基于改进麻雀搜索算法的CCHP型微电网优化调度

CCHP Microgrid Optimal Dispatching Based on Improved Sparrow Search Algorithm

随着能量多级利用技术的发展,冷热电联供(CCHP)型微电网被广泛地应用,其能源间合适的出力调度显得极为重要。传统的麻雀搜索算法(SSA)求解时易出现局部最优的问题,因此在麻雀搜索算法的基础上进行了改进。首先,为提升种群的多样性,通过反向学习策略对种群进行了优化;其次,为了避免求解时算法陷入局部最优,通过纵横交叉策略对寻优过程进行了优化;然后,以典型日调度运行一天成本最低为目标函数;最后,设定各出力源的爬坡约束、储能设备的充放电效率等为约束条件,利用反向学习及纵横交叉策略改进后的麻雀搜索算法求解CCHP型微电网调度模型。对比传统SSA、粒子群(PSO)和鲸鱼算法(WOA)仿真结果发现改进的SSA在收敛速度和全局搜索能力上效果更优。

With the development of multi-level energy utilization technology,the combined cooling,heating and power(CCHP)microgrid is widely used,and the appropriate output scheduling between its energy sources is ex-tremely important.The traditional sparrow search algorithm(SSA)is easy to fall into local optimality.Therefore,the SSA is improved.First,in order to improve the diversity of the population,the population is optimized through the reverse learning strategy.Then,in order to avoid the algorithm falling into the local optimum during the solution,the optimization process is optimized through the vertical and horizontal cross strategy.Then,the objective function is to minimize the cost of a typical daily scheduling operation day.Finally,the climbing constraints of each output source and the charging and discharging efficiency of the energy storage equipment are set as the constraints,and the improved SSA based on reverse learning and cross strategy is used to solve the CCHP micro grid dispatching model.By comparing the simulation results of SSA,particle swarm optimization(PSO)and whale algorithm(WOA),it is found that the improved SSA has better convergence speed and global search ability.