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Simulated co-optimization of renewable energy and desalination systems in Neom, Saudi Arabia

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  • Jefferson A. Riera

    (King Abdullah University of Science and Technology, (KAUST))

  • Ricardo M. Lima

    (King Abdullah University of Science and Technology, (KAUST))

  • Ibrahim Hoteit

    (King Abdullah University of Science and Technology, (KAUST))

  • Omar Knio

    (King Abdullah University of Science and Technology, (KAUST))

Abstract

The interdependence between the water and power sectors is a growing concern as the need for desalination increases globally. Therefore, co-optimizing interdependent systems is necessary to understand the impact of one sector on another. We propose a framework to identify the optimal investment mix for a co-optimized water-power system and apply it to Neom, Saudi Arabia. Our results show that investment strategies that consider the co-optimization of both systems result in total cost savings for the power sector compared to independent approaches. Analysis results suggest that systems with higher shares of non-dispatchable renewables experience the most significant cost reductions.

Suggested Citation

  • Jefferson A. Riera & Ricardo M. Lima & Ibrahim Hoteit & Omar Knio, 2022. "Simulated co-optimization of renewable energy and desalination systems in Neom, Saudi Arabia," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31233-3
    DOI: 10.1038/s41467-022-31233-3
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    References listed on IDEAS

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    1. Steven A. Gabriel & Antonio J. Conejo & J. David Fuller & Benjamin F. Hobbs & Carlos Ruiz, 2013. "Complementarity Modeling in Energy Markets," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4419-6123-5, September.
    2. Dasari, Hari Prasad & Desamsetti, Srinivas & Langodan, Sabique & Attada, Raju & Kunchala, Ravi Kumar & Viswanadhapalli, Yesubabu & Knio, Omar & Hoteit, Ibrahim, 2019. "High-resolution assessment of solar energy resources over the Arabian Peninsula," Applied Energy, Elsevier, vol. 248(C), pages 354-371.
    3. Al-Nory, Malak & El-Beltagy, Mohamed, 2014. "An energy management approach for renewable energy integration with power generation and water desalination," Renewable Energy, Elsevier, vol. 72(C), pages 377-385.
    4. Pfenninger, Stefan, 2017. "Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability," Applied Energy, Elsevier, vol. 197(C), pages 1-13.
    5. Siddiqi, Afreen & Anadon, Laura Diaz, 2011. "The water-energy nexus in Middle East and North Africa," Energy Policy, Elsevier, vol. 39(8), pages 4529-4540, August.
    6. Benjamin F. Hobbs, 1991. "The "Most Value" Test: Economic Evaluation of Electricity Demand-Side Management Considering Customer Value," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 67-92.
    7. Lara, Cristiana L. & Mallapragada, Dharik S. & Papageorgiou, Dimitri J. & Venkatesh, Aranya & Grossmann, Ignacio E., 2018. "Deterministic electric power infrastructure planning: Mixed-integer programming model and nested decomposition algorithm," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1037-1054.
    8. Dagoumas, Athanasios S. & Koltsaklis, Nikolaos E., 2019. "Review of models for integrating renewable energy in the generation expansion planning," Applied Energy, Elsevier, vol. 242(C), pages 1573-1587.
    9. Teichgraeber, Holger & Brandt, Adam R., 2019. "Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison," Applied Energy, Elsevier, vol. 239(C), pages 1283-1293.
    10. Langodan, Sabique & Viswanadhapalli, Yesubabu & Dasari, Hari Prasad & Knio, Omar & Hoteit, Ibrahim, 2016. "A high-resolution assessment of wind and wave energy potentials in the Red Sea," Applied Energy, Elsevier, vol. 181(C), pages 244-255.
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