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Optimization of Virtual Power Plant in Nordic Electricity Market

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Optimization of Virtual Power Plant in Nordic Electricity Market DEGREE PROJECT IN ELECTRICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2019 Optimization of Virtual Power Plant in Nordic Electricity Market JWALITH DESU KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE KTH Royal Institute of Technology Master Thesis Optimization of Virtual Power Plant in the Nordic Electricity Market Author: Jwalith Desu Supervisor: Dr. Mohammad Reza Hesamzadeh Examiner: Dr. Mohammad Reza Hesamzadeh A thesis submitted in fulfilment of the requirements for the degree of Master of Science in the Electricity Market Research Group (EMReG) School of Electrical Engineering October 2019 Declaration of Authorship I, Jwalith DESU, declare that this thesis titled, 'Optimization of Virtual Power Plant in the Nordic Electricity Market' and the work presented in it are my own. I confirm that: This work was done wholly or mainly while in candidature for a research degree at this University. Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any other institution, this has been clearly stated. Where I have consulted the published work of others, this is always clearly at- tributed. Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work. I have acknowledged all main sources of help. Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself. Signed: Date: i Abstract With the world becoming more conscious about achieving 1.5-degree scenario as promised by the most powerful economies of the world, much needed push was received by the renewable energy technology providers. This has led to an increased a share of energy production from renewables and a decrease in the fossil-based energy production with the overall energy production. As a result, a large share of inertia of the system is lost and a big challenge in the name of flexibility is presented to the world of energy. Virtual Power Plant is quite a novel and new concept to address the new generation challenge of flexibility and can offer various other benefits like competitivity,reliability, accessibility etc. In this thesis, a commercial virtual power plant is studied by developing a mixed integer linear model to emulate the trading for short term markets with the risk mea- sures in a Nordic Electricity Framework. Further, the developed model is implemented in a quite a new mathematical programming language known as \Julia". The model is implemented using a hypothetical portfolio consisting of a dispatchable unit, a battery system and a wind farm in the SE3 bidding zone of Sweden. An investigation on varia- tion of imbalance costs in three different modes also has been carried out, to demonstrate the advantage of such a virtual power plant concept in reducing the imbalance costs. Keywords: Virtual Power Plant, mFRR market, spot market, CVaR, risk measures, Stochastic Optimization, Nordic Electricity Market. Abstract F¨oratt uppfylla 1,5-gradersm˚aletsom beslutats av v¨arldensledande ekonomier har olika typer av f¨ornybar energiproduktion f˚attett stort uppsving. Detta har lett till ¨okad en- ergiproduktion fr˚anf¨ornybara k¨alloroch minskad energiproduktion fr˚anfossila k¨allor. F¨orelsystemen inneb¨aren h¨ogreandel f¨ornybar produktion minskad sv¨angmassaoch ¨okat behov av flexibilitet f¨oratt kompensera f¨orvariationen hos f¨ornybara energik¨allor. Virtuella kraftverk ¨arett nytt koncept f¨oratt tillgodose behovet av flexibilitet och kan ¨aven ge andra f¨ordelarsom konkurrenskraft och tillf¨orlitlighet. I denna uppsats stud- eras ett virtuellt kraftverk genom att utveckla en optimeringsmodell f¨oratt emulera handeln i elmarknader med riskm˚attinom ett ramverk f¨orden nordiska elmarknaden. Modellen implementeras i det nya programmeringsspr˚aket Julia. Modellen inneh˚aller en hypotetisk blandning av resurser best˚aendeav ett planerbart kraftverk, ett batter- isystem och en vindpark i elomr˚adetSE3 i Sverige. Balanseringskostnaderna i tre olika modeller unders¨oksf¨oratt visa potentialen hos det virtuella kraftverket att minska dessa kostnader. Nyckelord: Virtuellt kraftverk, mFRR marknad, spotmarknad, CVaR, riskm˚att,stokastisk optimering, nordiska elmarknaden Acknowledgements I take this opportunity to express my gratitude to everyone who have been associated with this thesis directly or indirectly. I want to thank the team at GreenLytics AB for giving me an opportunity to associate with them on their amazing journey in decarbonizing the economy.Especially my Super- visor, Sebastian Haglund El Gaidi, and his team to answer all my questions patiently. I would like to thank my parents, family and friends for their continuous support during the whole thesis. Finally, I want to thank my supervisor and examiner at KTH, Dr. Mohammad Reza Hesamzadeh, for guiding and trusting me through the thesis. ii Contents Declaration of Authorshipi Abstract i Abstract i Acknowledgements ii Contents iii List of Figuresv List of Tables vi Abbreviations vii Nomenclature ix Nomenclaturex Nomenclature xi 1 Introduction1 1.1 Background & Motivation...........................1 1.2 Existing Literature...............................4 1.3 Goal of the Study................................6 1.4 Thesis Structure................................7 2 Nordic Electricity Market8 2.1 Introduction...................................8 2.2 Day Ahead Market- ELSPOT.........................9 2.3 Intraday Market- ELBAS........................... 10 2.4 Nordic Balancing Concept........................... 10 2.4.1 Manual Frequency Restoration Reserve (mFRR) - Tertiary Reserve 12 2.4.2 Automatic Frequency Restoration Reserve (aFRR) { Secondary Reserve................................. 13 2.4.3 Frequency Containment Reserve (FCR) { Primary Reserve.... 15 iii Contents iv 2.4.3.1 Frequency Containment Reserve { Normal Operation.. 15 2.4.3.2 Frequency Containment Reserve { Disturbed Operation. 15 2.4.3.3 Pre-qualification, Reporting, Bidding & Procurement of FCR.............................. 16 2.5 Imbalance Settlement and Pricing...................... 17 3 Methodology 20 3.1 Introduction................................... 20 3.2 Model Assumptions.............................. 21 3.3 Model Description............................... 23 3.3.1 Modelling a Dispatchable unit.................... 24 3.3.2 Modelling of Flexible loads...................... 27 3.3.3 Modelling of Storage Unit....................... 28 3.3.4 Modelling of Stochastic Units..................... 29 3.3.5 Formulation of the Objective Equation of the VPP......... 29 3.4 Energy Balance Constraints.......................... 31 3.5 Other Constraints............................... 31 3.6 Selection of Scenarios............................. 34 3.7 Flow chart of the Stochastic Optimization Model.............. 36 3.8 Investigating the Imbalance Costs per MWh for different modes of VPP. 37 4 Case Study 39 4.1 Scope...................................... 39 4.2 Input Data................................... 40 4.3 Implementation and Results.......................... 46 4.4 Investigation of Imbalance Costs....................... 51 5 Closure 56 5.1 Summary.................................... 56 5.2 Recommendations for Future Possibilities.................. 57 A Linearization of the Quadratic Fuel Cost Function 59 B Codes 61 Bibliography 76 List of Figures 1.1 VPP.......................................3 2.1 MCP.......................................9 2.2 FCR....................................... 16 2.3 IB........................................ 18 3.1 scenario generation............................... 35 3.2 Bidding Startegy................................ 37 4.1 day ahead price................................. 40 4.2 day ahead price scenarios........................... 41 4.3 Upregulation prices............................... 41 4.4 Upregulation prices Scenarios......................... 42 4.5 Downregulation price.............................. 42 4.6 Downregulation price Scenarios........................ 43 4.7 mFRR prices.................................. 43 4.8 mFRR prices scenarios............................. 44 4.9 Wind Power Forcast Scenarios........................ 45 4.10 hour1...................................... 47 4.11 hour2...................................... 47 4.12 hour23...................................... 48 4.13 hour24...................................... 48 4.14 scenario1.................................... 49 4.15 scenario2.................................... 50 4.16 scenario9.................................... 50 4.17 scenario10.................................... 51 4.18 imbalance costs month............................. 53 4.19 imbalance costs month............................. 53 4.20 imbalance costs................................. 54 4.21 cumulative profit................................ 55 A.1 fuel cost..................................... 59 v List of Tables 1.1 Comparison of all the existing literature...................6 4.1 Calculation of imbalance costs in different modes of operation....... 54 vi Abbreviations API Application Programming Interface ARIMA AutoRegressive Integrated Moving Average BM Balancing Market BRP Balancing Responsible Party BSP Balancing Service Provider CHP Combined Heat and Power CVaR Conditional Value at Risk DER Distributed Energy Sources EMS Energy Management and System ENTSO European Network of Transmission
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