引用本文:曹知奥,汪晋宽,韩英华,等.基于交叉-变异人工蜂群算法的微网优化调度[J].控制与决策,2020,35(9):2059-2069
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基于交叉-变异人工蜂群算法的微网优化调度
曹知奥1, 汪晋宽1,2, 韩英华2, 赵强2
(1. 东北大学信息科学与工程学院,沈阳110004;2. 东北大学秦皇岛分校,河北秦皇岛066004)
摘要:
随着大规模可再生能源接入微网,其不确定性直接影响微网的优化调度.鉴于此,以微网的产能利润最大化为目标,构建微网日前产能调度的优化模型,其中对储能单元和需求响应负荷进行调度,对可再生能源产能预测的误差进行处理.考虑优化模型中包含的非线性特征,提出一种基于交叉和变异的人工蜂群算法以求解微网最优调度策略.所提出算法在雇佣蜂和观察蜂阶段,引入遗传算法中的交叉和变异操作对邻域搜索策略进行更新,以确保子代种群的多样性;在侦查蜂阶段,构建基于全局搜索的初始化机制,以提高算法搜索全局最优解的能力.仿真结果验证了所构建模型的有效性和算法的优越性.
关键词:  可再生能源  需求响应  不确定性  微网日前产能优化调度  交叉与变异  人工蜂群算法
DOI:10.13195/j.kzyjc.2019.0506
分类号:TP273
基金项目:国家重点研发计划项目(2016YFB0901900);河北省自然科学基金项目(F2017501107);中央高校基本科研业务费专项基金项目(N182303037);东北大学轧制技术及连轧自动化国家重点实验室开放课题基金项目(2017RALKFKT003).
Crossover-mutation based artificial bee colony algorithm for optimal scheduling of microgrid
CAO Zhi-ao1,WANG Jin-kuan1,2,HAN Ying-hua2,ZHAO Qiang2
(1. College of Information Science and Engineering,Northeastern University,Shenyang110004,China;2. Northeastern University at Qinhuangdao,Northeastern University,Qinhuangdao066004,China)
Abstract:
With the access of large-scale renewable energy to the microgrid, its uncertainty directly affects the optimal scheduling of the microgrid. In this paper, aiming at maximizing the generation profits of microgrid, an optimization model of day-ahead generation schedule for the microgrid is constructed, with consideration of the energy storage, demand response and error handling of renewable generation prediction. Then an improved artificial bee colony algorithm based on crossover and mutation operations is proposed to solve this problem. In the employed bee phase and onlooker bee phase, crossover and mutation processing is introduced to improve the neighborhood search strategy, in order to ensure the diversity of offspring population. In the scout bee phase, an initialization mechanism based on global search is constructed to improve the ability of searching global optimal solution. Finally, the simulation results demonstrate the effectiveness of the model and the superiority of the algorithm.
Key words:  renewable energy  demand response  uncertainty  microgrid optimal day-ahead generation schedule  crossover and mutation  artificial bee colony algorithm

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