摘要
系统负荷的波动一定程度上限制了风、光发电并入电网,不利于对新能源的开发利用。分析了风光发电出力反调峰特性,提出利用储能装置对系统负荷进行削峰填谷,来减小系统负荷的波动幅度,拟合了风、光电出力与负荷特性,消纳更多新能源。建立了储能装置对系统负荷削峰填谷与相应配置储能容量间的数学模型,并以经济性为目标,储能的成本及寿命等问题为约束条件,综合储能带来的电量效益、环境效益、运行效益等,配置最优的储能容量,使储能达到经济实用效果。最后,以某区域风光互补发电系统配置储能容量实例进行对比分析,证明此储能配置数学模型的可行性。
Power load fluctuations limit the integration of wind-PV generation into the grid,which is not conducive to the development and utilization of new energy.This paper analyzes the inverse peak regulation characteristics of wind-PV power output,and puts forward the use of energy storage devices to cut the peak load and fill the valley load,and to reduce the fluctuation amplitude of the system load,fit the wind-PV output and power load characteristics to absorb more new energy.A mathematical model between the energy storage device’s peak load reduction of the system load and the corresponding configuration of the energy storage capacity is established.With economy as the goal,and with the cost and life of energy storage as constraints,considering the positive and negative benefits brought by energy storage,the optimal energy storage capacity is configured with the model to achieve economic and practical effect of the energy storage system.Finally,an example of configuring the energy storage capacity of a regional wind-solar hybrid power generation system is used for a comparative analysis to prove the feasibility of the mathematical model for energy storage configuration.
作者
罗庆
张新燕
罗君
刘闯
周二彪
LUO Qing;ZHANG Xinyan;LUO Jun;LIU Chuang;ZHOU Erbiao(School of Electrical Engineering,Xinjiang University,Urumqi 830008,Xinjiang,China;Xinjiang Xinneng Financial Leasing Co.,Ltd.,Urumqi 830000,Xinjiang,China;Power Dispatching Control Center,State Grid Xinjiang Power Co.,Ltd.,Urumqi 830063,Xinjiang,China;Economic and Technological Research Institute,State Grid Xinjiang Electric Power Co.,Ltd.,Urumqi 830001,Xinjiang,China)
出处
《电网与清洁能源》
2020年第2期91-97,共7页
Power System and Clean Energy
基金
国家自然科学基金资助项目(51367015,51667018)
国家国际科技合作专项资助项目(172013DFG61520)。
关键词
储能
削峰填谷
经济约束
接纳能力
最优容量
energy storage
peak cutting and valley filling
economic constraints
acceptance ability
optimal capacity
