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

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.

Investments in Electricity Distribution Grids: Strategic versus Incremental Planning

“…Moreover, it is of interest to the authors to compare the economic benefits resulting from the presented analysis to benefits arising from the application of other frameworks such as Backwards Induction methodology [35], machine-learning approaches [36] and stochastic optimisation [37]. Finally, the authors are interested in expanding the sensitivity analysis to include more parameters, such as changes in consumer behaviour, external economic factors and different geographical regions with varying energy demands and backgrounds.…”

Specialization Patterns for the Development of Renewable Energy Generation Technologies across Countries

“…At the same time, a further study could analyze the link between the geographic position, neighborhood, and natural resources in determining the pattern. The other developments of the present study could be considered the optimization models at the country level [75] or building scenarios for reaching carbon neutrality [76].…”

Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids

“…Examples of the former include the Dynamic Line Rating system, Energy Storage [7], Vehicle-to-Building (V2B), Vehicle-to-Grid (V2G), Gridto-Vehicle (G2V) [8,9], Demand-Side Response (DSR) [10], Coordinated Voltage Control (CV) and Soft Open Points (SOP) [11]. Such technologies have already been deployed in various regions such as in India [12], China, Brazil, the U.K. and countries of the European Union [13].…”