Planning and Decision-Making Method for Incomplete Information Game Among Multiple Energy Entities Considering Environmental Costs and Carbon Trading Mechanism

With the rapid development of integrated energy systems (IES) towards integration and marketization, the collaborative planning of multi-energy entities has become a research hotspot. However, in real-world market environments, various energy entities often face information asymmetry and competitive interests, posing significant challenges to the optimal scheduling of the system. To address the incomplete information and competitive constraints among multiple energy hubs (EH) within IES, this paper constructs a multi-entity game planning model that accounts for environmental costs and carbon trading mechanisms. The model employs Bayesian game methods to handle the incomplete information among EH and analyzes the dynamic interactive behaviors of market entities under different strategies through multilateral incomplete information evolutionary game theory. Meanwhile, this paper incorporates carbon trading mechanisms along with the coupling technologies of power-to-gas (P2G) and carbon capture systems (CCS) to balance the economic efficiency and environmental protection. Additionally, in response to investment uncertainty, the real options theory is utilized for evaluation, and then a multi-entity incomplete information planning model is constructed, which is solved by using a nested algorithm proposed in this paper. This approach balances the interests of various entities and enhances the comprehensive long-term investment returns considering options. Simulation results demonstrate that the model effectively reflects the game behaviors among multi-energy entities under incomplete information, yielding optimized scheduling solutions that closely align with real-world scenarios. It improves economic benefits while reducing environmental pollution, providing theoretical foundations and methodological support for the planning of integrated energy systems involving multiple entities in electricity market environments.