Economic optimization scheduling of combined cooling, heat, and power systems utilizing a multi-strategy enhanced PSO algorithm

Under the dual carbon goals, enhancing the energy efficiency of combined cooling, heating, and power (CCHP) systems while reducing carbon emissions is crucial. As the physical carrier of the energy internet, CCHP systems deliver comprehensive benefits, including energy savings, environmental improvement, and enhanced power supply reliability. This paper establishes a CCHP system model based on the principle of energy gradient utilization. Under time-of-use electricity pricing, the model comprehensively considers electricity consumption, heating, and cooling demands. Subsequently, a hybrid algorithm BC-GWOPSO is proposed, combining an improved Gray Wolf Optimization (GWO) algorithm with Particle Swarm Optimization (PSO). The specific improvement strategy involves using the β - distribution strategy to adjust the inertia weight in PSO and applying the cosine law to modify the convergence factor in GWO. For typical summer and winter days, total cost is adopted as the objective function for optimization scheduling, enabling more rational power allocation among units within the CCHP system and minimizing system costs. Finally, the BC-GWOPSO algorithm was experimentally compared with four other optimization algorithms. Friedman and Wilcoxon test results show that BC-GWOPSO algorithm is superior to the other four algorithms. The CCHP system optimization operation results demonstrate that the proposed method effectively reduces total operating costs, environmental costs, and load loss costs. Compared to other algorithms, it exhibits faster convergence speed and better stability, providing an effective scheduling solution for CCHP systems.