Competitive Pricing Strategy of Wind-Solar-Fire Coupling System in Monthly Concentrated Market Considering the Uncertainty of Renewable Energy
DOI:
https://doi.org/10.13052/spee1048-5236.4322Keywords:
Coupling system, electricity markets, bidding strategies, monthly centralized bidding, market clearedAbstract
Monthly centralized bidding is a key link in the transition from annual bilateral trading to spot trading, the research object of this paper is the multi-type power system (coupling system) which is integrated and coupled by thermal power and renewable energy under the same grid point, from the market point of view, this paper discusses its competitive strategy and revenue in the monthly centralized bidding market. First, an outer-level market clearing model that adapts to the participation of the coupling system is constructed to maximize the clearing in terms of social welfare. Secondly, considering the forecast error of scenery, the optimization model of the inner coupling system is established to analyze the cost of the coupling system, and the increment of the coupling system is evaluated quantitatively. Finally, a two-layer optimization model for coupling system to participate in the monthly centralized bidding market is formed, and then the optimal operation strategy of coupling system is studied. The simulation verification of the calculation example shows that participating in the monthly centralized bidding transaction in the mode of the coupling system will increase the income of each of the scenery and fire, the proposed coupling system model promotes changes in the energy structure of the power market, driven by improving the overall economic benefits, ensuring the economic benefits of traditional units and expanding the scope of the renewable energy market, so as to provide electricity to renewable energy and thermal power to improve auxiliary services. The development of the situation provides new ideas for the large-scale grid-connected consumption of new energy.
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Shu Yinbiao, Zhang Zhigang, Guo Jianbo, Zhang Zhengling. Study on key factors and solution of renewable energy accommodation[J]. Proceedings of the CSEE, 2017, 37(01):1–9 (in Chinese).
Zhang Yan, LÜ Quan, Zhang Na, Wang Haixia, Liu Rao, Sun Hui. Cooperative operation of power-heat regulation resources for wind power accommodation[J]. Power System Technology, 2020, 44(04): 1350–1362 (in Chinese).
Li Xingmei, Zhong Zhiming, Yan Jie. Flexibility Reformation Planning of Thermal Power Units with Large-scale Integration of Wind Power[J]. Automation of Electric Power Systems, 2019, 43(03):51–58 (in Chinese).
Lin Li, Gu Jia, Wang Qian. Optimal Dispatching of Combined Heat-power System Considering Characteristics of Thermal Network and Thermal Comfort Elasticity for Wind Power Accommodation[J]. Power System Technology, 2019, 43(10):3648–3661 (in Chinese).
Ai Xin, Zhou Shupeng, Zhao Yuequn. Research on optimal operation and bidding strategy of power system with virtual power plants[J]. Proceedings of the CSEE, 2016, 36(23):6351–6362+6600 (in Chinese).
Ma Chunyan, Dong Chunfa, Lv Zhipeng, Duan Qing, Wang Kunyu, Ai Xin, Li Yuling. Short-Term Trading and Optimal Operation Strategy for Commercial Virtual Power Plant Considering Uncertainties[J]. Power System Technology, 2016, 40(05):1543–1549 (in Chinese).
Zhang, G, et al. Bidding strategy analysis of virtual power plant considering demand response and uncertainty of renewable energy[J]. Iet Generation Transmission & Distribution, 2017. 11(13):3268–3277.
Ai Qian, Zhang Jian. Optimization bidding strategies of microgrids based on multi-agent system[J]. Power System Technology, 2010, 34(02):46–51 (in Chinese).
Kong Xiangyu, Zeng Yi, Lu Ning, Yan Jiahong, Hu Qian. Optimal Bidding Management for Agent-based Microgrid Operation[J]. Proceedings of the CSEE, 2017, 37(06):1626–1634.
Ma Tiannan, Peng Lilin, Du Ying, Gou Quanfeng, Wang Chao, Guo Xiaofan. Competition game model for local multi-microgrid market based on block chain technology and its solution algorithm[J]. Electric Power Automation Equipment, 2018, 38(05):191–203.
Fang Yuchen, Zhao Shuqiang. Joint optimal operation and bidding strategies of concentrating solar power plants with wind farms[J]. Proceedings of the CSEE, 2020, 40(01):39–49+372 (in Chinese).
Xiufeng L, Lulu Z, Jizhong Z, et al. Research on Wind & Thermal Power Joint Bidding Strategy Considering Penalties for Wind Power’s Bidding Deviation[C]// 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2018.
Laia, R, et al. Bidding strategy of wind-thermal energy producers[J]. Renewable Energy, 2016. 99:673–681.
Xie Chang, Wang Beibei, Zhao Shengnan, Tan Jian, Huang Junhui, Xie Zhenjian. Equilibrium solution for electricity market based on bi-level particle swarm optimization algorithm[J]. Power System Technology, 2018, 42(04):1170–1177 (in Chinese).
Yang Yan, Chen Haoyong, Zhang Yao, et al. Computing equilibrium for electricity market using solving system of polynomial equations algorithm[J]. Proceedings of the CSEE, 2010, 30(25):108–116 (in Chinese).
Peng Chunhua, Qian Kun, Yan Junli. A bidding strategy based on differential evolution game for generation side in power grid integrated with renewable energy resources[J]. Power System Technology, 2019, 43(06):2002–2010 (in Chinese).
Zhang Xiaodong, Gao Bo, Song Zhiping. Research on bidding strategy of power generation enterprises[J]. Proceedings of the CSEE, 2004 (09):157–161 (in Chinese).
Tian Jinyu, Wang Le. Research on coping analysis of load curve trading under basic rules of medium and long term electricity transactions[J]. Electric Power Science and Engineering, 2019, 35(02):7–13 (in Chinese).
Chen Siyuan, Wang Bo, Wang Jiali, Lin Gang, Wang Yajun. Spread-back Mechanism Based Simulation and Parameter Impact Analysis of Monthly-centralized Electricity Market[J]. Automation of Electric Power Systems, 2018, 42(16):111–118+235 (in Chinese).
Jing Zhaoxia, Zhu Jisong. Simulation experiment analysis of market rules for monthly electricity centralized bidding[J]. Automation of Electric Power Systems, 2017, 41(24):42–48 (in Chinese).
Zhu Jisong, Research on the assessment of monthly centralized bidding electricity market mechanism under the background of new electric power reform[D]. South China University of Technology, 2018 (in Chinese).
Wang Shengyu. Research on the theory and method of medium and long term trading decision of power products in power generation enterprises[D]. North China Electric Power University (Beijing), 2019 (in Chinese).
Haghighat H, Seifi H, Kian A R. Gaming analysis in joint energy and spinning reserve markets[J]. IEEE Transactions on Power Systems, 2007, 22(4):2074–2085.
Zhang Xiaodong, Gao Bo. Simulation analysis of regional electricity spot market based on supply function equilibrium model[J]. Power System Technology, 2005 (13):80–84 (in Chinese).
Karmarkar N. A new polynomial-time algorithm for linear programming[C] // Proceedings of the Sixteenth Annual ACM Symposium on Theory of Computing, 1984:302–311.
Yan Xiujuan, Wang Yongli, He Guoping. A Survey on the Active Set Methods for Box Constrained Optimization[J]. Mathematics in Practice and Theory, 2012, 42(03):100–107 (in Chinese).
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