| 引用本文: | 张福民,崔海波,李占凯,等.基于改进NSGA-II算法的微网交互式多目标优化[J].电力系统保护与控制,2018,46(12):24-31.[点击复制] |
| ZHANG Fumin,CUI Haibo,LI Zhankai,et al.Interactive multi-objective optimization of microgrid based on improved NSGA-II algorithm[J].Power System Protection and Control,2018,46(12):24-31[点击复制] |
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| 基于改进NSGA-II算法的微网交互式多目标优化 |
| 张福民,崔海波,李占凯,姜含,马晨阳,刘明亮 |
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| (省部共建电工装备可靠性与智能化国家重点实验室河北工业大学，天津 300130 ;河北省电磁场与电器可靠性重点实验室河北工业大学，天津 300130) |
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| 摘要: |
| 针对微网在不同时间段运行期望的不同，提出了一种基于改进NSGA-II算法的微网交互式多目标优化策略。建立以运行成本和污染物排放量为优化目标的优化模型，将全天的能量管理问题以每小时为尺度分为24个相互关联的优化子问题，可以更为灵活地选择满足各时段运行期望的最优调度计划。为满足这24个优化子问题与全天优化目标间的耦合关系，提出一种交互式搜索策略协调各小时储能系统的运行。通过改进NSGA-II算法获得每小时的pareto前沿，并建立隶属函数从pareto前沿中选择出最理想的折衷解，依次解决全天的优化问题。算例仿真验证了该优化模型和策略的有效性。 |
| 关键词: 微网 改进NSGA-II 交互式搜索 多目标优化 能量管理 |
| DOI：10.7667/PSPC170910 |
| 投稿时间：2017-06-17修订日期：2017-07-24 |
| 基金项目:国家自然科学基金项目资助(51477040, 51677052);河北省自然科学基金项目资助(E2015202263, E2016202134);河北省科技计划项目资助(16211827) |
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| Interactive multi-objective optimization of microgrid based on improved NSGA-II algorithm |
| ZHANG Fumin,CUI Haibo,LI Zhankai,JIANG Han,MA Chenyang,LIU Mingliang |
| (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China) |
| Abstract: |
| According to the difference of operating expectations of microgrid at different periods, this paper proposes an interactive multi-objective optimization strategy of microgrid based on improved NSGA-II algorithm. An optimized model considering operation cost and emission as objectives is built, the problem is may virtually regarded as 24 associated hourly optimal energy management sub-problems to provide more flexibility for selecting hourly optimal scheduling plan. An interactive search strategy coordinates the operation of energy storage systems at each hour to implement the coupling relationship between 24 separate hourly optimal sub-problems and the all-day optimal objectives. Then through an improved NSGA-Ⅱalgorithm, Pareto-optimal fronts of each hour in turn are obtained, and the membership function is established to select compromise solutions from the Pareto-optimal fronts. The example simulation shows that the optimization model and strategy are effective. This work is supported by National Natural Science Foundation of China (No. 51477040 and No. 51677052) and Natural Science Foundation of Hebei Province (No. E2015202263 and No. E2016202134). |
| Key words: microgrid improved NSGA-II algorithm interactive search strategy multi-objective optimization energy management |
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