Research on Power Grid Dispatching Based on Particle Swarm Optimization

The optimization and dispatching of microgrids is the main issue in achieving efficient operation of smart grids. In recent years, various optimization algorithms have been widely used in microgrid dispatching. This paper systematically reviews the research progress that has made in this field and compares the advantages and disadvantages of different optimization methods. First, the classic intelligent optimization algorithms, such as particle swarm optimization, genetic algorithm, and differential evolution are introduced, and in this essay, their applications and improvement strategies in microgrid dispatching are discussed. Secondly, the optimization methods based on reinforcement learning, including deep reinforcement learning (DRL), deep deterministic policy gradient (DDPG), and proximal policy optimization (PPO), are analyzed, focusing on their advantages in dealing with high-dimensional, nonlinear, and real-time dispatching problems. Additionally, the prediction plus optimization combination strategy, such as Bayesian optimization, metaheuristic optimization, and multi-scenario optimization methods based on machine learning, is discussed to deal with the uncertainty and robustness problems of microgrids. These three aspects represent three kinds of most popular research on microgrid optimization. Finally, this paper summarizes the applicability of different optimization methods and looks forward to future development trends. Comprehensive analysis shows hybrid optimization strategies (such as PSO +GOA) are also worthy of further study in improving robustness.