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作 者:付磊 李自成[1] 时悦 李永康 刘江 FU Lei;LI Zicheng;SHI Yue;LI Yongkang;LIU Jiang(School of Electrical Information Wuhan Institute of Technology,Wuhan 430205,China;Hubei Electric Power Equipment Co.,Ltd.,Wuhan 430035,China)
机构地区:[1]武汉工程大学电气信息学院,湖北武汉430205 [2]湖北省电力装备有限公司,湖北武汉430035
出 处:《电气应用》2025年第3期15-22,共8页Electrotechnical Application
基 金:湖北省高等学校中青年科技创新团队项目(T2022012);武汉市重点研发计划项目(2023010402010584)。
摘 要:针对光储充一体化直流微电网投资成本高、系统稳定性不足等问题,对其进行光储容量优化配置研究。首先,采用蒙特卡洛法构建了电动汽车(Electric Vehicle,EV)充电负荷预测模型,以系统安全稳定为约束条件,搭建了象征经济效益的年净成本和象征系统稳定性的功率偏差率为目标函数的容量优化配置模型。然后,针对鲸鱼优化算法(Whale Optimization Algorithm,WOA)求解具有非零解的优化配置模型时搜索能力不足且寻优精度低的问题,提出了融合准对立学习机制、自适应权重、非线性收敛因子、高斯差分变异及贪婪选择的多策略改进鲸鱼优化算法(Multi-strategy Improved Whale Optimization Algorithm,MIWOA)。最后,以某小区为应用对象进行算例分析,验证了模型的可行性以及改进算法的有效性。To address the issues of high investment costs and insufficient system stability in the integrated DC microgrid of photovoltaic storage and charging,research is conducted on optimizing the configuration of photovoltaic storage capacity.Firstly,a Monte Carlo method was used to construct a charging load forecasting model for electric vehicles(EV).With system safety and stability as constraints,a capacity optimization configuration model was established with annual net cost symbolizing economic benefits and power deviation rate symbolizing system stability as objective functions.Then,in response to the problem of insufficient search ability and low optimization accuracy of the whale optimization algorithm(WOA)when solving optimization configuration models with non-zero solutions,a multi strategy improved whale optimization algorithm(MIWOA)was proposed,which integrates quasi adversarial learning mechanism,adaptive weights,nonlinear convergence factor,Gaussian difference mutation,and greedy selection.Finally,a case study was conducted in a residential area in Wuhan to verify the feasibility of the model and the effectiveness of the improved algorithm.
分 类 号:TP3[自动化与计算机技术—计算机科学与技术]
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