, before any chosen strategy, the load should be studies to apply the correct battery management strategy.

In the case studies, the load was not adapted to a peak shaving strategy. The battery size increases exponentially for few percentage peak-shaving. This is due to the load shape which does not suit for such strategy. Indeed, peak-shaving is perfect for load with short, sudden and intense peaks. In this case, peaks are long, uniform and moderate. However, in the future where more and more of appliances will be

PV (self-consumption)

67%

PV (sold back to the grid)

1%

Battery (PV self-consumption)

6%

Battery (peak shaving)

25%

Battery (price arbitrage)

1%

57

dependent on electricity like electric-vehicle, peaks will be more visible with every load, allowing the use of such strategy for any load.

Furthermore, it has been seen that savings from each proposed services from batteries are low. To be competitive and enter the market, battery should be used for multiple services in order to maximize their utilization during their lifetime. Results shows indeed that batteries are not used during 65 % of the year if only one service is chosen as strategy. For example, photovoltaic surplus management is used only during the day and in the early evening and could be used for other purposes during nights, like frequency regulation, and in early morning, like peak shaving.

Thus, services to customers and to the grid must be maximize and not underutilize the battery to one service, every services being a source a revenue.

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7 Model limitations and future works First and foremost, the results were calculated from the case study and will be different from any other load. It gives nevertheless a general overview of the trend. However, results from other load should be explored in future works. Notably, an interesting work would consist in studying a full commercial center and not just the property load (which limits the study to ventilation, lighting, common electricity usage, etc.) in aggregating the shops’ loads to the study load. It was indeed difficult to obtain the loads of the shops which have individual meters, but it would be interesting to have them in order to see the potential of managing the energy of a whole commercial center under one local system operator.

Moreover, due to time constraints, the current modeling can be developed further and some other aspects added, like notably simulating comparative models, with different data and more battery characteristics. The modeling assumes notably perfect forecast of solar production and electricity demand, which should be rectified with a prediction error.

The techno-economic calculations could also be improved with notably loans, incentives and other government programs not taken into account in the modeling. Furthermore, the battery design has been simplified to no losses. Discharging, charging and battery self-discharge losses should be taken into account in future works.

Finally, a comparative sensitive analysis could be developed in order to identify optimal battery designs and strategies.

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8 Conclusion

In conclusion, three modelings have been developed to design and analyze the dynamic behavior of a photovoltaic battery system, besides a modeling for cost analysis of such system. Results shows that currently, energy storage is not cost-competitive with most strategy investments which are too high compared to savings; savings from a photovoltaic system only being at least three times higher than savings from any battery strategy. It is true that batteries are still expensive and have difficulties to reach the mainstream power system due to performance, regulatory barriers and resistance of utilities.

However, the energy sector has understood the whole potential of batteries to reach a complete renewable world despite their high up-front cost.

Many researchers and industries are working to develop cheaper and more efficient batteries and appropriate business plan to facilitate the access to the global market. Even the consumer starts to feel concern by the potential of batteries and the need to change his energy consumption notably thanks to awareness and marketing promotion. Their deployment by the consumer should be fostered through incentive programs and the development of local system operators who could promote such technologies by the end-user.

Batteries can offer lots of services and all possibilities should be considered during their design and management in order to increase the financial assessment. Storage can indeed help to lower consumers’ electricity bills, increase grid flexibility and foster the integration of renewable sources. Hence, a multiple-objective photovoltaic-battery system regarding arbitrage, peak-shaving and self-consumption or other services like frequency regulation should be investigated in order to realize the full potential of batteries.

“Supplying modern energy services to the billions who now lack electricity and clean fuels is not just a moral imperative but a unique business opportunity – a huge market in itself and one that will create new levels of prosperity and demand for goods and services of all kinds.”

Kandeh Yumkella and Chad Holliday, Co-Chairs of the High-level Group on Sustainable Energy for All (Sustainable Energy for All, April 2012)

60

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[5] C. Morris, “Citizens own half of German renewable energy”, Energy Transition, 29 October 2013

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[20] Norut Northern Research Institute, home website, available at http://norut.no/en

[21] A. Christensen, “Bright future for solar energy in the north”, ScienceNordic, November 2012

[22] E. F. A. Al-Showany, “The Impact of the Environmental Condition on the Performance of the Photovoltaic Cell”, American Journal of Energy Engineering, Vol. 4, No. 1, 2016, pp. 1-7, March 2016

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[26] E. Mannheimer, “Swedish government to invest in solar energy”, Mundus news, July 2015

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[29] E. Wesoff, “How Soon Can Tesla Get Battery Cell Costs Below $100 per Kilowatt-Hour?”, Greentech Media, March 2016

[30] M. Munsell, “Solar Module Prices Reached 57 Cents per Watt in 2015, Will Continue to Fall Through 2020”, Greentech Media, March 2016

[31] T. Randall, “The World Nears Peak Fossil Fuels for Electricity”, Bloomberg, June 2016

[32] Battery University, “Finding the Optimal Runtime and Power Ratio of Li-ion”, BU-206a, April 2016

[33] R. Carnegie, D. Gotham, D. Nderitu, P. Preckel, “Utility Scale Energy Storage Systems”, Purdue University, June 2013

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63

http://photovoltaic-software.com/PV-solar-energy-calculation.php

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64

APPENDIX A

A.1 Properties of the materials The appendix presents the documents and calculation tables done to better analyze Haga centrum as a case study.

Figure 1: Plans of Haga centrum shopping center1

1 ÖBO, Swedish housing company of the region of Örebro, available at http://www.obo.se

http://www.obo.se/sv/Bostader/lokaler/Stadsdelscentra/Haga-centrum/

65

A.2 Load data In the section, different graphs of the studied load can be found.

Figure 2: 2015 electricity load real-data of the facility of Haga Centrum

Figure 3: 2015 electricity load modified-data of the facility of Haga Centrum

0

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66

A.3 Load analysis Energy use Energy use Surface Surface Energy use Ratio Ratio

kBTU/ft^2 kWh/m^2 cm^2 m^2 kWh Share kWh/kWh Share m^2/m^2

Grocery store 480 1514.2 34 2011.834 3.05E+06 44.0% 11.6%

Library 235.6 743.2 14.52 859.1716 6.39E+05 9.2% 4.9%

Restaurant 432 1362.8 4.4 260.355 3.55E+05 5.1% 1.5%

Indoor swimming pool 96.8 305.4 17.83 1055.03 3.22E+05 4.6% 6.1%

Café place 432 1362.8 3.91 231.3609 3.15E+05 4.5% 1.3%

Dentist cabinet 116.7 368.1 12.72 752.6627 2.77E+05 4.0% 4.3%

Gym #1 96.8 305.4 12.58 744.3787 2.27E+05 3.3% 4.3%

Office 148.1 467.2 7.4 437.8698 2.05E+05 3.0% 2.5%

Sport center #1 (Martial arts) 96.8 305.4 10.83 640.8284 1.96E+05 2.8% 3.7%

Community youth center 69.8 220.2 13.44 795.2663 1.75E+05 2.5% 4.6%

Pharmacy 116.7 368.1 5.19 307.1006 1.13E+05 1.6% 1.8%

Gym #2 96.8 305.4 5.94 351.4793 1.07E+05 1.5% 2.0%

Sport center (Martial arts) #2 96.8 305.4 5.25 310.6509 9.49E+04 1.4% 1.8%



Florist shop 114.4 360.9 3.155 186.6864 6.74E+04 1.0% 1.1%

Sport center (Martial arts) 96.8 305.4 3.52 208.284 6.36E+04 0.9% 1.2%

Hairdresser 100.4 316.7 3.155 186.6864 5.91E+04 0.9% 1.1%

Massage center #1 100.4 316.7 3.12 184.6154 5.85E+04 0.8% 1.1%

Dressing room 47.6 150.2 5.06 299.4083 4.50E+04 0.6% 1.7%

Massage center #2 100.4 316.7 1.5 88.7574 2.81E+04 0.4% 0.5%

Tailor 104.4 329.3 0.88 52.07101 1.71E+04 0.2% 0.3%

TOT 3372.9 10640.1 178.3 10549.1 6589475.3 95.1% 60.6%

Facility 4.6 49.7 115.8 6854.438 3.41E+05 4.9% 39.4%

TOT 3377.52 10689.87 294.12 17403.55 6.93E+06 100.0% 100.0%

Table 6: Energy usage per activities in Haga centrum

67

A.4 Electricity data In the appendix, the total description of hourly prices is given except Nord pool prices which vary every hour and can be found directly on the Nordpool website.

DSO (E.ON)

The DSO yearly price includes a fixed grid subscription in order to have a connection point to the grid, a monthly power effect fee for the maximum power that is sent through the grid in a month and an electricity consumption fee for the amount of electricity that is sent through the DSO grid infrastructure:

Fixed grid subscription 7200 SEK/year

Monthly power effect fee 76.3 SEK/kW/month

Electricity consumption fee 5.8 öre/kWh

Retailer (E.ON)

The hourly retailer price includes an electricity consumption fee based on the Nordpool spot price market for the amount of electricity the retailer is supplying. It varies every hour but it is in a range of 22 öre/kWh. It includes also electricity certificates which are defined for each month. Adjusting prices, which are defined for each month, and a retailer premium interest, are finally added in addition. The retail premium interest is fixed for the whole year and is based on the energy consumption.

Nord pool electricity consumption fee

~ 22 öre/kWh

Electricity certificates ~ 2.63 öre/kWh

Adjusting prices ~ 0.44 öre/kWh

Retail premium interest 3.5 öre/kWh

Electricity certificates for each month In öre/kWh

January 2.71 July 2.37

February 2.48 August 2.43

March 2.31 September 2.62

April 2.41 October 2.72

May 2.43 November 2.66

June 2.43 December 4.01

Adjusting prices for each month In öre/kWh

January 0.38 July 0.16

February 0.45 August 0.58

March 0.14 September 0.30

April 0.10 October 0.73

68

May 0.20 November 0.87

June 0.34 December 0.97

Taxes and VAT

On top of it, taxes and VAT (Value-Added Taxes) should be considered.

Taxes 29.2 öre/kWh

VAT 25% of total price

69

APPENDIX B

B.1 Model logic flow chart

Run Model

Battery design for different strategies

PV surplus management Peak shaving management Price arbitrage management

Run DefaultParameters

Run Price_arbitrage

Run PV_surplus

Run Peak_shaving

Cost analysis

Run analysisCost

CALCULATE PERFORMANCE

STEADY STATE DESIGN

DYNAMIC BEHAVIOR

STEADY STATE DESIGN

DYNAMIC BEHAVIOR

STEADY STATE DESIGN

DYNAMIC BEHAVIOR

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