Data Driven Approach for User-owned Renewable Energy Sources Allocation in Community Microgrid

Massive integration of photovoltaic (PV) and energy storage systems (ESS) in the energy system increases the utilization of renewable energy and system complexity. Optimal and centralized scheduling and management of these sources can make them more effective and acceptable in the energy community. In this regard, this paper proposes a virtual scheduling of the PV-ESS system based on a deep learning-based data-driven approach. The proposed system divides the time span into two segments based on the availability of PV generation. A deep learning model is applied to predict the day-ahead PV generation and demand during the two-time segments. An optimization algorithm is then applied to schedule the PV and ESS based on the predictive outcomes. For validating the proposed system, a community microgrid framework is considered where multiple PV and ESS systems are connected. In addition, the user-owned PV-ESS systems are controlled by the microgrid. The simulation results demonstrate the proposed system’s performance in terms of reliability and sustainability.