Chaotic adaptive particle swarm algorithm based on tent mapping for multi-objective optimization of combined cooling, heating, and power source-store-load systems

Abstract

With the proposal and implementation of the "double carbon" goal in China, it is necessary to further improve the dynamic energy efficiency of the operation process of combined cooling, heating and power (CCHP) units to maximize the economic efficiency of the system. In this paper, the optimization algorithm based on the combination of chaos search of Tent map and nonlinear adaptive particle swarm optimization combines the schedulable resources of energy production, energy storage and energy consumption into a CCHP "source storage" system, which can simultaneously meet the power, heat and cooling needs of the user side. Taking the operation cost and pollutant emission of CCHP system as the objective, a multi-objective optimization model is established. Under the constraint conditions of equipment output, power balance and so on, the equipment operation hourly output with the best economic and environmental benefits is obtained. The calculation results show that the CCHP "source storage and load" system reduces the operation and maintenance costs by 22.31%, and carries out the economic and environmental advantages.