Multi-Park Integrated Energy Optimization Method Based on Nodal Carbon Intensity and Dual Game Theory
This study addresses the ambiguous carbon responsibility allocation and insufficient decarbonization incentives among multiple parks under carbon trading and green certificate mechanisms. A dual-game optimization method based on nodal carbon intensity (NCI) is proposed. First, a carbon intensity model with park energy subnets as nodes is established, and a carbon responsibility allocation method is derived. Second, a Stackelberg game model between integrated energy providers (IEPs) and load aggregators (LAs) within a single park is constructed, while a Nash bargaining model governs cooperation among multiple parks, forming a dual-game mechanism. The model is transformed using interval possibility degree conversion and Karush-Kuhn-Tucker (KKT) conditions, and an accelerated alternating direction method of multipliers (ADMM) algorithm is developed for solution. Case studies validate the correctness and effectiveness of the proposed model and improved algorithm. Results demonstrate precise carbon responsibility division, reduced carbon emissions across multiple parks, and enhanced overall benefits. The accelerated ADMM achieves convergence with combined residual satisfying c_k ≤ η^k c_0, where η is the acceleration factor, and restart iterations ensure monotonic residual reduction. The method effectively integrates demand-side influence in peer-to-peer trading, carbon-related economics, and accurate carbon responsibility allocation, providing a robust framework for low-carbon operation of multi-park integrated energy systems under carbon trading and green certificate mechanisms.