采用改进北方苍鹰算法的微电网优化调度研究

Research on optimal scheduling of microgrid usingimproved Northern Goshawk algorithm

微电网系统通常由多种分布式电源组成,为降低运行成本,常使用智能算法对微电网进行调度。智能算法在求解微电网调度模型时容易陷入局部最优解,导致求解精度差,因此在北方苍鹰算法的基础上,提出了一种混合策略改进的北方苍鹰算法(HNGO),利用反向学习、Metropolies准则以及自适应t分布变异提高求解精度,同时构建了考虑可再生能源出力特性的需求响应模型,使负荷曲线与可再生能源出力曲线更贴近,然后建立日运行成本最低的微电网优化调度模型,并利用HNGO求解。对比仿真结果显示所提算法具有更好的求解精度,且所提需求响应模型能显著降低燃料成本。

The microgrid system normally consists of a variety of distributed power sources.To cut the operating cost of the microgrid,intelligent algorithms are often employed to dispatch the microgrid.Intelligent algorithms are prone to fall into local optimal solutions when solving microgrid scheduling models,resulting in poor accuracy.Therefore,based on the Northern Goshawk algorithm,this paper proposes a hybrid strategy improved Northern Goshawk algorithm(HNGO),which uses reverse learning,Metropolies criterion and adaptive T-distribution variation to enhance its accuracy.Meanwhile,a demand response model considering the output characteristics of renewable energy is built,so that the load curve is closer to the output curve of renewable energy.Then,a microgrid optimization scheduling model with the lowest daily operating cost is established,and HNGO is used to find the solution.Our simulation results show the proposed algorithm achieves accuracy,and our proposed demand response model significantly reduces fuel costs.