基于改进多目标骨干粒子群算法的电力系统环境经济调度
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贵州大学

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中图分类号:

TP273

基金项目:

国家自然科学基金资助项目(51907035);贵州省教育厅青年科技人才成长项目(黔教合KY字[2018]108);贵州大学引进人才科研项目(贵大人基合字[2017]16)


Environmental economic dispatch of power system based on improved bare-bone multi-objective particle swarm optimization algorithm
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Affiliation:

Guizhou University

Fund Project:

Project supported by the National Natural Science Foundation of China (No. 51907035), the Guizhou Education Department Growth Foundation for Youth Scientific and Technological Talents (No. QianJiaoHe KY Zi[2018]108, and the Scientific Research Foun-dation for the Introduction of Talent of Guizhou University (No. [2017]16).

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    摘要:

    针对粒子群优化算法种群多样性不足,易陷入局部寻优的问题,本文提出了一种基于改进多目标骨干粒子群优化算法(improved bare-bones multi-objective particle swarm optimization, IBBMOPSO)的电力系统环境经济调度的求解方法。IBBMOPSO采用一种搜索权重非线性递减策略改进骨干粒子群的位置更新模式,并在不同搜索阶段对最差粒子设计不同的位置更新策略,以平衡算法的全局搜索能力和局部搜索能力。IBBMOPSO根据粒子拥挤距离选择全局最优解,采用距离评价指标来选择折衷最优解。最后对6机IEEE30节点的标准测试系统进行仿真计算,并与其他算法进行对比分析,结果显示IBBMOPSO在解决电力系统环境经济调度问题上优于其他算法,具有良好的可行性和有效性。

    Abstract:

    Aiming at the insufficient population diversity of the particle swarm optimization algorithm and easy to fall into the problem of local optimization, this paper proposes a power system environment based on the improved bare-bones multi-objective particle swarm optimization (IBBMOPSO) The solution method of economic dispatch. IBBMOPSO adopts a non-linear decreasing strategy of search weight to improve the position update mode of the bare-bones particle swarm, and designs different position update strategies for the worst particles in different search stages to balance the algorithm"s global search ability and local search ability. IBBMOPSO selects the global optimal solution according to the particle crowding distance, and uses the distance evaluation index to select the compromise optimal solution. Finally, the 6-machine IEEE30-node standard test system is simulated and compared with other algorithms. The results show that IBBMOPSO is superior to other algorithms in solving power system environmental economic dispatching problems, and has good feasibility and effectiveness.

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历史
  • 收稿日期:2020-10-19
  • 最后修改日期:2021-01-21
  • 录用日期:2021-02-10
  • 在线发布日期: 2021-03-03
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