A State Space Model-based Multi-objective Optimal Scheduling Strategy for Power Systems

In order to cope with the increasingly complex challenges in power system scheduling, especially in the context of large-scale integration of renewable energy and frequent fluctuations in load demand, this study aims to solve multi-objective optimization problems in power system scheduling, including economy, reliability, and environmental friendliness. First, by introducing a state space model (SSM) to accurately model and predict the dynamic evolution process of the power system in order to grasp the real-time state of the system. On this basis, a multi-objective optimization scheduling model was constructed, which combines various constraints of the power system, adopts an adaptive weight particle swarm optimization (PSO) algorithm for search, and further optimizes the solution using the Cauchy–Lorentz (CL) distribution, aiming to achieve the best balance between economy, environmental protection, and efficiency in power system scheduling and obtain the Pareto optimal solution set. The experimental results indicate that the strategy proposed in this study has significant advantages in improving the operational efficiency of power systems, enhancing system adaptability, and promoting green and low-carbon development. Not only does it provide new theoretical guidance and technical means for the field of power system scheduling, but it also provides strong support for promoting the optimization and transformation of energy structure and achieving sustainable development goals.