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Intra-regional renewable energy resource variability in long-term energy system planning

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  • Das, Partha
  • Kanudia, Amit
  • Bhakar, Rohit
  • Mathur, Jyotirmay

Abstract

This article presents a framework to capture intra-regional spatial variation of solar and wind resources’ potential for long-term energy system planning. Geographic Information System and statistical tools are utilized to quantify class and time slice wise capacity and generation potential at a geographical grid-cell level. This is incorporated in an energy system planning model with high spatial and temporal resolution. An advanced bottom-up energy system modeling framework is used to develop the model. The North-Indian power sector is considered as a case study with the analysis of renewable energy penetration and curtailment levels, technology capacity, and role of storage and inter-regional energy exchange for many future energy transition scenarios. Various model cases indicate system transition towards high renewable energy penetrated generation portfolio. Solar energy curtailment is prominent in high renewable scenarios. Coal-based power plants are important generation options unless drastic high CO2 price is considered. Storage systems work as energy arbitrage devices. Quantifying intra-regional renewable energy capacity and generation potential and incorporating them in a long-term planning model with many annual time slices is a contribution to the current modeling practices. Analysis of varied sensitivity cases adds value to long-term large-scale variable renewable energy integration planning approaches.

Suggested Citation

  • Das, Partha & Kanudia, Amit & Bhakar, Rohit & Mathur, Jyotirmay, 2022. "Intra-regional renewable energy resource variability in long-term energy system planning," Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222002055
    DOI: 10.1016/j.energy.2022.123302
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    2. Kim, Gyeongmin & Hur, Jin, 2023. "A probabilistic approach to potential estimation of renewable energy resources based on augmented spatial interpolation," Energy, Elsevier, vol. 263(PA).
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    5. Zaiter, Issa & Ramadan, Mohamad & Bouabid, Ali & El-Fadel, Mutasem & Mezher, Toufic, 2023. "Potential utilization of hydrogen in the UAE's industrial sector," Energy, Elsevier, vol. 280(C).

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