As the world struggles to reduce climate-warming carbon emissions, India has pledged to do its part, and its success is critical: In 2023, India was the third largest carbon emitter worldwide. The Indian government has committed to having net-zero carbon emissions by 2070.
To fulfill that promise, India will need to decarbonize its electric power system, and that will be a challenge: Fully 60% of India’s electricity comes from coal-burning power plants that are extremely inefficient. To make matters worse, the demand for electricity in India is projected to more than double in the coming decade due to population growth and increased use of air conditioning, electric cars, and so on.
Despite having set an ambitious target, the Indian government has not proposed a plan for getting there. Indeed, as in other countries, in India the government continues to permit new coal-fired power plants to be built and aging plants to be renovated and their retirement postponed.
To help India define an effective—and realistic—plan for decarbonizing its power system, key questions must be addressed. For example, India is already rapidly developing carbon-free solar and wind power generators. What opportunities remain for further deployment of renewable generation? Are there ways to retrofit or repurpose India’s existing coal plants that can substantially and affordably reduce their greenhouse gas emissions? And do the responses to those questions differ by region?
With funding from IHI Corporation through the MIT Energy Initiative (MITEI), Yifu Ding, a postdoctoral associate at MITEI, and her colleagues set out to answer those questions by first using machine learning to determine the efficiency of each of India’s current 806 coal plants and then investigating the impacts that different decarbonization approaches would have on the mix of power plants and the price of electricity in 2035 under increasingly stringent caps on emissions.
First step: Develop the needed dataset
An important challenge in developing a decarbonization plan for India has been the lack of a complete dataset describing the current power plants in India. While other studies have generated plans, they haven’t taken into account the wide variation in the coal-fired power plants in different regions of the country. “So, we first needed to create a dataset covering and characterizing all of the operating coal plants in India. Such a dataset was not available in the existing literature,” says Ding.
Making a cost-effective plan for expanding the capacity of a power system requires knowing the efficiencies of all the power plants operating in the system. For this study, the researchers used as their metric the “station heat rate,” a standard measurement of the overall fuel efficiency of a given power plant. The station heat rate of each plant is needed in order to calculate the fuel consumption and power output of that plant as plans for capacity expansion are being developed.
Some of the Indian coal plants’ efficiencies were recorded before 2022, so Ding and her team used machine-learning models to predict the efficiencies of all the Indian coal plants operating now. In 2024, they created and posted online the first comprehensive, open-sourced dataset for all 806 power plants in 30 regions of India. The work won the 2024 MIT Open Data Prize. This dataset includes each plant’s power capacity, efficiency, age, load factor (a measure indicating how much of the time it operates), water stress, and more.
In addition, they categorized each plant according to its boiler design. A “supercritical” plant operates at a relatively high temperature and pressure, which makes it thermodynamically efficient, so it produces a lot of electricity for each unit of heat in the fuel. A “subcritical” plant runs at a lower temperature and pressure, so it’s less thermodynamically efficient. Most of the Indian coal plants are still subcritical plants running at low efficiency.
Next step: Investigate decarbonization options
Equipped with their detailed dataset covering all the coal power plants in India, the researchers were ready to investigate options for responding to tightening limits on carbon emissions. For that analysis, they turned to GenX, a modeling platform that was developed at MITEI to help guide decision makers as they make investments and other plans for the future of their power systems.
Ding built a GenX model based on India’s power system in 2020, including details about each power plant and transmission network across 30 regions of the country. She also entered the coal price, potential resources for wind and solar power installations, and other attributes of each region. Based on the parameters given, the GenX model would calculate the lowest-cost combination of equipment and operating conditions that can fulfill a defined future level of demand while also meeting specified policy constraints, including limits on carbon emissions. The model and all data sources were also released as open-source tools for all viewers to use.
Ding and her colleagues—Dharik Mallapragada, a former principal research scientist at MITEI who is now an assistant professor of chemical and biomolecular energy at NYU Tandon School of Engineering and a MITEI visiting scientist; and Robert J. Stoner, the founding director of the MIT Tata Center for Technology and Design and former deputy director of MITEI for science and technology—then used the model to explore options for meeting demands in 2035 under progressively tighter carbon emissions caps, taking into account region-to-region variations in the efficiencies of the coal plants, the price of coal, and other factors. They describe their methods and their findings in a paper published in the journal Energy for Sustainable Development.
In separate runs, they explored plans involving various combinations of current coal plants, possible new renewable plants, and more, to see their outcome in 2035. Specifically, they assumed the following four “grid-evolution scenarios”:
Baseline: The baseline scenario assumes limited onshore wind and solar photovoltaics development and excludes retrofitting options, representing a business-as-usual pathway.
High renewable capacity: This scenario calls for the development of onshore wind and solar power without any supply chain constraints.
Biomass co-firing: This scenario assumes the baseline limits on renewables, but here all coal plants—both subcritical and supercritical—can be retrofitted for “co-firing” with biomass, an approach in which clean-burning biomass replaces some of the coal fuel. Certain coal power plants in India already co-fire coal and biomass, so the technology is known.
Carbon capture and sequestration plus biomass co-firing: This scenario is based on the same assumptions as the biomass co-firing scenario with one addition: All of the high-efficiency supercritical plants are also retrofitted for carbon capture and sequestration (CCS), a technology that captures and removes carbon from a power plant’s exhaust stream and prepares it for permanent disposal. Thus far, CCS has not been used in India. This study specifies that 90% of all carbon in the power plant exhaust is captured.
Ding and her team investigated power system planning under each of those grid-evolution scenarios and four assumptions about carbon caps: no cap, which is the current situation; 1,000 million tons (Mt) of carbon dioxide (CO2) emissions, which reflects India’s announced targets for 2035; and two more-ambitious targets, namely 800 Mt and 500 Mt. For context, CO2 emissions from India’s power sector totaled about 1,100 Mt in 2021. (Note that transmission network expansion is allowed in all scenarios.)
Key findings
Assuming the adoption of carbon caps under the four scenarios generated a vast array of detailed numerical results. But taken together, the results show interesting trends in the cost-optimal mix of generating capacity and the cost of electricity under the different scenarios.
Even without any limits on carbon emissions, most new capacity additions will be wind and solar generators—the lowest-cost option for expanding India’s electricity-generation capacity. Indeed, this is observed to be the case now in India. However, the increasing demand for electricity will still require some new coal plants to be built. Model results show a 10% to 20% increase in coal plant capacity by 2035 relative to 2020.
Under the baseline scenario, renewables are expanded up to the maximum allowed under the assumptions, implying that more deployment would be economical. More coal capacity is built, and as the cap on emissions tightens, there is also investment in natural gas power plants as well as batteries to help compensate for the now large amount of intermittent solar and wind generation. When a 500 Mt cap on carbon is imposed, the cost of electricity generation is twice as high as it was with no cap.
The high renewable capacity scenario reduces the development of new coal capacity and produces the lowest electricity cost of the four scenarios. Under the most stringent cap—500 Mt—onshore wind farms play an important role in bringing the cost down. “Otherwise, it’ll be very expensive to reach such stringent carbon constraints,” notes Ding. “Certain coal plants that remain run only a few hours per year so are inefficient as well as financially unviable. But they still need to be there to support wind and solar.” She explains that other backup sources of electricity, such as batteries, are even more costly.
The biomass co-firing scenario assumes the same capacity limit on renewables as in the baseline scenario, and the results are much the same, in part because the biomass replaces such a low fraction—just 20%—of the coal in the fuel feedstock. “This scenario would be most similar to the current situation in India,” says Ding. “It won’t bring down the cost of electricity, so we’re basically saying that adding this technology doesn’t contribute effectively to decarbonization.”
But CCS plus biomass co-firing is a different story. It also assumes the limits on renewables development, yet it is the second-best option in terms of reducing costs. Under the 500 Mt cap on CO2 emissions, retrofitting for both CCS and biomass co-firing produces a 22% reduction in the cost of electricity compared to the baseline scenario. In addition, as the carbon cap tightens, this option reduces the extent of deployment of natural gas plants and significantly improves overall coal plant utilization. That increased utilization “means that coal plants have switched from just meeting the peak demand to supplying part of the baseline load, which will lower the cost of coal generation,” explains Ding.
Some concerns
While those trends are enlightening, the analyses also uncovered some concerns for India to consider, in particular, with the two approaches that yielded the lowest electricity costs.
The high renewables scenario is, Ding notes, “very ideal.” It assumes that there will be little limiting the development of wind and solar capacity, so there won’t be any issues with supply chains, which is unrealistic. More importantly, the analyses showed that implementing the high renewables approach would create uneven investment in renewables across the 30 regions. Resources for onshore and offshore wind farms are mainly concentrated in a few regions in western and southern India. “So all the wind farms would be put in those regions, near where the rich cities are,” says Ding. “The poorer cities on the eastern side, where the coal power plants are, will have little renewable investment.”
So the approach that’s best in terms of cost is not best in terms of social welfare because it tends to benefit the rich regions more than the poor ones. “It’s like [the government will] need to consider the trade-off between energy justice and cost,” says Ding. Enacting state-level renewable generation targets could encourage a more even distribution of renewable capacity installation. Also, as transmission expansion is planned, coordination among power system operators and renewable energy investors in different regions could help in achieving the best outcome.
CCS plus biomass co-firing—the second-best option for reducing prices—solves the equity problem posed by high renewables, and it assumes a more realistic level of renewable power adoption. However, CCS hasn’t been used in India, so there is no precedent in terms of costs. The researchers therefore based their cost estimates on the cost of CCS in China and then increased the required investment by 10%, the “first-of-a-kind” index developed by the U.S. Energy Information Administration. Based on those costs and other assumptions, the researchers conclude that coal plants with CCS could come into use by 2035 when the carbon cap for power generation is less than 1,000 Mt.
But will CCS actually be implemented in India? While there’s been discussion about using CCS in heavy industry, the Indian government has not announced any plans for implementing the technology in coal-fired power plants. Indeed, India is currently “very conservative about CCS,” says Ding. “Some researchers say CCS won’t happen because it’s so expensive, and as long as there’s no direct use for the captured carbon, the only thing you can do is put it in the ground.” She adds, “It’s really controversial to talk about whether CCS will be implemented in India in the next ten years.”
Ding and her colleagues hope that other researchers and policymakers—especially those working in developing countries—may benefit from gaining access to their datasets and learning about their methods. Based on their findings for India, she stresses the importance of understanding the detailed geographical situation in a country in order to design plans and policies that are both realistic and equitable.