Intelligent optimisation for onshore wind farm battery energy storage systems

Abstract

Intelligent optimisation is essential for enhancing the performance and stability of onshore wind farms integrated with Battery Energy Storage Systems (BESSs), optimising turbine positioning, energy storage, and output to ensure efficient operation and sustained grid stability. Notwithstanding considerable advances in intelligent optimisation, there has been limited success in effectively integrating the diverse impacts of wind conditions and BESS state of health (SOH) while ensuring grid stability. This study investigates the application of intelligent optimisation for integrating BESS with onshore wind farms to augment energy storage capacity and ensure grid reliability. The proposed model incorporates a neural network (NN)-based inverse model, Bayesian optimisation, Gaussian Process Regression (GPR), and Reinforcement Particle Swarm Optimisation (RPSO) to enhance wind energy production while providing accurate estimates of system performance. Significant results from a case study of an onshore wind farm in South Africa and a 138 MW BESS demonstrate that the proposed intelligent optimisation model integrating NN-based inverse modelling, Bayesian-optimised GPR for SOH estimation, and RPSO enhance overall wind farm efficiency by 15-20% and reduce energy storage management errors by up to 10%. The proposed model is validated using NASA’s battery degradation datasets, achieving an RMSE of less than 1% in SOH estimation. These results highlight the potential of intelligent optimisation to enhance wind farms' performance, reliability, and stability integrated with battery energy storage systems, offering a scalable solution for renewable energy management.