Multi-Objective Optimization Scheduling of Integrated Energy System Based on Operational Characteristics Clustering

Abstract

In order to improve the rationality of integrated energy system (IES) scheduling strategy and promote carbon reduction planning, this paper proposes a multi-objective optimization scheduling of IES based on operational characteristics clustering. Firstly, the operation architecture of IES is constructed, and the dynamic supply and demand balance formula is established, and the analysis method of source and load uncertainties is further proposed. After the initial historical data is processed, the variation of each hour is calculated and the k-means method is used to cluster the source and load scenarios. Secondly, according to the clustering results, the operation scenarios of IES are classified, and the carbon emissions and economics of each scenario are planned. Finally, the multi-objective optimal scheduling model with the lowest carbon emissions and the lowest operating costs is established. In terms of example analysis, the effectiveness of the proposed method in carbon emissions and economics is verified from the 12-months scenario and the 4-seasons scenario.