Multi-objective optimization scheduling of integrated energy systems considering regional time-of-use electricity prices and weight sensitivity

The integration of Electric Vehicles (EV) and diverse energy users in an Integrated Energy System (IES) poses a key challenge in IES optimization dispatch. This paper presents an IES optimization dispatch method that incorporates EV zoning electricity prices and multi-objective weight optimization. Firstly, the power flow calculation is integrated into the IES optimization dispatch, establishing a comprehensive IES network power flow model that considers electricity, gas, and heat. Next, an innovative Regional Time of Use Price (RTOU) is developed based on regional energy redundancy and the division of IES regions. This RTOU guides the charging demand of EVs in different regions and at different times. Subsequently, an approach for multi-objective weight optimization is proposed, which takes into account the differences in weight response and the incongruity of indicator meanings in the IES context. This approach utilizes a single-indicator weight sensitivity matrix to optimize the weights and incorporates the EW-TOPSIS evaluation method to achieve multi-dimensional weight optimization in IES. Finally, the second-order cone programming method is employed to solve the IES power flow model. This verifies the practical effectiveness of the proposed weight optimization approach based on the single-indicator weight sensitivity matrix and analyzes the application effect of the EW-TOPSIS algorithm in multi-objective optimization. Simulation results demonstrate the positive outcomes of this research: (1) The use of RTOU reduces the additional charging cost of EVs by 5.24 % compared to the use of Time of Use (TOU), validating the effectiveness of the improved pricing optimization mechanism. (2) The weight optimization method based on weight sensitivity increases the weight utilization index by 1.73 % compared to the use of normalized weights. The findings in this paper serve as a valuable reference for addressing multi-objective optimization problems in future IES dispatch work.