As the globe endeavors to mitigate climate-increasing carbon emissions, India has made a commitment to play its role, and its achievement is vital: In 2023, India ranked as the third-largest carbon producer globally. The Indian administration has vowed to attain net-zero carbon outputs by 2070.
To realize that commitment, India must decarbonize its electric grid, and that presents a significant challenge: A staggering 60 percent of India’s electricity is generated from coal-burning facilities that are highly inefficient. Compounding the issue, the demand for electricity in India is expected to more than double in the next ten years due to population growth and rising usage of air conditioning, electric vehicles, and more.
Although the Indian government has established an ambitious goal, it has not yet put forth a plan to achieve it. Indeed, similar to other nations, the government in India continues to allow the construction of new coal-fired plants, as well as the renovation and delay of retirement for older facilities.
To assist India in formulating a practical — and achievable — strategy for decarbonizing its power grid, crucial questions need to be addressed. For instance, India is rapidly enhancing its carbon-free solar and wind energy production. What further opportunities exist for expanding renewable energy generation? Are there methods to retrofit or repurpose existing coal plants in India that can effectively and affordably minimize their greenhouse gas emissions? Additionally, do the answers to these questions vary by region?
With financial support from IHI Corp. through the MIT Energy Initiative (MITEI), Yifu Ding, a postdoctoral researcher at MITEI, and her team aimed to resolve these queries by initially employing machine learning to evaluate the efficiency of each of India’s current 806 coal facilities, and then exploring the effects that various decarbonization strategies would have on the power plant mix and electricity pricing in 2035 under progressively stringent emission limits.
First step: Construct the necessary dataset
A significant hurdle in devising a decarbonization strategy for India has been the unavailability of a comprehensive dataset detailing the existing power plants in the country. While other research has proposed plans, they have not accounted for the considerable differences in coal-fired power plants across various regions. “Thus, we first had to compile a dataset that encompasses and describes all operational coal plants in India. Such a dataset was absent from existing literature,” notes Ding.
Creating a cost-effective strategy for enhancing the capacity of a power system necessitates knowledge of the efficiencies of all plants operating within the system. For this study, the researchers utilized “station heat rate” as their metric, a standard measure of the overall fuel efficiency of a particular power plant. The station heat rate of each facility is essential for calculating its fuel consumption and electrical output during capacity expansion development.
Some efficiency records for Indian coal plants date back to before 2022, prompting Ding and her team to employ machine-learning models to predict the efficiencies of all currently operating coal plants in India. In 2024, they developed and published online the first comprehensive, open-source dataset for all 806 power facilities across 30 regions of India. This work earned the 2024 MIT Open Data Prize. The dataset includes each facility’s power capacity, efficiency, age, load factor (a measure indicating its operational frequency), water stress, and other aspects.
Moreover, they classified each plant based on its boiler design. A “supercritical” facility operates at relatively high temperatures and pressures, making it thermodynamically efficient, thus generating substantial electricity per unit of heat from the fuel. In contrast, a “subcritical” facility operates at lower temperatures and pressures, which results in lower thermal efficiency. The majority of India’s coal plants remain subcritical and operate at diminished efficiency.
Next step: Explore decarbonization alternatives
Armed with their comprehensive dataset covering all coal power facilities in India, the researchers were now prepared to investigate alternatives for addressing strict carbon emission limits. For this analysis, they utilized GenX, a modeling platform developed at MITEI to assist decision-makers in planning investments and future strategies for their power systems.
Ding constructed a GenX model reflecting India’s power system in 2020, incorporating details regarding each power plant and the transmission network across 30 regions of the country. She also input data on coal pricing, potential resources for wind and solar installations, and other characteristics for each region. Based on the provided parameters, the GenX model would determine the least expensive combination of equipment and operational conditions capable of meeting a defined future demand level while adhering to specified policy constraints, including emissions limits. The model and all data sources were also made available as open-source tools for public use.
Ding and her colleagues — Dharik Mallapragada, a former principal research scientist at MITEI now serving as an assistant professor of chemical and biomolecular energy at NYU Tandon School of Engineering and a visiting scientist at MITEI; 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 employed the model to examine options for fulfilling demands in 2035 under increasingly rigorous carbon caps, considering the regional disparities in coal plant efficiencies, coal pricing, and other factors. They describe their methods and findings in a paper published in the journal Energy for Sustainable Development.
In various runs, they explored strategies involving different combinations of existing coal plants, potential new renewable facilities, and more, to evaluate their outcomes in 2035. Specifically, they examined the following four “grid-evolution scenarios:”
Baseline: This baseline scenario anticipates limited development of onshore wind and solar photovoltaics and omits retrofitting options, reflecting a business-as-usual pathway.
High renewable capacity: This scenario advocates the growth of onshore wind and solar power without any supply chain limitations.
Biomass co-firing: This scenario adheres to baseline renewable limits but allows all coal facilities — both subcritical and supercritical — to be retrofitted for “co-firing” with biomass, wherein clean-burning biomass replaces portions of the coal fuel. Some coal power plants in India already employ coal and biomass co-firing, so this technology is familiar.
Carbon capture and sequestration plus biomass co-firing: This scenario builds on the same assumptions as the biomass co-firing scenario, with an additional factor: All high-efficiency supercritical facilities are retrofitted for carbon capture and sequestration (CCS), a technology that captures and eliminates carbon from a power plant’s exhaust and prepares it for permanent disposal. Up to now, CCS has not been utilized in India. This study specifies that 90 percent of all carbon in the power plant emissions is captured.
Ding and her team analyzed power system planning for each of these grid-evolution scenarios and four assumptions regarding carbon caps: no cap, which is the current state; 1,000 million tons (Mt) of carbon dioxide (CO2) emissions, reflecting India’s announced targets for 2035; and two more ambitious targets, namely 800 Mt and 500 Mt. For context, CO2 emissions from India’s
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The energy sector reached approximately 1,100 Mt in 2021. (It is important to note that the expansion of the transmission network is permissible in all scenarios.)
Principal conclusions
The assumption of carbon limits across the four scenarios produced a wide range of intricate numerical data. However, when viewed collectively, the findings reveal notable patterns in the cost-effective combination of generating capacity and electricity pricing across the various scenarios.
Even in the absence of restrictions on carbon emissions, the majority of new capacity installations will consist of wind and solar power — the most economical choice for augmenting India’s electricity generation capability. This trend is evident in India today. Nevertheless, the rising electricity demand will necessitate the construction of some new coal facilities. Model analyses project a 10 to 20 percent increase in coal plant capacity by 2035 compared to 2020.
In the baseline scenario, renewables are maximized up to the allowable limits based on the assumptions, indicating that further deployment would be economically viable. Additional coal capacity is constructed, and as emissions caps become stricter, investments in natural gas plants, as well as battery storage, are made to offset the substantial intermittent generation from solar and wind. When a 500 Mt cap on carbon is enforced, the cost of electricity production is twice as high as it was in the absence of a cap.
The scenario with heightened renewable capacity diminishes the need for new coal development and results in the lowest electricity costs among the four scenarios. Under the strictest cap of 500 Mt, onshore wind installations play a crucial role in reducing expenses. “Otherwise, reaching such stringent carbon limits will be prohibitively costly,” remarks Ding. “Certain coal facilities will only operate a handful of hours yearly, rendering them both inefficient and financially unfeasible. However, they are still necessary to support wind and solar.” She further explains that alternative backup power sources, like batteries, are even more expensive.
The biomass co-firing scenario is based on the same renewable capacity constraints as the baseline scenario, producing analogous results, primarily because biomass displaces such a minimal fraction — merely 20 percent — of the coal in the fuel mix. “This scenario would most closely resemble the current landscape in India,” states Ding. “It won’t reduce electricity costs, indicating that incorporating this technology does not significantly aid in decarbonization.”
Conversely, CCS and biomass co-firing present a distinct case. This scenario also assumes limitations on renewable growth, yet it stands as the second-most favorable option for cost reduction. Under the 500 Mt CO2 emissions cap, retrofitting for both CCS and biomass co-firing achieves a 22 percent decline in electricity costs when compared to the baseline scenario. Furthermore, with tighter carbon caps, this approach curtails the deployment of natural gas facilities and substantially enhances overall coal plant efficiency. The increased utilization “means that coal plants have transitioned from merely fulfilling peak demand to providing part of the baseline load, which decreases coal generation costs,” explains Ding.
Some challenges
While these trends are insightful, the analyses also highlight several challenges for India to contemplate, particularly concerning the two methods that yielded the lowest electricity costs.
The high renewables scenario is, according to Ding, “very optimistic.” It presumes minimal restrictions on wind and solar development, which makes it unrealistic regarding supply chain issues. More crucially, the analyses suggest that adopting a high renewables strategy could lead to uneven investment distribution across the 30 regions. Resources for onshore and offshore wind facilities are predominantly located in a few regions of western and southern India. “Thus, all the wind farms would be concentrated in those areas, near prosperous cities,” remarks Ding. “The less affluent cities on the eastern side, where coal plants are situated, will see scant renewable investments.”
This means that the most cost-effective strategy does not necessarily align with social welfare priorities, as it tends to favor wealthier regions over impoverished ones. “The government will need to weigh the trade-offs between energy justice and cost,” says Ding. Setting state-level renewable generation targets could promote a more equitable distribution of renewable capacity installation. Additionally, as transmission expansion is in the works, collaboration among power system operators and renewable energy investors across various regions could result in optimal outcomes.
The CCS and biomass co-firing method — the second-best alternative for cost reduction — addresses the equity challenges posed by the high renewables scenario while assuming a more pragmatic level of renewable energy adoption. However, CCS technology has yet to be deployed in India, meaning there are no established costs. Hence, researchers relied on CCS cost data from China and then increased the investment requirement by 10 percent, applicable under the “first-of-a-kind” index established by the U.S. Energy Information Administration. Based on these costs and other assumptions, the team infers that coal plants incorporating CCS could begin operations by 2035 when carbon emissions from power generation fall below 1,000 Mt.
However, will CCS truly be implemented in India? There has been talk of employing CCS in heavy industry, yet the Indian government has yet to reveal any plans for integrating this technology within coal-fired power stations. In fact, India stands “very cautious about CCS,” states Ding. “Some analysts argue it won’t be feasible due to its high cost, and without a direct application for the captured carbon, the only option is sequestering it underground.” She adds, “It’s a contentious issue whether CCS will be rolled out in India within the next decade.”
Ding and her team aspire that other researchers and policymakers — particularly those operating in developing nations — will benefit from their data sets and methodological insights. Drawing from their findings regarding India, she emphasizes the significance of comprehending the specific geographical context of a country to formulate plans and policies that are both pragmatic and equitable.
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