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Addressing Uncertainty in Renewable Energy Integration for Western Australia’s Mining Sector: A Robust Optimization Approach

Author

Listed:
  • Mehrdad Ghahramani

    (School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia)

  • Daryoush Habibi

    (School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia)

  • Seyyedmorteza Ghamari

    (School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia)

  • Asma Aziz

    (School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia)

Abstract

The mining industry is a key contributor to Western Australia’s economy, with over 130 mining operations that produce critical minerals such as iron ore, gold, and lithium. Ensuring a reliable and continuous energy supply is vital for these operations. This paper addresses the challenges and opportunities of integrating renewable energy sources into isolated power systems, particularly under uncertainties associated with renewable energy generation and demand. A robust optimization approach is developed to model a multi-source hybrid energy system that considers risk-averse, risk-neutral, and risk-seeking strategies. These strategies address power demand and renewable energy supply uncertainties, ensuring system reliability under various risk scenarios. The optimization framework, formulated as a mixed integer linear programming problem and implemented in Python using the Gurobi Optimizer, integrates renewable energy sources such as wind turbines, photovoltaic arrays, and demand response programs alongside traditional diesel generators, boilers, combined heat and power units, and water desalination. The model ensures reliable access to electricity, heat, and water while minimizing operational costs and reducing reliance on fossil fuels. A comprehensive sensitivity analysis further examines the impact of uncertainty margins and the value of a lost load on the total system cost, providing insights into how different risk strategies affect system performance and cost-efficiency. The results are validated through three case studies demonstrating the effectiveness of the proposed approach in enhancing the resilience and sustainability of isolated power systems in the mining sector. Significant improvements in reliability, scalability, and economic performance are observed, with the sensitivity analysis highlighting the critical trade-offs between cost and reliability under varying uncertainty conditions.

Suggested Citation

  • Mehrdad Ghahramani & Daryoush Habibi & Seyyedmorteza Ghamari & Asma Aziz, 2024. "Addressing Uncertainty in Renewable Energy Integration for Western Australia’s Mining Sector: A Robust Optimization Approach," Energies, MDPI, vol. 17(22), pages 1-35, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5679-:d:1520267
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    References listed on IDEAS

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    1. Zheng, Shanshan & Hai, Qing & Zhou, Xiao & Stanford, Russell J., 2024. "A novel multi-generation system for sustainable power, heating, cooling, freshwater, and methane production: Thermodynamic, economic, and environmental analysis," Energy, Elsevier, vol. 290(C).
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    Cited by:

    1. Mehrdad Ghahramani & Daryoush Habibi & Seyyedmorteza Ghamari & Hamid Soleimani & Asma Aziz, 2025. "Renewable-Based Isolated Power Systems: A Review of Scalability, Reliability, and Uncertainty Modeling," Clean Technol., MDPI, vol. 7(3), pages 1-37, September.
    2. Mehrdad Ghahramani & Daryoush Habibi & Asma Aziz, 2025. "A Risk-Averse Data-Driven Distributionally Robust Optimization Method for Transmission Power Systems Under Uncertainty," Energies, MDPI, vol. 18(19), pages 1-29, October.

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