High-Voltage Treatment for a Comatose Elephant

Unshackling the Elephant by Anand Prasad: A Review

by Siddarth Raman.

Anand Prasad's Unshackling the Elephant provides an interesting clean-sheet critique of the Indian legal system. India's development story into the 21st century is shackled by a judicial system that is stuck in the past. The book succeeds incredibly well at applying economic logic and management principles to the judiciary. Many of the suggested reforms have the weight of common sense - the kind that becomes visible once someone has taken the trouble to articulate it well. It stumbles into uncertain territory when suggesting we succumb to indigenous instinct, especially given the fragility of India's current state capacity and my own preference for cautious reforms that avoid the treacherous currents of populism.

This book matters. It is precise in its diagnosis and bolder in its questions than most will venture. Systems rest on assumptions we stop interrogating; surfacing these debates is important because knowing why we got here tells us what needs to be fixed and what doesn't need relitigating.

The Low-Hanging Fruit of Process Modernisation

On several fronts, Prasad's prescriptions will invite broad agreement - particularly from those who have witnessed the impact of process modernisation in other fields and wondered why the legal system has resisted it for so long.

The law is both judicial reasoning and process management. Other fields have walked the journey of process improvement. IT services, consulting, accounting, even corporate law in some aspects. Templatised pleadings and standardised procedures need not be recreated from first principles for every matter; translation tools chip away at language barriers; and the knowledge management systems Prasad envisions - his "legal knowledge grid" - would make legal corpora accessible to law graduates and large language models alike. Ideas of virtual hearings and asynchronous proceedings push this further, reducing the friction of physical presence without necessarily compromising procedural integrity.

These are safe bets. They enhance capacity without demanding discretionary overreach. Early efforts have begun and should be embraced (see here, here, here); many of these ideas deserve serious piloting as part of the eCourts Phase III modernisation effort.

Prasad also questions the figure of the "all-purpose judge" -the expectation that foundational legal education alone equips one to adjudicate matters spanning technology, finance, and specialised commercial arrangements. The assumption may have held in a simpler era. Whether it holds today is less certain.

A note of caution on artificial intelligence. The justice system functions because society accepts the state's monopoly on coercive power and consents to have it applied through due process. This is a social contract grounded in human accountability. AI may assist judges. But the decision must remain with someone who can be questioned, overruled, and held responsible. As much as the engineer in me might wish law were code, it is not.

Fixing the Incentive Systems

Courts are not just forums for dispute resolution. They are markets where incentives shape behaviour.

Consider the interest rate regime applied to legal disputes. Courts award simple interest at rates that don't match any reasonable cost of capital. The effect is predictable: delay becomes a strategy. A defendant who owes money has every reason to stretch proceedings; time works in their favour. Prasad is correct to argue that judges need to understand the time value of money. Aligning judicial interest rates with economic reality would shift the calculus against strategic delay.

On damages, Prasad makes the case for stronger deterrence -particularly through punitive awards for corporate fraud. On costs, he argues that allowing successful litigants to recover expenses would reduce barriers to access, enabling those confident in their position could invest in quality representation without bearing the full risk.

He also correctly identifies information asymmetry and power imbalance as distortions in the current system. Those with deeper pockets hire better lawyers, and in an adversarial system, this matters considerably. His proposals - removing restrictions on contingency payments, allowing lawyers to advertise, and litigation funding - would make legal representation function more like a market, creating a more level playing field.

Like Chief Justice Suryakant, Prasad echoes the need for judicial performance reviews. This deserves serious consideration, though any implementation must balance accountability against judicial independence. For the bar, he calls for high penalties for malpractice and stricter consequences for perjury. Whether the profession will police itself remains an open question. Prasad's experience understanding lawyers and litigants shines through in this section - he dissects how courts, like any market, respond to incentives.

The Temptations of an Overhaul

Anand Prasad's frustration with the legal system is understandable. Stepping into a courtroom feels like walking back through time.

While some structural reforms like splitting the Supreme Court into a constitutional court and court of appeals or curbing judicial legislation merit serious debate, some of the more radical proposals warrant caution. Moving towards an inquisitorial system would transform judges from neutral adjudicators into active investigators. In a country where discretionary power is prone to abuse, such a shift is a dangerous gamble.

The most troubling temptation is the urge to decolonise the system. Weakening foundational principles like the presumption of innocence in favour of indigenous traditions risks legitimising majoritarian sentiment as law, especially when honour killings persist in the 21st century and extrajudicial encounters meet public approval. The philosophical questions in attempting an Indic reinterpretation are formidable - attempting to balance ideas of karma against retributive justice, uniform civil codes against community specific practices, or the contextual obligations of Raja Dharma against the common law tradition that all stand equal before the law.

The Contradictions

There is an internal tension in the book's vision. It praises codification for its clarity and bemoans lack of consistency in judgements while simultaneously advocating for inquisitorial discretion and culturally-responsive justice. Codification demands predictability. Instinctive justice invites its opposite. A similar tension runs through the hope that algorithms will fix what humans could not. AI may detect patterns across thousands of judgements, but it cannot bear responsibility for any one of them.

The deepest contradiction is one of trust. The book hopes to build a high-trust society where precedent holds and contracts are sacred. This is hard to reconcile with privileging cultural instincts that are fluid and contested. One cannot ask people to trust in precedent while empowering judges to override it.

Conclusion

The boldness of the project deserves recognition. Unshackling the Elephant is a precise diagnosis of a system that has resisted reform for too long. Many treatments align with modernity. But there is a rebel streak - perhaps born of frustration - that carries risk. Giant shocks may end up killing the elephant rather than reviving it.

References

Prasad, Anand. 2025. Unshackling the Elephant: Transforming Indian Law, Culture and Economy. Bloomsbury India.

Siddarth Raman is Senior Research Lead at XKDR Forum.

Can technology augment order writing capacity at regulators?

by Natasha Aggarwal, Satyavrat Bondre, Amrutha Desikan, Bhavin Patel and Dipyaman Sanyal.

Indian regulators have extensive quasi-judicial powers that they express through adjudicatory orders. It is critical that these powers are exercised in a proportionate, legitimate, and well-reasoned manner, as they not only impact the persons directly involved, but also the wider ecosystem in which they operate. Arbitrary actions, unsubstantiated by clearly articulated reasoning, can raise serious concerns around the legitimacy of regulatory actions and lead to a loss of confidence in the regulator. Such actions may also be set aside by appellate and review fora. Clearly written, well-researched, and reasoned orders help provide clarity, predictability, and knowability of the law, which are key indicators of a rule of law system (Aggarwal, Patel and Singh, 2025). Our study of the state of Indian regulatory order writing shows there is room for improvement in this regard.

We notice a growing interest in the use of Generative Artificial Intelligence (Gen AI) to resolve procedural inefficiencies at quasi-judicial and judicial authorities in India (Supreme Court Committee on AI, 2025; Kerala High Court, 2025), coupled with concerns around the potential dangers of using such technologies without adequate safeguards. Against this background, in a new working paper titled, 'Can technology augment order writing capacity at regulators?' we critically examine the opportunities and challenges of using technology, in particular Large Language Models (LLMs), to assist regulatory order writing in quasi-judicial settings.

The paper proposes augmenting rather than replacing human decision-makers, aiming to improve regulatory order writing practice through responsible use of LLMs. It identifies the core principles of administrative law that must be upheld in these settings - such as application of mind, reasoned orders, non-arbitrariness, rules against bias, and transparency - and analyses how inherent limitations of LLMs, including their probabilistic reasoning, opacity, potential for bias, confabulation, and lack of metacognition, may undermine these principles.

While the available Indian literature on the topic focuses largely on these limitations, and on critiquing proposals based on an over-reliance on technocratic means to improve state capacity, this paper's contribution lies in its integrative work: we draw upon the design principles articulated in frameworks developed in other jurisdictions and relate them to the applicable principles of Indian administrative law. We use this synthesis to develop a Problem-Solution-Evaluation (PSE) framework that is attentive to international practice, the legal principles underpinning quasi-judicial decision-making in India, and problems and limitations inherent to GenAI and LLMs.

The PSE framework proposed in the paper maps specific technical, design, and systemic solutions to each identified risk, and outlines evaluation strategies - end-to-end, component-wise, human-in-the-loop, and automated - to ensure ongoing alignment with legal standards. An overview of the framework is set out in the table below:

Table 1: Applying the Problem-Solution-Evaluation framework. This table illustrates how the PSE framework can be operationalised to align the design, development and use of LLMs for order writing assistance with the requirements of Applicable Law.

Problem	Applicable law	Solution	Evaluation
Non-application of mind	Non-application of mind; Failure to provide reasons; Arbitrariness	Interface Checkpoints; Confidence Score Display; Dual-Prompt Pipelines; Functionality Limitation; Constraint Enforcement; Workflow Design for; Review Role-Based Access	Edit Rate; Turnaround Time (TAT); Prompt Divergence Rate; Coherence Score
Black-box problem	Failure to provide reasons; Transparency	Chain-of-thought prompting; Input Token Influence Identification Symbolic Reasoning Systems Traceability tools; Visualisation; Simplified model explanations	Clarity rating; Audit Trail Incidence Document Traceability Rate
Potential for bias	Rules against bias; Arbitrariness	Data Preprocessing; Bias penalisation; Domain-specific content filters; Automated Bias Flagging Tools; Establishment of Legal Fairness Criteria; Mandatory Periodic Benchmarking	Bias Flag Rate Override Percentage; Fairness Benchmark Scores
Confabulation problem	Non-application of mind; Failure to provide reasons; Arbitrariness	Retrieval Augmented Generation; Post-Generation Verification; Legal Knowledge Graph Integration; Mandatory reviewer verification; Watermarking for traceability; Communicate technical limitations	Secondary LLM ''Judge'' for Fact-Checking; End-to-End Evaluation Tools Hallucination Rate; Retrieval Precision@k/ MRR NLI Coherence Checks; Self-Consistency Rate
Lack of metacognition	Non-application of mind; Arbitrariness	Prompt engineering; LLM as a judge; Iterative improvement from feedback	Closeness Metric; Human evaluation on overconfidence in output
Training corpus	NA	Adaptive Scraping Frameworks; Sector-specific pre-training; Structured Entity; Extraction and Legal Knowledge Graphs; Isolated Model Containers; Source inclusion; Perplexity tracking; Legal Retrieval Benchmarking; Curate sector-specific legal databases	Crawl coverage; OCR Error Reduction; Validation perplexity; Retrieval lift
Data security and privacy	NA	Stringent access control; Synthetic supervision-based PII detectors; NLP filters for information masking; Isolated Model Containers; On-premise infrastructure	Unauthorised access attempts; Mean Time To Remediation (MTTR); Penetration Test Pass Rate; PII Detection Accuracy

By itself the framework may be insufficient. It must be supplemented with systemic measures taken at the regulatory level. We offer stage-wise recommendations on how LLM-based order review tools can be built for and used in regulatory adjudication.

References

Natasha Aggarwal, Bhavin Patel, and Karan Singh, "A Guide to Writing Good Regulatory Orders" [2025] Trustbridge Rule of Law Foundation Working Papers.

Anurag Bhaskar and others, "White Paper on Artificial Intelligence and Judiciary" Centre for Research and Planning, Supreme Court of India, 2025.

High Court of Kerala, "Policy Regarding the Use of Artificial Intelligence (AI) Tools in District Judiciary" Official Memorandum HCKL/7490/2025-DI-3-HC Kerala, 2025.

Natasha Aggarwal, Amrutha Desikan and Bhavin Patel are researchers at the TrustBridge Rule of Law Foundation. Satyavrat Bondre and Dipyaman Sanyal work on AI and technology at dōnō consulting.

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.