Research on Collaborative Optimization Method of CCHP Regional Integrated Energy System Based on Improved Multivariate Universe Algorithm

Aiming to address the prevailing issue where Cold Heat and Power (CCHP)-type integrated energy systems are primarily optimized for either economy or environmental friendliness, this paper conducts an exhaustive synergistic optimization analysis of the CCHP system, focusing on the dual objectives of economy and environmental friendliness. In this study, an optimization model of the CCHP system encompassing units such as gas turbines, gas boilers, and electric chillers is formulated. By integrating Pareto theory, an adaptive grid method, and a roulette strategy into the multiverse algorithm, an enhanced multi-objective multiverse algorithm is developed, which notably enhances the convergence accuracy, convergence speed, and stability of the solutions. A case study conducted in a representative northern region yielded the following experimental results: When compared with both the traditional particle swarm algorithm and an improved version of it, the CCHP-type integrated energy system optimized using the enhanced multi-objective multiverse algorithm reduced operating costs by 7.98% and carbon dioxide emissions by 12%, relative to the original system. This outcome underscores the remarkable capability of the improved multiverse algorithm in balancing the economic and environmental aspects of the system, thereby providing a robust foundation and valuable reference for the planning and design of subsequent energy supply systems. Through this synergistic optimization analysis, a win-win scenario is achieved, balancing both economic and environmental benefits, which lays a solid groundwork for the sustainable development of future energy systems.