Abstract
In recent years, the Combined Cooling Heating and Power (CCHP) system has received widespread attention and application. The CCHP system mainly uses clean energy as fuel and has high economy, which solves many energy problems at present. Therefore, the target optimization research of the CCHP system has been attended. The article takes the daily operating cost of the CCHP system as the optimization objective, establishes an optimization model and sets constraints based on the mathematical models of various equipment in the energy station. To enhance global search capability of the model, multi particle information sharing and asymmetric acceleration coefficients are introduced into Particle Swarm Optimization (PSO). In order to avoid particles getting stuck in local optima and increase population diversity, mutation and crossover operators from Genetic Algorithm (GA) are introduced into the PSO. Finally, an improved Genetic Algorithm and Particle Swarm Optimization (GA-PSO) algorithm is proposed and this new algorithm is studied among the operation optimization of Shanghai Qiantan Energy Station system. The improved GA-PSO algorithm is found to have the lowest daily operating cost after optimization, which proves the superiority of the combined system compared to traditional energy supply modes.