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作 者:徐桂芝 梁丹曦 宋洁 康伟 赵雪莹 郜捷 XU Guizhi;LIANG Danxi;SONG Jie;KANG Wei;ZHAO Xueying;GAO Jie(Global Energy Interconnection Research Institute Co.,Ltd,Changping District,Beijing 102211,China)
机构地区:[1]全球能源互联网研究院有限公司,北京市昌平区102211
出 处:《现代电力》2023年第6期939-946,共8页Modern Electric Power
基 金:国家电网公司科技项目(百千瓦级氢利用关键技术研究及示范)(5400-201919487A-0-0-00)。
摘 要:为了实现风–光–氢–储综合能源系统实时跟踪电网日前发电计划的优化调度,并考虑峰谷价电价规则,结合储能系统时空位移特性,使综合能源系统制氢、氢燃料电池发电经济效益最高,首先建立了风–光–氢–储各子系统数学模型及以电力经济最优为目标的日前优化调度模型,综合考虑系统功率平衡、运行状态、安全状态等指标约束,设定氢储系统优先出力对应的权重系数,采用智能群优化算法对系统目标函数进行迭代优化求解;最后根据某地风光负荷数据及分时电价信息,对所提优化调度方法在Matlab中进行分析验证,得到风–光–氢–储综合能源系统各子系统功率分配情况以实现最优经济性以及对电网日前调度指令的实时跟踪。The objective of this paper is to realize the optimal scheduling of wind-photovoltaic-HESS-BESS integrated energy system for real-time tracking of power grid day-ahead generation schedule.Under the peak-valley electricity price mechanism,combined with the spatial-temporal displacement characteristics of the energy storage system,the comprehensive energy system has the highest income from controlled hydrogen production and hydrogen fuel cell power generation.In this paper,we established the mathematical model of subsystems of wind,photovoltaic,HESS and BESS,and proposed a day-ahead optimal scheduling model aiming at the optimal economic benefits of the power system.Comprehensively considering the constraints of system power balance,operation state and safety state,the paper set the weight coefficient corresponding to the priority output of the HESS and the BESS.The intelligent swarm optimization algorithm was used to solve the system objective function iteratively.Finally,according to the wind data,photovoltaic data,load data and time-of-use price information of a certain place,the proposed optimal scheduling method was analyzed and verified in Matlab,and the power distribution of each subsystem of the wind-photovoltaic-HESS-BESS integrated energy system was obtained to achieve the optimal economy and real-time tracking of the day-ahead dispatching instructions of the power grid.
关 键 词:风–光–氢–储综合能源系统 日前经济调度 氢储能 智能群优化算法 多能耦合
分 类 号:TM72[电气工程—电力系统及自动化]
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