Supply, stress and shortfalls: How India met its high electricity demand in September 2026

by Upasa Borah and Renuka Sane.

India has been touching new electricity demand peaks this year: 256.1 GW on 25th April, 265 GW on 20th May, and an all-time peak of 270.8 GW on 21st May. This demand has also been higher than the levels projected by the Central Electricity Authority (CEA). As demand grows, the ability of the system to provide sufficient supply when it is needed becomes increasingly important.

September 2026 offers a useful window into how the system balances demand and supply under pressure. Despite higher electricity generation relative to the previous year and high utilisation of the coal fleet, electricity shortages were the highest recorded since August 2023. The power exchange was also stressed during the month: average market clearing prices rose by 105% compared with September 2025, while purchase bids increased by 281% and sell bids fell by 45%. Over 100 captive power plants have been ordered to run at full capacity from October and sell surplus power on the exchanges to meet rising demand. Reports of critically low coal stocks at several coal power plants have added to concerns about supply for the coming months.

Against this backdrop, we examine electricity demand, supply and shortages at the transmission level in September 2026, defining shortages as the gap between electricity requirement and available supply. The article is descriptive: it sets out how the system behaved during the month rather than offering causal analysis. Establishing how demand, supply and shortages moved through the month gives a factual baseline against which causes and policy responses can be assessed. We ask three questions:

1. When did shortages emerge, and how did they vary across the days over the month?

   Daily and intra-day variations show how the system responded to high demand, and identify points at which it came under stress. This high-frequency analysis provides a snapshot of the state of the system, offering a useful starting point for understanding the operational challenges that are to become more important as electricity demand and renewable generation grow.

2. How did the power system respond to changes in demand?

   1. What was the composition of supply and its variation on the days of highest and lowest demand?
   2. How intensively were different generation sources utilised?
   3. How much renewable generation was curtailed?

   Installed capacity alone does not tell us how much electricity is available to meet demand at any given time. This depends on how much different sources can generate, how quickly they can respond to changes in demand and whether electricity can be delivered where it is needed. In the absence of storage, solar generation can meet a substantial share of daytime demand but falls as the sun sets. Thermal generation then needs to increase output, often quickly, though it can create technical and cost constraints. Hydro, oil and gas generation and storage can help manage these fluctuations, so their availability and responsiveness also matter. Solar and wind generation can get curtailed at times even when the plants are capable of generating more electricity due to transmission constraints limiting evacuation, or because the system has more power than it can absorb. Understanding these patterns is important for assessing how the power system manages both periods of scarcity and surplus.

3. How was the power exchange used during periods of high and low demand?

   Short-term markets also show how the system responds to changes in demand. Most of India's electricity is procured through long-term power purchase agreements, but buyers can buy additional power on the country's three exchanges. These offer segments across different time horizons, including the Day-Ahead Market (DAM), where electricity is traded a day before delivery, and the Real-Time Market (RTM), where trades take place an hour before. Such near-real-time markets help buyers respond to unexpected shifts in demand and generation, but their usefulness depends on how much electricity is offered and at what price. Both the DAM and RTM have a regulated price ceiling of Rs 10/kWh, which is often hit during high demand, leaving markets to clear at volumes well below what buyers bid for. A well-functioning market matters because it lets prices signal scarcity and guide decisions by generators and buyers.

Data sources

The study uses the following data sources:

1. Grid-India's Power Supply Position Reports: These reports are compiled by Grid India using data reported by State Load Dispatch Centres to Regional Load Dispatch Centres, which are then compiled at the national level. They provide information on energy supplied, energy shortages, peak demand met during solar and non-solar hours, and shortages during those periods. These values are measured at transmission buses and do not capture behind-the-meter generation like rooftop solar, or distribution-level constraints and outages. Our analysis thus focuses on system-level supply adequacy rather than delivery to end consumers or more localised grid constraints.

   The power supply position reports define shortage as the difference between unrestricted requirement and energy available, as per the Central Electricity Authority (Furnishing of Statistics, Returns and Information) Regulations, 2007. Energy availability implies supply at ex-bus, while unrestricted requirement is the sum of available energy, frequency correction, scheduled and unscheduled load shedding. The precise definitions and methodologies of these terms, however, could not be located, and it has been flagged in some Regional Power Committee meetings that there are inconsistencies in the methodologies adopted by states and a lack of transparency in the estimation of shortages. The results presented in this article should thus be interpreted with this caveat and a closer examination of the methodology underlying these estimates remains an important avenue for future work.

2. National Power Portal Daily Generation Reports: These are used to analyse how the generating fleet was operating and include data on daily capacity utilisation of thermal, nuclear and hydro power plants. Capacity utilisation here is defined as the ratio of a day's maximum generation to the available capacity on line, where available capacity is the installed capacity minus the capacity under outage. It is worth noting that these are daily aggregates rather than hourly measures. An hourly or 15-minute interval measure would provide a better indication of how much generation capacity was actually available to meet demand at different points in the day, but such data was not available.

3. Grid India's Daily VRE Report: These reports are used to measure the quantum of solar and wind generation that was curtailed daily. Data for 28, 29 and 30 September were not available.

4. India Energy Exchange's Day Ahead Market (DAM), Real-Time Market (RTM) and High Price Day Ahead Market (HP-DAM): These provide daily 15-minute blockwise data on aggregated purchase and sell bids, market-clearing quantities and market-clearing prices.

Demand and shortages in September 2026

India's electricity requirement in September 2026 stood at 1,62,736 GWh. Of this, about 545 GWh went unmet, implying a shortage of around 0.3% of total demand. These shortages varied across days and hours, but occurred on almost every day of the month, with 25 September being the only exception. Figure 1 shows the daily electricity required and met. Daily demand averaged around 5,424 GWh, with the highest requirement of 5,791 GWh recorded on 9 September; 51 GWh of this went unmet. The highest daily shortage was 54 GWh, recorded on 11 September. As demand declined towards the end of the month, shortages also fell.

Figure 1: Daily energy required and met

Peak demand refers to the highest quantum of electricity drawn from the grid at a point in time. There are two peaks generally observed, one during solar hours of 6 am to 6 pm, and another in the remaining non-solar hours. Figure 2 shows the daily peak demands during solar and non-solar hours, along with the shortage during these peaks. With the exception of a few days towards the end of the month, non-solar peak demand was lower than the solar-hour peak demand. Shortages, however, occurred during the non-solar peaks. On 9 September, for instance, the peak shortage was 7.72 GW at around 7:06 pm.

Figure 2: Solar and non-solar peak demand and shortages

Shortages emerging in non-solar hours, while the system met its highest solar-hour demand, is a defining feature of a grid with a growing share of solar. India's solar generation has increased significantly over the years, allowing the system to meet high levels of daytime demand. But this also leaves the question of how to meet demand as solar generation declines in the evening, particularly when demand remains high or increases.

Composition of electricity supply

Thermal plants generate around 71% of India's electricity, and non-fossil sources (renewables, nuclear and large hydro) the remaining 29%. The mix shifts by time of day and season. In September, coal accounted for an average of 65% of total generation, followed by renewables at 17% and hydro at around 11%.

When it comes to grid balancing, supply matters more in real time rather than in daily or monthly aggregates. We, therefore, study two days in September: 9 September, the day of highest electricity requirement, and 27 September, the day of lowest requirement. Figure 3 plots demand and supply at 15-minute intervals on the two days: On 9 September, solar accounted for 23% of total generation during solar hours (6 am to 6 pm), reaching a peak share of 33% at 1:15 pm when it generated 84.25 GW. On 27 September, solar accounted for 21% of generation during solar hours, reaching a peak of 30% at 1:45 pm when it generated 61.8 GW.

Figure 3 also plots the net load (dark solid line), defined as demand minus generation from solar and wind. The resulting profile shows the duck curve: as solar generation increases during the day, net load declines, reducing the amount of generation required from other sources. As solar generation wanes in the evening, while demand often stays high or increases, net load rises, and other sources must ramp up supply to meet the gap. This pattern is visible on both days, but on 9 September, higher demand meant that the evening ramp-up began earlier, around 2 pm onwards. Gas and hydro generation also increased during the morning and evening periods of lower solar generation.

This flexibility is important for grid operations. Thermal power plants often need time to increase and decrease output, and frequent, fast changes in generation can damage equipment or impose additional costs. Hydro and gas-based generation are better suited to respond quickly to changes in net load, and their greater use on 9 September reflects this role. Storage systems like pumped hydro plants and battery storage can also provide flexible, fast response, and play an important role in shifting generation across time, smoothing the net load associated with increasing renewable generation (Sanyal & Dev 2026). While storage was used on both 9 and 27 September, charging during solar hours and discharging in non-solar hours, its contribution was relatively small.

Figure 3: Source-wise generation at 15-minute intervals, on 9 and 27 September

Capacity utilisation

Figure 4 plots the maximum daily output of thermal, nuclear and hydro plants as a share of the generation capacity that was available to operate (installed capacity minus the capacity under outage). Hydro output remained high throughout the month, exceeding 90% and nearing 95% on several days. The high utilisation may partly reflect lower hydro availability this year due to weaker rainfall. Thermal generation also operated at close to 90% of available capacity. It is worth noting that daily aggregates hide real-time variations within a day. Maximum daily output need not coincide with the national solar or non-solar peak, and available capacity may not be fully dispatched because some generation is retained as operating headroom or reserves. Nonetheless, high utilisation rates suggest that there may be relatively little headroom to respond to unexpected increases in demand or sudden outages elsewhere in the system (Spencer and Vautrin 2026).

Figure 4: Capacity utilisation (maximum power output as a percentage of capacity on line)

Curtailment of renewable energy

On the other side of the supply story is the curtailment of solar and wind generation. Curtailment can occur for different reasons. Transmission constraints, grid-security requirements and other technical constraints may restrict the amount of renewable power being evacuated. Curtailment can also occur when there is surplus generation, particularly when demand is low or when thermal generators cannot reduce their output sufficiently. In such scenarios, the system operator can use the TRAS Down mechanism under the ancillary services framework to ask renewable generators to reduce their output even when they are technically able to generate more. This means available renewable energy is not fully utilised. In the first quarter of 2026, 300 GWh of solar and wind generation was curtailed. Better transmission infrastructure and deployment of storage systems can reduce such curtailment by allowing renewable energy to be evacuated or shifting surplus generation to periods when it is needed.

Figure 5 shows daily wind and solar curtailment for September, distinguishing between curtailment due to technical constraints and curtailment through TRAS Down. As electricity requirements eased by the end of the month, TRAS Down curtailment increased, reaching 93.58 GWh on 26 and 82.6 GWh on 27 September.

Figure 5: Solar and wind curtailment

Role of power exchanges

When the long-term contracted capacity of distribution companies is insufficient to meet demand, power exchanges provide a platform for short-term procurement. There are three power exchanges in India, but most of the trade happens on the India Energy Exchange (IEX). In our analysis, we focus on three of IEX's market segments that reflect procurement under different system conditions; Table 1 summarises their key features.

Table 1: Comparison of DAM, RTM and HP-DAM

	Day Ahead Market (DAM)	Real-Time Market (RTM)	High Price Day Ahead Market (HP-DAM)
Purpose	Procurement planned one day in advance	Balance demand and supply close to real time	Additional day-ahead procurement during periods of expected scarcity
Bids	One day before delivery	Every 15 minutes for delivery one hour ahead	One day before delivery
Activation	Used routinely for scheduled short-term procurement	Used to manage unexpected changes in demand and generation	Activated when expected prices exceed the DAM price ceiling
Price discovery	Market-clearing price at the point of demand and supply intersection, capped at Rs 10/kWh	Market-clearing price at the point of demand and supply intersection, capped at Rs 10/kWh	Market-clearing price at the point of demand and supply intersection, capped at Rs 20/kWh

In September, both the DAM and RTM were heavily stressed. Purchase bids were high in both markets, while sell bids and, consequently, market clearing volumes, were significantly lower. In the DAM, purchase bids were, on average, four times the volume of sell bids, reaching a maximum of eight times the sell bid on 17 September. In the RTM, purchase bids were, on average, five times sell bids. It was the highest at 13 times the sell bid on 16 September. The gap narrowed towards the end of the month as demand pressures eased (See Figures 6 and 7).

Figure 6: Purchase and sell bids, and market-clearing volumes in the IEX DAM

Figure 7: Purchase and sell bids, and market-clearing volumes in the IEX RTM

The divergence between purchase and sell bids can be understood by looking at both the available supply on the exchange and the market-clearing prices. Since much of India's generation capacity is tied up in long-term contracts, the amount of generation available for sale on the exchange can be limited, particularly during days of high demand. At the same time, the DAM and RTM operate under a CERC-mandated price ceiling of Rs 10/kWh. Figures 6 and 7 show the number of 15-minute blocks in each day during which the prices reached this ceiling. On most days, prices hit the ceiling in more than 60 of the 96 blocks. On 17 September, when the DAM recorded its highest purchase-to-sell bid ratio, the market clearing price reached the ceiling in 75 blocks. Similarly, in the RTM, prices reached the ceiling in 72 blocks on 16 September when the purchase-to-sell bid ratio was at its highest.

Figures 8 and 9 plot the purchase bids, sell bids, market clearing volumes and market clearing prices for each 15-minute block on 9 and 27 September in the DAM and RTM, respectively. The contrast between the two days illustrates how market conditions changed with changes in electricity requirements. On 9 September, the day with the highest electricity requirement, purchase bids were substantially higher than sell bids. Supply increased during the day, bringing down market clearing prices (MCP): in the DAM, the MCP fell to around Rs 2/kWh at 1 pm, while the RTM saw the lowest MCP of Rs 2.6/kWh at the same time block. But as solar generation declined and less supply was available, prices rose sharply and repeatedly hit the Rs 10/kWh ceiling.

On 27 September when electricity requirement was lower, market clearing volumes followed the quantum of purchase bids more closely and prices were considerably lower. The average MCP was Rs 2.76/kWh in the DAM and Rs 4.05/kWh in the RTM.

The difference in available short-term supply is also visible when comparing the two days: sell bids were consistently lower on 9 September than on 27 September for comparable time periods.

Taken together, these patterns suggest that both factors played a role during the period of highest demand. First, the price ceiling likely limited the market's ability to attract additional supply, and second, the small volume of sell bids indicates that spare generation capacity available for short-term trading may itself have been limited.

Figure 8: 15-minute blockwise buy and sell bids, MCV and MCP in the DAM

Figure 9: 15-minute blockwise buy and sell bids, MCV and MCP in the RTM

Another market in the IEX is the high price day ahead market (HP-DAM), which allows trades at prices above the ceiling price. The market was active from 4 till 17 September but traded volumes remained small, averaging around a total of 120 MWh per day. Unlike the DAM and RTM, it had higher sell bids relative to purchase bids, but on days that the market was active, HP DAM also hit its regulatory price ceiling of Rs 20/kWh (Figure 10).

Figure 10: Purchase and sell bids, and market-clearing volumes (MCV) in the IEX HP-DAM

Figure 11 plots market conditions in 15-minute blocks on 9 and 27 September. On 9 September, the HP-DAM was active in just 16 of the 96 blocks. Prices hit the Rs 20/kWh ceiling in all 16 blocks, with an average of only 9.5 MW clearing in each block. On 27 September, the market was not active, with no purchase or sell bids.

Figure 11: 15-minute blockwise buy and sell bids, MCV and MCP in the HP-DAM

Conclusion

India's electricity system is entering a period in which both demand and renewable generation are increasing rapidly. Our analysis of September 2026 shows that the power system was strained in four connected ways:

1. Shortages emerged on most days, and were concentrated during non-solar high-demand periods, when solar generation was low and the system had to rely more heavily on other sources. On 9 September, the peak shortage was 7.72 GW at around 7 pm.
2. Thermal and hydro plants ran near or above 90% of available capacity. This suggests little room to absorb demand spikes or outages.
3. The system faced both scarcity and surplus within the same month, as there was high curtailment of renewables through TRAS Down.
4. Prices on the IEX were lower when solar generation was abundant, and rose sharply as solar output declined and demand stayed high or increased. Prices hit the Rs 10/kWh ceiling in more than 60 of 96 blocks on most days.

As demand peaks rise and the share of renewable generation grows, ensuring reliability will increasingly depend not just on how much capacity is installed, but on when and where electricity is available, how quickly supply can respond, and whether it can be delivered to consumers. September's experience highlights the importance of examining these dimensions together when assessing India's electricity system.

References

Sanyal, Sanjeev and Dev, Satvik. 2026. The Duck and The Camel: Tracing the Net Load on the Indian Power Grid. EAC Working Paper Series EAC-PM/WP/53/2026.

Spencer, Thomas and Vautrin, Anthony. 2026. India's electricity demand grows at night: Managing rising cooling demand. International Energy Agency.

The code used to extract the monthly data and generate the figures in this blog is available on our GitHub repository.

The authors are researchers at TrustBridge Rule of Law Foundation. We thank Aryan Anand and Akshat Shukla for their assistance with the initial data extraction, and two anonymous referees for their helpful comments.

Evaluating India's Energy Ambitions: Evidence from Electricity Generation Project-Level Data

by Upasa Borah, Akshay Jaitly and Renuka Sane.

India's electricity demand has been growing rapidly, at 9% per annum since 2021. Meeting this demand by 2030 would require around 777 GW of installed capacity, as estimated by the Central Electricity Authority (CEA). At the same time, India has committed to achieving 500 GW of installed non-fossil capacity by 2030. A study by CEEW (2025) finds that meeting this target would require adding around 56 GW of non-fossil capacity every year between 2025 and 2030, failing which India would need an additional 10 GW of coal-based capacity to meet future demand. There is little doubt that renewable energy in India has seen a sharp growth, with 74 GW in 2018 to 162 GW by the end of 2024 (excluding large hydro and nuclear projects), driven by falling renewable energy prices, and policy support like subsidies for developers, waivers on inter-state transmission charges, Green Energy Corridor investments, changes in Green Open Access Rules and various state-level initiatives that signal policy commitment to the sector. In 2025 alone, the country added 45 GW of renewable capacity.

However, the next phase of the transition is likely to be more complex. India is now facing new challenges regarding grid integration and transmission infrastructure, leading to delays in commissioning projects and curtailment of operational projects. As of June 2025, around 50 GW of awarded renewable capacity was stranded due to a lack of buyers, transmission constraints or disputes over land and environmental clearances. This results in time and cost overruns, dampening investor confidence.

In this backdrop, our paper Evaluating India's Energy Ambitions: Evidence from Electricity Generation Project-Level Data studies how electricity generation projects evolve from announcement to completion. Using project-level data from the Centre for Monitoring Indian Economy (CMIE) CapEx database, we analyse 8,540 projects announced between January 1957 and December 2024 to understand how project size, cost, ownership, energy technology and location influence project timelines. We ask,

1. How many projects have been announced and of them, how many have been implemented and completed? What is the time taken?
2. Given the projects currently in the pipeline, how likely is India to meet the 2030 targets?
3. How do factors like project size, geography and developer characteristics influence the completion timelines and probabilities?

From announcement to completion

We find a significant divergence between projects announced and completed: of the total announced conventional (CE) and renewable (RE) capacity, only 15% and 9% have been completed, respectively. Announcement here refers to events like signing of MoUs, inviting bids, seeking approvals or preparing feasibility reports and may differ from official statistics that use alternative definitions of project status (Borah et al., 2025). The next stage in a project lifecycle is beginning implementation, which includes events like awarding contracts, securing financing, obtaining approvals or beginning construction, indicating a deeper commitment of resources. Even among this set of projects that have been implemented, completion rates remain low: 30% of CE and 22% of RE capacity have been completed. The timelines from announcement to implementation and implementation to completion vary significantly among different technologies, with solar and wind having the shortest timelines.

How much capacity will be added by 2030?

We used an accelerated failure time survival model to estimate the completion probabilities of projects currently in the pipeline (i.e. announced or under implementation as of December 2024). Applying a probability threshold of 0.5, i.e. excluding projects with less than 50% chance of completion by 2030, and scaling our dataset to match the capacities reported by the CEA, we find that India is likely to fall short of its capacity targets.

If the current completion trends continue, total installed capacity would fall short of the 777 GW target by around 56 GW for CE and 45 GW for RE. Similarly, for the 500 GW non-fossil target, the projected shortfall is around 77 GW. It is important to note that our analysis does not include new projects that may be announced after 2024. In that sense, our findings imply that meeting the 500 GW target would require announcing and completing 77 GW of projects within the next six years.

Explaining the capacity additions

We find that project characteristics play an important role in influencing implementation and completion timelines:

• Project size: Larger projects take longer to begin implementation and get completed.
• Ownership: Privately developed projects tend to be completed faster.
• Developer ranking: For RE projects, those developed by top firms (by market share) perform better.
• Location: RE projects in certain states such as Gujarat, Rajasthan and Andhra Pradesh complete faster than those in states with weaker RE ecosystems. Location is less important for CE projects.
• Year of announcement: RE projects announced after 2022 have longer implementation timelines compared to those announced before 2018.

These findings hold taking into account disruptions caused by the COVID-19 lockdown, which we explicitly model.

Finally, we compare completion timelines of large-scale solar and wind projects across states with benchmark timelines in the literature and find that even in RE-rich states, large projects face delays in commissioning.

Taken together, our findings suggest that the challenge is not just the announcement of new capacity but ensuring projects are implemented and completed on time. Bridging this gap will be critical to meeting India's future energy goals.

The authors are researchers at TrustBridge Rule of Law Foundation.

An Analysis of Electricity Outages in Delhi: 2024-25

by Upasa Borah and Renuka Sane.

Introduction

In a previous article, A Review of Outage Reporting by Indian DISCOMs, we examined the state of outage data reporting across India. We studied which distribution companies (DISCOMs) report such data and the variations in the way they do so. A natural next step is to thus look more closely at the available data to understand the kinds of analyses they enable.

This article focuses on the three privately owned DISCOMs operating in Delhi. Delhi's DISCOMs rank below the top 20 in the Ministry of Power's annual ranking of DISCOMs, all three graded B minus in the 13th Ranking exercise in 2025. They are similarly situated in terms of their billing and collection efficiency, power procurement portfolios and costs. There are, however, notable differences in the availability, structure and clarity of their reported outage data.

It is important to note that not all outages at a feeder level translate into outages for consumers due to the presence of redundancy in power systems. Most modern systems can re-route electricity through alternate feeders in case of faults. Understanding whether and how redundancy is accounted for is thus crucial to interpreting outage data. For instance, one of Delhi's DISCOMs, BSES Rajdhani Power Ltd., reports outages at the feeder level, but there is no information on which feeders have redundancy systems or how many outages were rerouted and thus did not cause interruptions for end consumers. On the other hand, Tata Power Delhi Distribution Ltd. reports outage data by zones and the number of consumers affected, allowing us to infer the extent of consumer impact. BSES Yamuna Power Ltd., however, reports outages by division and subdivisions and does not note the feeders or consumers impacted.

Given these data limitations, our analysis does not directly compare performance between DISCOMs. Instead, we study the available data to demonstrate the kinds of insights that can be drawn about the frequency, duration and spatial patterns of outages in Delhi. Specifically, we ask:

1. What is the pattern of outages on the following parameters:
1. Duration and frequency,
2. Intensity,
3. Geography,
4. Reasons for outages
2. What is the relationship between outages and electricity demand?

Methodology

There are four distribution companies operating in Delhi: i) BSES Rajdhani (BRPL) covering the southern and western areas, ii) BSES Yamuna (BYPL) covering the southeast and northeastern regions, iii) Tata Power (TPDDL) in the north and northwest areas, and iv) New Delhi Municipal Corporation (NDMC), which supplies to government buildings in central Delhi. Excluding NDMC, the first three DISCOMs are privately owned and supply to 93% of consumers in Delhi; BRPL supplies to 31 lakh consumers covering an area of approximately 700 sq km, TPDDL supplies to 20 lakh consumers in 510 sq km, and BYPL supplies to 19 lakh consumers in an area of around 200 sq km (Chitnis et al., 2025). In 2024-25, Delhi's electricity requirement stood at 38,287 MU, with peak demand hitting 8,685 MW.

We collected outage data from each DISCOM's website (see Data appendix). Lack of data for NDMC limited our analysis to the remaining three DISCOMs. The reported data includes date and time of outages, durations, areas affected, reasons for outages and measures taken to rectify the issue. However, there are inconsistencies in the data reported by the three. Table 1 summarises the variations in the availability of outage data for the three DISCOMs under study.

Table 1: Availability of data on power outages

DISCOM	Days of data availability	Spatial unit of reporting data	Number of spatial units
TPDDL	April, May, July and August 2024	Zones	12 zones
BRPL	April 2024 to March 2025	Grid and feeder	428 grids, 2,951 feeders
BYPL	April 2024 to March 2025	Division and sub-division	28 divisions and 108 subdivisions

TPDDL data is available only for April, May, July and August 2024. It reports data on zone-wise outages and the number of consumers impacted. BRPL, on the other hand, provides data on grid and feeder levels, without noting how many consumers were affected. Since outages at the feeder level may not always indicate consumer-level interruptions, understanding redundancy systems is important, but data on these was not available. There is also no data on how many consumers are serviced by a grid or feeder. Finally, BYPL reports outage data at the division and sub-division level without specifying feeder details or the number of consumers affected.

Aside from these differences, we also noticed inconsistencies in the way data is recorded, in terms of structure, format and number formatting. We extracted outage data from PDFs, conducted thorough cleaning and reorganisation. Although the datasets included reported outage durations, we recalculated the duration of each outage for all three DISCOMs based on recorded start and end dates and times. In terms of reasons for outages, TPDDL lists six broad reasons, which we retained. In contrast, BRPL and BYPL record a wider and more open-ended set of reasons, which we analysed and classified into six broad categories using text search.

TPDDL: consumer-facing outages

Between April and August 2024 (excluding June), the parts of Delhi serviced by TPDDL recorded an average of around 87 outages per day. Across all zones and feeders, these outages cumulatively amounted to roughly 159 hours of interruptions per day, and affected around 46,000 consumers. Figure 1 shows the daily frequency and total cumulative hours of outages across all TPDDL zones. On most days, outages occurred in 11 of the 12 reported zones.

Figure 1: Aggregate frequency and duration of outages for TPDDL

Over the four months for which data is available, we analysed outage days and duration for each TDPPL zone, and then averaged the results across zones. The median and mean values are presented in Table 2.

Table 2: Average days of outages, intensity and number of consumers impacted in the four reported months

Total number of zones	Number of consumers facing outages (lakhs)		Days of outages		Intensity of outages per outage day* (hours)
	Median	Mean	Median	Mean	Median	Mean
12	4.17	4.75	121	116	8.39	13.59

* cumulative value across all feeders

On average, a TPDDL zone experienced outages on 116 days, affecting around 4.7 lakh consumers. It is important to note that these are aggregate zone-level values, i.e. they do not represent outages faced by an average consumer but rather the cumulative outages across all feeders within a zone, covering multiple subdivisions and localities. For instance, Narela, Badli, and Bawala zones have the highest number of outage days, with Narela having the highest intensity (40 hours cumulatively per outage day) across the various areas in the zone, affecting 9.15 lakh consumers. The total duration exceeds 24 hours because a single zone has several feeders whose outages are aggregated when they occur simultaneously.

Around 16% of all outages reported by TPDDL are due to planned events. Figure 2 shows the share of outages by reason. 71% of outages, accounting for 60% of total outage hours, are due to external factors where the specific cause is not reported. A more detailed classification of these categories would help identify the underlying causes of outages more accurately. It also remains unclear what is included under "EODB compliance" outages, which account for 12% of all outage hours, and "Industrial weekly off" that accounts for 3% of outage hours.

Figure 2: Reasons for outages for TPDDL

BRPL: Feeder-level outages

On an average day, around 48 feeders under BRPL experience outages, amounting to a cumulative total of 50 outages and 119 total hours of interruptions across all feeders. Figure 3 shows the daily frequency and duration of these outages. The highest number of outages occurred on 7 January 2025, when 104 feeders were affected, resulting in a combined total of 386 cumulative outage-hours.

Figure 3: Aggregate frequency and duration of outages for BRPL

Of the 428 BRPL grids, an average grid had around 8 feeders under outages, with a mean of 28 days of outages in a year. Cumulatively, this results in approximately 1.8 hours of interruption per outage day across its multiple feeders. Table 3 presents the median and mean values of feeders under outage, days of outages and intensity of outages across the grids. The median values are lower than the means, indicating that while most grids experience relatively fewer and shorter outages, a few grids have significantly higher levels of outages. For instance, in 2024-25, the most outages occurred in Jaffarpur grid (187 days of outages with a cumulative intensity of 9.9 hours per outage day), followed by Nilothi grid (247 days, 4.6 hours), Mitraon grid (182 days, 6 hours), Hastal grid (236 days, 4.4 hours) and C-Dot grid (185 days, 5.39 hours).

Table 3:Average days of outages, intensity and number of feeders impacted 2024-25

Total number of grids	Number of feeders under outages		Days of outages		Intensity of outages per outage day* (hours)
	Median	Mean	Median	Mean	Median	Mean
428	2	8	2	28	0.75	1.80

* cumulative value across all feeders

Figure 4 shows the share of outages by reason. Planned events account for 54% of all outages and 82% of total outage hours. Fault-related outages follow, making up 31% of outages and 9% of total outage hours. Most outages of BRPL are thus planned rather than caused by unforeseen circumstances.

Figure 4: Reasons for outages for BRPL

BYPL: Area-wise outages

On an average day, BYPL areas recorded 16 outages, with a cumulative duration of 12.5 hours across all affected feeders. Figure 5 shows the daily frequency and duration of these outages. The highest number of outages occurred on 28 June 2024, when 98 outages were recorded, lasting a combined total of about 100 hours.

Figure 5: Aggregate frequency and duration of outages for BYPL

For an average subdivision serviced by BYPL, outages occurred on about 22 days in a year, with a cumulative average of 58 minutes per outage day. The median values are lower at just three days of outages (Table 4), indicating that most subdivisions experienced fewer days of outages, while a few faced disproportionately higher outages. Sonia Vihar recorded the most outages (201 days with a cumulative intensity of 1.86 hours per outage day), followed by Nand Nagri (196 days, 1.84 hours) and Karawal Nagar (179 days, 1.62 hours).

Table 4: Days of outages, intensity per outage day during the year 2024-25

Total number of subdivisions	Days of outages		Intensity of outages per outage day* (hours)
	Median	Mean	Median	Mean
108	3	22	0.92	0.96

* cumulative value across all feeders

Figure 6 shows the share of outages by reason. BYPL has zero outages explicitly listed as "planned". 51% of outages accounting for 47% of outage duration were due to faults, followed by maintenance outages and outages due to infrastructure damage.

Figure 6: Reasons for outages for BYPL

Electricity demand and outages

The lack of consistent and comparable data makes it difficult to analyse the yearly correlation between Delhi's electricity demand and outages. However, looking at BRPL and BYPL's outage data reveals contrasting results. BRPL's daily outage hours show no correlation with Delhi's electricity demand (Figure 7), while BYPL outages are positively correlated, significant at the 1% level (Figure 8).

Figure 7: BRPL outages and Delhi's total electricity demand

Figure 8: BYPL outages and Delhi's total electricity demand

Moreover, when we look at the time when most outages occur, we find similar divergence. Most of the outages of TPDDL and BRPL were recorded to have occurred between 6am to 12pm, which is different from Delhi's peak demand hours which are generally from 2 pm to 5 pm, and 11 pm to 1 am. BYPL's outages, on the other hand, seem to mostly occur around 12pm to 6pm. A detailed share of total outages by time of day is given in Table 5.

Table 5: Proportion of total outages and duration by time of day

Time of day	Share of TPDDL's total outages (%)		Share of BRPL's total outages (%)		Share of BYPL's outages (%)
	By frequency	By duration	By frequency	By duration	By frequency	By duration
12am - 6am	8.4	6.4	7.2	2.3	21.1	22.3
6am - 12pm	38.7	51.5	53.1	73.2	22.0	21.9
12pm - 6pm	37.3	28.6	31.3	21.8	32.5	31.3
6pm - 12am	15.5	13.4	8.4	2.6	24.4	24.6

Conclusion

Our analysis finds that the lack of a common standard and clarity in reporting makes it difficult to draw definitive conclusions about the frequency, duration, and causes of outages in Delhi. There seems to be a substantial number and hours of outages, but in the case of BRPL and BYPL, we do not know how many of those lead to consumer-facing outages, and thereby cannot assess the reliability of supply.

Several other issues also stand out. For example, TPDDL's outage reasons are not clearly defined: what exactly counts as EODB and Industrial weekly off outages? Meanwhile, most of BRPL's outages are marked as "planned". It is unclear if they translate to interruptions for consumers, but it is worth asking why such a large share is planned. On the other hand, BYPL does not report a single planned outage, which seems equally puzzling.

There are also differences in the spatial units used for reporting. That TPDDL reports 12 zones, BRPL 428 grids and BYPL 108 subdivisions implies that TPDDL's higher outages could be due to its larger geographical units. Even between BSES's two DISCOMs, outage data are reported differently, with no information on how many consumers are connected to a feeder or fall under a subdivision, making it difficult to assess the real impact of outages.

While much attention is paid to the financial performance of DISCOMs, it is also important to study the reliability of the electricity they supply. Internationally, countries like the United States and the United Kingdom publish country-wide, disaggregated outage data that enable detailed analyses of reliability, causes and impacts. For instance, studies using US Department of Energy data examine reliability and causes across states (Ankit et al., 2022) and counties (Richards et al., 2024), while data from the UK's National Fault Interruption Reporting Scheme has been used to analyse trends in outages and weather data (Shouto et al., 2024). These highlight the potential of regular, consistent and transparent reporting, which is missing in India.

As we discussed in our previous article, several independent studies in India have tried to estimate outage data, largely through household surveys (Agrawal et al., 2020; Bigerna et al., 2024; Khanna & Rowe, 2024). However, DISCOMs are better positioned to provide granular, feeder-level data in an accessible and comparable form, but as of the writing of this article, they are not mandated to make this information public. There is also no command standard of reporting, which make it impossible to make meaningful assessments. While DISCOMs are investing in redundancy systems and infrastructure, they must also clarify which recorded outages translate into consumer-facing interruptions. Doing so would, in fact, allow for a more accurate evaluation of the measures undertaken to improve reliability.

Aklin et al. (2016) had conducted a household survey in six Indian states and found that not only are outages very frequent, but that increasing the reliability of supply has effects comparable to electrifying an unelectrified household. Improving reliability of supply, however, first requires an understanding of where, when and why outages occur, which in turn requires better data. We recommend adopting a common standard of reporting outage data that includes daily, consumer-facing feeder-level outages, with information on the outage start and end times, durations, reasons, the number of consumers and the localities impacted. A first-level reason can broadly indicate whether an outage is planned or unplanned, and then provide a detailed description of the underlying cause. The data should be updated regularly and historical archives should be publicly available. This would enable more accurate and regular analyses of outage patterns, across DISCOMs and states.

References

Factors affecting household satisfaction with electricity supply in rural India by Aklin, M., Cheng, C. Y., Urpelainen, J., Ganesan, K., & Jain, A., 2016, Nature Energy, 1(11), 1-6.

Stalemate - How Consumers are Losing in the Fight Between the Regulator and Discoms in Delhi by Chitnis, A., Dmonty, A. N., & Singh, D., 2025, CSEP.

Data appendix

The data on outages was extarcted from:

• BRPL, accessed on 2 June, 2025
• BYPL accessed 7 June, 2025
• TPDDL accessed on 7 July, 2025

Delhi's daily electricity demamd was accessed from Grid-India on 7 July, 2025

The cleaned datasets and code used in this analysis are available on our GitHub repository.

The authors are researchers at TrustBridge Rule of Law Foundation. They thank an anonymous referee for useful comments.

A Review of Outage Reporting by Indian DISCOMs

by Upasa Borah and Renuka Sane.

In 2023, 99.5% of India's population had access to electricity. This statistic, however, should be measured along with the data on consistency and quality of electricity supply. Frequent power outages and low and fluctuating voltage can adversely affect appliances, reduce productivity, increase the cost of production and reduce standards of living (Jha et al., 2021). It adds a financial burden on both households and firms, who are forced to invest in costly backup options like inverters and diesel generators (Pargal and Banerjee, 2014). As India expands its electricity access, it is useful to measure how it is faring on the quality of its electricity supply. The aggregate data does not appear promising. According to the 2019 Global Competitiveness Index by the World Economic Forum, India ranked 108 out of 141 countries in electricity supply quality.

A response to the question of quality should first begin with assessing its measurement. In this article, we examine the availability of outage data in India. Outages refer to any interruptions in the supply of electricity to end consumers, and are classified into three types, depending on the location of the interruption in either generation, transmission or the distribution segments of the electricity system. We focus on outages happening in the distribution system, as it captures the final impact on consumers and takes into account upstream interruptions. These fall under the purview of distribution companies (DISCOMs), so we study all the DISCOMs in the country, and ask:

1. How many DISCOMs report data on outages?
2. Is the format of available data consistent across DISCOMs on
1. methodology,
2. period of data availability, and
3. the spatial unit of reporting?
3. Is there consistency in the reporting of outage data across states?
4. Is there a relationship between DISCOM characteristics like fiscal health and ranking, ownership and location and the availability of outage data?

Measuring outages

The quality and reliability of electricity supply are estimated by relying on a measure of either frequency or duration, or a combination of both, of interruptions faced by consumers. Feeders, which could be underground or overhead wires connecting substations to service areas, are the backbone of the distribution network, and distribution outages are typically measured using data from these feeders.

Two of the most widely used reliability indices are the System Average Interruption Frequency Index (SAIFI), and the System Average Interruption Duration Index (SAIDI). The former measures how often an average customer experiences an interruption, while the latter denotes the total minutes (or hours) of interruption an average customer faces. For example, as an illustration, if a distribution network serves 1,000 customers and experiences 200 supply interruptions in a given year, SAIFI would be 200/1000, or 0.2, interruptions per customer. If the total duration of interruptions in the same network were 3,000 minutes, then SAIDI would be 3000/1000, i.e. 3 minutes of interruptions per customer.

Supply interruptions or outages can be planned or unplanned, where planned outages are those that have been scheduled in advance, like maintenance work, which the DISCOM is supposed to disclose to customers in advance. Unforeseen outages due to disruptions, faults in the distribution system, extreme weather events, etc., are unplanned outages. From the perspective of the consumer, however, both planned and unplanned outages disrupt daily consumption and production activities and thereby have costs associated with them. Moreover, many households report not receiving prior information on planned outages (Agrawal et al., 2020), and it is often unclear which specific events are categorised as planned.

Methods

As per the Electricity Act 2003 and the National Electricity Policy 2005, the Central Electricity Authority (CEA) is tasked with collecting and publishing reliability indices for DISCOMs. However, this is not a statutory mandate, and compliance remains voluntary (Sekhar et al., 2016). The State Electricity Regulation Commissions have Standards of Performance regulations that outline metrics for reliable supply and guidelines such as time taken to restore supply, penalties, etc. (Athawale, 2021) Further, the Electricity (Rights of Consumers) Rules, 2020 mandate that DISCOMs should supply power 24x7 as the norm, with the State Commissions specifying the acceptable levels of SAIDI and SAIFI values for unavoidable interruptions. It also states that DISCOMs should have a mechanism to monitor and restore outages and disclose feeder-wise outage data and efforts made to minimise outages. The Service Rating of DISCOMs by the Ministry of Power & Rural Electrification Corporation Limited factors outages in its rating of DISCOMs; however, this data on actual outages is not publicly available.

We compiled a list of all the DISCOMs in the country using the annual ranking of DISCOMs by the Ministry of Power. While the CEA publishes annual reliability indices, not all DISCOMs are included in their lists. Moreover, such annual data masks the granular, day-to-day variations needed to meaningfully study the reliability of electricity supply.

We reviewed each DISCOMs official website to assess their current reporting practices. We restricted our search to official websites, and on encountering broken or unsafe links, we considered the data to be unavailable.

Findings: Availability of data

As per the Ministry of Power, there are a total of 72 DISCOMs in the country, all of which are included in our dataset. Among them, 36 (50%) have some form of outage data available on their websites, although irregular. The remaining 36 DICSOMs have no mention of outage or interruption data anywhere on their websites. Among the DISCOMs for which data is available, a closer look reveals the inconsistency and sporadic nature of the reported data. Broadly, there are three types of inconsistencies: i) the type of data reported and the methodology used, ii) the time period for which data is available, and iii) the spatial unit of measurement.

Reporting of data

The first inconsistency lies in the way outage data is reported. 17 out of the 36 DISCOMs use the SAIFI and SAIDI indices. The rest report interruptions by date and time, without noting how many customers were affected. Among the ones that report SAIFI and SAIDI, there is an inconsistency in the way the reliability indices are calculated. For instance, the Delhi Standard of Supply Code states that planned outages and outages less than five minutes shall not be included in calculating the reliability indices. On the other hand, the Haryana Standard of Supply Code includes planned outages in the calculation of the indices, while excluding outages of less than three minutes. DISCOMs like Karnataka's Chamundeshwari Electricity Supply Corporation (CESC) reference a "Reliability Index" without specifying which one. Other Karnataka DISCOMs provide feeder or area-wise frequency and duration of interruptions without calculating the SAIFI and SAIDI indices. Yet others, like Adani Electricity Mumbai Limited (AEML), report only the number of complaints registered and the duration taken to resolve them. Additionally, six of the 36 DISCOMs only reported scheduled or planned outages, and there was no data on unplanned outages.

Time period for which data is available

The second inconsistency concerns the time for which the data is reported. Only eight out of the 36 DISCOMs had data going back at least five years. For the rest, data availability was patchy and lacked any clear patterns. Some have data only for the past year, while others have data for sporadic years like 2022, or 2019 to 2024 and so on. Five DISCOMs had outage data only for the current date (as of visiting the website), and past archives were not available. In another instance, like that of West Bengal State Electricity Distribution Company (WBSEDCL), viewing outage data was allowed only for 60 days prior to the current date. Table 1 summarises the time period covered by the DISCOMs. There are also variations in the frequency of reporting outage data; some publish daily figures, others have data weekly, monthly or quarterly. 15 DISCOMs reported monthly SAIDI and SAIFI data, while two reported them daily.

Table 1: Coverage period of outage data

Time period covered	No. of DISCOMs
Last five years	8
Sporadic years	23
Current day	5

Spatial unit of measurement

The final inconsistency relates to the spatial unit of reporting, summarised in Table 2. 16 DISCOMs report interruptions both by feeders and areas, while four reported only feeder-wise data. Among these, some report outages in 33kV and 11kV feeders separately, while others club them together. The distinction is important because 33kV feeders carry electricity from high-voltage substations to 33/11kV substations where voltage is stepped down, and 11kV feeders then deliver power to local service areas through distribution transformers that further reduce voltage for end-users. 16 DISCOMs report outages in terms of geographic area, like zones, divisions or areas affected. However, it is unclear if these area lists are comprehensive; for instance, Assam Power Distribution Company (APDCL) reported district-wise data, but did not include all districts.

Table 2: Spatial units used in reporting outages

Unit of reporting	No. of DISCOMs
Area and feeder	16
Only feeder	4
Circles, divisions, towns, cities	13
Zones	2
Areas affected	1

Findings: Does DISCOM ranking, ownership, or state matter?

Next, we examined whether a DISCOM's characteristics, like the state where it is located, its ownership and ranking are correlated with the availability of outage data.

There were no visible patterns of data availability observed across states. In states with multiple DISCOMs like Uttar Pradesh, Gujarat and Maharashtra, most did not report outage data. In contrast, all four DISCOMs of Odisha and all three of Andhra Pradesh had outage data available on their websites. The three inconsistencies discussed earlier were also evident within states. For instance, among the four DISCOMs in Delhi, only three had data available, and among them, there were variations in the spatial units used (area vs feeder) and the time period for which data were reported.

We used the 13th DISCOM ranking by the Ministry of Power to see if better-performing DISCOMs tended to have better data availability. However, there was no clear correlation; both high-ranking and low-ranking DICSOMs seemed equally likely or unlikely to make outage data available. There was also no correlation between ownership and data availability.

Finally, we compared the DISCOMs that publish outage data on their websites to those for whom CEA has compiled annual reliability indices. Of the 49 DISCOMs included in CEA's 2021-22 list, only 26 had data available on their websites. Alternatively, among the 23 DISCOMs not included in the CEA list, 10 had outage data available on their websites. It is worth noting that for eight of these 10 DISCOMs, the data was available for sporadic years, which may explain their exclusion from the CEA's lists. Nonetheless, these findings point to the disconnect between the CEA and DISCOMs reporting practices.

Accuracy of reported data

The availability of data does not guarantee its accuracy. Several studies have raised concerns about the unreliability of outage data reporting, particularly in developing countries (Min et al., 2017). For instance, in January 2017, the National Load Dispatch Centre reported only a 0.9% shortfall in power supply in Uttar Pradesh, while Prayas Energy Group recorded a daily average of nine hours of power outages in rural areas and two hours in urban areas. Other studies point to similar discrepancies: scheduled power cuts in India often last longer than officially noted (Baskaran et al., 2015), there are logical inaccuracies in reported data (Mandal et al., 2019), and household survey data do not align with government-reported outage statistics (Agrawal et al., 2020).

These findings suggest that simple reporting of outage data is not sufficient. There is an urgent need for independent and transparent monitoring systems that complement official reporting and allow for verification of accuracy. Independent studies have attempted to fill this gap, using surveys (Agrawal et al., 2020; Bigerna et al., 2024; Khanna & Rowe, 2024), satellite night-light data (Min et al., 2017; Dugoua et al., 2022) or initiatives like the Supply Monitoring Initiative (ESMI) by Prayas Energy Group, but these efforts are usually restricted to specific regions and limited time periods. The lack of a single agency reporting outage data, combined with the inconsistencies in reporting practices by DISCOMs further complicates the process of data verification.

Conclusion

In July 2024, the National Feeder Monitoring System was inaugurated, which has data on around 2.5 lakh 11kV feeders across the country. Its dashboard provides data on hours of supply in rural and urban areas by state and DISCOM. However, to the best of our knowledge, it does not offer access to historical, granular data on daily hours of supply by feeders, state or DISCOM. The CEA reports annual reliability indices, but it should cover all DISCOMs in its list and augment it by including more granular data. A logical next step, however, is to ensure that the available data is accurate, which requires independent monitoring systems. Prayas Energy Group's ESMI has minute-wise data on supply from November 2014 to December 2018, recording not just outages but voltage fluctuations. Such efforts should be scaled up and maintained on an ongoing basis.

Having accurate and accessible data on hours of supply and areas of outage is crucial not only for consumers to understand and plan their production and consumption but also for a thorough review of DISCOMs' performance. While much of the discussion on DISCOMs centres around their financial health, it is also important to assess their ability to supply reliable power to their customers. Standard of performance indicators should include data on feeder-wise outages, distribution transformer failure rates and SAIDI, SAIFI (Pargal & Banerjee, 2014; Mandal et al., 2019). Reliability indices are valuable for providing consistent, comparable measures of service quality over time, but daily reporting of feeder-wise data that includes time, duration, cause of outage and measures taken to resolve the issue, also has its benefits in allowing for spatial, minute-by-minute analysis to pinpoint weak links in the network. Whichever approach is used, however, it should be standardised across DISCOMs and reported collectively, with a common format agreed upon by all stakeholders. If the sector moves towards reliability indices, their calculation methods should be consistent and published more frequently to ensure meaningful assessments, comparisons and verifications.

References

State of Electricity Access in India: Insights from the India Residential Energy Survey (IRES) by Agrawal, S., Mani, S., Jain, A., & Ganesan, K., October 2020, CEEW Report.

India's electric grid reliability and its importance in the clean energy transition by Athawale, R., May 2021, Regulatory Assistance Project.

Election cycles and electricity provision: Evidence from a quasi-experiment with Indian special elections by Baskaran, T., Min, B., & Uppal, Y, June 2015, Journal of Public Economics.

India's Statistical System: Past, Present, Future by Bhattacharya, P., June 2023, Carnegie Working Paper.

An empirical investigation of the Indian households' willingness to pay to avoid power outages by Bigerna, S., Choudhary, P., Jain, N. K., Micheli, S., & Polinori, P., November 2024, Energy Policy.

Assessing reliability of electricity grid services from space: The case of Uttar Pradesh, India by Dugoua, E., Kennedy, R., Shiran, M., & Urpelainen, J., June 2022, Energy for Sustainable Development.

Blackouts: The Role of India's Wholesale Electricity Market by Jha, A., Preonas, L., & Burlig, F., December 2021, NBER Working Paper.

The long-run value of electricity reliability in India by Khanna, S., & Rowe, K., April 2024, Resource and Energy Economics.

Five Stitches in Time: Regulatory and policy actions to ensure effective electricity service by Mandal, M., Nhalur, S., Pandey, A., & Josey, A., May 2019, Prayas (Energy Group).

Whose Power Gets Cut? Using High-Frequency Satellite Images to Measure Power Supply Irregularity by Min, B., O'Keeffe, Z., & Zhang, F., June 2017, World Bank Research Working Paper 8131.

More Power to India: The Challenge of Electricity Distribution by Pargal, S., & Banerjee, S. G., 2014, World Bank Directions in Development.

Evaluation and Improvement of Reliability Indices of Electrical Power Distribution System by Sekhar, P. C., Deshpande, R. A., & Sankar, V., 2016, IEEE.

The authors are researchers at TrustBridge Rule of Law Foundation. They thank an anonymous referee for useful comments.

Powering AI with Reliable Grids and Networks

by Renuka Sane.

There is much action and anticipation related to the AI decade, and especially about the potential of building large data centres, in India. As of April 2025, at least five hyperscale data centres were in the works. It is expected that India's data centre capacity will surpass 4,500 MW by 2030, backed by $25 billion in investments. Recently, OpenAI has indicated its interest in setting up a data center in India. Before we celebrate these investments, we should ask if the economics of locating them in India adds up? Even leaving aside issues such as land and taxes, do power prices, network quality, and the reliability of both electricity and the internet make us globally competitive? Some investment will come anyway because of data-localisation rules. But that is compulsion, not strategy. Real scale will only occur when a rational firm would choose India even without a localisation mandate, because the numbers and the policy risk both make sense. The AI story has two prerequisites: abundant, reliable electricity and reliable connectivity.

Reliable electricity

Let's start with electricity consumption. A small data center requires about 1-5 MW of power, while a "hyperscale" data centre draws about 100 MW of power at full load. Assuming a power usage effectiveness (PUE) of a data center of 1.2, one hyperscale data center will require 100 MW * 1.2 * 8,760 h = 1.0512 TWh. over a year.

Where is this power going to be sourced from, and how much will it cost? There are three supply options: (1) grid supply,(2) round-the-clock renewable energy plus storage (RTC-RE+storage) contracted from a developer under green open access or (3) a captive plant powering the data centre.

1. Grid supply: If we assume an industrial tariff of Rs. 7.5 per kWh (which is close to the tariffs in Maharashtra and Tamil Nadu,the two states at the forefront of the data center business), the electricity bill for uninterrupted grid power is roughly Rs. 7.9 billion (US$ 90 million) per year. Except that power supply is not guaranteed, and significant power outages imply that data centres have to build alternatives to ride through grid outages. At Rs. 25-30/kWh, diesel generators routinely cost an order of magnitude more to almost US 236-300 million.

2. Round-the-clock renewables with storage (RTC-RE + storage) via green open access: Here we have to consider three cost items. The first is the cost of the RTC power purchase agreement (PPA), which will be higher than plain solar/wind because it includes storage and portfolio diversity. The second are the network charges and losses that include intra-state transmission, wheeling (distribution) charges, and transmission + wheeling losses before it reaches the meter. If this is inter-state then one has to start considering the prevailing ISTS regime. The third is the cross-subsidy surcharge (CSS) and additional surcharge (AS) to the DISCOM. In Maharashtra, for example, these are quite high, potentially making the final price above the grid, even if the landed cost of the RTC PPA is significantly lower than the grid price. An approx overall price of Rs.9/kWh hour, gives us a total cost of around US$110 million.

3. Group-captive RTC: In this case, CSS and AS charges wouldn't apply and the price may come close to (or be lower than) the grid price. The data centre, however, would need to hold the required equity and off-take, and would have its own governance challenges. It is these that can become the binding constraints, not just price. A group-captive cost of Rs.7/kWh leads to a cost of US$83 million.

How do these numbers compare to say the US or Germany? The table below gives some indicative answers.

Country	Unit cost (US$)	Annual costs (US$)
India (grid average)	0.085	89.4 m
India (RTC, group captive)	0.076-0.080	80-85m
India, (RTC, third-party)	0.104-0.112	109-118 m
Germany, (grid average)	0.196	205.5 m
United Kingdom (grid average)	0.249	261.6 m
US, (grid average)	0.0886	93.1 m
US (Texas)	0.063	66.6m

As the table shows, despite the difficulties in India, it remains competitive vis-a-vis countries such as Germany and the UK as far as electricity prices are considered. However, India is not competitive vis-a-vis the US, where prices in places such as Texas and Virginia (US$0.091) are lower than the third-party open access option in India. The economics of electricity will further change based on the amount of cooling required, which will be relatively higher in India, requiring more energy than in countries with ambient temperatures much lower than India.

Reliable connectivity

There is much more awareness regarding the impediments of the electricity sector for our AI ambition. The issue of reliability of connectivity is less talked about - reliability that gets compromised because of our world-leading record of internet shutdowns. India sees routine network blackouts for reasons related to mobile data bans during exams, to internet suspensions in conflict for months on end. In 2024 India accounted for 28% of all government ordered shutdowns globally - the highest by any country. There have already been 28 shutdowns in 2025. One study estimates the economic cost of shutdowns to the Indian economy at $968 million.

One could argue that data centres used leased lines, and a mobile only shutdown would not matter much. Except that mobile only bans knock out end-user access potentially affecting any product whose customers are on these networks. Shutdowns can affect logistics, field engineering, and remote operations. A shutdown freezes the demand for AI inference in that region. Such vanishing demand equals loss of demand of electricity, leading to idle capacity, another indirect cost for an entrepreneur to handle. One could also argue that shutdowns primarily occur in conflict zones, or districts that are on the periphery of economic activity, and therefore not likely to materially affect the AI story. While that may be true at the moment, routine ad-hoc shutdowns undermine trust in India as a location for latency-critical workloads and cross-border data partnerships. They can lead to a sovereign reliability discount, raising the hurdle rate for capital. Even without shutdowns, India routinely has to deal with connectivity problems owing to frequent power cuts causing internet outages.

Way forward

There are two ways to look at the issue of data centres. The first is whether India can become the regional hub and service clients across Asia and the Middle East. On this question, the answer is clear. Firms will make rational decisions - unless it makes economics sense, firms will prefer to rent equipment or make API calls to the cheapest data centres elsewhere in the world. The second is what it costs firms in India to be forced to place data centres here owing to data localisation mandates. If the cost of training and inference is lower in the US (or other overseas markets) than in India, then firms in India will be at a considerable disadvantage if forced to use local facilities.

Blackouts and shutdowns are not compatible with the way in which AI services evolve and deploy. Foundation models, hyperscale data centres, and exportable AI services demand 24x7 supply and connectivity. India needs to get its grid electricity to world-class levels. It needs to reform its charge structure that makes firm green more expensive than grid making exit difficult. While nuclear energy remains an option, there is no clarity yet on the resolution of supplier-liability laws, making its future still uncertain. Additionally, India needs a radical overhaul of its policy on shutdowns. They should be the absolute last instrument; a rule-of-law state first exhausts narrower tools such as content take-downs, site-specific throttling, geofenced blocks, and targeted law enforcement, and only then even contemplates turning off the network. Our AI strategy should focus on building on two pillars: dependable power and dependable networks.

The author is a researcher at the TrustBridge Rule of Law Foundation. I thank Ajay Shah and Anand Venkatanarayanan for useful comments.