), 余涛2,3 1.昆明理工大学 电力工程学院, 昆明 650500
2.华南理工大学 电力学院, 广州 510640
3.广东省电网智能量测与先进计量企业重点实验室, 广州 510640
收稿日期:2021-10-08出版日期:2021-12-28发布日期:2021-12-30通讯作者:曹璞璘E-mail:pulincao_kust@sina.com作者简介:杨 博(1988-),男,云南省昆明市人,教授,主要从事新能源发电/储能系统优化与控制,以及人工智能在智能电网中的应用研究.基金资助:国家自然科学基金(61963020);云南省基础研究计划(202001AT070096);国家自然科学基金委员会-国家电网公司智能电网联合基金资助项目(U2066212)Optimal Sizing and Placement of Distributed Generation Based on Adaptive Manta Ray Foraging Optimization
YANG Bo1, YU Lei1, WANG Junting1, SHU Hongchun1, CAO Pulin1(), YU Tao2,3 1. School of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
2. School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
3. Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou 510640, China
Received:2021-10-08Online:2021-12-28Published:2021-12-30Contact:CAO Pulin E-mail:pulincao_kust@sina.com摘要/Abstract
摘要： 建立了考虑有功功率损耗、电压分布、污染排放、分布式电源(DG)成本以及气象条件的DG选址定容规划模型,其中选址、定容工作分别是一个离散、连续变量,是一个高度非线性、含离散优化变量的复杂模型.因此,应用自适应蝠鲼觅食优化 (AMRFO) 算法获取最优Pareto解集,其具有丰富多样的搜索机制,个体更新机制以及先进的Pareto解筛选机制,针对该模型能够获得更加优异的高质量解.为回避权重系数人为设置主观性带来的影响,采用基于马氏距离的理想决策点法进行Pareto最优解集决策.最后,基于IEEE 33, 69节点配电网和孤网运行的IEEE 33, 69节点配电网进行仿真分析.研究结果表明:与传统的多目标智能优化算法相比,AMRFO算法能够获得分布更加广泛、均匀的Pareto前沿,在兼顾经济性的同时,配电网的电压分布、有功功率损耗的改善效果显著优于其他算法.
关键词: 配电网, 分布式电源, 选址定容, 自适应蝠鲼觅食优化算法
Abstract: In this paper, an optimal sizing and placement model for distributed generation (DG) is established, which includes active power losses, voltage profile, pollution emission, DG costs, and meteorological conditions. Since optimizing placement and sizing are discrete and continuous variables respectively, the model established is a highly nonlinear complex one with discrete optimization variables. Therefore, the adaptive manta ray foraging optimization (AMRFO) algorithm is applied to obtain the optimal Pareto front, which has a rich and diverse search mechanism, individual updating mechanism, and advanced Pareto solution selection mechanism. For this model, a better solution of high quality can be obtained. In order to avoid the influence of subjective setting of weight coefficient, the ideal point method based on Mahalanobis distance is used to make Pareto front decision. Finally, the simulation based on the IEEE 33, 69-bus distribution network and the IEEE 33, 69-bus distribution network in isolated network operation are implemented. The results show that compared with the traditional multi-objective intelligent optimization algorithm, AMRFO algorithm can obtain a more widely distributed and uniform Pareto front. While considering the economy, the optimized distribution network voltage profile and active power losses can be significantly improved.
Key words: distribution network, distributed generation (DG), optimal sizing and placement, adaptive manta ray foraging optimization (AMRFO) algorithm
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