基于交叉-变异人工蜂群算法的微网优化调度

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.

Crossover-mutation based artificial bee colony algorithm for optimal scheduling of microgrid

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.