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.

Examining the performance of ERCs at APTEL

by Chitrakshi Jain, Bhavin Patel, and Renuka Sane.

Introduction

The efficiency of the State Electricity Regulatory Commissions (SERC)s, the Central Electricity Regulatory Commission (CERC) and the Joint Electricity Regulatory Commission (JERC) influence investability and growth of the electricity sector. For example, it costs regulated entities time and resources to petition the relevant ERC for decisions and potentially, to challenge decisions taken by the ERC at the appellate tribunal (APTEL), which exercises supervisory control over the ERCs and reviews their decision-making.

This article studies how ERC decisions perform at APTEL. We collect information about aspects of the ERCs' functioning from the text of orders passed by APTEL. This helps us (a) identify the most-litigious areas across ERCs and (b) examine how the ERCs' decisions perform in appeal. We use sub-national comparative analysis to understand the variation in the functioning of the different ERCs and the litigiousness of issues in different states.

We ask the following questions:

1. Which ERCs contribute the most appeals at APTEL?
2. What are the most litigious issues at APTEL?
3. In how many appeals was the ERC's decision:
   1. Upheld, i.e. appeal was dismissed by APTEL?
   2. Partially upheld, i.e. appeal was partly allowed at APTEL?
   3. Overturned, i.e. appeal was fully allowed at APTEL?
4. How often were the ERCs ordered to reconsider their decisions, i.e. the matter was remanded?

Our results suggest that issues related to tariff determination and restructuring are the most litigated issues at APTEL across ERCs, with the exception of Maharashtra. ERCs are differently situated in their ability to defend their decisions at APTEL and in the quality and clarity of their orders. We argue that such assessments, if regularised, can assist ERCs in improving the quality and form of their decision-making by creating a feedback loop, and can also assist in identifying areas for policy reform at the sub-national level.

Methods: Data

We obtained the orders passed by APTEL between the years 2013-2022 where the ERCs are a party to the challenge before APTEL. We excluded interim orders given that they do not include the outcome of the case. We focused on ten states, including Andhra Pradesh, Karnataka, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, and West Bengal. These were selected keeping in mind geographical coverage, size of the state, and installed renewable energy (RE) capacity in the state.

We collected information on 26 indicators from the orders, related to the following categories:

1. Time-related: e.g., date of orders, date of impugned order
2. Party-related: names of appellants, respondents
3. Bench-related: e.g., quorum, members' names
4. Subject-matter related: e.g., prayers, issues
5. Outcome-related: e.g., disposition and remand

We processed the text of the orders through LLMs, which we prompted to collect the information by placing reliance on explicit language in the text. After collecting the information for the relevant indicators, we ran verifications based on rules of legal consistency and logic to ensure that the collected information is accurate and reliable. For indicators related to outcome, we have made subjective inferences when the explicit information regarding its outcome was not articulated in the order. We have relied on individual appeals as the unit of analysis, given that outcomes are typically uniform for all parties in an order. We have integrated human verification at every stage of data collection to ensure reliability. Our final dataset consists of 513 orders and 919 appeals. The data is available here.

Methods: Issue categorisation

In order to study the issues that were being agitated before APTEL, we identified themes from the statement of issues in appeals which explicitly articulated them. There were 318 appeals (out of 919) that did not include a statement of issues. After classifying the issues thematically, we decided upon the final categories presented in Table 1 in consultation with practitioners. We ran keyword searches to sort the statement of issues into identified categories and verified the classification by reading the statements when they yielded unclear results for accuracy and reliability.

Table 1: Categorisation of Issues

Issue Category	Coverage
Tariff determination and restructuring	Challenges to tariff determination and adoption under Sections 62 and 63; inadequate attention to principles in arriving at tariff; revision of tariff and truing up.
Contractual disputes	Liquidated damages, outstanding payments, renegotiation or termination of contracts, excluding change in law and force majeure.
Change in law and force majeure	Subset of contractual disputes, relating to change in law and force majeure clauses in the contracts.
Procedural and jurisdictional	Procedural lapses, violation of principles of natural justice, challenges to ERC's jurisdiction.
Open access consumers	Wheeling and banking charges, and issues relevant to open access consumers.
Transmission and grid-related	Connectivity, ISTS and grid-related issues, including compliance with grid code.
Specific compliance with regulations	Mandatory non-tariff-related requirements for obligated entities, such as RPOs and RECs.
Captive status	Captive status of power plants or group captive power plants.
Others	Issues not falling under previous categories, e.g., distribution licensing.

Methods: Limitations

For the categorisation of issues we have relied on the statement of issues as determined by APTEL, in the instances it was explicitly identified in the order. Understandably, analysing the full text of the order will give deeper insights on the litigated questions. The outcomes, such as appeal allowed or dismissed, also do not provide information about outcomes on specific issues. This would entail reading the full orders and making subjective inferences. While the outcomes at APTEL have been used to assess the performance of ERCs, they do not measure the functioning of ERCs holistically, especially because studying the performance of APTEL is beyond the scope of this research.

Results

1. Distribution of litigation

Between the ERCs under study, Maharashtra followed by Karnataka, contribute to the most litigation at APTEL, as represented in Figure 1. These results indicate that the two states have a relatively larger private industry. However our analysis excludes writ proceedings, which are also used as a way to challenge ERC decisions. The total number of orders passed by ERCs is also not available uniformly across the ERCs to accurately calculate the rate of appeal.

Figure 1: Distribution of litigation at APTEL

2. Most litigious issues

ERCs are empowered to decide a wide range of issues. As a consequence, the issues dealt with by the APTEL in appeals are also varied. An appeal may involve more than one issue, and hence the number of issues involved is more than the number of appeals. As represented in Table 2, tariff determination and restructuring are the most litigated upon issues at APTEL across ERCs with the exception of Maharashtra. In Maharashtra, procedural and jurisdictional issues emerge as the most litigious. While the high incidence of such issues is concerning, issues of the procedural and jurisdictional variety can be resolved easily if ERCs invest in capacity building and follow the procedure under the law faithfully.

Our results corroborate the findings of an earlier study (Prayas 2018) which had found that a third of the issues being litigated before APTEL were concerned with tariff. Pertinently, the ERCs have enacted specific regulations related to tariff determination and made the calculation of tariff an exercise which is assisted by detailed delegated legislation. In this context, it is worrying that tariff continues to be the predominant category with regard to appellate litigation.

Table 2: Issue portfolio at different ERCs (Values in percentages)

State	Total Issues	Tariff related	Procedural & Jurisdictional	Contractual disputes	Specific Compliance	Change in law & force majeure	Open access	Trans-mission & grid	Captive status	Other
Maharashtra	248	26.61	32.66	2.82	3.63	3.63	6.85	4.44	16.53	2.82
Karnataka	171	30.41	26.90	22.81	1.75	2.34	6.43	6.43	0.00	2.92
Tamil Nadu	138	28.99	12.32	7.25	20.29	0.72	5.80	7.25	10.87	6.52
Punjab	96	33.33	11.46	15.62	14.58	8.33	7.29	4.17	1.04	4.17
Rajasthan	76	25.00	11.84	19.74	10.53	13.16	5.26	7.89	1.32	5.26
Madhya Pradesh	75	41.33	17.33	13.33	0.00	1.33	9.33	4.00	9.33	4.00
Andhra Pradesh	65	47.69	26.15	9.23	3.08	1.54	1.54	7.69	0.00	3.08
Uttar Pradesh	59	33.90	16.95	20.34	8.47	5.08	1.69	13.56	0.00	0.00
Odisha	49	34.69	16.33	6.12	16.33	0.00	8.16	10.20	6.12	2.04
West Bengal	29	62.07	13.79	3.45	13.79	3.45	0.00	3.45	0.00	0.00

In addition to the most litigated issues, we could also identify the states which contributed most to the litigation of a particular issue at APTEL. Maharashtra contributes the most to issues related to tariff, and procedure and jurisdiction. This outcome is also a function of Maharashtra being involved in the highest number of appeals in our dataset. The findings are presented in Table 3 below.

Table 3: Distribution of Issues

ERC which contributes most to the litigation of a particular issue at APTEL and the percentage share of their contribution

ERC	Issue	Contribution (%)
Maharashtra	Tariff related	20.1
Maharashtra	Procedural and jurisdictional	37.5
Karnataka	Contractual disputes	33
Tamil Nadu	Specific compliance with regulations	34.5

3. Outcomes

We focus on indicators related to the 'disposition' of the appeal from the information we had collected. We scored the performance of the ERCs relative to each other by making the number of decisions that were upheld, overturned or modified by APTEL as the basis of comparison. The results have been compiled in Table 4.

At an outcome level, if an ERC succeeds in defending its decisions, then it would indicate that the orders are well-reasoned, and the ERC follows the procedure under the law. A high overturn rate would indicate weak decision-making capacity.

Table 4: Dispositions at APTEL across ERCs

ERC	Allowed	Dismissed	Partly Allowed	Other	Remanded	Total
Andhra Pradesh	27	32	7	4	11	70
Karnataka	103	49	14	5	67	171
Maharashtra	92	61	31	62	35	246
Madhya Pradesh	22	17	11	8	13	58
Odisha	17	15	17	2	6	51
Punjab	18	28	22	6	13	74
Rajasthan	32	45	7	3	20	87
Tamil Nadu	22	33	19	9	20	83
Uttar Pradesh	15	26	11	5	10	57
West Bengal	4	8	6	4	5	22
Total	352	314	145	108	200	919

_{The categories "allowed", "dismissed", "partly allowed", and "other" are mutually exclusive. That is, if an appeal is allowed, it cannot be dismissed. However, the appeals that are remanded form a subset of either allowed or partly allowed.}

We find that ERCs are differently situated in their ability to defend their decisions at APTEL and the quality and clarity of their orders. Rajasthan found the most success at APTEL, Maharashtra had the least.

Typically, matters are remanded when APTEL is of the opinion that the relevant ERC did not, amongst other things, follow the procedure or frame the issues or determine question of facts sufficiently well. A high remand rate is worrying since it implies that either the ERCs in question are ill-equipped to resolve disputes in the first instance or that APTEL, unless it has insufficient evidence to make the decision, is abdicating its mandate.

Remands lengthen the resolution of disputes and burden regulated entities with legal and compliance costs. This can stymie the growth of the electricity sector, especially in states like Karnataka, for KERC has been asked to reconsider most number of its decisions when compared to other ERCs.

Recommendations

Both APTEL and ERCs are empowered to implement these recommendations.

1. Regularise assessments through use of emerging technologies

We recommend that such comparative assessment exercises be regularised through the use of emerging technologies. The composition of ERCs is constantly changing, and members would benefit from information about the performance of their decisions at APTEL closer to the date of the decisions. This can be made possible by creating a customised tool that leverages LLMs and the competence of researchers and practitioners familiar with the sector.

2. Publish granular statistics

APTEL can improve upon the collection and publication of litigation statistics and include the subject matter of litigation and the relevant laws that are under litigation, amongst other categories, in this exercise. Similarly, while some ERCs publish the number of orders they hear and decide annually, they can include more relevant details in this publication and also publish these at shorter intervals. Collecting this data at source would make the identification of litigious issues, which are often proxies for policy problems, easier.

3. Identify areas for policy reform

ERCs should study the precise reasons for disputes that correspond with the litigious issue categories in their states and respond by changing and adapting their regulations to minimise them. The persistence of tariff as the most litigious category is concerning, given that detailed regulations on calculation and imposition of tariff have been enacted by the regulators.

Conclusion

In summary, we find that:

• Between the ERCs under study, Maharashtra, followed by Karnataka, together contribute to the most litigation at APTEL.
• Issues related to tariff determination and restructuring are the most litigated issues at APTEL across ERCs. This is worrisome given the detailed subordinate legislation that govern the regulation of retail and other categories of tariff.
• ERCs are differently situated in their ability to defend their decisions at APTEL and the quality and clarity of their orders. We find that Rajasthan found the most success at APTEL, while Maharashtra had the least.
• Remands lengthen the resolution of disputes and burden regulated entities with legal and compliance costs. This can stymie the growth of the electricity sector, especially in states like Karnataka, since KERC has been asked to reconsider the most number of its decisions when compared to other ERCs.

References

Amicus Populi? A public interest review of the Appellate Tribunal for Electricity , by Vaishnava S, Chitnis A and Dixit S, 2018, Prayas Energy Group

The authors are researchers at TrustBridge Rule of Law Foundation. They would like to acknowledge and thank Natasha Aggarwal, Madhav Goel, Abhinav Hansaraman, Amol Kulkarni, Praduta Singh, Aparna Jha, Varun Soni, Gaurav Aswani, Tarang Rathi and Sumedh Gadham for compiling, collecting, and verifying the data used in our analysis. We would also like to thank Upasa Borah for helping with verifying, cleaning, and consolidating the dataset.

Pumped storage plants in India: assessing policies and progress

by Upasa Borah, Chitrakshi Jain and Renuka Sane.

The transition to renewable energy faces challenges related to intermittency and variability in energy availability. Energy storage systems (ESS) play a crucial role in addressing these issues by storing excess renewable energy (RE) during periods of low demand and releasing it during peak hours. This enhances the scalability of renewable energy systems worldwide, reducing reliance on fossil fuels and supporting the integration of renewables into the grid. ESS technologies enable the conversion of electricity into other forms of energy for storage and later use. Among these, pumped storage plants (PSPs) remain one of the oldest and most widely relied upon solutions. These are adaptations of conventional hydropower plants.

India has set a target to achieve 50% cumulative installed capacity from non-fossil fuel-based energy resources and to reduce the emissions intensity of its GDP by 45% by 2030. India has also seen policy changes in ESS over the last few years. Legal recognition to ESS was granted in 2022, and new policy guidelines for PSPs were notified in 2023. The Central Electricity Authority (CEA) has estimated the storage capacity requirements, which will enable greater integration of renewable energy sources. These include 26.69 GW of pumped storage capacity and 47 GW of battery energy storage system (BESS) capacity by 2031-32. Among the two commercially viable technologies, BESS and PSPs, the latter present several advantages. Batteries are restricted by their storage capacity and their lifespan, and will have to be replaced frequently. PSPs, on the other hand, have the longest service life of 50 to 150 years and can store and generate energy on a much larger scale.

Given the importance of ESS and PSPs for India's energy transition, our recent paper titled "Pumped Storage Plants in India: Assessing Policies and Progress" presents the evolution of policy on PSPs and their performance in India.

The paper addresses the following questions:

• Where do PSPs feature in the overall storage policy?
• How many PSPs are under various stages of development? How many are eventually being completed?
• Are the policy measures encouraging the private sector to participate in the development of PSPs?
• Is the stated requirement of adding 26.69 GW of PSPs storage capacity by 2032 likely to be completed in the current context?
• What lessons from our experience of executing hydropower projects are relevant for the development of PSPs?

To study these questions, it builds a dataset of PSP projects from the information published by the Central Electricity Authority (CEA) and the CapEx dataset maintained by the Centre for Monitoring Indian Economy (CMIE).

Our analysis finds that the policy environment has become conducive to the development of energy storage systems in general and PSPs in particular. The participation of the private sector in the development of PSPs has increased considerably since 2018. Out of the 130 GW capacity that is under various stages of planning, 102 GW is being developed by the private sector. However, the ratio of projects which receive concurrence and are eventually completed remains low. Of the 91 projects in the dataset, 17 are under implementation, and six have been completed. The completed projects account for 3.3 GW of storage capacity. The low ratio of PSPs that are completed, combined with the experience of delay in executing hydropower projects, implies that the requirements of storage capacity addition from PSPs by 2026-27 and 2031-32 will be met only if the capacity under planning is realised and the projects are completed within six years.

The authors are researchers at the TrustBridge Rule of Law Foundation.

Judicial overreach: Bypassing expert tribunals in the electricity sector

by Natasha Aggarwal and Bhavin Patel.

A 2023 decision of the Supreme Court (The Southern Power Distribution Company of Telangana State v. Agarwal Foundaries Private Limited and Another, SLP (C) No. 14047-14066/2019) underscored the importance of judicial deference to expert bodies, stating that the High Court should have remanded a technical matter to the Appellate Tribunal for Electricity (APTEL) instead of adjudicating it itself.

The Electricity Act, 2003 establishes a framework under which appeals from orders of the Central Electricity Regulatory Commission and State Electricity Regulatory Commission (SERCs) may be filed before the APTEL. In 2021-22, only 12 appeals from the Telangana State Electricity Regulatory Commission (TSERC) were filed before the Appellate Tribunal for Electricity (APTEL), while 85 appeals were filed before the Telangana High Court (that is, more than seven times the number of appeals before the APTEL). Therefore, a large number of challenges to the TSERC's orders were filed before the High Court, and not the APTEL, a sector-specific expert body. Notably, this problem is not unique to Telangana and exists in other states from time to time. For example, in 2019-20, 21 appeals from the Odisha Electricity Regulatory Commission were filed before the APTEL while 34 writ petitions were filed before the High Court.

The trajectory of the TSERC's orders, from the TSERC to the Telangana High Court, raises questions on the grounds and scope of judicial review of these orders and their adherence to well-established principles of administrative law. These principles caution against judicial overreach in reviewing regulatory decisions. Over time, the Supreme Court of India has established the circumstances in which judicial review is permitted as well as the considerations that may be relevant in deciding to exercise judicial review.

In a recent paper, Bypassing expert tribunals through writs: Judicial overreach in review of the Telangana State Electricity Regulatory Commission's orders, we study 179 writ petitions and 181 writ appeals involving the TSERC before the Telangana High Court between 2014-2022 and examine whether judicial review of the TSERC's orders by the High Court is within the permitted limits in administrative law.

Our study reveals that 52.5% of the writ petitions in our subset and 58% of the writ appeals in our subset fall squarely within the scope of the matters for which the Electricity Act provides an appellate mechanism through the APTEL. Therefore, the largest number of writ petitions and writ appeals relate to 'substantive matters', which we identify as those that the Electricity Act contemplates as falling within the scope of TSERC's quasi-judicial powers and APTEL's appellate jurisdiction.

The existence of an efficacious alternative remedy, such as an appeal before the APTEL is not a complete bar on judicial review. However, well-established principles of administrative law limit the situations in which courts should entertain matters when such an alternative remedy exists, particularly because specialised tribunals and appellate authorities have the technical expertise to examine the facts and merits of a case. Moreover, the rationale for providing such an appellate mechanism is the requirement of technical expertise, and the APTEL has such expertise while the High Courts may not, and therefore the exercise of judicial review in such situations undermines the objectives of the Electricity Act.

The authors are researchers at TrustBridge Rule of Law Foundation.