Analysis of Demand Response Solutions for Congestion Management in Distribution Networks

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Degree project in

for Congestion Management in Distribution Networks

Daniel Brodén

Stockholm, Sweden 2013 ICS Master thesis

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Abstract

According to the 20-20-20 targets set by the European Union, 50 percent of the Swedish electricity share is to be provided by renewable energy sources by 2020. The Smart Grid Gotland (SGG) project has emerged as a response to this target. The project aims at demonstrating a proof of concept on how smart grid solutions can be used to integrate large quantities of renewable energy sources in an existing network. The outcomes of the project are intended to pave the way for future renewable energy integration projects in Sweden.

The Thesis focuses on one of the technical objectives of the SGG project, i.e. to increase the hosting capacity of wind power on Got- land from 195 MW to 200 MW by using Demand-Response (DR) from households and industries. DR consist of shifting peak-loads to peak- production hours. The integration of additional wind power causes a risk of exceeding the transmission capacity of the power export cable between Gotland and the Swedish mainland.

The approach considered for this Thesis is to use an Ancillary Ser- vice (AS) toolbox scheme based on multi-agent systems. The AS toolbox consist of flexibility tools such as DR on long-term, short-term, a battery energy storage system and a wind curtailment scheme. The DR activity includes space heating and domestic hot water consumption from detached houses on Gotland.

The simulation results indicate that 1900 household participants are sufficient to balance the additional 5 MW for worst case scenarios. Furthermore, it is shown that the DR participation from industries contributes in some cases to a reduction of 700 household participants.

The findings helped conclude that using an AS toolbox solution on Gotland is fully possible from a technical perspective. However, barriers that stand against its realisation are of economical nature and need to be investigated in future studies.

Keywords: smart grid, demand side management, demand- response, load shift, wind power integration, distribution net- work, stationary battery

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Enligt EU målen 20-20-20 har Sverige som målsättning att 50 procent av all energiproduktion ska utgöras av förnybara energikällor till år 2020. Smart Grid Gotland projektet är ett initiativ som dragits igång för att möta dessa mål. Projektet strävar efter att påvisa hur smarta applikationer i elnätet kan användas för att integrera stora mängder av förnybar energi i ett befintligt elkraftsystem. Lärdomarna från SGG projektet kommer att användas som underlag för framtida elnätsprojekt i Sverige.

Examensarbetet fokuserar på en av de tekniska målsättningarna för SGG, att öka produktionskapaciteten av vindkraft från 195 MW till 200 MW genom att med hjälp av konsumentrespons skifta hushåll och indu- strilaster till timmar av hög produktion. Produktionsökningen medför en ökad risk för energiexportproblem mellan Gotland och fastlandet.

Detta problem uppstår pågrund av begränsningar i överföringskapaci- tet.

Tillvägagångssättet för examensarbetet har varit att använda sig av ett sidotjänstverktyg som baserar sig på ett multi-agent system. Detta verktyg består av konsumentrespons på lång och kort sikt, ett batteri- lagringssystem samt vindreducering.

Resultaten påvisar att 1900 hushållskunder är tillräckligt för att ba- lansera 5 extra MW vindkraft under svåra omständigheter. Industrikun- derna lyckades i vissa fall minska behovet av antal hushållskunder med så mycket som 700 hus.

I slutsats kan man påstå att det är teoretiskt möjligt att använda sig av ett sidotjänstverktyg på Gotland för att lösa de exportproblem som kan inträffa. Dock så begränsas sidotjänstverktyget av marknadsstruk- turer och ekonomiska utmaningar. Detta bör vara en utgångspunkt för framtida studier inom ämnet.

Nyckelord: smarta elnät, lasthantering, konsumentrespons, skifta last, vindkraftsintegrering, distributionsnät, stationärt batteri

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Acknowledgment

This Master Thesis study truly captured my interest from the very beginning. The high relevancy of the study with on-going projects in the industry has made me realise the importance of researchers in this field. I believe Smart Grid applications will play a very important role for the future of our energy infrastructure, as we will have to rely on innovative solutions to ensure a sustainable future.

I would like to thank everybody that has assisted me during the Master Thesis study. Special thanks to my supervisors from Vattenfall, Erica Lidström and David Erol, for believing in me and giving me the opportunity to study the challenging issues on Gotland. They have made me feel at ease during my time at Vattenfall and have been a great guidance throughout the study. The work environment at Vattenfall has been engaging and I have met some wonderful people that have made my stay very pleasant. I sincerely hope that the findings of my Thesis will be valuable for the Smart Grid Gotland project and that I will have the occasion to work with people from the R&D department at Vattenfall in the near future.

Special thanks to Claes Sandels, my supervisor at the Royal Institute of Tech- nology (KTH). He has shown a lot of engagement and enthusiasm in my Thesis.

Claes has been more than just a supervisor he has been a true mentor. I sincerely hope that my Thesis results will be valuable for your research at KTH.

Last but not least, I want to thank all of whom I have interacted with for the purpose of the Thesis. I felt at all times that I was encouraged to stay creative and confident of trying alternative routes. Thank you for that, it has been a true learning experience.

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List of Symbols

KTH Kungliga Tekniska Högskolan EU European Union

DR Demand Response SGG Smart Grid Gotland DSP Demand-Side Participation AS Ancillary Service

LT Long-Term (day-ahead) ST Short-Term (hour-ahead) BESS Battery Energy Storage System PM PowerMatcher

PSS/E Power System Simulator for Engineering RMSE Root Mean Square Error

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Contents

1 Introduction 1

1.1 Background . . . 1

1.2 Purpose of Master Thesis . . . 3

1.3 Thesis Goals and Objectives . . . 3

1.4 Delimitation of Study . . . 3

2 Gotland Background Study 5 2.1 Electric Power System . . . 5

2.1.1 Power Grid Description . . . 5

2.1.2 Power Production & Prognosis . . . 6

2.1.3 Consumption Characteristics . . . 6

2.1.4 Future Objectives and Export Challenges . . . 9

2.2 Demand-Response . . . 11

2.2.1 What is Demand-Response? . . . 11

2.2.2 Demand-Response in Detached Houses . . . 12

2.2.3 Demand-Response in Industries . . . 14

2.3 Ancillary Service Toolbox . . . 15

2.3.1 Purpose & Functionality . . . 15

2.3.2 Flexibility Tools Description . . . 16

2.4 Simulation Tools . . . 17

2.4.1 PowerMatcher Technology . . . 17

2.4.2 MATLAB Optimization Toolbox . . . 19

2.5 Summarized Problem Illustration . . . 20

3 Modeling Flexibility Tools 21 3.1 Demand-Response Participants . . . 21

3.1.1 Detached House Flexibility . . . 21

3.1.2 Industry Flexibility . . . 26

3.2 Battery Energy Storage System & Wind Curtailment . . . 26

3.3 Summarized Model Illustration . . . 28

4 Optimization Method 29

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4.1 Problem Formulation . . . 29

4.2 Long-Term Cluster . . . 32

4.3 Short-Term Cluster . . . 32

4.4 Required Input Data . . . 33

4.5 Optimization Flowchart . . . 34

5 Simulation Set-Up 35 5.1 Defining Simulation Scenarios . . . 35

5.2 Applying Data Offset . . . 35

5.3 Applying Production Prognosis Errors . . . 37

5.4 Setting Start Parameters . . . 38

6 Simulation Results & Analysis 41 6.1 Scenario 1: Winter days . . . 41

6.1.1 Expected Power Export . . . 41

6.1.2 Optimized Consumption . . . 42

6.1.3 BESS Operation & Wind Curtailment . . . 45

6.1.4 Sensitivity Analysis . . . 45

6.2 Scenario 2: Spring days . . . 48

6.3 Scenario 3: Summer days . . . 50

6.4 Scenario 4: Autumn days . . . 52

6.5 Scenario Comparison . . . 53

6.5.1 Space Heating Flexibility . . . 54

6.5.2 Export Problem Occasions . . . 55

7 Discussion 57 7.1 Model Limitations . . . 57

7.2 Scenario Limitations . . . 58

7.3 Validity & Reliability of Results . . . 58

7.4 Thesis Benefits for the SGG project . . . 59

8 Conclusion 61

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Bibliography 65

A Model Calculations 69

A.1 Space Heating Slopes . . . 69 A.2 Domestic Hot Water Slopes . . . 70

B Scenario Result Figures 71

B.1 Scenario 2: Spring days . . . 71 B.2 Scenario 3: Summer days . . . 75 B.3 Scenario 4: Autumn days . . . 80

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Chapter 1

Introduction

1.1 Background

Today’s electricity networks are facing challenges as the pressure increases from governments and political institutions to reduce fossil fuel dependent energy production and replace them with large amounts of renewable energy production. In March 2007, the European Union (EU) set the 20-20-20 targets committing Europe to become a highly energy efficient, low carbon economy and cleaner energy pro- ducer [1]. Most parts of the European electricity networks were built during the early 20th century, oblivious to the rising environmental concerns that would form new energy policies. The recent interest of integrating renewable energy sources in the existing electricity networks poses technical challenges in maintaining grid stability, matching demand and supply, and transmission capacities. In recent years the "smart grid" concept has emerged as a response to these challenges. The idea has been to use information exchange and communication technology to improve efficiency and distribution of power in electricity networks. The technology targets both producers and consumers where the electricity distribution and usage is optimized such that the grid can safely account for renewable energy source integration.

The intermittent nature of renewable energy sources makes it difficult to accurately predict power production as it is closely tied to complex weather dynamics. The uncertainty in prognosis and high production variation accentuates the difficulties in maintaining safe system operation. This is one of many challenges electricity networks are facing when dealing with mass integration of renewable energy sources.

An interesting solution aims at involving the electricity consumer by making them consume in a proactive manner, for example shift the consumption to periods when there is a need from the system to maintain balance. This concept is called Demand- Response (DR) and has become a popular solution. DR solutions has received a lot of attention in the scientific community. One example is a published scientific report [2] presenting residential load models for space heating/cooling and tap water systems. Another example is a study conducted for massive wind power integration in the Netherlands [3]. The paper shows how DR from households is controlled

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intelligently to accommodate for mass integration of wind power production. The research has now extended to companies such as Vattenfall AB focusing on implementing DR as part of the smart grid applications [4].

The target for the Swedish share of renewable energy by 2020 is approximately 50% which has encouraged the development of projects aiming at increasing renewable energy production. The largest Swedish ongoing project today is the Smart Grid Gotland (SGG) project, which is a collaboration between Vattenfall, GEAB, ABB, Energimyndigheterna, Svenska Kraftnät, Schneider Electric and the Royal Institute of Technology (KTH). The project has three overall objectives [4]: Cost efficiently increase the hosting capacity for wind power in an existing distribution system. Show that novel technology can improve the power quality in a rural grid with large quantities of installed wind power. And to create possibilities for DR in the electricity market, in order to shift load from peak load hours to peak production hours. There are several reasons why Gotland is chosen for a smart grid project.

Gotland is in fact an ideal candidate for a small-scale pilot study before extending the concept to other parts of Scandinavia. The island is an electrically closed system, with its own frequency, high wind power production and which only link to the continent is through an HVDC cable. These characteristics greatly reduces the economical and technical complexity of the project and encloses the potential risks within the island. The project is a proof of concept of smart grid applications and the outcomes are intended to pave the way for similar Scandinavian energy projects in the future.

The Master Thesis focuses on the wind power integration aspect of the SGG project where 5 MW additional wind power needs to be integrated in the existing grid without making any changes to the infrastructure. Currently the network on Gotland can withstand a maximum installed capacity of 195 MW. Increasing the wind power to 200 MW will cause a risk of exceeding the transmission capacity of the export HVDC link from Gotland to the mainland. The study focuses on using flexibility tools such as DR to balance those 5 MW in the network while ensuring that the export capacity is not being exceeded. The DR activity includes space heating and domestic hot water consumption from detached houses on Gotland.

Detached houses are interesting DR candidates as they represent 20% of the total consumption on Gotland [16]. Moreover, 75% of the total electricity consumption in a detached house [5] comes from space heating and domestic hot water activities. The DR activity also includes participation from industries on Gotland where specific operation strategies are deployed after receiving requests for DR. Using industries as DR participants can help shift considerable loads and reduce the need for more household participants.

The approach is to model a technical Ancillary Service (AS) toolbox suited for the environmental set-up of Gotland. The AS toolbox is a multi-agent system consisting of DR on Long-Term (LT), DR on Short-Term (ST), a stationary Battery Energy Storage System (BESS) and a wind power curtailment scheme.

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1.2. PURPOSE OF MASTER THESIS

1.2 Purpose of Master Thesis

The main purpose of the Thesis is to study whether it is technically feasible to implement a technical AS solution on Gotland to balance 5 MW additional wind power capacity.

1.3 Thesis Goals and Objectives

The following Master Thesis goals and objectives have been formulated:

• Choosing Agent Model

A literature study will be performed on two alternative ways of modeling the AS toolbox. The first, using PowerMatcher (PM) simulation tool and the second using MATLAB optimization toolbox. The advantages between the two will be assessed to justify the choice of agent model.

• Defining Simulation Scenarios

Feasible simulation scenarios will be derived on production and consumption occasions on Gotland.

• Modeling Flexibility Tools

The flexibility tools will be modeled either by using PM or MATLAB. The developed models need to reflect the conditions and prerequisites of Gotland.

The DR participants will include industries and detached houses where flexibility will be provided from appliances used for space heating and domestic hot water. An operation strategy for the BESS will be proposed to absorb the production prognosis errors.

• Performing Simulations

Simulations will be performed in PM or MATLAB for a set of feasible scenarios. The simulation results will serve as reference material to conclude on the technical feasibility of implementing an AS toolbox on Gotland.

1.4 Delimitation of Study

The study has been limited in certain aspects to ensure reasonable and qualitative deliverables within the thesis time frame:

• Geographical Delimitation

The study is limited to the island of Gotland. However the models can be used and rescaled to study other regions where the conditions are similar. The study will not focus on the influence of individual loads and production units, instead the total load and production of the island will be considered as a whole.

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• DR Participants

The DR participants considered are detached houses and industries. Only industries with documented consumption activity are eligible to participate in DR. Furthermore, only industries having a large influence on the overall consumption on Gotland will be studied.

• DR Consumption for Detached Houses

Space heating and domestic hot water are the only two consumption activities for detached houses that will be used for ST and LT DR. This is done to reduce model complexity,

• Prognosis Errors

It is assumed that the network can handle 195 MW of installed wind power capacity. This include the prognosis errors from electricity production and consumption. Therefore the only prognosis errors considered in the study will be on the 5 MW of additional power production for both LT and ST prognosis [4].

• DR time resolution

The DR participants will operate on an hourly resolution for the simulations.

In reality, the SGG project needs faster ST DR and BESS operation, typically every 5 minutes.

• Network Simulator

The multi-agent model needs to communicate with a network simulator to verify the feasibility of the power flows within the network. The network simulator of choice is Power System Simulator for Engineering (PSS/E). The communication with the network simulator is important to send readjustment signals back to the agent model when power flow limitations are encountered.

This process will be performed manually although it is intended to be an automated process. The results from PSS/E will be used internally by Vattenfall and will not be included in the Thesis report.

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Chapter 2

Gotland Background Study

2.1 Electric Power System

2.1.1 Power Grid Description

The island of Gotland is located in the Baltic Sea, east from the town of Västervik.

The power distribution grid on Gotland consist of approximately 300 km of 70kV lines, 100km of 30 kV lines and 2000 km of 10 kV lines [7]. A representation of the network is presented in Figure 2.1.

Figure 2.1. The power distribution grid on Gotland [6].

The electric power system on Gotland is a closed system connected only to the mainland by two HVDC cables. The current HVDC cables were installed in 1983 and 1987 to increase the safety of supply as well as the electricity need on the island. The HVDC cables separate Gotland and the rest of Sweden into two different frequency control areas. The frequency on Gotland is therefore independent of the frequency changes on the mainland. The HVDC cables are used for import and export of power and consist of two poles with a rating of 130 MW per pole operating at 150 kV of rated voltage [8]. The HVDC cables are 100 km long submarine

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cables stretching from the town of Västervik in the mainland to Ygne on Gotland.

Power is imported from the mainland during peak loads of approximately 170 MW and in order to assure N-1 criterion, the HVDC cables do not transmit power simultaneously in the same direction. The importing HVDC cable runs often on full capacity because of the sometimes low local production. This has lead authorities to plan the construction of two additional HVDC cables with 2x500 MW power capacity. The cables are planned to be operational by year 2017 and 2020 [9].

2.1.2 Power Production & Prognosis

The power production on Gotland consists of wind power. There are gas and diesel based power production on the island with enough installed capacity to cover the total demand in case of unexpected outages [10]. In recent years, many small wind farms have been replaced by larger ones, thereby increasing the total installed wind power capacity to approximately 170 MW [12]. Hence, the installed capacity is approaching the maximum grid limit of 195 MW [4]. The intermittent nature of wind power makes it difficult to establish a typical yearly power production trend.

Generally, wind power production is higher during cold seasons such as winter and autumn because of stronger, more frequent wind blows and greater air density.

Figure 2.2 presents the wind power production on Gotland during year 2012.

Rarely, the wind production on Gotland generates at its full capacity. In 2011, the annual wind power production was 340 GWh equating to about 38% of Gotlands total consumption [13]. Furthermore, wind power production is difficult to forecast.

The production prognosis is based on climatology models taking into account factors such as wind speeds, forces and other types of weather conditions. As of today the production prognosis error increases rapidly the further ahead in time a prediction is made. The maximum forecast length of most models today are 48-178 hours ahead [14]. Naturally, a one hour-ahead prognosis is much more accurate and reliable than a day-ahead prognosis. Figure 2.3 presents the wind power production prognosis error on Gotland for different hours.

2.1.3 Consumption Characteristics

Today, Gotland has approximately 57,000 inhabitants. The population has been stagnating since 1995 and no forecasts have been made on heavy population growth [15]. This means that the overall consumption profiles on the island will not be subject to any drastic changes during the next coming years. The average total consumption since 2002 has been measured to approximately 860 GWh/year, i.e., 100 MWh/h [16]. The power consumption from January 1st to December 31st resembles a valley where the peaks occur during cold seasons such as winter and autumn. Figure 2.4 presents an average of the total daily consumption on Gotland during year 2012.

Gotland accounts for approximately 0.65 % of the total Swedish consumption [16]. The high-consuming loads are typically industries, detached houses and col-

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2.1. ELECTRIC POWER SYSTEM

Seasonal Average Production Yearly Average Production Daily Average Production

Power[MWh/h]

Days

0 50 100 150 200 250 300 350

0 20 40 60 80 100 120 140 160 180

Figure 2.2. Daily averages on the total power production on Gotland during year 2012 [11]. Day 1 corresponds to January 1^st2012. The yearly average production is 47 MWh/h. Examples: winter average production is 51 MWh/h and summer average production is 35 MWh/h. The installed wind power capacity during 2012 was 170 MW [12].

lective apartments. Figure 2.5 presents the different load profiles and shares based on statistics from 2011.

Detached Houses

In 2012 there were 20,590 detached houses on Gotland [16]. According to statistics from Figure 2.5, 20% of the total consumption on Gotland is consumed by detached houses. This typically includes high-consuming activities such as space heating, domestic hot water and the use of electric appliances.

Collective Apartments

Collective apartments represent only 4% of the total consumption on Gotland. The apartments are heated from district heating power plants. The plants are operated by Gotlands Energi AB (GEAB), the local power utility company, and cover the most populated areas on Gotland, i.e. Visby, Klintehamn, Hemse and Slite [17].

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Normalized RMSE (hourly)

Percentage

Time [hours]

18.79 % 17.79 %

6.12 %

0 4 8 12 16 20 24 28 32 36 40 44 48

6 8 10 12 14 16 18 20

Figure 2.3. Normalized Root Mean Square Error (RMSE) on hourly wind power production prognosis on Gotland. The graphs are compiled based on hourly wind power production data from Gotland [11]. The production prognosis is estimated using a persistence method, where one assumes that the prognosis for time step t + 1 will produce as much as the current time step t. The process is repeated for larger steps up to t + 48. The prognosis errors presented are by no means conclusive. They are only used for illustrative purposes and a lot more advanced techniques are used to determine wind power production prognosis errors in reality [14].

Industry

Although Gotland is not an industrial region, the industry accounts for approximately one third of the total electricity consumption. The company Cementa, operating in the cement industry, is the main player responsible for this. Their activity is estimated to cover 86% of the total industrial consumption. The remaining 14% are divided among the companies Arla (5%), Nordkalk (6%) and others (3%). A field study carried out by ABB presents detailed information about the consumption activities from these three companies [18].

Others

The consumption corresponding to ’others’ comprise commercial entities, the public sector, transportation and other services. These services represent approximately 40 % of the total consumption.

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2.1. ELECTRIC POWER SYSTEM

Seasonal Average Consumption Yearly Average Consumption Daily Average Consumption

Power[MWh/h]

Days

0 50 100 150 200 250 300 350

60 80 100 120 140 160 180

Figure 2.4. Daily averages on the total consumption on Gotland during 2012 [11].

Day 1 corresponds to January 1^st 2012. The yearly average consumption is 107 MWh/h. Examples: winter average consumption is 136 MWh/h and the summer average consumption is 87 MWh/h.

Others

358 GWh/year 42%

Industries 288 GWh/year 34%

Apartments 34 GWh/year 4%

Small houses 170 GWh/year 20%

Figure 2.5. Electricity consumption per category in GWh/year during 2011 [16]

(small houses = detached houses).

2.1.4 Future Objectives and Export Challenges

The installed wind power capacity has increased rapidly during the last years on Gotland. In August 2011, there were 158 wind turbines on Gotland with a total

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installed capacity of 118 MW which during 2010 produced 0.2 TWh of electricity [19]. Today, the installed wind power capacity is close to the maximum grid limit of 195 MW. The regional ambition is to produce an annual of 2.5 TWh of electricity which means that at least 500 additional wind power plants need to be built. The challenges Gotland faces are to integrate the production in the existing distribution network without having to make extensive investments in the infrastructure. The problem that arises when the installed capacity exceeds 195 MW in the current network is that the transmission capacity of the export HVDC link gets overloaded during hours of high production and low consumption. Historical data suggests that consumption has never been as low as to provoke an export problem. These findings are presented in Figure 2.6.

HVDC transmission capacity Hourly power export 2012

Power[MWh/h]

Time [hours]

0 1000 2000 3000 4000 5000 6000 7000 8000 0

20 40 60 80 100 120 140 160

Figure 2.6. The exported power between Gotland and the mainland during 2012.

The transmission capacity of the HVDC cable is 130 MW [8]. The installed capacity during 2012 is estimated to around 170 MW [12].

One has to consider a worst case scenario where the production generates at the maximum grid limit of 195 MW and where the consumption is greatly reduced to provoke an export problem. Figure 2.7 presents observed correlations between time of the day and export occasions. The figure show that there are no correlations between export occasions and the day of the week. The export occasions are more likely to occur during night hours (i.e. between 22:00 - 04:00) because of the overall low consumption. The bar charts complement the accumulated export power

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2.2. DEMAND-RESPONSE

Figures for a better statistical analysis.

Accumulated power export occasions per hour of the day (year 2012)

Exportoccasions

Time [hours]

Accumulated power exported per hour of the day (year 2012)

Power[MW]

Time [hours]

0 3 6 9 12 15 18 21 24

0 20 40 60 0 500 1000 1500

Accumulated power export occasions per day of the week (year 2012)

Exportoccasions

Days Monday-Sunday

Accumulated power exported per day of the week (year 2012)

Power[MW]

Days Monday-Sunday

1 2 3 4 5 6 7

0 5 10 15 20 25 1000 1500 2000 2500 3000 3500

Figure 2.7. The left figures show the correlation between export occasions and the hours of the day during year 2012. The right figures show no or little correlation between export occasions and the day of the week.

There are also some issues concerning the import of power during the year.

During those occasions the local diesel and gas power plants are operated to avoid overloading the HVDC cable.

2.2 Demand-Response

2.2.1 What is Demand-Response?

To maintain the stability of an electric power system there has to be a constant balance between power production and consumption. Traditionally, power producers ensure the balance by either increasing or decreasing their production according to demand. Demand side management on the other hand, consists of doing the complete opposite, i.e., to adjust consumption according to what is being produced.

One increasingly popular example of demand side management is DR where consumers change their consumption behaviour after receiving a DR signal. Examples of DR participants comprise households, industries or the public sector. The in- centives for consumers to engage in DR activities are mostly driven by economical benefits. This means that the DR signal sent is often an indication of low electricity market prices. DR has become a popular research topic in the field of smart grid applications thanks to the technical and economical benefits it provides. One example is presented in [22] where DR allows the integration of large quantities of wind power in an existing grid.

Active DR

Active DR is the process where consumers actively change their consumption based on requests. In detached houses, active DR includes all consumption that is man-

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ually controlled, e.g. computers, ovens, stoves, dishwashers, showers, baths, etc.

For industries, it is any type of electricity consuming activity that is not operated by a control system. Being an active DR participant is a sign of engagement and commitment. These participants have good knowledge of their consumption and are willing to trade comfort for other benefits.

Remote DR

When consumers engage in remote DR they sign up to let their consumption be controlled by an external entity. For example, it might be the control of appliances used for space heating or domestic hot water which offers a certain degree of consumption flexibility. The appliances are controlled within a select region which is judged to have no, or very little, impact on the comfort level of the consumer.

Hence, remote DR allows consumers to maintain regular activities with the illusion of being a passive user. The automated process that this type of DR provides makes it easier for large scale implementation. Moreover, the barriers of realization are considerably lower than active demand since the consumer commitment is mini- mized. Remote DR offers a lot more reliability than active DR which is crucial from a power system stability perspective.

2.2.2 Demand-Response in Detached Houses

The average Swedish detached house uses approximately 55% of its electricity for space heating, 20% for domestic hot water and the remaining percentage for household equipments [5]. Among these activities both space heating and domestic hot water can be used as remote DR.

Space Heating

Almost every house on Gotland consume electricity for space heating purposes.

The indoor temperature is generally maintained close to a reference temperature of 20 degrees Celsius. During days of low outdoor temperature the consumption for space heating increases to prevent the indoor house temperature from dropping below the reference. Figure 2.8 presents the most common energy sources used for space heating in detached houses on Gotland.

The most abundant form of energy used is a combination of oil, electricity and bio. Details about the methods used for space heating are further explained on the website of the Swedish Energy Agency [5]. Direct and water carried energy are common space heating methods where the thermal energy spreads through the radiators of the house. These processes can be used as remote DR where a constraint is set for a maximum and minimum allowed indoor temperature. The temperature boundaries are chosen in such a way to allow room for consumption flexibility while avoiding experienced consumer discomfort.

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2.2. DEMAND-RESPONSE

Others 14.7%

District Heating 15%

Heat Pump 7%

Bio 2.3%

Oil+Elec 15.9%

Oil+Elec+Bio 21.6%

Elec.(w) 10.8%

Elec.(d) 9.2%

Figure 2.8. Energy sources used for space heating in detached houses on Gotland (2010) [16]. Elec.(d) is direct electricity whereas Elec.(w) denotes water carried electricity.

Figure 2.9. Direct electricity (Elec.(d)) on the left and water carried electricity (Elec.(w)) on the right. Images are taken from the Swedish Energy Agency website [5].

Domestic Hot Water

Most detached houses which are not part of district heating use water tanks equipped with a boiler to maintain the hot water at a reference temperature. The hot water from the tank is drained for showers, baths, hand-washing and similar activities.

Every time water is drained, the tank temperature drops and the boiler needs to consume more to prevent the temperature from dropping below 60 degrees C^◦. This constraint is set to minimize the occurrence of legionella bacterias in the water [20].

The typical temperature interval of a water tank is between 60 and 100 degrees C^◦, leaving enough margin for boiler consumption flexibility. One could for example let the boiler consume more during hours of low water drainage and less during other hours. This type of flexibility is ideal for remote DR purposes.

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2.2.3 Demand-Response in Industries

Industries on Gotland account for a high share of electricity consumption (see section 2.1.3), having an industry participate in DR can help shift considerable loads.

Industries offer mostly active DR solutions. It might for example involve moving production hours from the morning to the evening or utilize existing buffers by consuming more during certain hours. The following examples illustrate potential day-ahead DR strategies for high power consuming companies on Gotland [18].

Cementa

The first three steps of the company process flow is presented in Figure 2.10. The company performs stone quarry activity during day shifts, twice a week on weekdays.

During times when the production is lagging the demand, the company can be issued a permission from the county for one extra weekday of stone quarry activity. The stone crushing activity consumes 2.8 MW and the crushed stones are later sent for storage where they can remain for 4 days. If the company can be issued a similar permission from the county for DR purposes then power consumption activity can be utilized.

Figure 2.10. Cementa process flow [18]. Step 1 is the stone quarry activity. Step 2 is the stone crushing activity consuming 2.8 MW. Step 3 is the storage of the crushed stones where it can stay for a maximum of 4 days before it moves on to the next step.

Arla

The company operates in the dairy industry and deliver its product to grocery stores. 80% of the electricity consumption goes to fabricate the milk powder. This process involves 20 hours of drying and 4 hours of washing. As soon as one batch is dried then the washing process begins, followed by the next batch. For hygienic purposes the washing has to occur within 20 hours. Relative to drying, the washing process uses a lot less energy. The DR potential is to relocate the hours when the washing occurs.

Nordkalk

This company operates in the mineral industry, one of their activity is limestone quarry which occurs twice a day. The stones are crushed on sight through 2x250 kW stone crush motors. The stones are transported to the docks where they are crushed once again in the correct sizes and then categorized accordingly. The DR

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2.3. ANCILLARY SERVICE TOOLBOX

possibilities for the company are limited, however loading the boat with stones consumes close to 0.5 MW which can be relocated to other hours of the day since the boats stays at the dock for 24 hours. The loading activity is estimated to take between 6 and 8 hours which opens up a possibility for DR.

2.3 Ancillary Service Toolbox

2.3.1 Purpose & Functionality

The purpose of the AS toolbox is to communicate with flexibility tools to balance additional power production in the existing network without overloading the export HVDC cable. The additional power production is 5 MW where the installed capacity has been increased from 195 MW to 200 MW. A worst case scenario is considered where the wind production generates at installed capacity. The AS toolbox is a multi-agent model using LT and ST production prognosis data as input. The agents of the AS toolbox are presented in Figure 2.11.

Figure 2.11. AS toolbox communication channels. The agent model operates the flexibility tools every time there is an export problem prognosis. The information is exchanged with PSS/E to verify the feasibility of the power flow in the network.

PSS/E sends readjustment signals to the agent model when power flow limitations are experienced (refer to section 1.4 for delimitation of study).

The data input to the agent model will be both LT and ST production and consumption prognosis. The ST production prognosis is much more accurate than LT prognosis. This was presented earlier in Figure 2.3.

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2.3.2 Flexibility Tools Description

The model uses flexibility tools such as LT DR, ST DR, a BESS and a wind curtailment scheme to balance additional power production in the network when an export problem occurs.

LT DR

LT DR refers to DR scheduled 24-48 hours in advance (day-ahead). This activity include the control of space heating and domestic hot water for detached house participants. Industry participants are also part of LT DR since it is assumed that they need at least 24 hours to reschedule and plan their consumption activity for the upcoming day. Once the day-ahead consumption has been set then the space heating and domestic hot water consumption for detached houses will be controlled the following day and industries will have to consume what they agreed upon.

ST DR

Short-term DR refers to DR scheduled one hour in advance (hour-ahead). This activity include the control of space heating and domestic hot water for detached house participants. It is assumed that industries are not flexible for DR on a hour- ahead basis since most of their high consuming activities are planned well ahead.

BESS

The BESS is used to absorb prognosis errors from wind power production. Even though ST production prognosis have high accuracy, the ST DR participants will not be able to account for all imbalances. The BESS has a capacity of 280 kWh and has the ability to absorb and deliver power to the network. It is assumed that the charge rate is 280 kWh. Electricity is consumed when the BESS is absorbing power and the production is increased when the BESS is delivering power.

Wind Curtailment

Wind curtailment is used as a last resort when other flexibility tools are unable to balance the power in the network. When curtailment is needed a signal is sent to wind turbines to either shut down or run at lower speeds. This process is very costly and has a damaging effect on the turbines.

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2.4. SIMULATION TOOLS

2.4 Simulation Tools

2.4.1 PowerMatcher Technology PM in Brief

PM is a communication and coordination technology used for supply demand matching in electricity networks. It is a multi-agent based system that uses electronic exchange markets to coordinate the supply and demand of a cluster of devices.

The multi-agent is used to represent complex networks consisting of devices such as electricity production units, electricity storage devices (e.g. BESS), electricity consumption patterns, etc. The agents interact with each other in order to reach an optimal solution. The main purpose of PM is to facilitate the implementation of smart grids for conventional and renewable energy sources. Using intelligent clus- tering, small electricity producing or consuming units gain in operation flexibility which can add value to the electricity market or in the case of the thesis, help balance additional production capacity in an existing network. [21]

Figure 2.12. Logical tree of PM cluster. Image taken from PM website [21].

PM Structure

The PM technology has a tree structure consisting of agents such as the auctioneer, the concentrator, the device and the objective agent (see Figure 2.12). All agents try to operate the process associated with its child in an economical optimal way.

The information exchanged between different agents is bids and prices which express how much agents are willing to pay or to be paid for a certain amount of electricity.

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The root of the tree is the auctioneer agent which collects the bids and calculates the market price. The market price is communicated to the device agents who based on the information make an assessment on production or consumption of electricity.

In some cases neither of the two alternatives is chosen and the device agent waits instead until the market price changes. The agents are described in more detail:

Auctioneer agent

It is the root of the logical tree and collects all bids from its children. It forms an equilibrium price based on the price signals received from its children and communicates the market price back to them.

Concentrator agent

It mimics the auctioneer by collecting all bids from the device agents and forms a bid price which is communicated to its root.

Device agent

It is a control agent that operates the device in an economical optimal way. This agent communicates with all other agents in the cluster by buying or selling the consumed or produced electricity of the device. The market price and the latest price of the device agent determine how much electricity that will be produced or consumed.

Objective agent

It determines the objective of the clusters which most often is to maintain balance between electricity production and consumption but additional constraints could also be added on network transmission capacity.

PM Limitations

The PM technology was successfully used for a similar study in the Netherlands which showed that DR in households could accommodate mass integration of new wind power [22]. However, the PM software presented limitations for the Gotland study. All models used for flexibility tools such as space heating, domestic hot water were not configurable from the source. The user is only able to toggle few parameters such as maximum and minimum capacity. The original source files needed modification for the models to reflect realistic conditions on Gotland. Furthermore, the selection of wind parks only included those from the Netherlands. Modifying the source files implies extensive programming and reconfiguration. Unfortunately, the lack of documentation, thesis time frame, and lack of transparency in the software lead to re-evaluate the necessity of using PM as an agent model for this study. This decision does not imply that PM was unfit for the study but rather that extensive

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2.4. SIMULATION TOOLS

programming and further research is needed to make a rightful assessment on the matter.

2.4.2 MATLAB Optimization Toolbox

[23] The MATLAB optimization toolbox is an extension of the standard MATLAB version. The toolbox provides algorithms for optimization problems of any sizes and kinds. The software include linear programming, quadratic programming, binary integer programming, nonlinear optimization, nonlinear least squares, systems of nonlinear equations and multi-objective optimization. These functions can be used to compute an optimal solution subject to a set of constraints and boundaries. The toolbox is of high relevancy for the Gotland study where the power export occasions can be formulated as a minimization problem subject to transmission capacity constraints. The advantage of using MATLAB as a simulation tool is the high degree of freedom that is offered. Any data, variables or models are easily modifiable which is highly desired, especially for Gotland, having such unique characteristics in production, consumption and environmental set-up.

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2.5 Summarized Problem Illustration

The purpose and approach of the Thesis are summarized in the illustration below.

LONG-TERM SHORT-TERM

REQUIRES

PROBLEM CAUSE

SOLUTION?

AS TOOLBOX

DOMESTIC HOT WATER

SPACE HEATING

INDUSTRY OPERATION

STRATEGY

DEMAND RESPONSE

BATTERY SYSTEM

WIND CURTAIL.

SIMULATION

POWER MATCHER

MATLAB

ABSORB PROGNOSIS

ERRORS

MODELING

EXPORT OVERLOAD

200 MW INSTALLED

WIND CAPACITY

DATA COLLECTION

SCENARIO DESCRIPTION

Figure 2.13. Note that MATLAB was chosen as the most suitable software for this study.

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Chapter 3

Modeling Flexibility Tools

3.1 Demand-Response Participants

3.1.1 Detached House Flexibility

Every detached house has its own set of characteristic such as the house area, the number of windows, the exposure to solar radiation, heat losses through surrounding walls, etc. The difference in characteristics makes it hard to build an accurate model to simulate household consumption profiles. One approach explored in [24]

uses Markov chain methodology to establish an electricity consumption profile of a typical household. The model takes into account three consumption modules, space heating, domestic hot water and the use of electric appliances. Using this methodology one can produce synthetic electricity demand profiles. The model results were validated with empirical data for the consumption of 41 Swedish residents living in detached houses showing high statistical correlation with the actual consumption.

These results indicate that the mathematical model used for the three consumption modules can be used to simulate the consumption profile for detached houses on Gotland when the parameters are adjusted to reflect the conditions on the island.

The following subsections will present the mathematical model and the parameters used to simulate the electricity consumption activities of detached houses on Gotland.

Space Heating Model & Parameters

To use the space heating model proposed in [24], one first has to define detached house parameters. Similar parameters from the study in [24] were chosen to reflect detached house characteristics on Gotland. The parameter values correspond to those of an average Swedish detached house. They are summarized in Table 3.1.

These parameters are used to construct the space heating model. The indoor temperature of a detached house will deviate from the reference value due to the following reasons: space heating consumption Q_heat(t), outdoor temperature vari- ations T_out(t), solar radiation Q_sun(t), presence of occupants Q_occ(t), the use of

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Table 3.1. Model parameters for a detached house on Gotland [24].

Parameter Value Unit Description

A_{f loor} 100 m² Total floor area

A_roof 100 m² Total roof area

A_wall 79 m² Total wall area

A_door 4 m² Total door area

A_window 20 m² Total window area

Aside,window 5 m² Total side-window area

U_{f loor} 0.4 W/(m²· C^◦) Transmission factor of floor U_roof 0.25 W/(m²· C^◦) Transmission factor of roof U_wall 0.3 W/(m²· C^◦) Transmission factor of wall U_door 1.5 W/(m²· C^◦) Transmission factor of door U_window 3 W/(m²· C^◦) Transmission factor of window

d_height 2.5 m Height of one-floor house

V_house 250 m³ Volume of one-floor house

ρ_air 1.20 kg/m³ Density of air at room temperature

Cp_air 0.28 W/(kg · C^◦) Heat capacity factor of air at room temperature

αred 1 - Heat reduction factor

α_rc 0 - Heat recycling factor

N_vent 0.2 h⁻¹ Exchange rate of air

τ 100 h Time it takes to drop 3C^◦ during a cold snap [25]

electric appliances Q_app(t) and space heating losses Q_SH,loss(t). There are also two other sources of loss considered: the transmission losses of heat λ_trans and the ven- tilation losses λ_vent caused by the exchange of heated and surrounding air. These losses constitutes the inertia of the system and are calculated from the detached house parameters as followed:







λ_trans= ^P

j∈J

U_j· A_j

λ_vent= V_house· N_vent· Cp_air· (1 − α_rc)

(3.1) Where the set J includes all building components of the house except the side- window (the side-window is modeled as the window exposed to solar radiation).

The overall dynamics of the indoor temperature of a detached house on Gotland at time step t + 1 is described as:

T(t + 1) = T(t) +_{τ ·(λ} ¹

trans+λvent) · (Q_heat(t) + Q_sun(t) + Q_occ(t) + . . . [C^◦] Q_app(t) − Q_SH,loss(t))

(3.2) The expression _{τ ·(λ} ¹

trans+λvent) denotes the inertia of the system. The reader is referred to [25] for a better understanding on how the inertia of the system was determined. Having all time dependent variables in hourly resolution allows one

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3.1. DEMAND-RESPONSE PARTICIPANTS

to make predictions about the indoor temperature variation for the following hour.

Thereby, the relation between space heating consumption and indoor temperature becomes predictable. The following list will detail on how the different variables Q(t) from Equation 3.2 are calculated:

(i) The heat consumption Qheat(t) is equal to the consumed electricity for space heating P_SH(t). The minimum value that is consumed per household has been set to 0 W while the maximum consumption is calculated from the detached house parameters as:

P_SH,max= (λ_trans+ λ_vent) · (T_ref − T_{DU T}) [W] (3.3) Where T_ref is a reference indoor temperature and T_{DU T} the dimensioning winter temperature on Gotland [25]. Note that the heat consumption Q_heat(t) is the optimization variable that affects the indoor temperature variation.

(ii) The heat contribution from solar radiation Q_sun(t) at time t is expressed as:

Q_sun(t) = α_red· P_sun(t) · A^side_window [W] (3.4) Where P_sun(t) is the radiated sun heat per square meter for a detached house at time t (W/m²). The other variables in the expression are detached house parameters.

(iii) The heat contribution from house occupants Qocc(t) is assumed to be on average 100 W/person. It is also assumed that the average household in Sweden consist of 3 persons/household. The assumptions are based on the study in [24].

(iv) The heat contribution from electric appliances in the house Q_app(t) is dif- ficult to estimate, it is assumed that the consumed electricity is on average 380 W/household. The assumptions are based on the study in [24].

(v) The total space heating loss Q_SH,loss(t) of the system at time step t is expressed as:

Q_SH,loss(t) = (T(t) − T_out(t)) · (λ_trans+ λ_vent) [W] (3.5) Using the expressions formulated in (i)-(v) one can solve Equation 3.2 and thus estimate the indoor temperature at the next time step. Some of the expressions in (i)-(v) require outdoor temperature or solar radiation data for calculations. Table 3.2 summarizes the space heating parameters derived and the comfort interval for indoor temperature in a detached house based on the study made in [24].

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Table 3.2. Space heating parameters and base assumptions for a detached house on Gotland [24].

Variable Value Unit Description λ_trans 154.7 W/C^◦ Transmission losses λvent 14 W/C^◦ Ventilation losses

P_SH,min 0 kWh Minimum space heating consumption

P_SH,max 4.61 kWh Maximum space heating consumption T_ref 20 C^◦ Reference indoor temperature

T₀ 20 C^◦ Initial indoor temperature T_min 18 C^◦ Minimum indoor temperature T_max 22 C^◦ Maximum indoor temperature

T_{DU T} -9.7 C^◦ Dimensioning winter temperature on Gotland [26]

Domestic Hot Water Model

The domestic hot water module is modeled as a simplified tank with a water inflow of constant temperature T_inlet and a consumer controlled outflow of assumed constant temperature T_outlet. The outflow depends on every day activities such as showering, baths and hand-washing. It is assumed that the water level in the tank is always kept at a constant level, i.e., the water inflow equals outflow at all times. The water dynamics in the tank are more easily understandable for DR purposes when expressed in terms of energy produced and energy consumed. The energy produced in the tank is the energy provided by the boiler. The energy consumed from the tank is Q_drain,i(t) from showers, baths and hand-washing where i denotes either weekday or weekend activity. The tank is also drained on energy from losses, where Q_DHW,loss(t) denotes the losses from the surrounding walls of the tank. The water outflows are expressed in terms of consumed energy through the transformation in Equation 3.6:

( Q_drain,i(t) = V_{f low,i}(t) · Cp_water· (T_outlet− T_inlet) [W]

Q_DHW,loss(t) = λ_tank· (T_tank(t) − T_amb(t)) [W] (3.6) Where V_{f low,i}(t) denotes the drained water from the tank at time t in litres with i indicating whether it is a weekday or a weekend, Cp_water the heat capacity of water, λ_tank the insulation factor of the water tank and T_amb(t) the ambient air temperature at time t. The heat capacities in the water tank are calculated from Equation 3.7:







M_min(t) = V_tnk· Cp_water· (T_tnk,min− T_inlet) [W]

M_max(t) = V_tnk· Cp_water · (T_tnk,max− T_inlet) [W]

M_ref(t) = V_tnk· Cp_water· (T_tnk,ref − T_inlet) [W]

(3.7) Where V_tnk denotes the volume of the tank and T_tnk,min and T_tnk,max the minimum and maximum allowed tank temperatures. The model parameters and constants are summarized in Table 3.3.

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3.1. DEMAND-RESPONSE PARTICIPANTS

Table 3.3. Parameters and constants for domestic hot water model for a detached house on Gotland [24].

Variable Value Unit Description

T_min 60 C^◦ Minimum tank temperature

T_max 100 C^◦ Maximum tank temperature

T_ref 80 C^◦ Reference tank temperature

T_amb 20 C^◦ Ambient air temperature

T_inlet 10 C^◦ Inlet water temperature to tank

T_outlet 40 C^◦ Outlet water temperature from tank

V_tnk 300 L Volume of water tank

Cp_water 1.17 Wh/(kg C^◦) Heat capacity of water

λ_tnk 1 W/C^◦ Insulation factor of water tank

M_min 31.6 kWh Minimum heat capacity in water tank M_max 17.6 kWh Maximum heat capacity in water tank M_ref 24.6 kWh Reference heat capacity in water tank

P_boil,max 3 kWh Maximum boiler consumption capacity

The consumption of the domestic hot water tank P_boil(t) should be equal to the sum of the losses from the tank and the drained water at all time steps to maintain the tank temperature at reference level, i.e.

P_boil(t) = Q_DHW,loss(t) + Q_drain,i(t) (3.8) Note that energy drained can exceed the maximum boiler capacity P_boil,max. In that case the boiler consumes at maximum capacity, i.e. P_boil(t) = P_boil,max. Required Input Data

The detached house model requires input data for the calculation of some of the space heating and domestic hot water parameters. The required input data is presented in Table 3.4.

Table 3.4. Required input data for detached house model. Note that the resolution of the data is on hourly basis and that all vectors are of same length (minimum allowed length 24x1)

Data Input Consumption Activity Unit Description Q_sun(t) Space heating W Solar radiation

Q_occ(t) Space heating W Occupant heat contribution Q_app(t) Space heating W Electric appliance consumption

Q_SH,loss(t) Space heating W Heat losses

Qdrain,weekday(t) Domestic hot water W Weekday hot water drained Qdrain,weekend(t) Domestic hot water W Weekend hot water drained

Q_DHW,loss(t) Domestic hot water W Heat losses to surrounding tank walls

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Q_drain,i(t) and V_{f low,i}(t) are interchangeable and in some cases it is easier to get a hold of domestic hot water data in Watt. In that case the first expression in Equation 3.6 is omitted.

3.1.2 Industry Flexibility Cementa Consumption Model

The DR consumption of Cementa is assumed to be operational only during weekdays and day shift hours, i.e. hours between midnight and noon. The hourly DR consumption P_cementa(t) becomes:

( P_cementa(t) = 2.8 MW for all t = 1, . . . , 12

P_cementa(t) = 0 MW otherwise (3.9)

Where t denotes the hour of the day. Note that the industry participation will only occur if there is an export problem prognosis during day shift weekdays.

3.2 Battery Energy Storage System & Wind Curtailment

The BESS is modeled as a simple battery where the discharge of energy increases the power export due to more power flowing in the network. The charging on the other hand decreases the export as a consequence of consuming more power.The BESS has a capacity of 280 kWh and all power excess that is not absorbed by the BESS is cut through the wind curtailment scheme. The proposed operation strategy is summarized in flowchart 3.1.

The BESS operation will result in a number of battery cycles depending on daily conditions. A critical cycle consist of charging the battery close to it’s maximum capacity and fully discharging it in a very short time interval. Battery cycles, especially critical ones, have a damaging effect on the BESS by reducing its life expectancy. It is desired to have as low battery cycles as possible because of the high cost associated with replacing a BESS.

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3.2. BATTERY ENERGY STORAGE SYSTEM & WIND CURTAILMENT

No Yes

step step

No

No Start

Export problem after hour t?

Wind curtailment until export problem solved BESS fully

charged?

Charge BESS as much as

possible Discharge

BESS as much as possible while avoiding

new export problems

Export problem solved?

BESS empty?

Yes

No Yes t = t + 1

t <= 24 Stop

Yes No

Yes

Start at t=0

Figure 3.1. Proposed BESS and wind curtailment operation strategy.