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Development of wind farm integrated battery energy storage system to mitigate the variability of power generation
Author(s)
Mbanjwa, Sibonelo Lucky-boy
Date Issued
2026
Type
master thesis
Publisher
Cape Peninsula University of Technology
Abstract
Wind power generation, while a key renewable energy source, suffers from inherent variability due to fluctuating wind conditions, leading to grid instability, voltage fluctuations, and unreliable power supply. This study addresses these challenges by developing and assessing a wind farm integrated Battery Energy Storage System (BESS) at the 21 MW Chaba Wind Farm in South Africa, comprising seven 3.075 MW turbines. The integration aims to mitigate power generation fluctuations, enhance grid stability, and ensure compliance with grid codes, while accounting for economic and environmental factors to support sustainable renewable energy adoption. Quasi-dynamic simulations were conducted using DIgSILENT PowerFactory to evaluate system performance under various scenarios, including normal operations, a 3-phase fault at cable, and a 15 MW load increase. Data was sourced from Chaba Wind Farm's operational records via SCADA (Vestas Business Online Client). The BESS control strategy involved charging during high-wind periods and discharging when output fell below a set threshold. The BESS integration achieved up to a 30% reduction in voltage fluctuations, frequency stabilisation within 0.1 Hz, and a 50% faster fault recovery compared to non-BESS scenarios. It improved Fault Ride-Through (FRT) capabilities for double-fed induction generators and stabilised active/reactive power and current profiles. Despite limitations such as single-site focus and high initial costs, the model demonstrated cost-efficiency through energy arbitrage, validating BESS's role in enhancing power output reliability and grid resilience. This research provides a replicable assessment for global wind-BESS integration, recommending 50-75% BESS capacity with predictive controls, while highlighting the need for policy support to address costs and battery lifespan. Future work should explore AI-driven optimisation and multi-source renewables to advance further South Africa's clean energy transition and sustainable energy infrastructure worldwide.
Additional information
Thesis (MEng (Electrical Engineering))--Cape Peninsula University of Technology, 2026
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Name
Mbanjwa SL_216150450 (1).pdf
Size
7.11 MB
Format
Adobe PDF
Checksum
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