  With some of the world's most ambitious renewable energy (RE) growth targets,
especially when normalized for scale, India aims more than quadrupling wind and
solar by 2030. Simultaneously, coal dominates the electricity grid, providing
roughly three-quarters of electricity today. We present results from the first
of a kind model to handle high uncertainty, which uses parametric analysis
instead of stochastic analysis for grid balancing based on economic despatch
through 2030, covering 30-minute resolution granularity at a national level.
The model assumes a range of growing demand, supply options, prices, and other
uncertain inputs. It calculates the lowest cost portfolio across a spectrum of
parametric uncertainty. We apply simplifications to handle the intersection of
capacity planning with optimized despatch. Our results indicate that very high
RE scenarios are cost-effective, even if a measurable fraction would be surplus
and thus discarded ("curtailed"). We find that high RE without storage as well
as existing slack in coal- and gas-powered capacity are insufficient to meet
rising demand on a real-time basis, especially adding time-of-day balancing.
Storage technologies prove valuable but remain expensive compared to the 2019
portfolio mix, due to issues of duty cycling like seasonal variability, not
merely inherent high capital costs. However, examining alternatives to
batteries for future growth finds all solutions for peaking power are even more
expensive. For balancing at peak times, a smarter grid that applies demand
response may be cost-effective. We also find the need for more sophisticated
modelling with higher stochasticity across annual timeframes (especially year
on year changes in wind output, rainfall, and demand) along with uncertainty on
supply and load profiles (shapes).
