Distributed robust hybrid game mechanism for interconnected multi-energy systems considering speculative behavior

Multi energy flow coupling and clean energy substitution are significant characteristics of Multi-Energy Systems (MES). For the coordinated operation of interconnected MESs, it is crucial to construct an operation mechanism that considers both regional autonomous operation and multilateral interaction. However, the intertwined coupling between intermittent renewable energy and speculative behaviors of participants poses considerable challenges to the game decision making of multiple stakeholders. To this end, this paper proposes a distributed robust hybrid game mechanism to support the collaborative operation of interconnected MESs. This enables us to construct a bilevel robust game model within the MES to coordinate the multilevel interactions between the operator and photovoltaic (PV) prosumers. In the upper level, the Multi Energy System Operator (MESO) adopts distributionally robust optimization based on multiple discrete scenarios to formulate operation plans satisfying system constraints. In the lower level, the energy interactions among PV prosumers are modeled as a Generalized Nash Equilibrium model with distributionally robust chance constraints, and variational inequalities are utilized to precisely characterize its optimality conditions. At the inter-MES interaction level, an asymmetric Nash bargaining model is introduced to pursue the maximization of social welfare. Subsequently, a quantitative analysis model of MES speculative behavior is proposed to reveal the specific impact of such behavior on the distributed operation mechanism. To efficiently solve the aforementioned hybrid game model, this paper proposes a distributed optimization algorithm incorporating robust operation constraints to obtain the optimal scheduling strategy. Numerical case studies demonstrate that the proposed method effectively quantifies speculative behavior and enhances system economy and robustness under uncertain scenarios.