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Rahul Tongia

Publications and source records attributed to Rahul Tongia.

2 recordsLinked to original sources

Sustained cost declines in solar PV and battery storage needed to eliminate coal generation in India

Unabated coal power in India must be phased out by mid-century to achieve global climate targets under the Paris Agreement. Here we estimate the costs of hybrid power plants - lithium-ion battery storage with wind and solar PV - to replace coal generation. We design least cost mixes of these technologies to supply stylized baseload and load-following generation profiles in three Indian states - Karnataka, Gujarat, and Tamil Nadu. Our analysis shows that availability of low cost capital, solar PV capital costs of at least $250/kW, and battery storage capacity costs at least 50% cheaper than current levels will be required to phase out existing coal power plants. Phaseout by 2040 requires a 6% annual decline in the cost of hybrid systems over the next two decades. We find that replacing coal generation with hybrid systems 99% of the hours over multiple decades is roughly 40% cheaper than 100% replacement, indicating a key role for other low cost grid flexibility mechanisms to help hasten coal phaseout. Solar PV is more suited to pairing with short duration storage than wind power. Overall, our results describe the challenging technological and policy advances needed to achieve the temperature goals of the Paris Agreement.

econ.GN

Balancing India's 2030 Electricity Grid Needs Management of Time Granularity and Uncertainty: Insights from a Parametric Model

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).

econ.GN