摘要
相变储能系统作为一种新型的具备高弹性、高可靠性与绿色性的分布式储能形式,能够以柔性可调负荷形式为电网提供灵活可靠的辅助服务。为进一步挖掘相变储能系统的应用潜力,提出一种基于共享聚合相变储能系统的区域联合削峰填谷策略。首先,构建分布式相变储能单元的聚合可控模型,在此基础上,设计一种基于共享相变储能的区域联合控制可行架构;然后,在非合作博弈、合作博弈等不同商业模式下,建立区域内及区域间的联合削峰填谷控制策略;最后利用量子粒子群算法对所提控制策略进行有效求解。算例仿真结果验证所提策略在降低负荷方差及区域电力系统运行成本等方面的优越性,可作为电网辅助服务的一种新手段。
As a distributed energy storage with high flexibility,high reliability,and a green economy,the energy storage system with phase change materials can provide flexible and reliable auxiliary services for the grid as a kind of adjustable load.For the purpose of further investigating the application potential of distributed energy storage systems with phase change material,a kind of regional-joint peak-load shifting strategy is presented based on the aggregated system with shared phase change energy storage.Firstly,an aggregated controllable model for units of distributed phase change material energy storage is proposed.After that,a feasible regional-joint control architecture based on shared phase change energy storage is designed.Then,a joint peak-load shifting strategy of intra-regional and inter-regional joint peak-shaving and valley-filling control strategies is established under different business models such as the non-cooperative game and the cooperative game.Finally,the proposed control strategy is effectively solved by the quantum particle swarm algorithm.The simulation results verify the feasibility and effectiveness of the proposed joint peak-shaving strategy in reducing the load variance and regional economic cost.The proposed method is also a new approach to grid auxiliary service.
作者
张超
贾长杰
何文俊
钟泰军
冯忠楠
ZHANG Chao;JIA Changjie;HE Wenjun;ZHONG Taijun;FENG Zhongnan(Power China Hubei Electric Engineering Co.,Ltd.,Wuhan 430040,China;State Key Laboratory of Advanced Electromagnetic Engineering and Technology,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《电力科学与技术学报》
CAS
北大核心
2022年第5期25-34,共10页
Journal of Electric Power Science And Technology
基金
湖北省电力勘测设计院科技项目(K2020-2-02)。
关键词
相变储能
聚合储能
共享储能
区域联合削峰填谷
量子粒子群优化
phase change material energy storage
aggregated storage
shared energy storage
regional-joint peak-load shifting
quantum particle swarm optimization