A decarbonization-oriented and uncertainty-aware energy management strategy for multi-district integrated energy systems with fair peer-to-peer trading

Multi-District Integrated Energy Systems play a crucial role in achieving decarbonization goals and enhancing renewable energy utilization. However, the inherent uncertainties in source-load side and market fluctuations pose significant challenges for stable energy management and fair market participation. To address these issues, this study presents a decarbonization-oriented and uncertainty-aware energy management strategy, integrating a Newton-Raphson-Based Optimizer-enhanced Density-Based Spatial Clustering of Applications with Noise clustering algorithm, an enhanced Alternating Direction Method of Multipliers optimization framework, and an Asymmetric Nash Bargaining model for Fair Peer-to-Peer Trading. The proposed Newton-Raphson-Based Optimizer-enhanced Density-Based Spatial Clustering of Applications with Noise dynamically optimizes clustering parameters, reducing scenario Root Mean Square Error by 40 % compared to baseline Density-Based Spatial Clustering of Applications with Noise. The enhanced Alternating Direction Method of Multipliers solver accelerates system convergence by 50 %, enabling real-time scheduling optimization. Furthermore, the fairness-aware Asymmetric Nash Bargaining model incorporates carbon emissions, demand-side response, and prosumer contributions, ensuring equitable revenue allocation while reducing carbon emissions by 51.4 % and system costs by 18.5 %. The findings demonstrate that decarbonization-oriented and uncertainty-aware energy management strategy offers a robust and scalable solution for the next generation of sustainable energy markets, supporting both carbon neutrality and economic efficiency.