Publication
Energy storage systems can improve the performance of home energy management systems and also provide ancillary services in electricity markets. However, the economic and technical insufficiency of individual home storage units, makes employing them inefficient for domestic storage and market applications, respectively. Therefore, central storage units with capacity sharing capability to small consumers as Cloud Energy Storages (CESs) are becoming a promising solution, and are also potentially able to contribute to ancillary service provision in the market. In this context, this paper aims to develop a novel stochastic day-ahead distributed management framework for CES to simultaneously adopt the optimal strategy of storage allocation for photovoltaicintegrated homes and participation in energy and ancillary service markets. The developed distributed framework is formulated within the Benders decomposition approach in which CES strategy and home energy management systems are optimized through master and subproblems based on linear mathematical models, respectively. Furthermore, the uncertainty of market prices is handled by the Conditional Value at Risk (CVaR) method. The developed model is assessed through real-world case studies of ERCOT. The results highlight the effectiveness of the collaborative service framework for both CES and households. Energy arbitrage through local storage services compensates for CES's challenges such as degradation and low power rating in the provision of energy services. These improvements benefit CES owners with higher net revenues and households with reduced cost of electricity supply.