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邵振,邹晓松,袁旭峰,等. 基于改进多目标粒子群优化算法的配电网削峰填谷优化[J]. 科学技术与工程, 2020, 20(10): 3984-3989.
Shao Zhen,Yuan Xu-Feng,et al.Optimization of Peak load shifting in distribution network based on improved MOPSO algorithm[J].Science Technology and Engineering,2020,20(10):3984-3989.
基于改进多目标粒子群优化算法的配电网削峰填谷优化
Optimization of Peak load shifting in distribution network based on improved MOPSO algorithm
投稿时间:2019-07-19  修订日期:2020-01-09
DOI:
中文关键词:  储能系统  削峰填谷  多目标优化  改进粒子群算法  Pareto最优  模糊隶属度
英文关键词:energy storage system  peak load shifting  multi-objective optimization  improved particle swarm optimization  Pareto optimal  fuzzy membership
基金项目:(51667007)和贵州省科学技术([2019]1128)、([2018]5615)。资助
                 
作者单位
邵振 贵州大学 电气工程学院
邹晓松 贵州大学 电气工程学院
袁旭峰 贵州大学 电气工程学院
熊炜 贵州大学 电气工程学院
袁勇 贵州大学 电气工程学院
苗宇 贵州大学 电气工程学院
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中文摘要:
      电力系统削峰填谷优化作为负荷管理的重要手段,而储能系统在削峰填谷的功能显得尤为突出,以负荷峰谷差为目标的单目标优化已经无法全面评价储能系统在削峰填谷上的优势,为更好的体现储能系统在负荷管理上的优势,考虑以经济效益为调度目标的多目标优化问题(multi-objective optimization problem,MOP)显得尤为重要。基于此本文以负荷峰谷标准差和分时电价构建了配电网削峰填谷的多目标优化模型进行研究。提出基于拥挤距离排序的改进多目标粒子群算法(multi-objective particle swarm optimization,MOPSO),为改善算法陷入局部最优提出了变异机制的二次寻优,通过设置一定容量的外部档案存储非支配的Pareto最优解,最终获得Pareto最优前沿面。最后通过采用模糊隶属度法求解折中最优解,算例分析验证了本文所提模型的实用性和改进算法的有效性。
英文摘要:
      Power system peak load shifting optimization is an important means of load management, and the function of energy storage system in peak load shifting is particularly prominent. Single target optimization with load peak-to-valley difference has been unable to fully evaluate the energy storage system in the advantage of valley peak load shifting. in order to better reflect the advantages of energy storage system in load management, it is particularly important to consider the multi-objective optimization problem with economic benefits as the scheduling goal. Based on this paper, a multi-objective optimization model for peak load shifting of distribution network is constructed based on load peak-to-valley standard deviation and time-sharing electricity price. An improved multi-objective particle swarm optimization algorithm based on crowded distance sorting is proposed. The second optimization of the mutation mechanism is proposed to improve the algorithm"s fall into local optimum. By setting a certain volume of external archives to store the non-dominated Pareto optimal solution, Pareto is the most Excellent frontier. Finally, the fuzzy membership method is used to solve the optimal solution. The example analysis shows the practicability of the proposed model and the effectiveness of the improved algorithm.
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