Multi-objective optimization design and multi-attribute decision-making method of a distributed energy system based on nearly zero-energy community load forecasting

Abstract Currently, many scholars have researched distributed energy systems (DES) from different dimensions. However, the load forecasting on both source-side and load-side, the optimal design of DES, and the multi-attribute decision making need further research. Therefore, a novel DES combining solar photovoltaic and hybrid energy storage is proposed. Combining the Monte Carlo method and improved K-means clustering is presented to predict the load on the source-side and load-side. Then, an optimal design method considering system independence and solar energy utilization scale is proposed, and the novel system is optimized. The entropy method and TOPSIS method are combined to make the multi-attribute decision on the optimization results of the system. The novel system is used to supply energy to nearly zero-energy communities (NZEC) in different scenarios. The research results show that the proposed load forecasting method can more accurately reflect the actual load demand of users. When “cost - external energy dependence - solar fraction” is taken as the target, the comprehensive energy import rate with nearly zero-energy office communities is only 36.7%, showing excellent independence. Finally, this paper can provide a method for load forecasting of NZECs, and provide certain theories for the optimization and decision-making of DESs.