The development and utilization of low-carbon energy systems has become a hot topic of energy research in the international community. The construction of a multi-energy complementary distributed energy system (MCDES) is researched in this paper. Based on the multi-objective optimization theory, the planning optimization of an MCDES is studied, and a three-dimensional objective-optimization model is constructed by considering the constraints of the objective function and decision variables. Aiming at the optimization problem of building terminals for the MCDES studied in the paper, two genetic optimization algorithms—Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Sorting Genetic Algorithm III (NSGA-III)—are used for calculation based on an example analysis. The constraint conditions of practical problems were added to the existing algorithms. Combined with the comparison of the solution quality and the optimal compromise solution of the two algorithms, a multi-decision method is proposed to obtain the optimal solution based on the Pareto optimal frontier of the two algorithms. Finally, the optimal decision scheme of the example is determined and the effectiveness and reliability of the optimization model are verified. Under the application of the MCDES optimization model studied in this paper, the iteration speed and hypervolume index of NSGA-III are found to be better than those of NSGA-II. The values of the life cycle cost and life cycle carbon emission objectives after the optimization of NSGA-III are indicated as 2% and 14% lower, respectively, than those of NSGA-II. The primary energy efficiency of NSGA-III is shown to be 20% higher than that of NSGA-II. According to the optimal decision, the energy operation strategies of the example MCDES with each typical day in the four seasons indicate that good integrated energy application and low-carbon operation performance are shown during the four-seasons operation process. The consumption of renewable energy is significant, which effectively reduces the application of high-grade energy. Thus, the theoretical guidance and engineering application reference are provided for MCDES design planning and operation optimization.

Multi-energy complementary systems (MCSs) are complex multilevel systems. In the process of system planning, many aspects—such as power planning, investment cost, and environmental impact—should be considered. However, different decision makers tend to have different levels of control objectives, and the multilevel problems of the system need to be solved effectively with comprehensive judgment. Therefore, based on the terminal MCS energy structure model, the optimization method of MCS planning and operation coordination, considering the influence of planning and operation in the system’s life cycle, is studied in this paper. Consequently, the research on the collaborative optimization strategy of MCS construction and operation was carried out based on the bi-level multi-objective optimization theory in this paper. Considering the mutual restraint and correlation between system construction and operation in practical engineering, a bi-level optimization model for collaborative optimization of MCS construction and operation was constructed. To solve the model effectively, the existing non-dominated sorting genetic algorithm III (NSGA-III) was improved by the authors on the basis of previous research, which could enhance the global search ability and convergence speed of the algorithm. To effectively improve and strengthen the reliability of energy supply, and increase the comprehensive energy utilization of the system, the effects of carbon transaction cost and renewable energy penetration were considered in the optimization process. Based on an engineering example, the bi-level model was solved and analyzed. It should be noted that the optimization results of the model were verified to be applicable and effective by comparison with the single-level multi-objective programming optimization. The findings of this paper could provide theoretical reference and practical guidance for the planning and operation of MCSs, making them significant for social application.

Multi-energy complementary system (MCS), integrating with renewable energy and new energy, is an effective way to promote low-carbon development and clean energy utilization. Reasonable system configurations and operation scheduling schemes play key roles in maintaining the long-term and efficient operations of the system. In general, multi-objective optimization of system integration is employed to achieve the optimal decision strategies for MCS operations. However, different weights used to determine the importance of each objective during the optimization may cause various optimization results. Thus, reasonable weight adopted is critical to the credibility of the optimization solutions and the resulting system efficiency. In this paper, a comprehensive weight analysis is implemented during the multi-objective optimization for the decision-making of the MCS. The weight determination of MCS design optimization objective is analyzed from three aspects namely subjective, objective, and the combination of subjective and objective. Based on a case analysis, it isfounded that when the weight is determined by subjective weighting method, the weight of economic index is the largest. When the objective weighting method is adopted, the environmental protection index is obviously greater than the economic index and the energy efficiency index. Due to the different information entropy carried by each target in each group of data, the weights obtained by objective weighting and subjective and objective comprehensive weighting are different. With different weight decision schemes, the capacity of each MCS equipment varies greatly. While using the intelligent optimization algorithm for multi-objective optimization, it is necessary to analyze the weight of each objective in the process of multiple independent experiments and make comprehensive decisions according to the objective preference of decision makers to determine the optimal solution. The results indicatethat the research provides an effective reference for the analysis of the weight of each objective and the decision of the optimal solution in the MCS optimization research process.