A bi-Level collaborative optimization strategy for power quality in distribution networks based on fuzzy triple black hole multi-objective optimization algorithm

IF 5.9 Q2 ENERGY & FUELS
Xiaohui Yang, Jiajing Xu, Chilv Wu, Lingjun Guo, Zhicong Wang, Rui Zhong, Zekai Tu, Peng Yang
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引用次数: 0

Abstract

With the large-scale integration of renewable energy units and electric vehicles (EVs) into distribution networks, enhancing the power quality of these networks has emerged as a critical issue requiring immediate attention. Meanwhile, existing solution methods are inadequate for meeting the multi-objective optimization needs of distribution networks. This study establishes a bi-level collaborative optimization strategy for improving power quality in distribution networks. Specifically, the upper planning tier aims to minimize comprehensive costs through multi-component collaborative planning. The lower operational tier, based on the comprehensive performance evaluation decision model (CPEDM), conducts coordinated scheduling of multiple components by considering both economic benefits and power quality indicators. Furthermore, a fuzzy triple black hole multi-objective optimization algorithm (MOFTBH), which boasts high solution quality, uncertainty handling capabilities, and high adaptability, is developed and employed to solve the bi-level collaborative model. The study focuses on the IEEE-33 system as the research subject, leveraging the MOFTBH for analysis. Simulation results indicate that the optimization strategy presented in this study improves economic benefits and power quality by 45.43% and 19.90%, respectively, compared to the case without any optimization. Specifically, indices such as voltage deviation, voltage fluctuation, and harmonic distortion have improved by 39.01% , 127.45% and 113.14% , MOFTBH demonstrates a 30% faster Pareto front convergence rate compared to benchmark algorithms, with a 25% improvement in solution set uniformity. Under equivalent iteration counts, the objective function values show an optimization range of 18.7%–23.4%. This planning model aims to provide intelligent and green strategies for future smart grid construction and facilitate the commercial expansion of distribution network operators.
基于模糊三黑洞多目标优化算法的配电网电能质量双级协同优化策略
随着可再生能源机组和电动汽车在配电网中的大规模集成,提高配电网的电能质量已成为一个迫切需要关注的关键问题。同时,现有的求解方法无法满足配电网的多目标优化需求。本研究建立了一种改善配电网电能质量的双层协同优化策略。具体而言,上层规划层旨在通过多组件协同规划使综合成本最小化。下层运行层基于综合性能评价决策模型(CPEDM),综合考虑经济效益和电能质量指标,对多部件进行协调调度。在此基础上,提出了求解质量高、不确定性处理能力强、适应性强的模糊三重黑洞多目标优化算法(MOFTBH),并将其应用于双层协同模型的求解。本研究以IEEE-33系统为研究对象,利用MOFTBH进行分析。仿真结果表明,与未进行优化的情况相比,该优化策略的经济效益和电能质量分别提高了45.43%和19.90%。具体而言,MOFTBH算法的电压偏差、电压波动和谐波失真等指标分别提高了39.01%、127.45%和113.14%,Pareto front收敛速度比基准算法提高了30%,解集均匀性提高了25%。在等效迭代次数下,目标函数值的优化范围为18.7% ~ 23.4%。该规划模型旨在为未来的智能电网建设提供智能化、绿色化的策略,促进配电网运营商的商业扩张。
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来源期刊
Renewable Energy Focus
Renewable Energy Focus Renewable Energy, Sustainability and the Environment
CiteScore
7.10
自引率
8.30%
发文量
0
审稿时长
48 days
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