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A data-driven optimization model for renewable electricity supply chain design

Author

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  • Panahi, Homa
  • Sabouhi, Fatemeh
  • Bozorgi-Amiri, Ali
  • Ghaderi, S.F.
Abstract
The rising electricity demand, driven by technological progress and population growth, has made the design of the electricity supply chain a major challenge. The transition to renewable energy sources has led to a substantial reduction in the costs and greenhouse gases emissions of electricity production. This study proposes a hybrid approach consisting of a recurrent neural network and an optimization model for designing a resilient electricity supply chain based on wind and solar energies. First, the electricity demand is forecast using the long short-term memory algorithm. Second, based on the forecast demand, a two-stage stochastic programming model is developed for designing an electricity supply chain under disruption risks. Partial and complete disruptions in power lines and facilities, including power plants, distribution and subtransmission substations, and low-power solar panel farms are considered. The objective of the proposed model is to minimize the total cost of the supply chain by making optimal decisions on facility location; technology selection for power plants; setting up transmission lines; and the quantity of power generation, power transmission, and power shortage in low- and high-voltage energy consumption systems. Three strategies are adopted to enhance the supply chain's resilience: multiple renewable energy sources, multiple power transmission lines, and lateral power lines. Lastly, the Sistan and Baluchestan Electric Power Distribution Company in Iran is used as a case study for this work to demonstrate the applicability of the proposed approach, analyze the findings, and derive relevant managerial insights.

Suggested Citation

  • Panahi, Homa & Sabouhi, Fatemeh & Bozorgi-Amiri, Ali & Ghaderi, S.F., 2024. "A data-driven optimization model for renewable electricity supply chain design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:rensus:v:202:y:2024:i:c:s1364032124003447
    DOI: 10.1016/j.rser.2024.114618
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