S2 Proceedings-Keynote V2 Public

7/31/2019 S2 Proceedings-Keynote V2 Public

1/30

PROPRIETARY RIGHTS STATEMENTThis document contains information, which is proprietary to the EU-DEEP Consortium. Neither this document nor the

information contained herein shall be used, duplicated or communicated by any means to any third party, in whole or inparts, except with prior written consent of the EU-DEEP consortium

23

rd

-26

th

March 2004Workshop Proceedings

Keynote paper

Seminar 2

End-User acceptanceand potential for LTS:

Experiments and

modelling

[FP6 Project: SES6-CT-2003-503516]


2/30

Document Name: End-User acceptance and potential for LTS: experiments and modelling Date: 28/06/2004ID: S2_PRO~2 Security: PublicRevision: Public Page 2/30

Document Information

Document Name: End-User acceptance and potential for LTS: experiments and modelling

ID: S2_Proceedings-Keynote_V2_Public

WP : 3

Task : 3.1

Revision: Public

Revision Date: 28/06/2004

Author: IIE-UPV

Diffusion list

EU-DEEP Partners Contact Points

Approvals

Name Company Date Visa

Author Carlos lvarez IIE-UPV

Work Package Leader Seppo Krkkinen VTT 13/09/04 YES

Rapporteur Ingelo Cobelo Labein

Internal contradictor Petros Dokopoulos AUTHInternal contradictor Seppo Krkkinen VTT

Reviewer C. Protogeropoulos CRES

Coordinator Etienne Gehain GdF

Documents history

Revision Date Modification Author

Draft 09/03/04 March workshop seminar version IIE-UPV

V0 16/04/04 Workshop proceedings version IIE-UPV

V0.1 21/04/04 Format modifications Etienne Gehain

V1 14/05/04 Workshop proceedings version CRES modifications IIE-UPV

Public 28/06/04 Public version


3/30


General purpose of this document

The aim of these document is to provide a discussion basis for Seminar 2: End useracceptance and potential for LTS: Experiments and modelling of the WP2-WP3 March Workshops(Brussels, 23rd-26th March).


4/30


Content

1. Scope of The Review____________________________________________________ 5

2. Key issues____________________________________________________________ 5

2.1. Paper organization __________________________________________________ 6

3. State Of The Knowledge _________________________________________________ 7

3.1. Literature review. ___________________________________________________ 7

3.1.1. Reported experiments: ____________________________________________________73.1.2. Modelling methodologies: __________________________________________________83.1.3. Others models___________________________________________________________93.1.4. Distributed Generation ___________________________________________________10

3.2. The Economic Problem ______________________________________________ 11

3.3. Models for processes identification ____________________________________ 13

3.4. Physically Based Load Modelling ______________________________________ 17

3.4.1. Air Conditioning and Heat Pumps models _____________________________________173.4.2. Thermal Storage Loads (TES) ______________________________________________193.4.3. Aggregation Methodologies ________________________________________________213.4.4. Validation _____________________________________________________________21

4. Areas Where Knowledge Lacks But Must Be Gathered _________________________ 25

4.1. Lack of linkage between physical knowledge and historical data _____________ 25

4.2. Lack of linkage between consumer and market possibilities _________________ 26

5. What must be abandoned from the past in this area to favour DER? ______________ 26

6. What needs to be operational five years from now? __________________________ 27

7. References __________________________________________________________ 27


5/30


1. Scope of The Review

This keynote paper is an attempt to review the existing knowledge (modelling methodologiesand experiments) and identify the areas in which the work has to be directed in EU-DEEP in order topredict and validate the acceptance and potential that the Local Trading Strategies to be developed

and proposed in the framework of EU-DEEP will probably have in the end-user.These acceptance and potential may be basic to enhance the expansion of Distributed Energy

Resources in Europe over the next 5-15 years.

The International Energy Agency (IEA) identifies the following barriers for the expansion ofDER:

Technical barriers

Structural barriers

Legal Barriers

Lack of Capacity response Financial barriers

Tradition

The objectives of the seminars of the actual workshop cover these barriers.

Issues referring to technical barriers are tackled in seminars: S7 (metering needs andstandards), S8 (Data communication needs and standards) and S9 (automation and control issues).Market structures to eliminate the barriers to the expansion of DER are the objective of S5(methodologies to build market configurations suited for DER deployment). Seminar S10 (Networkregulation under large development of DER) deals with legal barriers and S6 (methodologies for DER

economic appraisal) involves the financial barrier. End-users have not traditionally directly involvedin electricity markets and the expansion of DER and LTS requires a change in their behaviours. Thistraditional barrier should be tackle through the possible solution to the rest ones.

This keynote paper, as basis document for S2 (end-user acceptance and potential for LTS:experiments and modelling) addresses the lack of capacity response obtained from thecustomers. Evaluating the acceptance and potential for LTS could detect the reasons and possiblesolutions for this lack of capacity response.

This paper has basically been generated with the information provided by the contributors:AUTH, IIE-UPV, VTT

2. Key issues

The main issues to perform the analysis in the end-user, that includes its acceptance and thepotential benefits that can be derived for the customer energy bill has to rely on the evaluation ofthe impact that any considered trading strategy may have on:

Service provided by the energy consumption (production, comfort, etc.)

Total energy and service cost

The methodologies to evaluation of this impact of LTS in the end user need to rely in a

comprehensive knowledge of the behaviour of the customer and its consumption processes indifferent supply scenarios. This knowledge can be obtained by means of two complementarymethodologies:


6/30


By performing suitable experiments and surveys.

By implementing simulation studies based on suitable models.

The availability of this knowledge provides information about the perception by the end-userof the modifications on the service provided by a specific trading strategy and a further evaluation ofits economical impact has to be addressed in order to perform a cost/benefit analysis for thisstrategy.

The models suitable for impact evaluation can be based on:

Data gathered on the customer facilities, both in normal or LTS test conditions

Physical knowledge of the customer load s and possesses

Consequently, the evaluation of the impact and potential in end-user facilities to beconsidered in this paper is to be addressed from the two perspectives: models and experimentalcampaigns.

2.1. Paper organization

In order to develop the key issues mentioned this paper is organized as follows: Next section(section 3) is devoted to the state of the art. This section begins with a literature review on thefollowing subjects (subsection 3.1):

1. Models

2. Experimental campaigns

The tools reported so far by the contributors to solve the problems involved in theassessment of the end-user acceptance and potential will be presented next:

- Economical problem: Model provided by AUTH (Subsection 3.2)

- Processes identification: Model obtained from the PRIMES project (Subsection3.3)

- Load element response modelling: Physically based models developed by IIE-UPV andvalidation through the comparison of simulation results with metered data ofexperimental campaigns provided by IIE-UPV (Assessing the air conditioner and heatstorage load models.) and VTT (Direct load control response experiments)(Subsection3.4).

Conclusions from the contributions will be drawn in sections 4 and 5 where the areas to befurther researched and the concepts that must be abandoned from the past are identified. Section 6refers to what needs to be operational five years from now.

Section 7 establishes some orientations for EU-DEEP research and proposes a methodology to

organize the customer demand for LTS purposes, by fully consider the physical knowledge of theconsumer processes with the models described in previous sections. This methodology was proposedin IEE-UPV contribution. Another proposal that establishes the link between some of the modelsexplained in previous sections is also described in the section.

Finally, allies stakeholders and treaths for EU-DEEP in this area within the next 18 areidentified in sections 8 and 9.


7/30


3. State Of The Knowledge

3.1. Literature review.

The customer flexibility is basic for the assessment of end user acceptance and potential forLTS. This flexibility has been investigated by test field experiments and simulations, being theobjective to find the customer response to prices and tariffs.

Modelling customer demand and response capabilities and willingness. This modeldevelopment is often associated, for validation purposes, to measurement campaigns. More difficultto model is the actual reaction of the customers, where some additional issues may interfere such asattitude, mood, result of football scores, etc. In this case, experiments and enquiries seem to be theonly way to identify it.

3.1.1. Reported experiments:

[1] investigated the customers reaction to price changes by measuring the hourlyfluctuations in the customers use of electricity. The tariff consisted of two price steps that were

changed in real time using ripple control. Read signal light was used to show the high price periodsto the customers. The response of 20 households and 10 farms were studied. Electric heatinghouseholds were excluded from the tests. [2] reports how this research continued and what werethe results. A yellow signal light was added. It indicated that high price is coming the next day. Alsonew customer groups were added. These included electric heating customers, services and small tomedium size industries, and commerce, education and offices. The results show that dynamic tariffsinfluenced the peak consumption of electricity. The end report of this project is in Finnish [3] but anoverview of the results is in [4] and[5].

[6] reports customers reactions to real time prices. The customers belonged to industry suchas pulp, paper and sawmills. The energy consumption of the customers increased in the off-peakperiods. There was price elasticity and customers reacted to different levels of prices.

[7]explores the potential benefits of real time tariffs to the individual customer using priceand demand data from California. The response consists of rescheduling the use of residentialappliances and air conditioning. [7] asserts that load management can have a dramatic effect on theutility bill for individual consumers and potentially on wholesale electricity prices, if marketsencourage price responsive demand and consumers are able to predictably respond to high pricesignals by shifting load. Exposing customers to real time pricing provides the needed incentive tocreate demand elasticity.

The financial benefits of real time tariffs for both the customer and the utility are estimatedby [8], [3] p. 370-371 and [9] p. 63-71. In all of them the costs or inconvenience caused by loadshedding to the users of the building are neglected in the benefit analysis. They are implicitly taken

into account in the design of the automatic load control strategy by [8] and [9] while in the fourexperiments described by [3] the customer response was manual and based on light panelinformation. The omission of other customer costs than the electricity costs from the analysis isnatural, when the benefits are estimated from the electricity companys point of view, because it isonly the customer who knows the time to time other losses and costs or the value of inconvenience.However, it is important to remember this when interpreting the results.

[8], [10] and [11] present the combination of real time pricing and automated control oflarge commercial buildings. The described buildings are very large hotels with a power consumptionof several megawatts. In this scale dynamic tariffs are clearly beneficial to both the customer andthe power company. Automatic control is reported to be superior to manual response to real timetariffs. The prices are hourly price profiles that are sent in advance, probably the previous evening.

In this scale dynamic tariffs are clearly beneficial to both the customer and the power company.


8/30


Linking automated energy management systems in commercial buildings and industrialfacilities to hourly electricity price signals can be beneficial to power suppliers and consumersalike.[12] reports some such systems

3.1.2. Modelling methodologies:

Modelling of residential appliance loads for use in Demand Management programs (DSM,DSB,..), has been a concern for the last two decades, [13]. In this way, models based only onhistorical data have been developed,[14][15], but they do not offer results with a sufficientlyenough accuracy: on one hand, the model parameters are obtained during the normal state ofPower System, which is altered by LM actions; and on the other hand, these parameter values aredifferent for each Power System, so it is not possible to apply a specific load model on any otherPower System. Walker and Pokoski,[16], suggested an empirical model based on historical data andlifestyle, but weather fluctuations were neglected and thus results were not as accurate as it wasexpected.

PBLM methodologies have been widely used, because they are able to predict the individualload dynamic response and allow to obtain the aggregated response of these loads efficiently.Therefore, the problem can be decomposed into two sub-problems: modelling individual loads, atthe elemental level, and subsequently devising schemes to aggregate these elemental load models.A great quantity of models based on this methodology have been developed and used, but, in ouropinion, they have not generally given the results expected by the utilities, due to the hypothesisand simplifications that have normally been assumed. The main parameters which have beenneglected are the following: the solar radiation, [13], [17][19], which can be an important internalload contribution and it should be taken into account, for example the portion of solar radiationwhich introduces through glazed surfaces and is converted into internal load immediately. Duty-cycle and consumption of thermal electric loads (HVAC, TES) depend on the room orientation andcan vary substantially between rooms with different orientations. Thermal capacity of walls isanother parameter that in most cases has not been explicitly considered, [13], [17][19]. This lackhas produced internal temperature evolutions and consumption predictions not as accurate as itshould be desired; since indoor air is not really subjected to the thermal difference between theoutdoor and indoor temperatures, but it is exchanging energy with the internal surfaces of walls andpartitions, which have a temperature evolution significantly different of the outdoor temperature,[20]. Other PBLM models have explicitly considered thermal capacity of walls and partitions as wellas solar radiation on external wall surfaces, [21], however, they have neglected the solar radiationthrough the fenestration areas and the furnishing thermal capacity, which considerably modifies thebuilding thermal behaviour, [22]. Therefore, it seems that the use of excessively simplified modelsbased on this methodology can offer results very far from the real results. In this way, Reed,Broadwater and Handrasekaran [23] applied the model suggested in [13] to predict duty-cyclevariations and the effect of load control intensities on the indoor temperature. They simulateddifferent Direct Load Control programs for four hours. Their simulated results indicated that theinterior temperature tended asymptotically to the external temperature with a slope around 5 C per15 minutes whereas the maximum difference between internal and external temperature was 8 C.

This slope is, according to our measures, much larger than a normal value for a typical room.Mainly, these discrepancies are due to neglect the thermal capacity of walls as well as the internalmass, obtaining results which are excessively far from the reality.

Other authors have suggested a simplified dynamic first order model. They have introduced atarget temperature which was approached asymptotically by the internal temperature when theconditioner device remained off. Therefore, this proposed model is simple, but the main difficulty isto determine that target temperature which is related to weather parameters and internal variables.This model has also been used in several cases, assuming that this target temperature was equal tothe external temperature, [24].

A lot of control policies have been considered during the last decade as not acceptable to the

customers, since those do not maintain the minimum comfort levels. From the supply side, theysuffer an important secondary effect of DSM programs implantation: the increase of residential loadcurve peak as a consequence of the loss of diversity after the control period -payback effect-. An


9/30


intelligent control strategy could partially mitigate these problems, but, in our opinion, only throughthe use of detailed models.

3.1.3. Others models

Others models have been proposed and tested for real time and time of use tariffs responseand optimisation, as reported in:

[25] categorizes various types of consumer loads to facilitate optimal consumer response totime variable tariffs. It uses linear and integer programming as solution techniques. [26]investigated in theory some consumer response models and their interaction with utility operations.

Real time pricing can be implemented in different forms. [27] provided simulation examplesof response differences for some different types of real time pricing.

Also[28], [29] and [30] tell about the customer responses to real-time pricing of electricity.[31] is concerned with the analysis and development of electricity tariffs for indirect load control.These tariffs use price messages. In addition it presents consumer load models.

Some customers respond to the real time pricing by modifying or rescheduling electricityusage. This makes the short-term load forecasting more complicated than with fixed tariffs. [32]discussed this issue and represented an artificial neural networks based solution model for real-timepricing related short term load forecasting. [33] applied fuzzy logic for this problem. [34] proposed atwo stage short term load forecasting system that consists of an artificial neural network basedprise-insensitive short-term load forecasting along with fuzzy logic based module that transformsthe price insensitive forecast to a price sensitive forecast. [35] addresses short term loadforecasting under spot pricing. [36] studied by simulation the customer response under differentconsumer behaviour scenarios. [37] is a revised and more complete version of the same paper. [38]adds inter-temporal factors to such customer behaviour models and compares day-ahead dynamictariffs with a tariff where the price is declared online.

[39] describes a customised electricity pricing mechanism and a knowledge-based end userdemand response modelling tool. A case study in included, where the response of an industrialcustomer to dynamic electricity prices is modelled. [40] analyses the potential of fictitious industrialcustomers for cost savings through real time pricing and demand rescheduling. The analysis isidealised because of several assumptions such as no losses due to load scheduling occur.

[41] presents an overview of an algorithm for scheduling of single storage electricityconsuming processes. [42] and [43] report experiments on real time based control of heat storagein three commercial buildings, two of these with water storage and one with earth storage. Forminimising the heating cost this experiment used a fast non-simplex algorithm, but any linearprogramming algorithm can be used. The system model was a linear model with first degreedynamics and constrained state and control. Operational results of the experiment were reported.

The savings were compared to those estimated for a time of use tariff. The savings were increasedclose to 50%. [44] studies how the sizing of thermal storage affects the benefits of real-timepricing. The results of the examples show that under real time prices higher utility savings can berealised for both under-sized and over-sized electric thermal storage systems when compared totime of use tariffs. The benefits do not diminish due to incorrect storage size as fast as with time ofuse rates. Operational and capital costs of the system were also discussed.

Building HVAC (Heating, Ventilation and Air Conditioning) controls are typically operated tominimise operating costs. [45] describes an optimisation algorithm designed to minimise overalloperating costs under real time electricity prices. It examines the mathematical properties of a setof linear difference equations that give a very simple model of the thermal dynamics of the building.It finds them to be asymptotically stable, positive dynamic systems. Impulse response vector was

used when calculating the elementary direction vectors in order to reduce the calculation time. This


10/30


permits a much more rapid and elegant series of calculations than is possible with conventionallinear programming techniques.

[46] reports how cost reflective messages combined and optimisation of electricity use havebeen applied in UK already for several years. Two load control techniques are considered. One isCELECT that was first implemented using Echelon LonWorks building control networks and had localcontrol for each heater in the house [47]. Later CELECT versions used the European Home Systems(EHS) home automation network technology, centralised the control system and incorporated

domestic hot water management. Optimisation is based on a model that predicts the consequencesof planned actions. Cost and a quantitative measure of comfort are outputs of the model. Thecontrollable inputs are the heat charging schedules and the uncontrollable inputs are occupancyprofile, weather forecast and the cost reflective messages. The optimisation requires a model with10-minute time steps and covering 48 hours. Thus the search space is too large for exhaustivesearch and a complicated heuristics search based approach was applied. Both running costs andcomfort were improved in the trials. However, the costs of the system and its installation were highcompared to these benefits. The other control technique is GeMMS (Generic Modular ManagementSystem. It has been applied in ice storage for air conditioning, water pumping and dairy farm energymanagement. In the farms the controllable loads consist of pumping water, heating water, and milkchilling and multiple room cool stores. The optimisation is based on a genetic scheduler and amodule that predicts the cost of any schedule. The practical feasibility of the CELECT and GeMMS

concepts has been demonstrated, but the systems have so far been relatively expensive comparedto the benefits. However, it is expected that technical development will reduce the costs enough inthe near future.

[48] studied by simulation three cases. The first was space heating and ventilation under realtime prices. The second was inclusion of continuous controllable loads such as electrolysis directly inthe optimisation of heat and power generation and purchase. The third was scheduling a steel plantunder real time prices. In the space heating the controllable inputs included heating and ventilation.Uncontrollable inputs were weather forecast, schedule of hourly prices and planned occupancy.Among other things the results show that co-ordinating the ventilation and heating controlsimproves the load control. The optimisation horizon was short due to the limitations of the SQP-optimisation (Sequential Quadratic Programming) method and improved optimisation method would

be needed for a practical implementation. The prototype steel making scheduling system givesamong other things the energy costs of the planned schedules. Thus the real time prices ofelectricity and gas can be taken into account when comparing and refining the schedules. In somefigures in [49] hourly supply and demand prices of electricity are compared in the energymanagement of a company consisting of several different base metal production plants.

[50] and [51] describe agent based power load management. The auctioneer agent definesthe real time price based on the demand functions of the consumer agents and the supply functionsof the producer agents. This market price is then broadcast to all agents.

[52] discuss optimal control of building thermal storage. The building under study includesmultiple operational modes. These include a heating mode and three cooling modes. Because of

switching between these modes of operation the optimisation problem is not smooth. The problemwas solved using a direct search complex method that does not assume smoothness. The dynamicoptimisation problem is repeated over increasing number of hour increments until 24 hours arecovered. Experimental and simulated results of such a system for space cooling are reported by[53]. Significant reductions in energy costs and peak demand were reported compared to aconventional night setback control.

3.1.4. Distributed Generation

Some reported methodologies include Distributed Generation.

Cogeneration of heat and electricity has long been used for demand side management

purposes and its importance is increasing. Customers use it as a load management mechanism inorder to gain maximum financial benefit from the agreement. Real time pricing increases potential


11/30


for this benefit. [54] concentrates on the scheduling of cogeneration under real time pricing. It dealswith the extended value that real time pricing gives to cogeneration. Results for two cogenerationplants are reported.

3.2. The Economic Problem

Assessing the end user acceptance and capability to benefit from tailored trading strategies

will finally lead to an economical problem whose main components are described next.Energy and power flow in a facility using Distributed Energy resources has the form depicted

in Fig. 1.

1. Power exchange with grid PGR

2. Power of DER PDER

3. Power exchange with storagePSTO

4. Power flowing into the Load PL

PGR PL

PSTOPDER

Fig. 1. Power flows in a facility using DER

The power sources, in each of the 4 above categories, can be more than one, e.g. we mayhave one or more feeding lines or one or more DER generators. Power can also be positive ornegative, following the above convention.

Considering operation for a time ahead, PL is a forecasted value. Also PDER is a forecastedvalue in case of wind, PV and other renewable energy sources.

The enterprise which owns the site has expenses (+E) and revenues (-R) for running theabove mentioned power park for a period of time T ahead, as explained in a next paragraph. T can

be any reasonable time (usually a few days) and it is discredited in n equally spaced intervals .Our interest is to minimize expenses or to maximize revenues; therefore we define the followingobjective function

1

n

j

F Ej Rj

(1)

Objective of LTS is to minimize F by selecting suitably PDER and PSTO, and fulfilling at anytime t all imposed constrains related to operation as well as other factors as defined in the nextparagraphs.

It is an optimisation problem which can be solved using optimisation platforms (such asLINDO) or customized software evolution algorithms. The problem is becoming very demanding ifenergy storages are present.

Running expenses

Running expenses considered are as follows:

1. Fuel consumption cost, they depend on PDER, while consumption as a function of DERis given by the manufacturer

2. Start up cost (i.e. in case of thermal units)

3. Shut down cost (i.e. in case of thermal units)

4. Expenses for energy import coming from the grid, i.e. PGR, depending on the marketsituation


12/30


Revenues

1. Revenues from energy export (PDER - PL - PSTO)

2. Revenues from selling services to the system (Transmission System Operator)reactive power, reserves etc.

Constrains

1. Initial state of the plant, t=02. Power balance: Input power equals output power

PDER+PGRID=PL+PSTO

3. Unit operating constrains:

3.1. Time depended upper and lower limits are important for thermal plants.Upper and lower limits depend on the time the plant needs to changes itspower, i.e. we may want to change the power to Pmax within a time t butthe plant is too slow.

3.2. Minimum Up and Down times of the DER.

3.3. Unit status i.e. the unit must run, availability.

4. Plant crew constrains5. Spinning reserves

6. Storage constrains:

6.1. Discharge Rate limits

6.2. Charge Rate limits

6.3. Continuity limit, i.e.tPWW

STOjj 1

6.4. Storage limits, i.e.Wmin


13/30


3.3. Models for processes identification

In order to determine, for all customers suitable to implement DER and LTS the power andenergy flows pointed out in the previous section, it is necessary to identify the processes that arepresent in a typical customer of the studied segment.

Different methodologies have been proposed in the literature for that purpose. The one that isdescribed next corresponds to one developed in the framework of an EC project (developed in Lab

E3M of NTUA)

The demand-side sub-models of PRIMES V.2 have a uniform structure. Each sub-modelrepresents a sector that is further decomposed into sub-sectors and then into energy uses. Atechnology operates at the level of an energy use and utilises energy forms (fuels). The followinggraphic (Fig. 2) illustrates the hierarchical decomposition of the demand-side models.

Fig. 2 Hierarchical decomposition of the demand-side models

The data that is necessary to calibrate the model for a base year (1995) and a country (all EUmember-states) can be divided in the following categories.

- Macro-economic data that correspond to demographics national accounts, sectorialactivity and income variables. These data usually apply to sectors.

- Structure of energy consumption along the above-described tree in the base year andstructure of activity variables (production, dwellings, passenger-kilometres, etc.).Some indicators regarding specific energy consumption are also needed for calibration.The databases MURE, IKARUS, ODYSSE and national sources have been used.

- Technical-economic data for technologies and sub-sectors (e.g. capital cost, unit

efficiency, variable cost, lifetime, etc.).

The basic source of data for energy consumption by sector and fuel is Eurostat (detailedenergy balance sheets). By using additional information (surveys of cogeneration operation andcapacities and surveys on boilers), the balance sheets have been modify in order to representexplicitly the production of steam.

According to PRIMES definitions, steam includes industrial steam and distributed heat (atsmall or large scale). In the balance sheets, Eurostat reports on steam production in thetransformation input/output only if the producers sell that steam. If the steam, irrespectively of theway it is produced (e.g. a boiler or a CHP plant), is used for self-consumption only, Eurostataccounts for only the fuels used to produce that steam and includes these fuels in final energy

consumption. The PRIMES database consists in introducing that steam (for self-consumption) in thefinal energy consumption tables of the balance sheets and inserting the fuels used to produce thatsteam in the table of transformation input and output. This is necessary for the model to calibrate to


14/30


a base year that properly accounts for the existing cogeneration activities (even if they are used forself-generation of steam).

The fuel types are as follows:

1. Solid fuels except lignite andpeat

2. Lignite and Peat

3. Residual Fuel Oil4. Diesel Oil

5. Liquefied Petroleum Gas

6. Kerosene

7. Gasoline

8. Naphtha

9. Other oil products

10.Bio-fuels

11.Natural and derived gas

12. Thermal Solar (active)

13.Geothermal low enthalpy

14.Steam (industrial anddistributed heat)

15.Electricity

16.Biomass and Waste

17.Hydrogen

Industrial Sector

The industrial sector consists of nine sectors. For each sector different sub-sectors aredefined. At the level of each sub-sector a number of different energy uses are represented. Atechnology at the level of an energy use may consume different types of fuels (one of which issteam generated from the power and steam sub-model of PRIMES, so only steam distribution anduse costs are accounted for in the demand-side, together with a price for steam).

The structure for the industrial sector is given in Table I:

Table I. Structure for the industrial sector

SECTORS SUB-SECTORS ENERGY USESAir compressors Low enthalpy heat

Blast furnace Motor drives

Electric arc Process furnacesElectric process Rolled steel

Foundries Sinter makingIron and Steel

Electric arcIron and Steel integrated

Lighting Steam and high enthalpyheatAir compressors

LightingMotor drives

Electric furnaceElectrolysis

Process furnacesElectric kilns

Low enthalpy heat

Non ferrousmetals

production

Primary aluminiumproduction

Secondary aluminiumproduction

Copper productionZinc productionLead production

Steam and high enthalpy heatAir compressors

Low enthalpy heatLighting

Motor drivesElectric processes

Steam and high enthalpy heatThermal processes

Chemicalsproduction

FertilizersPetrochemical

Inorganic chemicalsLow enthalpy chemicals

Energy use as raw materialElectric kilns Low enthalpy heatCement kilns Glass annealing thermal

Air compressors Glass tanks thermalLighting Material kilns

Motor drives Drying and separation

Buildingmaterials

production

Cement dryCeramics and bricks

Glass basic productionGlass recycled production

Other building materialsproduction Glass annealing electric Tunnel kilns


15/30


Glass tanks electricLighting Low enthalpy heat

Motor drives Pulping steamPulping electric Drying and separationRefining electric Refining steam

Paper and pulpproduction

Chemical paperMechanical pulp and

paperSteam and high enthalpy

heatAir compressors Low enthalpy heat

Cooling and refrigeration Space heating

Lighting Drying and separationthermal

Motor drives Specific heatDrying and separation electric Direct heat

Food, Drink andTobacco

production

Food, Drink and Tobaccogoods

Steam and high enthalpyheat

Air compressors Steam and high enthalpyheat

Lighting Low enthalpy heatMotor drives Space heating

Drying and separation electric Drying and separationthermal

Machinery Coating thermal

Coating electric Foundries thermal

Engineering Engineering goods

Foundries electric Direct heat

Air compressors Steam and high enthalpyheat

Cooling and refrigeration Low enthalpy heatLighting Space heating

Motor drives Drying and separationthermal

Drying and separation electric Direct heat

Textilesproduction Textiles goods

MachineryLow enthalpy heat

Space heatingDrying and separation

thermalSpecific heatDirect heat

Other industrialsectors

Other industrial sectorsgoods

Air compressorsLighting

Motor drives

Drying and separation electricMachinerySteam and high enthalpy

heat

Tertiary Sector

The tertiary sector comprises of 4 sectors. At the level of the sub-sectors, the model structuredefines groups of energy uses, which are further subdivided in energy uses defined according to thepattern of technology. The structure is as follows:

Table II. Tertiary sector structure

SECTORS ENERGY USES ENERGY TECHNOLOGIESLighting LightingSpace heating Heating/CoolingElectrical uses Greenhouses

Pumping PumpingAgriculture

Motor energy Motor drivesLighting Lighting

Space heating Electric heating/coolingAir conditioning Gas heating/coolingElectrical uses Boiler heating/coolingWater heating District heating

Greenhouses

Offices andServices

Electrical equipment

Lighting Lighting

Services

Trade Space heating Electric heating/cooling


16/30


Air conditioning Gas heating/coolingSteam uses Boiler heating/cooling

Electrical uses District heatingWater heating Greenhouses

Electrical equipmentLighting Lighting

Space heating Electric heating/coolingAir conditioning Gas heating/cooling

Steam uses Boiler heating/cooling

Electrical uses District heatingWater heating Greenhouses

Public services

Electrical equipment

Residential Sector

The residential sector distinguishes five categories of dwelling. These are defined according tothe main technology used for space heating. They may use secondary heating as well. At the level ofthe sub-sectors, the model structure defines the categories of dwellings, which are furthersubdivided in energy uses. The electric appliances for non-heating and cooling are considered as aspecial sub-sector, which is independent of the type of dwelling. The structure is as follows (TableIII):

Table III. Residential sector structure

SECTORS HOUSEHOLD TYPES ENERGY USES

Central boiler households that may also use gas connected to the central boiler(flats)

Space heating

Households with mainly electric heating equipment (non partially heated) CookingHouseholds with direct gas equipment for heating (direct gas for flats and gas

for individual houses)Water heating

Households connected to district heating Air conditioning

Dwellings

Partially heated dwellings and agricultural householdsWashing

machinesDish washers

DryersLighting

Refrigerators

ElectricEquipment

Television sets

Transport sector

The transport sector distinguishes passenger transport and goods transport as separatesectors. They are further subdivided in sub-sectors according to the transport mean (road, air, etc.).At the level of the sub-sectors, the model structure defines several technology types (car technologytypes, for example), which correspond to the level of energy use. The structure is as follows (TableIV):

Table IV. Transport sector structure

SECTORS SUB-SECTORS ENERGY TECHNOLOGIES

Busses Internal combustionengines

Motorcycles Electric motors and hybridPrivate cars Fuel cell

Passenger trains Gas turbine and CNGAir transports

Passenger transports

Navigation passengers

Trucks Internal combustionengines

Trains Electric motors and hybrid

Navigation Fuel cell

Goods transports

Gas turbine and CNG


17/30


3.4. Physically Based Load Modelling

Modelling of the processes identified provides a powerful tool to evaluate the load demandflexibility and dynamic response during a control period that may be required when implementing aLocal Strategy.

In order to demonstrate the powerfulness of PBLM, physically based electrical load models ofHeating, Air Conditioning and Thermal Energy Storage residential loads are presented in this section.

These models are based on energy balances between the internal air, the dwelling constructiveelements, the conditioner appliances, energy storage capabilities and the external environmentthrough a discrete state-space stochastic equation system.

Air Conditioning and Thermal Storage Loads are typical devices to be used in LTSalternatives, since they are related to some kind of energy thermal storage. These thermal inertiasgenerally allow us to separate the demanded electrical energy intervals (electrical demand) from theintervals in which that energy is used by the customers (service demand).

The individual models proposed here are based on energy balances between the externalenvironment, the conditioner device and the internal air together with the indoor mass through astate-space equation system.

3.4.1. Air Conditioning and Heat Pumps models

The first one, which is related to air conditioner and heat pump appliances, can be adequatelyrepresented by the following thermal balance, which is showed in Fig. 3. This elemental system isflexible enough to model any conditioned room, with only modifying its parameters. The model is anextension of one of the models previously proposed by IIE-UPV jointly with UPCT1 [19], [56].

Solar Radiation

External Walls + Glazed Surfaces

Internal Load Generation

Air Cond.

External Energy

Exchange

INDOOR ENVIRONMENT

Internal WallsInternal Energy

Exchange

OUTDOOR ENVIRONMENT

Fig. 3. Energy balance of the AC elemental load

For simplicity, the analogy between the heat transmission processes and the electrical circuittheory has been used. Therefore, it is possible to establish an electrical circuit equivalent to theenergy balance of the elemental system (Fig. 4).

1 UPCT: Universidad Politcnica de Cartagena


18/30


extXwe

I weC intC

HVACI

wiC radjX

lieII

ceR weR ' weR '

surfgR

wiR ' wiR '

OffOnm

Fig. 4. Equivalent electrical circuit for AC elemental load

Where:

e wC : Thermal capacity of the external walls

intC : Indoor thermal capacity (Internal air + Furniture)

i wC : Thermal capacity of the internal walls

e i lI I : Solar radiation that introduces through glazed surfaces plus internal load generation

e wI : Solar radiation on external wall facesHVACI : Value associated with the power supply

On Offm : Discrete variable that represents the operating state of the device (1 for ON and 0 for OFF)

e cR : External convection resistance between external environment and external wall faces

'e wR : Half equivalent thermal resistance of the external walls

g surfR : Equivalent thermal resistance of external glazed surfaces

'i wR : Half equivalent thermal resistance of the internal walls

extX : External temperature evolution

adj rX : Adjoining room temperature evolution

The way of modelling external as well as internal walls is a simplification of the electricalmodel proposed by Dominguez, Herrera and Alvarez, [57], for multilayer walls. In the same way,heat transmission between the test room and the upper and lower floors have been neglected fromthe collected data of internal temperatures. The air conditioner model has initially been implementedby means of a linear relationship between the external and internal temperatures, its nominalCoefficient Of Performance (COP) and nominal electrical power. The state-space equation system is:

d

x A x B udt

(2)

y C x D u (3)

Where:inte w i wC C C

x X X X

: State-space variable vector

ext e w e i l HVAC adj r u X I I I I X

: Input parameter vector

intCy X : Output variable

DCBA ,,, : Matrix of State-space system

0x : Column vector of initial conditions

The state-space equation system has been implemented using Matlab as shown in Fig. 5.


19/30


2

1x' = Ax+Bu

y = Cx+Du

State-Space

Selector

6

5

3

2

1

External Temp.

Solar Rad. on Walls

Solar Rad. on Glazed-Surf.

Adjoining Room Temp.

Initial State of Air Condit.

Target Indoor Temp.

Indoor Temp. Simul.Interior Global Load

Cooling Power

4

Thermostat

Deadband

Fig. 5. State-space equation subsystem simulation

3.4.2. Thermal Storage Loads (TES)

The second elemental model is focused on individual houses with TES heaters. An energybalance analysis has been applied again to the system integrated by the housing, the externalenvironment outdoors temperature and radiation- and the TES device, obtaining a discrete state-space equation system. The load model response relies on information about physical loadcharacteristics, internal control mechanisms Thermostat performance-, usage and environmental

parameters. In order to obtain this model it has been necessary to study several appliances of thepresent market. Specifically, our model is focused on ceramic storage devices. So, Fig. 6 showsgraphically this global energy balance. It can be seen that basically the elemental heat transferprocesses are very closed between the two kinds of load (HVAC and TES).

External Walls + Glazed Surfaces

Internal Energy

Exchange

Ceramic Bricks

Radiation

Forced convect.

Internal Load Generation

Internal Walls

External Energy

ExchangeSolar Radiation

OUTDOOR ENVIRONMENT

INDOOR ENVIRONMENT

Fig. 6. Energy balance of an elemental heat storage system

Storage losses -due to radiation and natural convection- through the TES external surface tothe indoor environment during the off-peak periods do not suppose and additional energyconsumption, since these losses contribute partially to heat the room. Two kinds of TES residentialdevices have been studied:

- Static Electric Thermal Storage loads (TES)

- Dynamic Electric Thermal Storage loads (DTES)

While SETS devices have a thinner insulation layer and their discharge regulation is based onthe opening/closing of a grille driven by a bimetallic mechanism, DTES devices have a higherinsulation layer and an internal fan to circulate the stored heat, helping to maintain the indoortemperature around the thermostat set point. Therefore, two control mechanisms are needed: Aninternal thermostat which controls the ceramic brick charge and the electric demand, and anexternal thermostat which is related to the target indoor temperature.


20/30


For simplicity, the analogy between the heat transmission processes and the electrical circuittheory proposed in paragraph 3.4.1 (see Fig. 4) has been used again to obtain the equations of theelemental model.

The difference of TES loads and HVAC loads is the global heat flux from the TES device to theindoor environment. In this way, an equivalent electric sub-circuit has been developed which takesinto account the different heat fluxes that are present. Fig. 7 shows this equivalent sub-circuit:

RInternalX

cbC

cbrR

rcI

fcIOFFONET OFFONIT

Fig. 7. Equivalent electrical sub-circuit of ETS device

Where:cbC : Ceramic brick thermal capacity

ON OFFET : External Thermostat that controls the indoor temperature

ON OFFIT : Internal Thermostat that controls the ceramic brick charge during the off-peak periods

fcI : Heat flux by Forced Convection from the ceramic bricks to the indoor -controlled by the External

Thermostat-.

rcI : Equiv. heat flux by Radiation and Natural Convection from the TES external surface to the

indoor

r cbR : Thermal resistance of ceramic bricks during the charge periods

Internal RX : Temperature of internal charge resistances

It is possible to know several variables related with the ETS performance from this equivalentelectrical sub-circuit: Ceramic brick evolution; loss heat flux during the charge phase due to naturalconvection and radiation; and electric consumption through the power related to X Internal-R.

Therefore, the global state-space equation system has the same mathematicalrepresentation:

dxA x B u

dt (4)

y C x D u (5)Where:

e w i wC Indoor C x X X X

: State-space variable vector

ext e w e i l Heat Storage adj r u X I I I I X

: Input parameter vector

Indoory X : Output variable

DCBA ,,, : Matrix of Space-state system

0x : Column vector of initial conditions

Therefore, this elemental model consists in a discrete state-space equation system thatcomprises continuous states Temperatures- as well as discrete states Thermostats-. These kind ofmodels were proposed in [58], [59], and now they have been improved in order to achieve moreaccurate results.


21/30


3.4.3. Aggregation Methodologies

The aggregation problem consists, for a given load control group (i.e. a set of elementalelectrical devices), consists on describing approximately the expected value of the total powerdemand due to the group. An ensemble of various mathematical tools can solve this problem:Fokker-Planck equations [19], Monte-Carlo methods or Kernel estimators. Each method hasadvantages and drawbacks. For example Fokker-Planck stochastic equations allows the user toconsider the simplest elemental model: first order differential equation and devices described by

nearly identical parameters and subjected to the same control by the utility-, called HomogeneousControl Groups (HCG). The stochastic equation is quite easy to solve, and supply a considerableamount of information (mean operating state of the HCG and temperature) at real time, but themodel does not suit very well for broad control periods.

The second alternative is Monte-Carlo Method (MCM). In our case, the use of this methodarises to imitate the dynamical behaviour of a real aggregate load (perhaps a hundred of HVACappliances with different, but close parameters: a quasi-homogeneous control group (QHCG). Themathematical solution of such a system is very difficult to solve by Fokker-Planck equations, due tothe higher order of stochastic equations at elemental level third or fourth order-. Thus the easierand exact method is to use MCM methods because among all numerical methods that rely on a Bsample size evaluation elemental loads-, the absolute error of estimates decreases as N.

21 21,5 22 22,5 23 23,5 24 24,5 250

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

Indoor temperature (C)24,4 24,6 24,8 25 25,2 25,4

0

0,5

1

1,5

2

2,5

3

Indoor temperature (C)

Fig. 8. Euler discretisation and Kernel aggregation methods

The last alternative is to refine the aggregation methods through the use of smoothingtechniques to the same set of simulated elemental houses. Triangle and Gaussian kernels have beenused for the kernel estimates, obtaining similar results in both cases. The results for internaltemperature correlated by mean load demand- is shown in Fig. 8. The density distribution functionof the aggregated group suits well a normal distribution, as can been seen in the figure.

3.4.4. Validation

In order to validate the models, simulation results have been compared with collected data ofdifferent field tests. The advantages of the models proposed here are also compared with modelspreviously developed and described in the specialized literature.

Assessing the air conditioner and heat storage load models.

For this purpose, the models have been tested using data belonging to houses with differentorientations, lifestyles and situated in several cities with different outdoor temperature profiles. Thedata have been collected for a period of one year, in order to assess the air conditioner load modelas well as heat storage load model. For example, the main outputs of the implemented system are:the consumption of the HVAC or TES appliance, its duty cycle and the evolution of internaltemperature. The system inputs are basically formed by: weather data outdoor temperature andradiation levels- and time. If radiation data would be not available, it has been implemented asubsystem which allows us to estimate the radiation levels according to the solar hour, theorientation and the specific day of the year. In order to show the model performance, a simulationof the air conditioner model is compared with real data in this section. The main thermal and

constructive characteristics of the test room are the following:Table V. Main Thermal and constructive characteristics


22/30


Main thermal and constructive characteristics

Room orientation: South-WestPercent of ext. walls: 25 %Thermal cap. of ext. walls : 1.875 MJ/CIndoor thermal capacity: 1.2 MJ/CThermal cap. of int. walls : 3.1 MJ/CExternal convect. Resistance: 0.005 C/WEquivalent thermal resist. of ext. walls : 0.028 C/W

The following set of curves compare the evolution of real data internal temperature,electrical power consumption and duty cycle- with the simulated results, taking into account theexternal temperature evolution for that same time period (Fig. 9).

24

25

26

27

28

29

30

31

10:000

200

00

600

800

1000

1200

1400

10:30 11:00 11:30 12:00 12:30 13:00

10:00 10:30 11:00 11:30 12:00 12:30 13:00

Time

Time

Temp.

(C)

E.

Power(w)

Outdoor Temp

Real

Simulated

Real

Simulated

Fig. 9. Indoor temperature and electrical load demand comparison in HVAC test appliance

As seen in that figure, the simulated and real indoor temperature evolutions are very close.The differences between the real and simulated consumptions are mainly due to discrepanciesbetween switching ON and OFF moments -as a consequence of the real thermostat performance-and COP real variations. In this case, it has been supposed a constant average demanded power,

whereas the real demanded power has fluctuations that are related to the indoor and outdoortemperature. Nevertheless, the rest of the obtained results, which can be deduced from the previousfigure, show the suitability of this model to forecast air conditioner performance.

Table VI. Comparison of simulated results and data collected

Parameter Simulated results Real data

Duty Cycle : 45 % 45 %Energy demanded : 1.53 kWh 1.56 kWh

Total ON Time : 82 minutes 83 minutesNumber of Cycles: 14 16

It has been studied many tests for other periods of the day and houses, obtaining values veryclose to the real data, just as the previously presented. Maintaining all the previous thermal andconstructive parameters, the same room with different orientations has been simulated varying the

solar radiation levels. Table VII presents the energy demand for each main orientation, showing thatit is necessary to take into account the radiation levels, since those can clearly affect the outputvariables. In all cases, it has been taken the same period time as the previous case two hours anda half, between 10 and 12:30 AM- in order to be able to compare the obtained results.

Table VII. Comparison of results for different orientations

Orientation Duty-Cycle Total ON Time Total Energy

North 45 % 82 min. 1.53 kWhNorth-East 59 % 108 min. 2 kWh

East 77 % 140 min. 2.55 kWhSouth-East 75 % 135 min. 2.50 kWh

South 56 % 102 min. 1.88 kWhSouth-West 45 % 82 min. 1.53 kWh

West 45 % 82 min. 1.53 kWh

North-West 45 % 82 min. 1.53 kWh


23/30


A third model could be derived from the previous HVAC load models by means of neglectingthe energy exchange between the test room and its adjoining rooms which is normally around a15% of the total load-. This consideration would simplify both equivalent electrical circuits, since itwould be possible to remove one of their branches and decrease the number of state variables. Themain advantage of this simplification is to eliminate the adjoining room temperature evolutionparameter, which is very difficult to fix with accuracy, since it can be changing during the simulationperiod and it depends on several parameters very heterogeneous and difficult to determine utilityof rooms, constructive differences, orientations, lifestyle-. If this simplification is not accomplished, a

first approximation for this parameter could be to take the average value between the test roomtemperature and the external temperature.

Direct load control response experiments

Similar models (also physically based and developed by VTT) were also validated in field testsin Finland. The verification field tests with direct load control systems were carried out between 16th

December 1996 and 24th January 1997 in Finland in rather northern areas. The tests aredocumented in Finnish [60] but a summary in English is in [61]. In these load control systems therewere 8283 load control terminals controlling various space-heating loads such as small houses andski resort holiday homes. The power at substations was measured. However power measurements ofsome substations were not used in the research due to too coarse time resolution or datacommunication failures. Thus the tests included 463 holiday home terminals (most of whichcontrolled several holiday homes each) and about 5666 small houses. Based on the measured afterpeaks, it can be estimated that the total maximum power of the controlled loads included in the testwas about 20 MW. Of course the actual controllable power is much smaller except for very coldweather.

An example of measurement data during one test day is shown Fig. 10. Four load groupswere controlled to off-state for half an hour, each group at a different time. Controls started at10:15, 11:30, 13:15 and 14:20. (After 15:00 a data communication failure and half an hour later asystem failure show their effects on the recordings. The ripple in the data is mainly caused by therather large measurement pulse size.) The weather conditions were not good for load control tests,because all the cold periods were too short. It was impossible to identify the parameters of the

traditional load control response models with required accuracy directly from this data. However, thephysically based model structure worked better.

8 9 10 11 12 13 14 15 16 17 18 19

aikah

teho

asuntoalueetloma-alueet

Power

Time h

residential areas

holiday resorts

Fig. 10. Example of primary substation load measurements, 16 Dec. 1996Four load groups were controlled one at a time, controls start at 10:15, 11:30, 13:15 and 14:20

In Fig. 11 an example of the response of the model is shown. The measured load represents

two ski resort areas (raw measurement data depicted in Fig. 10) and the measurement of areference day has been subtracted in order to reduce the effect of other load variations from the


24/30


results. Four different load control groups were controlled, one at a time. The outside temperaturewas around -19 oC. The parameters of the prediction model were identified from load control tests ofnormal houses in another nearby utility. However, advance information on the total controllablepower and heat storage capacity was used to scale the respective parameters of the model. Thepower was known. It turned out that slow dynamics are hidden behind other load variations andthus advance information of large heat storage capacities is useful in the model.

0

1000

2000

3000

4000

5000

9 10 11 12 13 14 15 16 17 18 19

time [hours]

PowerkW

SimulatedMeasured

Fig. 11. Example of the prediction performance of the model, Dec. 16th 1996

In Fig. 12 and Fig. 13 the response of the newer model [60] is compared with a load controlresponse model [62] developed earlier at VTT. Fig. 12 shows also the measured response to whichthe models were fitted. The measurement curve shown is the difference of a test day and theaverage of several reference days. During the test the temperature was around -7 oC. In Fig. 13 theresponses of the two models are compared when the temperature is -30 oC.

-300

-200

-100

0

100

200

300

400

500

18 19 20 21 22 23 24

aika h

eo

vaste

simulointi

Rouvali

PowerMW

Time h

measurement

physical model

old model

Fig. 12. Comparison of the load response model of [60] and [62]. Both models are fitted to the measured response that isalso shown. Outdoors temperatures is -7C


25/30


- 400

- 300

- 200

- 100

0

10 0

20 0

30 0

40 0

1 8 1 9 2 0 21 2 2 2 3 24

ai ka h

tehoMW

simulointiRouvali

PowerMW

Time h

phys. model

old model

Fig. 13. Comparison of the load response models of /1/ and 3 in a different outdoor temperature, -30C

Advanced load control terminals may limit the after-peak for example by reducingtemperature set-point temporarily. It is easy and straightforward to include such features in thephysically based model. With the earlier model that would have been very difficult. The physicallybased load response model is also more suitable than the old one for applications with theoptimisation of price control.

4. Areas Where Knowledge Lacks But Must Be Gathered

The implementation and expansion of Distributed Energy Resources may be definitelyenhanced by its combination with local trading strategies, defined as trading mechanisms for abetter management of customer consumption/production through the interaction with the supplymechanisms in which end-users play an active role.

A revision of tools developed to solve the problems associated to the evaluation of thecustomer acceptance and potential of typical segment customers, i.e. process identification and load

response and organisation has been performed in previous sections.

4.1. Lack of linkage between physical knowledge and historical data

Process identification can be performed either by sector analysis based on the followinginformation:

1. Macro-economic sector data (demographics national accounts, sector activity andincome variables, etc.).

2. Structure of energy consumption, based on yearly activity variables (production,dwellings, passenger-kilometres, etc.).

3. Technical-economic data for technologies and sub-sectors (capital cost, unitefficiency, variable cost, lifetime, etc.).

Physically Based Load Modelling Methodologies (PBLM) seems to suit very well for loadresponse evaluation purposes. Models to simulate LTS have been described by contributors and theircomputational performances, despite of their rather complex mathematical formulation, appearreasonable. The powerfulness and accuracy of these models have been widely tested throughcomparison between collected data and simulated results, obtaining values very close to the realperformances. Therefore, it can be concluded that this modelling methodology is valid to assessvarious LTS opportunities.

Probably, detailed PBLM for process description in combination whit accurate methodologies

to identify the presence of different processes in customer segments (along with the associatedenvironmental variables such as climate, market structure, etc.) could render in a completecustomer demand description suitable for EU-DEEP purposes.


26/30


Therefore further research should be performed to develop a methodology that combinesphysical knowledge with historical data.

4.2. Lack of linkage between consumer and market possibilities

Another significant point is the linking step between the end-user demand description and itspreparation to trade in the market. Once the customer potential has been evaluated through a

thorough demand description it is necessary to organize it in order to be able to use this potential inthe market.

The IEA identified a time frame for the demand-side structure that is summarized in Fig. 14

Fig. 14 Demand-side time frame (IEA)

Different products can be traded in different time-bands according to their characteristics.Fig. 15 depicts a tutorial scheme of a demand reduction that can be traded in as an offer tobalancing or ancillary services markets.

Fig. 15 Demand reduction offers (IEA)

Therefore further research should be performed to develop a methodology to transform end-user potential in market products that fit in this structure.

5. What must be abandoned from the past in this area to

favour DER?

It is necessary to abandon the idea of Demand Side Management controlled exclusively bythe Utility. The voluntary aspect of the management and a bilateral collaboration between the utilityand the customer is a must. However, in order the customer to agree on LTS, an adequatemethodology of the economical advantages must be presented.


27/30


6. What needs to be operational five years from now?

According to section 4 a methodology for consumer demand description and preparation forparticipation in the market should be operational in five years from now in order to expand LTSstrategies. This methodology has necessarily to be based on customer data and related information(sector economical information and data).

The new methodology should be well suited for the customer engagement in medium andshort term trades and has necessarily to be based on the detailed knowledge of the load elements(through physically based models) and processes involved in the customer load mix.

Moreover, the demand description would require an accurate forecasting of the loadcomponents over the time so that the customer may produce demand bids and offers that can besuccessfully traded.

This organization should be adapted to the flexibility and time predictable horizon. The maincriteria that should be taken into account should be:

Loads priority

Storage capability Rescheduling possibilities

7. References

[1] Uusitalo J., Yrjl E., Research on real-time pricing of electricity, Metering Apparatus and Tariffs forElectricity Supply, 1990., Sixth International Conference on, 3-5 Apr 1990. pp. 48-51.

[2] Kallio A., Research on real-time pricing of electricity, Metering Apparatus and Tariffs for Electricity Supply,1992, Seventh International Conference on, 3-5 Apr 1990. pp. 13-17.

[3] Kallio, A., Helminen H., Shkn reaaliaikaisen hinnoittelun tutkimus. Loppuraportti vuosilta 1988 1993.IVO-A-05/94. (Research on real-time pricing of electricity, final report for the years 1988-1993, InFinnish.)

[4] Kallio A., Research on real-time pricing of electricity, Energy Efficiency and DSM Conference, September21-23, 1993, Stockholm, Sweden, pp. 497-503.

[5] Kallio A. and Salo T., Research on real-time pricing of electricity, DA/DSM94 Europe ConferenceProceedings, September 27-29, 1994, Paris, France, Pennwell, Vol I., pp 365-376.

[6] Magnabosco P. W., OSheasy M. T., Real time pricing for purchased electricity: An innovative pricing optionfor electricity as used by the pulp and paper industry. Conference Record of the Annual Pulp and PaperIndustry Technical Conference 1996, 10-14 Jun 1996, pp. 45-54.

[7] Ilic M., Black J. W., Watz J. L., Potential benefits of implementing load control, Power Engineering SocietyWinter Meeting, 2002. IEEE. Vol. 1. pp. 177-182.

[8] Cirillo N. C., Vold P., Gabel S., Flood J. D., Carmichael L., Automated control of commercial and industrialfacilities using real time pricing - a winning combination for electric utilities and their customers,DA/DSM95 Europe Conference Proceedings, November 21-23, 1995, Rome, Italy, Pennwell, Vol. I, pp 349-360.

[9] Rouvali J., Shkkuormien ohjauksen mallintaminen ja erityyppisten ohjaustapojen arviointi (Modelling ofelectric loads and assesment of the potential of different load control methods). VTT, Laboratory of electricaland automation engineering, Report SH-1/93, Espoo 1993, 76 p. + app. 7 p. (in Finnish)

[10] Vold P., Carmichael L. and Flood J. D., Automated Control and Communications Technologies forCommercial and Industrial Customers in the US. DA/DSM96 Europe Conference Proceedings, October 8-10, 1996, Vienna, Austria, Pennwell, Utrecht, the Netherlands, Vol. III, pp 385-398.

[11] Flood J., Carmichael L, Sheldon M. A., Culp C., Real time automated building control and real time pricing:

an effective combination. Proceedings of the 4th International Symposium on Distribution Automation andDemand Side Management. Orlando, Florida, 17-20 January 1994. pp. 604-611.


28/30


[12] Hoffman S., Renner R., Drenker S., Carmicheal L, Flood J., Taking advantage of real-time pricing. IEEEPower Engineering Review, September 1997, Vol.17 No. 9, pp. 9-12.

[13] Bargiotas, D., Birdwell, J.D.: Residential air conditioner dynamic model for Direct Load Control, IEEETrans. Power Delivery, 1988, 3, (4), pp. 2119-2126

[14] Virk, G.S., Loveday, D.L.: Model-based control of HVAC applications, IEEE Conference on ControlApplications, 1994, 3, pp. 1861-1866

[15] Tomiyama, K., Daniel, J.P., Ihara, S.: Modeling air conditioner load for power system studies, IEEE Trans.Power System, 1998, 13, (2), pp. 414-420

[16] Walker, C.F., Pokoski, J.L.: Residential load shape modeling based on customer behavior, IEEE TransPower System, 1985, 104, pp. 1703-1711

[17] Ihara, F., Schweppe, C.: Physically based modeling of cold load pickup, IEEE Trans. Power System, 1981,100, pp. 4142-4150

[18] Mortensen, R.E., Haggerty, K.P.: A stochastic computer model for heating and cooling loads, IEEE Trans.Power System, 1988, 3, (3), pp. 1213-1219

[19] lvarez, C., Malham, R.P., Gabaldn, A., A class of models for load management application andevaluation revisited, IEEE Trans. Power System, 1992, 7, (4), pp. 1435-1443

[20] Massouros, P., Athanassouli, G., Massouros, G.: A model of the thermal transient state of a wall of a roomduring the heating by a heating system, Intern. Journal of Energy Research, 2000, 24, pp. 779-789

[21] Shweppe, F.C., Daryanian, B., Tabors, R.D.:Algorithms for a price responding residential load controller,IEEE Trans. Power System, 1989, 4, (2), pp. 507-516

[22] Antonopoulos, K.A., Koronaki, E.P.: Effect of indoor mass on the time constant and t he thermal delay ofbuildings, Intern. Journal of Energy Research, 2000, 24, pp. 391-402

[23] Reed, J.H., Broadwater, R.P., Chandrasekaran, A.: Air conditioner model study using Athens load controlexperiment data, Energy and Information Technologies in the Southeast, 1989, 1, pp. 390-394

[24] Uak, C., aglar, R.: The effects of load parameter dispersion and Direct Load Control actions onaggregated load, International Conference on Power System Technology, 1998, 1, pp. 280-284

[25] David A. K., Optimal consumer response for electricity spot pricing, IEE Proceedings, Vol. 135 Pt C, No. 5,September 1988, pp. 378-384.

[26] David A. K., Lee Y. C., Dynamic Tariffs : Theory of Utility Consumer Interaction, IEEE Transactions onPower Systems, Vol. 4. , No. 3., August 1989, pp. 904-911.

[27] David A. K., Li Y. Z., A comparison of system response for different types of real time pricing. APSCOM1991, International Conference on Advances in Power System Control, Operation and Management, 5-8November 1991, vol. 1, pp. 385-390.

[28]

[29] Rsnen M., Modelling processes in the design of electricity tariffs. Dissertation, Helsinki University ofTechnology, Systems Analysis Laboratory, Research Reports A 60, 1995, 35 p.

[30] Rsnen M., Ruusunen J. and Hmlinen R. P., Identification of consumers price responses in thedynamic pricing of electricity, Proceedings of the 1995 IEEE International Conference on Systems, Man &Cybernetics, Vancouver, Canada, October 22-25, 1995, Vol. 2, pp.1182 -1187.

[31] McDonald J.R., Lo K.L., Dynamic price structures and consumer load reaction. Metering Apparatus andTariffs for Electricity Supply, 1990., Sixth International Conference on , 3-5 Apr 1990 pp 6 10.

[32] Chang C. S., Minjun Yi, Real-time pricing related short-term load forecasting, Proceedings of 1998International Conference on Energy Management and Power Delivery, EMPD98, 3-5 March 1998, Vol. 2,pp. 411-416.

[33] Hu A. S., Lie T.T., Gooi H. B., Load forecast for customers under real time pricing systems. DRPT 200.Proceedings of the International Conference on Electric Utility Deregulation and Restructuring and PowerTechnologies 2000, pp. 538-543.

[34] Khotanzad A., Zhou E., Elragal H., A neuro-fuzzy approach to short-term load forecasting in a price-sensitive environment. IEEE Transactions on Power Systems, Vol. 17, No. 4, November 2002, pp. 1273-1282.

[35] David A. K., Load forecasting under spot pricing, IEE Proceedings, Vol. 135 Pt C, No. 5, September 1988,

pp. 369-377.


29/30


[36] David A. K., Li Y. Z., Consumer rationality assumptions in the real time pricing of electricity. APSCOM1991, International Conference on Advances in Power System Control, Operation and Management, 5-8November 1991, vol. 1, pp. 391-396.

[37] David A. K., Li Y. Z., Consumer rationality assumptions in the real time pricing of electricity. IEEProceedings-C, Vol. 139, No. 4, July 1992, pp. 315 322.

[38] David A. K., Li Y. Z., Effect of inter-temporal factors on the real time pricing of electricity. IEEETransactions on Power Systems, Vol. 8, No. 1, February 1993, pp. 44-52.

[39] Roos J.G., Kern, C.F., Modelling customer demand response to dynamic price signals using artificialintelligence. Metering and Tariffs for Energy Supply, Eighth International Conference on (Conf. Publ. No.426) , 3-5 Jul 1996, pp. 213 217.

[40] Roos J. G., Lane I. E., Industrial power demand response analysis for one-part real-time prices. IEEETransactions on Power Systems, Vol. 13, No. 1, February 1998, pp. 159-164.

[41] Darynian B., Bohn. R. E.,Tabors R. D., Optimal demand-side response to electricity spot prices for storagetype customers. IEEE Transactions on Power Systems, Vol. 4, No. 3, August 1989, pp. 897-903.

[42] Daryanian B., Bohn R. E.,Tabors R. D., Control of electric thermal storage under real time pricing, APSCOM1991, International Conference on Advances in Power System Control, Operation and Management, 5-8November 1991, Vol. 1, pp. 397-403.

[43] Darynian B., Bohn. R. E.,Tabors R. D., An Experiment in real time pricing for control of electric thermalstorage systems. IEEE Transactions on Power Systems, Vol. 6, No. 4, November 1991, pp. 1356-1365.

[44] Darynian B., Bohn. R. E., Sizing of electric thermal storage under real time pricing. IEEE Transactions onPower Systems, Vol. 8, No. 1, February 1993, pp. 35- 43.

[45] Daryanian B., Norfolk L.K., Minimum-cost control of HVAC systems under real time prices. Proceedings ofthe Third IEEE Conference on Control Applications, 24-2 1994, Vol. 3. pp. 1855-1860.

[46] Hawley R., Advanced control of energy consumption. Practical experience with predictive control, IEESeminar on. (Ref. No 2000/23) 2000 s. 4/1-4/10.

[47] Strong D., CELECT system trial results and ETHOS. a presentation at the European Forum 95 From HomeAutomation to Information Highways, Paris, 4-5 December 1995, 20 p.

[48] Koponen, P., The interaction of the utility and its customers in load control. PSCC. Proceedings of theTwelfth Power Systems Computation Conference. Dresden, 20 - 23 August 1996. Vol. II. Power SystemComputation Conference. Zurich (1996), 749 756.

[49] Koponen P., Viherma R., Rm T., Uronen P., Control of electric energy consumption in steel industryusing knowledge based techniques. Proceedings of the IFAC Workshop on Expert Systems in Mineral andMetal Processing, Espoo, Finland, 26 28 August 1991. (Oxford, UK; Pergamon 1992) pp. 31-37.

[50] Ygge F., Market Oriented Programming and its Application to Power Load Management. Ph.D. thesis,Department of Computer Sciences, Lund Institute of Technology, 1998.

[51] Arnheiter T., Modeling and simulation of an agent-based decentralized two-commodity power market.Proceedings of the Fourth International Conference on Multi Agent Systems 2000, pp. 361-362.

[52] Braun J. E., Reducing Energy Costs and Peak Electrical Demand Through Optimal Control of BuildingThermal Storage, ASHRAE Transactions, 96(2), 1990, pp. 876-888.

[53] Morris F. B., Braun J. E., Treado S. J., Experimental and Simulated Performance of Optimal Control ofBuilding Thermal Storage, ASHRAE Transactions, 100(1), 1994, pp. 402-414.

[54] Pretorius H. M., Delport G. J., Scheduling of cogeneration facilities operating under the real-time priceagreement. ISIE98, Proceedings of the IEEE international Symposium on Industrial Electronics 7-10 July1998, Vol. 2, pp. 390-395.

[55] Yau, T.S., Huff, R.G., Willis, H.L.: Demand-side management impact on the transmission and distributionsystem, IEEE Trans. Power System, 1990, 5, (2), pp. 506-512

[56] Molina, A., Gabaldn, A., Fuentes, J.A., lvarez, A., Implementation and Assessment of Physically BasedElectrical Load Models: Application to Direct Load Control Residential Programs, IEE Proceedings Gen.Trans. Distr., Vol. 150, n1, January 2003.

[57] Dominguez, M., Herrera, O., Alvarez, I.: Resolution of heat transmission equation for multilayer walls bymeans of thermal impedance, Technical Report, Instituto del Frio C.S.I.C., Spain, 1985

[58] Gabaldn, A., lvarez, C., Cnovas, F.J., Fuentes, J.A., Molina, A.: Physically based load modeling of HVACloads with some kind of energy storage: Principles and aggregation, International Congress on Energy,Environment and Technological Innovation, 1999, Rome, Italy, pp. 329-335


30/30

[59] Molina, A., Gabaldn, A., lvarez, C., Gomez, E., Fuentes, J.A. A Physically Based Load Model ofResidential Electric Thermal Storage: Application to LM Programs, Int. Journal of Power and EnergySystems, Vol. 24, n1, January 2004.

[60] Koponen Pekka, Shklmmityskuorman suoran ohjauksen mallit (Load response models for direct controlof electric heating ) ( in Finnish), VTT Energy, report ENE6/9/97. Espoo, June 1997, 47 p. + 4 p.

[61] Kopone Pekka, "Optimisation of load control, Final report of the project", VTT Energy research reportENE6/12/97, Espoo, December 1997, 26 p. + 14 p.

[62] Rouvali. Juhani, Shkkuormien ohjauksen mallintaminen ja erityyppisten ohjaustapojen arviointi (inFinnish) VTT, Laboratory of Electrical and Automation Engineering, report SH-1/93, Espoo, January 1993,76 p. + 9 p.

[63] N.S. Rau Assignment of Capability Obligation to Entities in Competitive Markets: The Concept of ReliabilityEquity. IEEE Transactions on Power Systems, Vol. 14, No 3, August 1999

[64] E. Hirst, B. Kirby. Retail-Load Participation in Competitive Wholesale Electricity Markets Prepared forEdison Electric Institute, Jan 2001.

[65] Instituto para la Diversificacin y Ahorro de la Energa, Spanish Government Technical Guide for EnergyEfficiency in Lighting, March 2001

[66] J.A. Fuentes, A. Gabaldn, A. Molina, E. Gmez. Development and Assesment of a load decompositionmethod applied at the distribution level. IEE Proceedings Generation, Transmission and Distribution. Vol150, n2, pp. 245-251, March 2003.

[67] D. Grayson, C. Heffner, Ch. A. Goldman, Demand Responsive Programs. An Emerging Resource forCompetitive Electricity Markets?. Proceedings of the International Energy Program Evaluation Conference(2001 IEPEC)

[68] G. Strubac, D. Kirschen Assessing the competitiveness of Demand-Side Bidding. IEEE Transactions onPower Systems, Vol. 14, n 1, pp. 120-125, Feb. 1999.