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

Spyros Giannelos, Anjali Jain, Stefan Borozan, Paola Falugi, Alexandre Moreira, Rohit Bhakar, Jyotirmay Mathur and Goran Strbac
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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

Energies, 2021, vol. 14, issue 22, 1-27

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.

Keywords: energy storage; flexibility; India; nested benders decomposition; network planning; option value; stochastic optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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