Stochastic Energy Management for Battery Storage System-Based Microgrid Considering Different Forecasting Models

As a result of the recent innovations in the deployment of distributed Energy Storage Systems (ESS) such as Battery Energy Storage Systems (BESS), this technology can play an important role as a distributed energy resource in supplying the system demand for the new generation of power systems. BESSs are considered promising technology in the microgrid to reduce reliance on fossil fuels and mitigate the intermittency from renewable energy sources in a microgrid in which operators adjust electricity procurement and storage decisions in dynamic response to changes in demand, supply, and pricing in real-time to increase the profit. Optimization approaches have been proposed as a way to increase the user’s profits from microgrid participation. However, renewable energy resources are highly uncertain and cannot be effectively predicted which affects the operation of the microgrid-based BESS. Therefore, this study presents a layout design of energy planning for microgrids using the stochastic energy management model. The proposed management algorithm aims to find a day-ahead optimal operation of the microgrid including the photovoltaic (PV) system, and BESS considering demand response programs in the grid-connected mode. The stochastic model operates a grid-tied PV-BESS-based microgrid asset to inject the power into the main grid and store the surplus energy to maximize the profit. Besides, to create the stochastic energy management model, machine learning and deep learning models are used to generate proper and accurate scenarios for the PV system. Finally, a systematic simulation based on time-varying electricity prices was carried out using a day-ahead profile of a single load profile.