Onshore wind farm battery energy storage systems optimisation

This study enhances the integration of Battery Energy Storage Systems (BESS) into onshore wind farms by improving efficiency, reliability, stability, and sustainability, thereby addressing significant challenges in renewable energy systems. Utilising advanced methodologies such as intelligent optimisation, hybrid forecasting models, and complex algorithms, the research provides innovative solutions for enhancing grid integration and energy management. The study aligns with the United Nations Sustainable Development Goals (SDGs), specifically SDGs 7 (Affordable and Clean Energy), 13 (Climate Action), and 8 (Decent Work and Economic Growth), advocates for economic prosperity, environmental stewardship, and social equity through sustainable renewable energy technologies. 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. The study further introduces hybrid forecasting models that combine Long Short-Term Memory (LSTM) networks, Complementary Ensemble Empirical Mode Decomposition (CEEMD), and hybrid optimisation (ACO-GA-PSO) to attain accurate long-term predictions of wind energy variability. Lastly, the study reviews and applies a comprehensive techno-economic framework spanning Levelized Cost of Energy (LCOE), Levelized Cost of Storage (LCOS), Levelized Avoided Cost of Energy (LACE), and analysis together with layout and storage optimisation models to determine economically efficient BESS configurations for onshore wind. These frameworks undergo thorough validation through simulations, empirical case studies, and data analysis, with case studies of the 138 MW Gouda wind farm and the 69MW Jeffreys Bay wind farm in South Africa. The research also includes identifying resilient techno-economic models that align technical efficiency, cost-effectiveness, and long-term sustainability for BESS integration. The research significantly advances global renewable energy development by tackling challenges such as wind energy variability, battery energy storage system stability, and grid reliability. It achieves a 15-20% increase in energy efficiency and a 10% reduction in energy management errors, demonstrating practical applicability. The research supports sustainability objectives by reducing carbon footprints and greenhouse gas emissions, promoting environmental preservation, and generating economic and social benefits, including job creation and sustainable development. These advancements enhance operational efficiency, decision-making, and energy storage management, establishing onshore wind farms as fundamental components of a robust energy future. The study's innovative frameworks, validated through peer-reviewed publications and real-world applications, provide a scalable, transformative approach to renewable energy management. This research enhances global initiatives for a sustainable, low-carbon, and equitable energy framework by incorporating intelligent systems into renewable energy infrastructure, facilitating the expedited adoption of renewable energy worldwide.

Grantor dc:publisher

University of Pretoria

Year dc:date.issued

2025

Advisor dc:contributor.advisor

• Bansal, Ramesh C.

• UCTD
• Sustainable Development Goals (SDGs)
• Onshore
• Wind farm
• Energy storage systems
• Battery
• Optimisation

Statement dc:rights

• © 2024 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.

Language dc:language.iso

en

DOI

https://doi.org/10.25403/UPresearchdata.30189925

Dc Identifier Other

A2026

OAI identifier oai:identifier

oai:repository.up.ac.za:2263/108041