Energy Internet Multi-Energy Complementary Trading Model and Algorithm Based on Multi-Objective Optimization

This paper proposes a multi-energy complementary trading model for energy Internet based on multi-objective optimization. The model aims to solve the coordination problem between multiple energy types and optimize the balance of energy supply and demand through a multi-objective optimization algorithm. The core objectives of the model include improving economic benefits, reducing environmental pollution and energy waste, and ensuring the smooth operation of energy supply and demand. PSO and GA are used in this paper, and the performance of these algorithms is compared in terms of optimization result, convergence rate and computing efficiency. The effectiveness of the model is verified through many experiments and simulations. The experimental results show that the trading model based on multi-objective optimization has improved the economic benefits by about 12% compared with the traditional model, and effectively reduced energy waste and carbon emissions, with carbon emissions reduced by about 8%. In addition, the performance analysis results of the optimization algorithm show that the particle swarm optimization algorithm shows a faster convergence speed and higher accuracy when dealing with complex multi-objective problems, and is suitable for the optimization and scheduling of large-scale energy Internet systems.