Optimal allocation method of multi-energy system based on hybrid optimization algorithm

With the rapid development of industry, the research of energy storage technology and renewable energy continues to be hot, and the energy industry opens the era of diversification. Multi-energy complementary has become a new trend in sustainable energy development, leading the energy industry to a new energy system of deep integration and integration of multiple energy sources. This paper proposes a hybrid optimization algorithm that combines particle swarm algorithms and Hooke–Jeeves​ (HJ) with a comprehensive evaluation index as the optimization objective, aiming to improve the speed of solving the capacity optimization of integrated energy systems. The multi-energy system configuration optimization platform that covers the index system, optimization model, and system analysis module was established to systematically solve the integrated energy system optimization configuration problem, moreover provide an important reference for integrated energy system design and implementation. Besides, the influence of optimization algorithms on the configuration results was analyzed. Taking the combination of soil source heat pump system and combined cooling, heating and power system as an example, this study quantifies and compares the optimization results and solution speeds of the hybrid algorithm and the traditional single optimization calculation. It is shown that the hybrid optimization algorithm reduces the amount of iteration steps by approximately 31% compared with the particle swarm algorithm and by approximately 48% compared with the HJ algorithm. This significantly improves the speed of the optimization computation while ensuring the accuracy of the computation results.