多交易模式下混合式抽水蓄能电站优化调控策略OA北大核心CSTPCD
Optimization Scheduling Strategy for Hybrid Pumped-Storage Power Station Under Multi-Transaction Modes
"双碳"目标下,实现常规水电与抽水蓄能二者功能结合的混合式抽水蓄能电站是水力发电的新趋势,而如何制定合理的调控策略是混合式抽水蓄能适应电力市场发展的关键难点.因此,提出了一种适用于混合式抽水蓄能电站的多交易运行模式,同时根据常规水电与抽水蓄能的特点提出了混合式抽蓄电站内部协同调控方法.多交易模式下混合式抽水蓄能的常规水电部分实行峰谷电价结算、抽水蓄能部分实行两部制电价结算,同时允许电站保留容量电费参与日前现货市场出清.基于该运行模式和调控方法,以混合式抽水蓄能电站利润最大化和社会福利最大化为目标提出了双层优化调控模型.最后通过算例分析证明了所提模型的有效性,同时分析结果表明所提混合式抽水蓄能调控策略有利于实现水电资源的合理利用,对比不同单一交易运行模式电站利润至少提高1.2%.
Under the"Dual Carbon"target,the hybrid pumped storage power station combining conventional hydropower with pumped storage functions is a new trend in hydropower generation.Developing rational scheduling strategies is a key challenge for hybrid pumped storage adaptation to the electricity market.Therefore,a multi-transaction operation mode suitable for hybrid pumped storage power station is proposed,and an internal collaborative control method for the station is proposed based on the characteristics of conventional hydropower and pumped-storage.The conventional hydropower part implements the peak-valley price mechanism,while the pumped storage part implements the two-part tariff mechanism,allowing the station to retain capacity price and participate in the spot market clearing.Based on this operating mode and regulation method,a bi-level optimization scheduling model is proposed to maximize power station profits and social welfare.Finally,the effectiveness of the proposed model is verified through case studies,and the results show that the proposed model is conducive to achieving the rational utilization of hydropower resources.Compared with different single transaction operation modes,the profit of power station increases by at least 1.2%.
王玥瑶;延肖何;刘念
华北电力大学新能源电力系统国家重点实验室,北京 102206
动力与电气工程
混合式抽水蓄能电站两部制电价现货市场优化调度
hybrid pumped-storage power stationtwo-part tariffspot marketoptimize scheduling
《南方电网技术》 2024 (005)
51-61 / 11
国家重点研发计划资助项目(2021YFB2400700);国家自然科学基金青年科学基金项目(52107090). Supported by the National Key Research and Development Program of China(2021YFB2400700);the National Natural Science Foundation for Young Scientists of China(52107090).
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