Multi-Stage Optimization of Community Microgrid Considering Fair Allocation and Risk Management

As renewable energy and environmental protection gain prominence, community microgrid has become crucial for promoting resource sharing and improving energy efficiency. This paper presents a multi-stage optimization strategy for community microgrid considering fair allocation and risk management, utilizing the Vickrey-Clarke-Groves (VCG) mechanism and the Glue Value at Risk (GlueVaR) method. This strategy integrates carbon with the collective self-consumption (CSC) framework, using GlueVaR to manage uncertainties in photovoltaic (PV) power generation by balancing economic performance with extreme risk management. Compared to traditional risk managements, GlueVaR offers a more comprehensive characterization of both tail risks and central tendency, enabling more robust decision-making under uncertainty. The VCG mechanism ensures accurate supply and demand reporting, thereby optimizing resource allocation. The strategy aims to promote fair allocation, enhance community welfare, reduce carbon emissions, and optimize energy utilization. A distributed alternating direction method of multipliers (ADMM) algorithm is employed to improve computational efficiency and preserve the privacy of community members, making the strategy scalable to various community microgrid sizes. Case studies confirm that this strategy significantly enhances community benefits, mitigates carbon emissions, and strengthens system stability and security. Furthermore, by fostering fair and transparent transactions among members, it reinforces community cohesion and long-term sustainability.