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

The interconnection of multiple regional integrated energy systems (RIES) can effectively enhance the lowcarbon 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.