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A data-driven robust optimization for multi-objective renewable energy location by considering risk

Author

Listed:
  • Reza Lotfi

    (Yazd University
    Behineh Gostar Sanaye Arman)

  • Bahareh Kargar

    (Iran University of Science and Technology)

  • Alireza Gharehbaghi

    (Sharif University of Technology)

  • Mohamad Afshar

    (Islamic Azad University Central Tehran Branch)

  • Mohammad Sadra Rajabi

    (University of Tehran)

  • Nooshin Mardani

    (Islamic Azad University)

Abstract

Using Renewable Energy (RE) is growing day by day. We need to locate RE in the best place to maximize energy production and supplier profit. As a result, we propose a novel method for RE location (REL). This model suggests a Data-Driven Robust Optimization (DDRO) for multi-objective REL by considering Risk (DDROMORELR). We consider risk by adding min function in energy and profit objectives (government and supplier objectives). A DDRO approach is added to the model to tackle uncertainty and be close to the real world. We utilize an improved Augmented ε-constraint (AUGEPS2) to solve objectives and produce a Pareto front. We compare problems with DDRO and without considering DDRO, and the profit and energy production without DDRO is less than with DDRO, and its gap is -7%. We change the conservativity coefficient, the Rate Of Return (ROR), and the scale of the problem. The supplier’s profit and energy production increase by decreasing the conservativity coefficient. By increasing the ROR, the profit function and energy production decrease. By increasing the scale of the problem, the time solution increased. Finally, we suggest regression between time solution and sets.

Suggested Citation

  • Reza Lotfi & Bahareh Kargar & Alireza Gharehbaghi & Mohamad Afshar & Mohammad Sadra Rajabi & Nooshin Mardani, 2025. "A data-driven robust optimization for multi-objective renewable energy location by considering risk," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(9), pages 20813-20834, September.
    • Handle: RePEc:spr:endesu:v:27:y:2025:i:9:d:10.1007_s10668-022-02448-7
    DOI: 10.1007/s10668-022-02448-7
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