Energy Saving Strategy for Variable Load Peak Regulation of Thermal Power Units Based on Adaptive Weighted Particle Swarm Algorithm

With the large-scale integration of new energy into the grid, frequent participation of thermal power units in peak shaving has led to an increase in coal consumption. Aiming at the optimization problem of coal consumption during the peak load regulation process of thermal power units, an energy-saving optimization strategy based on adaptive weighted particle swarm optimization algorithm(AWPSO) is proposed. Firstly, the coal consumption characteristics of thermal power units under different load rates were analyzed, and a peak shaving optimization model was established with the goal of minimizing coal consumption for power supply. Secondly, in response to the drawback of the standard particle swarm optimization algorithm being prone to getting stuck in local optima, an inertia weight strategy was designed that dynamically adjusts with the iteration process and particle fitness, and an improved AWPSO algorithm was proposed. The simulation analysis results show that compared with the standard PSO algorithm, the convergence speed of the AWPSO algorithm is improved by 25.3%, and the optimized peak shaving scheme can reduce the average coal consumption for power supply by 4.2 g/(kWh). This article provides a reference for peak shaving and energy conservation of thermal power units.