Optimal Onshore Wind Power Integration Supported by Local Energy Storages Paper Number: 15PESGM1230

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1 1 Optimal Onshore Wind Power Integration Supported by Local Energy Storages Paper Number: 15PESGM1230 Christian Klabunde, Natalia Moskalenko, Pio Lombardi, Przemyslaw Komarnicki, Zbigniew Styczynski

2 2 Agenda 1. Motivation 2. Renewable Energy Act vs. Direct Marketing 3. Wind Forecast Errors 4. Energy Storage Usage 5. Economic Results 6. Conclusion

3 3 Motivation Central Power Supply: generation of electricity from controllable conventional power plants Decentral Power Supply: high amount of decentralized renewable energy sources generation of electricity is characterized by forecast errors higher effort in balancing load and generation high potential for energy storage systems (ESS)

4 4 Renewable Energy Act vs. Direct Marketing management premium intensive bonus to compensate risks energy source installation date location bonuses feed-in tariff development to obligatory direct marketing market premium electricity price difference between feed-in tariff and average market value market clearing price on the day ahead market [7] renewable energy act (EEG) scheme of direct marketing

5 5 Direct Marketing Risks Direct Marketing: offer forecasted generation on the day ahead market partially high forecast errors activating balancing power Risks and Solutions: paying the balancing power costs is necessary offer wind power generation on the intraday market balancing the forecast error BPC FE CF > 0 < 0 BZR TSO > 0 > 0 TSO BZR < 0 < 0 TSO BZR < 0 > 0 BZR TSO BPC balancing power costs FE forecast error CF cash flow BZR balance zone responsible [8]

6 6 Wind Forecast Errors forecast error not symmetrical mostly forecasted to high intraday forecast significantly more accurate [9], [10] offer generation/load on the intraday market is highly suggested forecast mean value standard deviation day ahead 0.92 % 7.5 % intraday 0.36 % 2.0 %

7 7 Intraday Forecast Error Example: Wind farm with an installed wind power of 45 MW Characteristics: 80 % of the minimal and maximal forecast errors are between MW 80 % of the necessary daily balancing energy is between MWh

8 8 Energy Storage Systems Considered Energy Storage Systems: adiabatic compressed air energy storage (ACAES) lead acid batteries (LA) lithium ion batteries (Li-ion) parameter ACAES LA Li-ion power costs 910 /kw 160 /kw 160 /kw capacity costs 3.5 /kwh 200 /kwh 500 /kwh operation costs 1.5 % CAPEX 50 ct/kwh 30 ct/kwh start up time 4 10 min < 1 min < 1 min Efficiency 70 % 80 % 90 % Lifetime 50 years 10 years 12 years typical use case Energy arbitrage primary control (as a part of a pool) primary control (as a part of a pool) [11], [12]

9 9 Energy Storage Model Optimization Method: rule based optimization typical use case Objective Function: minimizing balancing energy costs Constraints: minimal and maximal power energy start up time using ESS in addition to other use cases only consider operation costs energy storage system provided balancing energy constraints

10 10 Simulation Scenarios General Conditions: installed wind power: 45 MW forecast error, day ahead market price, intraday market price and balancing energy costs of 2013 scenario scen1 scen2 scen3 scen4 scen5 description marketing by the EEG-bonus marketing by direct energy selling direct energy selling supported by an ACAES direct energy selling supported by a LA battery direct energy selling supported by a Li-ion battery

11 11 Renewable Energy Act vs. Direct Marketing management premium market premium DES profit 370 t (ca. 8 %) higher than EEG profit electricity price intraday costs total costs for compensating forecast errors: 300 t (ca. 6.5 %) balancing energy ESS usage can improve the revenue

12 12 Without ESS vs. ESS Usage Conditions: used ESS: Lead Acid Battery rated power: 5 MW capacity: 20 MWh (only 10 MWh usable) cycling around the optimal state of charge is necessary charge on the intraday market operation costs: 0,3 /kwh balancing energy costs Intraday costs operation costs ESS usage reduces the total balancing energy costs by 40 %

13 Comparison of Used ESS 13

14 14 Conclusion reducing balancing energy costs is not a negligible use case for energy storage systems high flexible electrochemical energy storage systems meet the requirements on the best way lead acid batteries and lithium ion batteries proceed similar results but: high number of cycles lithium ion batteries > lead acid batteries

15 15 Literature [1] Styczynski, Z.A.; Orths, A.; Rudion, K.; Lebioda, A.; Ruhle, O.: Benchmark for an electric distribution system with dispersed energy resources. Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, art. no , pp , [2] Stötzer, M.; Hauer, I.; Richter, M.; Styczynski, Z.A.: Potential of demand side integration to maximize use of renewable energy sources in Germany. Applied Energy, 146, pp , [3] Lombardi, P.; Stötzer, M.; Styczynski, Z.A.; Orths, A.: Multi-criteria optimization of an energy storage system within a virtual power plant architecture. IEEE Power and Energy Society General Meeting, art. no , [4] Lombardi, P.; Powalko, M.; Rudion, K.: Optimal operation of a virtual power plant. IEEE Power and Energy Society General Meeting, PES '09, art. no , [5] Lombardi, P.; Styczynski, Z.A.; Sokolnikova, T.; Suslov, K.: Use of energy storage in Isolated Micro Grids. Proceedings Power Systems Computation Conference, PSCC 2014, art. no , [6] Lombardi, P.; Styczynski, Z.A.: Electric energy storage systems: Review and modelling. CIGRE 2011 Bologna Symposium - The Electric Power System of the Future: Integrating Supergrids and Microgrids, 7 p., [7] Staubitz, H.: The German Electricity Market Current State and Issues. Germany Trade & Invest, [8] 50Hertz Transmission GmbH: Model for Calculation of consistent Control Area comprehensive Balancing Energy Prices (rebap) taking into Account the Federal Network Agency Decision BK of the [9] 50Hertz Transmission GmbH: wind energy. [Online], Available: [10] Kloubert, M.; Schwippe, J.; Müller, S.C.; Rehtanz, C.: Analyzing the Impact of Forecasting Errors on Redispatch and Control Reserve Activation in Congested Transmission Networks. Proceeding of the IEE PowerTech Eindhoven 2015, art. no , [11] Sterner, M.; Stadler, I.: Energy Storage Systems Demand, Technologies, Integration. Springer-Verlag, Berlin Heidelberg, [12] VDI-report 2058: Electric Energy Storage System Key Technology for Energy-Efficient Applications. 2009

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