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光储微网系统多目标协调控制策略 被引量:21

Multi-Objective Coordinated Control Strategy for Photovoltaic and Energy-Storage Microgrid System
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摘要 基于光伏储能+电采暖的光储微网系统可以有效解决西部农村社区清洁能源供应量不足问题。为了提高光储微网系统的能量控制效果和工程实用性能,该文通过构建包含电力费用、储能系统荷电状态(SOC)、供暖舒适度等变量的多目标函数,提出一种计算量较低的储能与电热负荷多目标协调控制策略。为了简化目标函数求解过程,引入求导和单调性分析等优化方法。为了降低系统计算量,以住宅建筑保温性能为评价标准,提出一种带宽能量控制模式,降低了被控单元的控制频率,提高了算法的工程应用价值。搭建Matlab/Simulink仿真平台,依据西部青海省某示范光储微网系统实际数据,仿真对比分析基于传统能量控制策略和基于多目标协调控制策略的光储微网系统性能,验证该文所提控制策略的有效性和优越性。 The problem of the clean energy shortage in western rural communities can be solved using photovoltaic and energy-storage microgrid system,which is composed of photovoltaic energy storage and electric heating.To improve the energy control effect and engineering practical performance of the photovoltaic and energy-storage microgrid system,a multi-objective function is constructed in this paper,the considered variables include power cost,energy storage system SOC,heating comfort,etc.And then a multi-objective coordinated control strategy is proposed.In order to simply the solution process of objective function,derivation and monotonic analysis are adopted.In order to reduce the computation of the system,a new bandwidth energy control mode was proposed based on insulation performance of residential,which can reduce the unit control frequency and improve the algorithm engineering application value.Finally,the simulation platform is established by Matlab/Simulink,the performance of photovoltaic and energy-storage microgrid system utilizing traditional EMS energy control strategy and multi-objective coordinated control strategy are analyzed based on actual data of a demonstration photovoltaic and energy-storage microgrid system in western Qinghai Province,and the effectiveness and superiority of the proposed control strategy are verified.
作者 郭立东 雷鸣宇 杨子龙 王一波 许洪华 Guo Lidong;Lei Mingyu;Yang Zilong;Wang Yibo;Xu Honghua(University of Chinese Academy of Sciences,Beijing 100149 China;Institute of Electrical Engineering China Academy of Science,Beijing 100190 China)
出处 《电工技术学报》 EI CSCD 北大核心 2021年第19期4121-4131,共11页 Transactions of China Electrotechnical Society
基金 国家重点研发计划项目(2018YFB1503004) 青海省重大科技专项(2019-GX-A9)资助。
关键词 光储微网系统 电采暖 多目标协调控制策略 带宽能量控制模式 Photovoltaic and energy-storage microgrid system electric heating multi-objective coordinated control strategy bandwidth energy control mode
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  • 1GB/T 12325-2008.电能质量 供电电压偏差[S]. 2008
  • 2Logenthiran T, Srinivasan D, Khambadkone A M.Multi-agent system for energy resource scheduling ofintegrated microgrids in a distributed system [J]. Electric Power Systems Research, 2011, 8 1 (1 ) : 138-148.
  • 3Wang Jianhui, Zhou Zhi, Botterud A. An evolutionarygame approach to analyzing bidding strategies inelectricity markets with elastic demand [J]. Energy,2011, 3 6 (5 ) : 3459-3467.
  • 4Langary D, Sadati N, Ranjbar A M. Direct approach incomputing robust Nash strategies for generating companiesin electricity markets [J]. Electrical Power and EnergySystems, 2014, 5 4 (5 4 ) : 442453.
  • 5Kazemi M, Ivatloo B M, Ehsan M. Risk-based biddingof large electric utilities using information gap decisiontheory considering demand response [J]. Electric PowerSystems Research, 2014, 1 1 4 (3 ) : 86-92.
  • 6Nojavan S, Zare K, Ashpazi M A. A hybrid approachbased on IGDT-MPSO method for optimal bidding strategyof price-taker generation station in day-ahead electricitymarket [J]. Electrical Power and Energy Systems,2015, 6 9 : 335-343.
  • 7Zakariazadeh A , Jadid S. Smart microgrid energy andreserve scheduling with demand response using stochasticoptimization[J]. Electrical Power and Energy Systems,2014, 6 3 (1 2 ) : 523-533.
  • 8Mazidi M, Zakariazadeh A , Jadid S, et al. Integratedscheduling of renewable generation and demand responseprograms in a microgrid [J]. Energy Conversion andManagement, 2014, 8 6 (1 0 ) : 1118-1127.
  • 9Handschin E, Slomski H. Unit commitment in thermalpower systems with long-term energy constraints [J]. IEEETransactions on Power Systems, 1990, 5 (4 ) : 1470-1477.
  • 10Green R G, Wang Lingfeng, Alam M. Training neuralnetworks using central force optimization and particleswarm optimization : insights and comparisons [J]. ExpertSystems with Applications, 2012, 3 9 (1 ) : 555-563.

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