【目的】为有效地求解电力系统环境经济调度问题，本文以同时优化燃料成本和污染排放量为研究目标，提出了一种基于灰狼优化(GWO)的改进粒子群优化(PSO)算法。【方法】首先对初始粒子群采用折射反向学习产生反向解，以此提高种群的多样性；在算法迭代过程中，结合GWO算法，引导粒子群中的优秀个体进行寻优搜索，提高PSO算法的寻优能力和收敛精度；在算法迭代后期，针对粒子群易陷入局部最优解的缺点，使用Tent混沌映射对最优粒子进行扰动，根据适应度值更新粒子群个体最优值、全局最优值的位置。【结果】将改进算法应用于不同负荷需求的6单元发电机组和40单元发电机组，对比本文算法与PSO算法和GWO算法求解电力系统的收敛曲线。结果表明，本文所提改进算法能更快收敛到最优值，且最后的燃料成本最低。【结论】本文所提改进算法能更好地解决复杂约束优化问题,在寻优精度和稳定性方面表现较好。

[Objective] To effectively solve the environmental economic dispatch problem in power systems, this paper proposed an improved particle swarm optimization (PSO) algorithm based on grey wolf optimization (GWO) to optimize both the fuel costs and pollutant emissions. [Methods]First, the refracted opposition-based learning of refraction was applied to the initial particle swarm to generate inverse solutions, hereby enhancing population diversity. During the algorithm’s iteration process, the GWO algorithm was combined with PSO to guide the elite individuals in the particle swarm to conduct optimal searches, improving PSO’s optimization capability and convergence accuracy. In the later stages of the algorithm, to address the drawback where the particle swarm easily fell into local optima, Tent chaotic mapping was used to perturb the optimal particles. The individual best and global best positions of the particle swarm were then updated based on the fitness values. [Results] The improved algorithm was applied to 6-unit and 40-unit generator systems with different load demands. The convergence curves of the proposed algorithm, PSO algorithm. And GWO algorithm were compared for solving the power system, and the results showed that the proposed improved algorithm converged to the optimal value more quickly and resulted in the lowest fuel cost. [Conclusion] The improved algorithm proposed in this paper effectively solves complex constrained optimization problems and performs well in optimization accuracy and stability.

辽宁省教育厅科学研究项目(LJKMZ20220828,LJKZ0489);四川省重点实验室开放基金项目(2020RYJ04)