A stochastic hierarchical optimization and revenue allocation approach for multi-regional integrated energy systems based on cooperative games

Abstract

The interconnection of multiple regional integrated energy systems (RIES) can effectively enhance the low-carbon and flexible operation capabilities of RIES, but the uncertainty and multi-energy interaction of the system pose challenges to the stable operation of RIES. Therefore, a stochastic hierarchical optimization and revenue allocation approach is proposed to optimize the operational strategy of multi-RIES with multi-operator participation, multi-energy interactions, and multiple uncertainties. Firstly, agent-based modeling, Latin hypercube sampling and simultaneous backward reduction based on the Wasserstein metric are proposed to capture the power-side and load-side uncertainties. Secondly, based on the Nash bargaining game and the alternating direction method of multipliers algorithm, multi-RIES peer-to-peer transactions of electricity, thermal, and natural gas are optimized through energy cooperation and scheduling strategies. Subsequently, a Nash-Harsanyi bargaining game revenue allocation method that considers both fairness and renewable energy accommodation is proposed to ensure the stable operation of the alliance. Finally, simulations are conducted on a multi-RIES in northern China, demonstrating that the proposed model and approach can achieve inter-grid flexible resource complementarity, improve RIES economics, increase local renewable energy accommodation rate, and reduce carbon emissions.