Stochastic robust optimization scheduling for integrated energy system cluster based on data-driven method

Considering the multiple uncertainties in integrated energy system (IES) clusters and the need for flexible and economically efficient coordinated operation, a stochastic robust hybrid optimization (SRHO) method for IES clusters based on different contribution factors is proposed. Firstly, a multi-stage SRHO model is developed to handle the uncertainties of electricity prices, renewable energy (RE), and load. The flexible robust level is calculated using the information gap decision theory (IGDT), while the stochastic scenarios of RE and load are generated based on the dynamic Bayesian network (DBN). Secondly, a cooperative game model for IES cluster is constructed using asymmetric Nash bargaining game theory. The benefit distribution of IES is carried out based on three contribution factors: electricity trading contribution rate, marginal contribution rate, and deviation rate of uncertainty factors, to improve the enthusiasm of IES to participate in cooperation. Finally, the stochastic dichotomy method (SDM) is used to decouple the IGDT and SRHO models, and the IES cluster cooperative game model is solved using the AOP-Looped column-and-constraint generation (C&CG) and the alternating direction method of multipliers (ADMM) algorithm. Additionally, the progressive hedging (PH) algorithm is employed to decompose the master problem into scenario-based subproblems for parallel solving, thereby accelerating the overall optimization process. The simulation results verify the effectiveness of the proposed method in IES cluster optimization scheduling.