摘要
随着清洁可再生能源的不断推广,分布式电源的渗透率日益增加。为提高清洁能源利用率、降低微电网成本,基于分时电价背景,首先建立双层风电预测误差模型,在综合考虑风电、光伏、火电、大电网联络线等多类型电源运行特性的基础上,以经济成本、环境成本与机组异步度为优化目标,建立了考虑风电不确定度与阀点效应的微电网协同优化模型,同时提出一种结合协同进化思想和非支配排序遗传算法的改进优化算法(CoNSGA-Ⅱ)。该算法在非支配排序遗传算法中使用了多种群进行种群间的相互合作、竞争,同时兼顾了非支配排序遗传算法优异的收敛性和协同进化算法的强大搜索能力。最后运用我国西北某微电网系统作为算例,并与传统快速非支配算法进行对比,验证了改进算法的全局最优解搜索能力和收敛性能上的优越性。
With the popularization of clean renewable energy sources, the permeability of distributed power sources is increasing. Firstly, in order to improve the utilization rate of clean energy and reduce the cost of micro-grid operation, we established a double-layer wind power prediction error model based on comprehensive consideration of time-of-use price and operating characteristics of different types sources such as wind power, photo-voltaic, thermal power and tie line. Secondly, aiming at minimizing the economic cost, the environmental cost, and the asynchronous degree of output, we established a micro-grid collaborative optimization model in which the wind power forecast uncertainty and valve point effect are taken into account. Thirdly, to solve the established multi-objective optimization model, we proposed an im proved intelligent optimization algorithm combined by co-evolution theory and non-dominated sorting genetic algorithm (CoNSGA-Ⅱ). In this algorithm, a variety of groups in the non-dominated sorting genetic algorithm are employed to compete and correct with each other, therefore, the proposed algorithm can combine the excellent convergence of the non-dominated sorting genetic algorithm and the powerful searching ability of the co-evolutionary algorithm. Finally, a practical micro-grid system in Northwest China was simulated as a case study, and the improved algorithm was compared with the traditional fast non-dominated algorithm. The simulation results demonstrate the superiority of the global search performance and the fast convergence performance of the improved algorithm.
作者
谭碧飞
陈皓勇
梁子鹏
林镇佳
陈思敏
TAN Bifei;CHEN Haoyong;LIANG Zipeng;LIN Zhenjia;CHEN Simin(School of Electric Power, South China University of Technology, Guangzhou 510641, China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2019年第10期3130-3139,共10页
High Voltage Engineering
基金
国家重点研发计划(2016YFB0900100)~~
