Optimized scheduling of integrated community energy stations based on improved NSGA-III algorithm

Abstract

To cope with the problem of building and using a large number of fast electric vehicle stations (FEVS), adding FEVS to the integrated community energy system (ICES) provides new ideas for solving the problems of interaction with the grid, overall system stability, revenue benefits, and environmental pollution. To this end, this paper designs an ICES model with FEVS (ICES-FEVS). This model considers economy, stability, and sustainability to achieve all-around consideration. To further solve the complex multi-objective problem, the multi-objective improvement strategy NSGA-III algorithm (IS-NSGA-III), combined with fuzzy comprehensive evaluation (FCE), is used to solve the model. In the solution phase, the advantages of the IS-NSGA-III algorithm are first verified by the benchmark function, and the validity of the ICES-FEVS model is verified by comparing different models. In the optimization results, daily revenue was increased by 6.3 %, daily battery life loss was increased by 26.03 %, and pollution emissions were reduced by 46.9 %. The analysis of the results shows that introducing Electric Vehicle (EV) fast charging stations in an integrated community energy system ensures economic improvement, sustainable development, and more favorable regulation of the energy storage system. The simulation results show that compared with other algorithms, the HV metrics of IS-NSGA-III are lower by about 6.8 %, Spread metrics are better by about 1.35 %, and the proposed IS-NSGA-III is better in terms of convergence and distributivity.