Predictive control and optimisation of battery energy storage system for wind power smoothing using machine learning and constrained optimisation

Abstract

Operating wind power plants with constant output is essential for grid integration and liberalised energy market participation. This study presents an integrated framework for predictive control and optimisation of Battery Energy Storage Systems (BESS) to stabilise wind power output. A triad machine learning model, combining Long Short-Term Memory (LSTM), Artificial Neural Networks (ANN), and Linear Regression (LR), achieves highly accurate wind forecasts (RMSE = 0.000,027 MW), outperforming existing benchmarks. These forecasts coupled with Sequential Least-Squares Quadratic Programming (SLSQP) algorithm to optimise BESS operation while satisfying constraints on state-of-charge, inverter capacity, and battery life, with maximum deviation limited to 0.07%. A mean-based neural network model reduces required BESS capacity to 11.5% of wind farm capacity, compared to 15%–30% in prior studies. Validated using operational data from the Thambapavani wind farm, the framework ensures constraint compliance, extends battery lifespan, reduces variability, and offers a scalable solution for reliable wind energy integration.