Optimizing Energy Storage Management in Hybrid Solar Wind Systems with Deep Learning-Based Forecasting and Control

Hybrid Solar-Wind Systems require effective Energy Storage Management to efficiently integrate intermittent renewable energy sources. This involves optimizing the charging and discharging of energy storage devices to maximize system efficiency, enhance grid stability, and improve economic returns. The inherent intermittency and variability of solar and wind power generation pose a significant challenge for effective energy storage management in hybrid systems. Furthermore, the substantial initial investment costs associated with energy storage technologies can significantly impact the economic viability of these systems. This approach utilizes Feedforward Neural Network (FNN) and Gated Recurrent Units (GRU), to model the time-dependent patterns in solar and wind power generation, addressing the issues of intermittency and variability, and enhancing energy storage management to boost the economic viability of integrated renewable energy systems. FNN and GRU, are highly effective at modelling long- term dependencies in time-series data, leading to more precise forecasts of energy generation and consumption in hybrid renewable systems. FNN and GRU capability to manage fluctuating and unpredictable energy inputs improves the stability of energy storage management, resulting in more efficient and economically viable operation of integrated solar- wind systems. Location 2 achieved the most accurate predictions with the lowest error values (MSE: 0.0111, RMSE: 0.1056, MAE: 0.0863), whereas Location 3 experienced the highest errors, highlighting the difficulties in forecasting energy generation across various locations.