Electricity Market Hypergraph Decision Optimization Model and Its Solution Method

In the process of establishing a unified national electricity market system, the diversification of market levels, transaction types, and market agents has led to complex dynamic networks formed by interactions such as strategy influence and learning among agents. This provides a favorable backdrop for certain entities to exercise market power and manipulate market prices. To address this, an electricity market hypergraph decision model and its optimization method are proposed to describe the interaction network and mitigate market agents’ manipulative behaviors. Firstly, market agents are treated as nodes, and hyperedges are constructed by splitting the agents’ declared capacities to mitigate the market power of agents. Then, based on the “majority rule”, a hyperedge pricing decision is made, with hyperedge prices replacing individual agents’ declared prices in the market clearing process. Finally, the optimal electricity market hypergraph(OEMH) is determined to minimize the total system cost. However, as the number of nodes increases, the number of potential hypergraphs grows exponentially. To manage this complexity, the HHI of hypergraph(THI) to is introduced to narrow the solution space and accelerate the solving process. Simulation calculations are conducted under three scenarios: baseline load, light load, and high load. The results show that the hyperedge decision model can accurately depict the dynamic nature of the interaction network, reduce its complexity, and significantly weaken agents’ market power; the optimal hypergraph achieves the lowest total system cost.