Operation Optimization Method of Integrated Cooling, Heating and Power Energy System in a Park Based on Neural Network and Particle Swarm Optimization

Multi-energy complementary and coordinated operation is one of the important technologies for the integrated energy system in the park to achieve the optimal economic benefits in operation. Since the existing technical methods cannot comprehensively consider the nonlinear factors in each link of energy input - conversion - output in the actual energy conversion process of the integrated energy system in the park, and thus cannot be used for accurate operation optimization and adjustment of the actual existing integrated energy system in the park, an operation optimization method for the combined cooling, heating and power integrated energy system in the park based on neural network and particle swarm optimization is proposed. Firstly, the nonlinear fitting and regression capabilities of the neural network are utilized to establish the regression models between the control strategies of each energy conversion equipment in the park's integrated energy system and the total power that can be supplied for electricity, heat and cooling of the integrated energy system. Then, the particle swarm optimization algorithm is used to search for the optimal control strategies of each energy conversion equipment in the park's integrated energy system with the optimization goal of minimizing the current operation cost of the integrated energy system under the constraint of meeting the overall electricity, heat and cooling loads required by the park's users, so as to achieve the purpose of accurate operation optimization and adjustment of the actual existing integrated energy system in the park.