Stationary Mine Electrification (FBI CRC No. 039)

The Challenge 

Western Australia’s remote mine sites face high energy costs and growing pressure to decarbonise. Integrating solar PV with battery storage into mine microgrids is complex due to intermittent generation, variable mining loads, and the need for fast, intelligent battery control to maintain stable grid operation. 

Key Findings 

High-accuracy digital twin models of vanadium redox flow (VRF) and sodium-sulphur (NaS) batteries were developed and validated against IGO Nova site operational data, with NaS modelling errors below 1.8%. Novel model-independent optimal control algorithms, integral reinforcement learning H∞ control for VRF and finite-control-set model predictive control for NaS, were designed and verified through hardware-in-the-loop testing. Results confirmed VRF effectively stabilises low voltage microgrids, while higher-rated batteries are required for MW-scale primary mining loads. 

Benefits to WA 

The project equips WA’s mining sector with validated digital tools and intelligent battery control strategies to integrate renewables at mine sites, reducing grid dependence and electricity costs while supporting decarbonisation. It also delivered industry-relevant battery sizing guidance and trained five PhD researchers, strengthening the State’s clean-energy workforce capability. 

DOI

Zhang et al., “Long-Term Energy and Peak Power Demand Forecasting Based on Sequential-XGBoost,” in IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 3088-3104, March 2024,doi: 10.1109/TPWRS.2023.3289400 

Yulin Liu, Ran Li, Binyu Xiong, Shaofeng Zhang, Xinan Zhang, Herbert Iu, Tyrone Fernando, “A novel vanadium redox flow battery modelling method using honey badger optimization assisted CNN-BiLSTM”, Journal of Power Sources, Volume 558, 2023, https://doi.org/10.1016/j.jpowsour.2022.232610  

Liu et al., “A Novel Integral Reinforcement Learning-Based Control Method Assisted by Twin Delayed Deep Deterministic Policy Gradient for Solid Oxide Fuel Cell in DC Microgrid,” in IEEE Transactions onSustainable Energy, vol. 14, no. 1, pp. 688-703, Jan. 2023,doi: 10.1109/TSTE.2022.3224179.  

Liu et al., “A Novel Adaptive Model Predictive Control for Proton Exchange Membrane Fuel Cell in DC Microgrids,” in IEEE Transactions on Smart Grid, vol. 13, no. 3, pp. 1801-1812, May 2022,doi: 10.1109/TSG.2022.3147475.