Multiobjective Optimization for Uncertain Integrated Energy Systems: Aggregating EVs in Demand Response via Photovoltaic-Energy Storage

In this article, a multiobjective optimization methodology is tailored for energy producers and sellers (Prosumers) and electric vehicle charging service providers (EVCS) in IES. The primary objective is to harmonize economic and safety aspects of system, while addressing uncertainties associated with renewable energy and involvement of electric vehicles (EVs) in integrated demand response (IDR). Initially, an orderly charging model for EVs is developed by driving behavior patterns, taking into account the influence of EVCS aggregating EVs in IDR via photovoltaic-energy storage charging stations. Subsequently, a stochastic programming method with conditional value at risk is utilized to alleviate potential risks from stochastic and intermittent nature of renewable energy outputs. Furthermore, the optimization framework incorporates operating costs of Prosumers and EVCS, and peak-to-valley difference of IES load as multiobjective functions. The optimal solution is determined by technique for order of preference by similarity to ideal solution. Ultimately, case studies confirm that the proposed approach effectively balances economic performance for multiple stakeholders and operational safety, and enhances system robustness under uncertain conditions.