摘要
电力储能系统接入电网在保证电力系统安全稳定运行方面具有诸多优势,执行储能系统调度策略能够实现削峰填谷,并从中获得经济效益。在不同的分时电价下,储能系统调度策略获得的经济效益往往具有差异性,因此定量研究不同分时电价下储能系统调度策略的效益潜力,对提高电力系统的整体经济效益具有重要意义。建立了考虑多重分时电价的储能系统调度策略效益潜力评估模型,量化储能系统调度策略在多重分时电价下的经济效益。以2018年某地的实际负荷数据为分析对象,采用改进粒子群优化算法验证所提储能系统调度策略的有效性,分析结果表明,所提储能系统调度策略效益潜力评估模型对提高电力系统的经济效益具有实用价值。
The grid-connection of electric ESS(Energy Storage System)has many advantages in ensuring the safe and stable operation of power system.The implementation of ESS scheduling strategy can realize peakload shifting and obtain economic benefits from it.Under different TOU(Time-Of-Use)electricity prices,the economic benefits of ESS scheduling strategy are often different.Therefore,it is of great significance to quantitatively study the potential benefit of ESS scheduling strategy under different TOU electricity prices to improve the overall economic benefits of power system.The potential benefit evaluation model of ESS scheduling strategy considering multiple TOU electricity prices is established to quantify the economic benefits of ESS scheduling strategy under multiple TOU electricity prices.Taking the actual load data of a certain place in 2018 as the analysis object,the improved particle swarm optimization algorithm is adopted to verify the validity of the proposed ESS scheduling strategy.The analysis results show that the proposed potential benefit evaluation model of ESS scheduling strategy is of practical value to improve the economic benefits of power system.
作者
杨贺钧
时瑞廷
马英浩
马静
沈玉明
YANG Hejun;SHI Ruiting;MA Yinghao;MA Jing;SHEN Yuming(Anhui Province Key Laboratory of Renewable Energy Utilization and Energy Saving,Hefei University of Technology,Hefei 230009,China;Institute of Economy and Technology,State Grid Anhui Electric Power Co.,Ltd.,Hefei 230071,China)
出处
《电力自动化设备》
EI
CSCD
北大核心
2021年第10期130-137,共8页
Electric Power Automation Equipment
基金
国家自然科学基金资助项目(51607051)
中央高校基本科研业务费专项资金资助项目(PA2021KCPY0053)
安徽省自然科学基金资助项目(1908085QE237)。
关键词
分时电价
电力储能系统
调度策略
效益潜力
粒子群优化算法
time-of-use electricity prices
electric energy storage system
scheduling strategy
potential benefit
particle swarm optimization algorithm