Abstract
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.
Highlights
It should be noted that the optimal solution of non-dominated sorting genetic algorithm (NSGA)-III algorithm is greatly affected by the randomness of initialization, and that the algorithm has better selection ability than the NSGA-II algorithm in this case
Compared with the calculation of the optimal decision scheme, the life cycle cost is reduced by 0.64%, the life cycle carbon emissions are reduced by 0.3%, and the primary energy utilization rate is increased by 0.45%
In order to promote the efficient utilization of energy system resources, low carbon emissions, environmental protection, and the friendly development of economic multienergy complementary distributed energy systems, taking the construction terminal of an multi-energy complementary distributed energy system (MCDES) as the research object, a multi-objective optimization planning model with the lowest system life cycle cost, the minimum life cycle carbon emissions, and the maximum primary energy utilization as the optimization objectives is proposed, which comprehensively considers the economy, environmental protection, and energy efficiency
Summary
Jing et al [13], based on environmental protection and economy, optimized the capacity design of a distributed integrated energy system with photovoltaic power generation by considering the carbon emissions and investment recovery period. NSGA-II, as a relatively mature method that became a benchmark algorithm for optimizing the performance comparison of other multi-objective optimization algorithms, has been widely used It has high solving efficiency, and can obtain multiple high-quality solutions in one calculation. As a new method for solving multi-objective optimization problems, NSGA-III has achieved good results in solving high-dimensional multi-objective optimization problems with a large number of objectives [24,25,26]; it has certain advantages in improving operation efficiency, increasing the diversity of solution space, and reducing computational complexity, which can effectively compensate for the shortcomings of other optimization algorithms. The final decision scheme of the system design optimization is comprehensively determined
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
