Two-stage stochastic robust optimization for capacity allocation and operation of integrated energy systems

Abstract

To address the challenges posed by various uncertainties in integrated energy systems (IES) for planning and operation, this paper considers the capacity configuration of IES equipment with energy storage systems under a stepped carbon trading mechanism, as well as the planning of electric vehicle (EV) charging stations under different charging modes. A two-stage stochastic robust planning method is proposed, taking into account both short- and long-term uncertainties in renewable energy generation and electric, thermal, and cooling loads. In the first stage, planning decisions are made with the objective of minimizing investment costs, while accounting for the seasonal characteristics of the “source-load” relationship in the planning phase. Stochastic programming is employed to handle long-term uncertainties. In the second stage, operational simulation is performed with the goal of minimizing energy dispatch costs and carbon trading costs, and short-term uncertainties in “source-load” operations are described using robust optimization. The nested column-and-constraint generation (NC&CG) algorithm is used to solve this two-stage model. Finally, the proposed model is applied to an IES in northern China. The results show that orderly EV charging within the IES can reduce both investment and operational costs, as well as carbon emissions. The consideration of a stepped carbon trading mechanism can reduce system carbon emissions and enhance environmental sustainability. IES planning with multiple energy storage types is more economical than with a single energy storage type, and the proposed stochastic robust planning method, which considers both long- and short-term uncertainties, demonstrates stronger reliability and economic performance under extreme conditions.