Optimal Energy Management System Using Probabilistic Day-ahead Forecasting

The power loss and economic revenue of a photovoltaic storage system (solar energy+storage system) can be optimized by integrating probabilistic photovoltaic (PV) forecasting into control strategies. An energy management system (EMS) aims to optimize energy utilization based on certain constraints (such as the grid code) and improve the reliability of energy injection into the electrical network. Solar energy production is dependent on factors such as weather conditions and time of day. Therefore, precise PV forecasting can aid an energy management system in predicting the solar energy quantity that will be available at any given time, allowing for optimal management of the storage system. In this article, we explore the performance of a PV storage system with integrated probabilistic forecasting under different scenarios. Our analysis focuses on evaluating the impact of the forecasting strategy on the energy balance and revenue maximization. We compare the results obtained using probabilistic, deterministic, and persistence-based forecasts over a one-year simulation period. Our findings demonstrate the effectiveness of the system in prioritizing electricity injection during peak hours, resulting in high electricity injection and low penalties. Overall, accurate PV forecasting plays a crucial role in optimizing the performance of a PV storage system and maximizing revenue.