基于飞蛾火焰优化算法的火电调峰负荷分配研究

Research on Peak Load Distribution of Thermal Power Based on Moth Flame Optimization Algorithm

在分析火电调峰成本的基础上,以调峰成本最小化为目标构建了火电调峰负荷分配模型,引入飞蛾火焰算法作为模型求解方法,以重庆电网为例,共设定3种负荷分配场景,同时以粒子群算法和蚁群算法求解效果与飞蛾火焰算法对比,验证了模型及求解算法的高效性和合理性。结果表明:3种场景下,随着调峰需求的增加,优化过程中需要部分机组进行深度调峰和启停调峰才能满足调峰需求,优化后系统调峰总成本分别达到了1976万元、2645万元和3287万元;3种算法求解过程中,飞蛾火焰优化算法收敛速度更快,求解效率更高,且经飞蛾火焰优化后的系统调峰总成本最低,相比传统优化算法具有更强的规避局部最优解能力。

Based on the analysis of peak load regulation cost of thermal power plants,a load distribution model for peak load regulation of thermal power plants is established with the goal of minimizing peak load regulation cost.The moth flame algorithm is introduced to solve the model,and the rationality of the model is verified by comparing the results of the Particle Swarm Optimization algorithm and the Ant Colony Optimization algorithm with those of the Moth Flame algorithm with setting up three kinds of load distribution scenarios in Chongqing Power Grid.The results show that with the increase of peak shaving demand,some units need to carry out deep peak shaving and start-up shutdown peak shaving to meet the peak shaving demand in the three scenarios.After optimization,the total peak shaving costs of the system reached 19.76 million yuan,26.45 million yuan and 32.87 million yuan respectively.Among the three algorithms,the convergence speed of the moth flame optimization algorithm is faster and the solution efficiency is higher,and the total cost of peak shaving is the lowest after the moth flame optimization.Compared with the traditional optimization algorithm,the algorithm has stronger ability to avoid local optimization.