Two-stage Multi-objective Optimization Coordination of Electro-thermal Coupled Integrated Energy System Based on Improved NSGA-II Algorithm

Abstract

With the growing proportion of clean energy in integrated energy systems (IES), energy supply uncertainty and spatial-temporal dispersion are becoming increasingly prevalent. System modeling and optimal scheduling are facing greater challenges. In this paper, we improve the non-dominated sorting genetic algorithm (NSGA-II) to address the above problems and propose a two-stage multi-objective benefit-equilibrium optimization coordination of the electric-thermal coupled integrated energy system. Firstly, this paper carries out the thermodynamic characteristics analysis of the equipment components of the electro-thermal coupled energy system, which reflects the structural features of the system, the performance of each equipment under different task conditions, and the mechanism of the system; based on the above characteristic analysis, a two-stage multi-objective optimization of electro-thermal coupled system optimization coordination is proposed to establish the objective function and carry out each objective balance constraint; the NSGA-II algorithm is as well as improved. According to the operation stage, operation generation and the NSGA-II algorithm are improved by dynamically adjusting the operating parameters of evolving individuals of the operation stage, operational generation, and the number of undominated individuals in the current temporary population. By making the algorithm adaptation to improve the adaptive capacity of the evolution operator, we solve the two-step model and obtain the Pareto optimal front for each energy device. In summary, the results of the analysis of the IES under the coupling of power system and thermal system show that the constructed model and the proposed algorithm can effectively improve the accuracy of the renewable energy system and the optimization decision. The results of the research further reflect the benefits of the proposed multi-objective optimization scheme in accounting for economic, renewable energy, and complex operating constraints which ensure the economical and stable operation of the system, as well as the robustness of optimal scheduling.