基于改进灰狼算法的混合发电系统优化设计

风光柴蓄混合发电系统的容量配置是系统优化设计中重要内容。针对基本灰狼算法在进化后期由于种群多样性的缺失而易于出现局部收敛或算法早熟的问题，提出一种具有全局寻优性能的改进灰狼优化算法(Improved Gray Wolf Optimization, IGWO)降低风光柴蓄混合发电系统的运行成本。引入收敛因子设置非线性调整策略来调节算法的全局探索与局部开发之间的平衡从而提高算法的收敛性；同时为了提高算法的全局寻优能力，通过柯西变异算子减少算法早熟收敛的概率。分析了风光蓄柴混合发电系统各发电单元特性，建立起以年均化系统成本最小化为目标的混合发电系统容量优化配置模型。分析优化结果，该改进灰狼优化算法能够有效对目标函数求解，从而证明了该算法的有效性和实用性。

Sizing optimization of hybrid generation system based on improved grey wolf optimization

An improved hybrid gray wolf optimization (IGWO) algorithm is presented to determine the optimal combination of a standalone photovoltaics, wind turbines, battery systems, diesel generator (PV-WT-BS-DG) hybrid generation system in order to achieve a cost effective and reliable hybrid generation system. As the traditional grey wolf optimization (GWO) algorithm has the disadvantages of slow convergence and high possibility of being trapped in local optimum, the Improved Gray Wolf Optimization (IGWO) algorithm used the dynamical convergence factor to maintain a better balance between global search and local search. Cauthy mutation operator is carried out on the current optimal individual of each generation, thus it could effectively jump out of local minima. The IGWO is applied for optimal sizing of PV-WT-BS-DG hybrid generation system, in which the optimization objective is minimizing of operation cost. The results show the effectiveness and applicability of the proposed method.