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Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty

Author

Listed:
  • Spyros Giannelos

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Anjali Jain

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Stefan Borozan

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Paola Falugi

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Alexandre Moreira

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Rohit Bhakar

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Jyotirmay Mathur

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Goran Strbac

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

Abstract

Considerable investment in India’s electricity system may be required in the coming decades in order to help accommodate the expected increase of renewables capacity as part of the country’s commitment to decarbonize its energy sector. In addition, electricity demand is geared to significantly increase due to the ongoing electrification of the transport sector, the growing population, and the improving economy. However, the multi-dimensional uncertainty surrounding these aspects gives rise to the prospect of stranded investments and underutilized network assets, rendering investment decision making challenging for network planners. In this work, a stochastic optimization model is applied to the transmission network in India to identify the optimal expansion strategy in the period from 2020 until 2060, considering conventional network reinforcements as well as energy storage investments. An advanced Nested Benders decomposition algorithm was used to overcome the complexity of the multistage stochastic optimization problem. The model additionally considers the uncertainty around the future investment cost of energy storage. The case study shows that deployment of energy storage is expected on a wide scale across India as it provides a range of benefits, including strategic investment flexibility and increased output from renewables, thereby reducing total expected system costs; this economic benefit of planning with energy storage under uncertainty is quantified as Option Value and is found to be in excess of GBP 12.9 bn. The key message of this work is that under potential high integration of wind and solar in India, there is significant economic benefit associated with the wide-scale deployment of storage in the system.

Suggested Citation

  • Spyros Giannelos & Anjali Jain & Stefan Borozan & Paola Falugi & Alexandre Moreira & Rohit Bhakar & Jyotirmay Mathur & Goran Strbac, 2021. "Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty," Energies, MDPI, vol. 14(22), pages 1-27, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7813-:d:684993
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    References listed on IDEAS

    as
    1. Mateusz Andrychowicz, 2021. "The Impact of Energy Storage along with the Allocation of RES on the Reduction of Energy Costs Using MILP," Energies, MDPI, vol. 14(13), pages 1-15, June.
    2. Mateusz Andrychowicz, 2021. "RES and ES Integration in Combination with Distribution Grid Development Using MILP," Energies, MDPI, vol. 14(2), pages 1-19, January.
    3. Spyros Giannelos & Predrag Djapic & Danny Pudjianto & Goran Strbac, 2020. "Quantification of the Energy Storage Contribution to Security of Supply through the F-Factor Methodology," Energies, MDPI, vol. 13(4), pages 1-15, February.
    4. Laha, Priyanka & Chakraborty, Basab, 2021. "Cost optimal combinations of storage technologies for maximizing renewable integration in Indian power system by 2040: Multi-region approach," Renewable Energy, Elsevier, vol. 179(C), pages 233-247.
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    Cited by:

    1. Spyros Giannelos & Stefan Borozan & Marko Aunedi & Xi Zhang & Hossein Ameli & Danny Pudjianto & Ioannis Konstantelos & Goran Strbac, 2023. "Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids," Energies, MDPI, vol. 16(13), pages 1-15, June.
    2. Aditya H. Bhatt & Mireille Rodrigues & Federico Bernardoni & Stefano Leonardi & Armin Zare, 2023. "Stochastic Dynamical Modeling of Wind Farm Turbulence," Energies, MDPI, vol. 16(19), pages 1-24, September.
    3. Riccardo Risso & Lucia Cardona & Maurizio Archetti & Filippo Lossani & Barbara Bosio & Dario Bove, 2023. "A Review of On-Board Carbon Capture and Storage Techniques: Solutions to the 2030 IMO Regulations," Energies, MDPI, vol. 16(18), pages 1-25, September.
    4. Spyros Giannelos & Alexandre Moreira & Dimitrios Papadaskalopoulos & Stefan Borozan & Danny Pudjianto & Ioannis Konstantelos & Mingyang Sun & Goran Strbac, 2023. "A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector," Energies, MDPI, vol. 16(6), pages 1-37, March.

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