基于改进NSGA-II算法的微电网多目标优化调度
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扬州大学电气与能源动力工程学院

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TM731

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江苏省高等学校自然科学研究资助项(19KJB470038)


Multi-objective Optimal Dispatching of Microgrid Based on Improved NSGA-II Algorithm
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School of Electrical and Energy Power Engineering, Yangzhou University

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    摘要:

    为解决微电网优化调度中存在的经济和环保问题,提出一种改进NSGA-II算法,具体改进方式为采用拉丁超立方抽样产生初始种群并在精英策略中融入禁忌搜索。以微电网系统经济收益最大和污染物排放成本最小为优化目标,采用峰谷分时电价机制,构建包含5个调度变量的并网型微网模型。通过MATLAB仿真对比GNSGA-II、NSGA-II、MOEA/D算法的适应度收敛曲线,初步证明该改进算法收敛较快且收敛性较高;对比GNSGA-II算法和NSGA-II算法的Pareto前沿解集,并结合微电网算例分析基于最优折中解下的24h微源出力曲线,结果进一步证实其可行性与优越性。

    Abstract:

    In order to increase the revenue of the microgrid optimized dispatch and reduce its pollution cost, an improved Non-dominated Sorting Genetic Algorithm (NSGA-II) is proposed. The specific improvement method is to use Latin hypercube sampling to generate the initial population and incorporate taboo search into the elite strategy. Taking the maximum economic benefit of the microgrid system and the minimum pollutant emission cost as the optimization goal, the peak-valley time-of-use electricity price mechanism is adopted to construct a grid-connected microgrid model containing 5 dispatch variables. Comparing the fitness convergence curves of GNSGA-II, NSGA-II, and MOEA/D algorithms through MATLAB simulation, it is preliminarily proved that the improved algorithm converges faster and has higher convergence; compare the Pareto frontier solutions of GNSGA-II algorithm and NSGA-II algorithm Based on the analysis of the 24h micro-source output curve based on the optimal compromise solution, the results further confirm its feasibility and superiority.

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张继勇,柏宗元,史旺旺. 基于改进NSGA-II算法的微电网多目标优化调度[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-07-13
  • 最后修改日期:2021-08-16
  • 录用日期:2021-08-16
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