Determining the Optimal Strategy of Multi Virtual Power Plants using GA-GT

Document Type : Research Article

Author

University of Tehran

Abstract

In the present work, determining the optimal strategy of multi virtual power plants (VPPs) as well as the objective of maximizing profit through the multi-level control of VPPs are discussed by the micro-grid utilization center including virtual power plants. VPPs include renewable resources such as wind farms, photovoltaic, and conventional resources such as fuel cell, micro turbine, hybrid heat and power include gas and also waste heat boiler integrated with electrical resources and energy storage devices such as batteries. During the market competition process, the method of biding for each VPP is determined according to the optimal generation capacity of each VPP. In order to reduce the effects of uncertainty and unpredictability of the output power of wind farms, a more precise method for predicting using wavelet transform and artificial neural network as well as genetic algorithm method has been provided. Two operational models are described in this paper: 1) specify the optimal independent strategy of each VPP; and 2) The game theory model to specify the optimized strategy of 9-bus IEEE system including multiple virtual power plants as well as a model of load response according to pricing mechanism for time use and also removable electrical loads.

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References

[1] Sources of Greenhouse Gas Emissions, in, United States Environmental Protection Agency(EPA), 2019.
[2] M.C. Kocer, C. Cengiz, M. Gezer, D. Gunes, M.A. Cinar, B. Alboyaci, A. Onen, Assessment of battery storage technologies for a Turkish power network, Sustainability, 11(13) (2019) 3669.
[3] N. Mazzi, J. Kazempour, P. Pinson, Price-taker offering strategy in electricity pay-as-bid markets, IEEE Transactions on Power Systems, 33(2) (2017) 2175-2183.
[4] W. Pei, Y. Du, W. Deng, K. Sheng, H. Xiao, H. Qu, Optimal bidding strategy and intramarket mechanism of microgrid aggregator in real-time balancing market, IEEE Transactions on Industrial Informatics, 12(2) (2016) 587-596.
[5] A. Thavlov, H.W. Bindner, Utilization of flexible demand in a virtual power plant set-up, IEEE Transactions on Smart Grid, 6(2) (2014) 640-647.
[6] H.T. Nguyen, L.B. Le, Z. Wang, A bidding strategy for virtual power plants with the intraday demand response exchange market using the stochastic programming, IEEE Transactions on Industry Applications, 54(4) (2018) 3044-3055.
[7] A.T. Al-Awami, N.A. Amleh, A.M. Muqbel, Optimal demand response bidding and pricing mechanism with fuzzy optimization: Application for a virtual power plant, IEEE Transactions on Industry Applications, 53(5) (2017) 5051-5061.
[8] M. Kazemi, H. Zareipour, M. Ehsan, W.D. Rosehart, A robust linear approach for offering strategy of a hybrid electric energy company, IEEE Transactions on Power Systems, 32(3) (2016) 1949-1959.
[9] A. Baringo, L. Baringo, A stochastic adaptive robust optimization approach for the offering strategy of a virtual power plant, IEEE Transactions on Power Systems, 32(5) (2016) 3492-3504.
[10]P. Moutis, N.D. Hatziargyriou, Decision trees-aided active power reduction of a virtual power plant for power system over-frequency mitigation, IEEE Transactions on Industrial Informatics, 11(1) (2014) 251-261.
[11]H. Ding, P. Pinson, Z. Hu, J. Wang, Y. Song, Optimal offering and operating strategy for a large wind-storage system as a price maker, IEEE Transactions on Power Systems, 32(6) (2017) 4904-4913.
[12]R. Henríquez, G. Wenzel, D.E. Olivares, M. Negrete-Pincetic, Participation of demand response aggregators in electricity markets: Optimal portfolio management, IEEE Transactions on Smart Grid, 9(5) (2017) 4861-4871.
[13]M. Shafie-khah, E. Heydarian-Forushani, M.E.H. Golshan, M.P. Moghaddam, M.K. Sheikh-El-Eslami, J.P. Catalão, Strategic offering for a price-maker wind power producer in oligopoly markets considering demand response exchange, IEEE Transactions on Industrial Informatics, 11(6) (2015) 1542-1553.
[14]M. Shabanzadeh, M.-K. Sheikh-El-Eslami, M.-R. Haghifam, An interactive cooperation model for neighboring virtual power plants, Applied energy, 200 (2017) 273-289.
[15] M. Yazdani-Damavandi, N. Neyestani, M. Shafie-khah, J. Contreras, J.P. Catalao, Strategic behavior of multi-energy players in electricity markets as aggregators of demand side resources using a bi-level approach, IEEE Transactions on Power Systems, 33(1) (2017) 397-411.
[16]M. Rahimiyan, L. Baringo, Strategic bidding for a virtual power plant in the day-ahead and real-time markets: A price-taker robust optimization approach, IEEE Transactions on Power Systems, 31(4) (2015) 2676-2687.
[17]M. Zdrilić, H. Pandžić, I. Kuzle, The mixed-integer linear optimization model of virtual power plant operation, in: 2011 8th International Conference on the European Energy Market (EEM), IEEE, 2011, pp. 467-471.
[18]J.-B. Park, B.H. Kim, J.-H. Kim, M.-H. Jung, J.-K. Park, A continuous strategy game for power transactions analysis in competitive electricity markets, IEEE transactions on power systems, 16(4) (2001) 847-855.
[19]H. Pandžić, I. Kuzle, T. Capuder, Virtual power plant mid-term dispatch optimization, Applied Energy, 101 (2013) 134-141.
[20]H. Pandžić, J.M. Morales, A.J. Conejo, I. Kuzle, Offering model for a virtual power plant based on stochastic programming, Applied Energy, 105 (2013) 282-292. [21]M. Peik-Herfeh, H. Seifi, M. Sheikh-El-Eslami, Decision making of a virtual power plant under uncertainties for bidding in a dayahead market using point estimate method, International Journal of Electrical Power & Energy Systems, 44(1) (2013) 88-98.
[22] S. Sučić, T. Dragičević, T. Capuder, M. Delimar, Economic dispatch of virtual power plants in an event-driven service-oriented framework using standards-based communications, Electric Power Systems Research, 81(12) (2011) 2108-2119.
[23]E. Mashhour, S.M. Moghaddas-Tafreshi, Bidding strategy of virtual power plant for participating in energy and spinning reserve markets—Part II: Numerical analysis, IEEE Transactions on Power Systems, 26(2) (2010) 957-964.
[24]Z. Bie, P. Zhang, G. Li, B. Hua, M. Meehan, X. Wang, Reliability evaluation of active distribution systems including microgrids, IEEE Transactions on power systems, 27(4) (2012) 2342-2350.
[25]K. Dietrich, J.M. Latorre, L. Olmos, A. Ramos, Modelling and assessing the impacts of self supply and market-revenue driven Virtual Power Plants, Electric Power Systems Research, 119 (2015) 462-470.
[26]J.Z. Riveros, R. Donceel, J. Van Engeland, W. D’haeseleer, A new approach for near real-time micro-CHP management in the context of power system imbalances–A case study, Energy Conversion and Management, 89 (2015) 270-280.
[27] S.R. Dabbagh, M.K. Sheikh-El-Eslami, Risk-based profit allocation to DERs integrated with a virtual power plant using cooperative Game theory, Electric Power Systems Research, 121 (2015) 368-378.
[28] J.Z. Riveros, K. Bruninx, K. Poncelet, W. D’haeseleer, Bidding strategies for virtual power plants considering CHPs and intermittent renewables, Energy Conversion and Management, 103 (2015) 408-418
[29]Q. Zhao, Y. Shen, M. Li, Control and bidding strategy for virtual power plants with renewable generation and inelastic demand in electricity markets, IEEE Transactions on Sustainable Energy, 7(2) (2015) 562-575.
[30]Y. Wang, X. Ai, Z. Tan, L. Yan, S. Liu, Interactive dispatch modes and bidding strategy of multiple virtual power plants based on demand response and game theory, IEEE Transactions on Smart Grid, 7(1) (2015) 510-519.
[31]M.H. Shoreh, P. Siano, M. Shafie-khah, V. Loia, J.P. Catalão, A survey of industrial applications of Demand Response, Electric Power Systems Research, 141 (2016) 31-49.
[32]L. Ju, Z. Tan, H. Li, Q. Tan, X. Yu, X. Song, Multi-objective operation optimization and evaluation model for CCHP and renewable energy based hybrid energy system driven by distributed energy resources in China, Energy, 111 (2016) 322-340.
[33] S.R. Dabbagh, M.K. Sheikh-El-Eslami, Risk assessment of virtual power plants offering in energy and reserve markets, IEEE Transactions on Power Systems, 31(5) (2015) 3572-3582.
[34] S.M. Nosratabadi, R.-A. Hooshmand, E. Gholipour, Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy, Applied energy, 164 (2016) 590-606.
[35]E. Jafari, S. Soleymani, B. Mozafari, T. Amraee, Optimal operation of a micro-grid containing energy resources and demand response program, International Journal of Environmental Science and Technology, 15(10) (2018) 2169-2182.
[36]E. Jafari, Determining optimal strategy of a micro-grid through hybrid method of nash equilibrium–genetic algorithm, International Journal of Emerging Electric Power Systems, 20(3) (2019).
[37]E. Jafari, S. Soleymani, B. Mozafari, T. Amraee, Scenario-based stochastic optimal operation of wind/PV/FC/CHP/boiler/tidal/ energy storage system considering DR programs and uncertainties, Energy, Sustainability and Society, 8(1) (2018) 1-14.
AUT Journal of Electrical Engineering
Volume 52, Issue 2
December 2020
Pages 243-254

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