Dariush Keihan Asl

Energy storage systems provide ancillary services, support grid flexibility, and balance energy in residential systems. Among them, cloud energy storage (CES) offers scalability for large markets, capacity-sharing for small consumers, and participation in multiple energy and ancillary service markets. This paper proposes a risk-based, day-ahead distributed energy management framework for coordinated optimization between CES and interconnected clustered residential houses (CRHs) within a multi-area energy management system (MAEMS). The framework models CES and CRHs as independent agents capable of direct power trading, virtual-capacity rental, and peer-to-peer (P2P) energy exchange, while maintaining local autonomy and data privacy. Market price uncertainty is incorporated using a conditional value-at-risk (CVaR) formulation, ensuring risk-averse scheduling under volatility. Coordination between CES profit maximization and CRH cost minimization is achieved via a compact Benders decomposition (CBDM)-based master–subproblem approach, enabling scalable, distributed optimization. Three case studies demonstrate that integrating multi-market CES participation, P2P trading, bidirectional energy exchanges, and virtual battery rentals significantly improves performance: CES net profit rises by approximately 30%, CRHs' total operating costs drop by ~75%, and CES degradation costs decrease through optimized charge–discharge management. Overall, the proposed MAEMS enhances both economic efficiency and operational resilience under risk-constrained conditions.