metadata: authors: 0: author: Min,Brian
1: author: O'Keeffe,Zachary
2: author: Zhang,Fan
count: India
docna: 0: docna: Whose power gets cut ? using high-frequency
            satellite images to measure power supply irregularity
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Industrial Economics,Energy Policies & Economics,Food Security,Economic Growth,Energy Demand,Energy and Environment,Energy and Mining,Global Environment
teratopic: Energy
abstracts: cdata!: In many parts of the developing world,
            access to electricity is uneven and inconsistent,
            characterized by frequent and long hours of power outages.
            Many countries now engage in systematic load shedding
            because of persistent power shortages. When and where
            electricity is provided can have important impacts on
            welfare and growth. But quantifying those impacts is
            difficult because utility-level data on power outages are
            rarely available and not always reliable. This paper
            introduces a new method of tracking power outages from outer
            space. This measure identifies outage-prone areas by
            detecting excess fluctuations in light outputs. To develop
            these measures, the study processed the complete historical
            archive of sub-orbital Defense Meteorological Satellite
            Program's Operational Linescan System (DMSP-OLS)
            nighttime imagery captured over South Asia on every night
            since 1993. The analysis computes annual estimates of the
            Power Supply Irregularity index for all 600,000 villages in
            India from 1993 to 2013. The Power Supply Irregularity index
            measures are consistent with ground-based measures of power
            supply reliability from the Indian Human Development Survey,
            and with feeder-level outage data from one of the largest
            utilities in India. The study’s methods open new
            opportunities to study the determinants of power outages as
            well as their impacts on welfare.
geo_reg_key: 517198,517191,517212
repnme: repnme: Whose power gets cut ? using high-frequency
            satellite images to measure power supply irregularity
display_title: Whose power gets cut using
            high-frequency satellite images to measure power supply irregularity
listing_relative_url: /research/2017/06/27688996/power-gets-cut-using-high-frequency-satellite-images-measure-power-supply-irregularity
topicv3: Agriculture and Food Security m1326205 435,Economic Growth m1326241 471,Energy and Extractives m1326595 507,Power and Electricity Sector m1327094 515,Environment, Natural Resources and Blue
            Economy m1327391 566
publishtoextweb_dt: 2017-06-29T00:00:00Z
historic_topic: Energy
no_of_pages: 30
author_keys: 0: author_key: 905148
model_extractions: WPS8131 Policy Research Working Paper 8131

# Whose Power Gets Cut?

Using High-Frequency Satellite Images to Measure Power Supply Irregularity _Brian Min Zachary O’Keeffe Fan Zhang_ South Asia Region Office of the Chief Economist June 2017
Policy Research Working Paper 8131

# **Abstract**

In many parts of the developing world, access to electricity is uneven and inconsistent, characterized by frequent and long hours of power outages. Many countries now engage in systematic load shedding because of persistent power shortages. When and where electricity is provided can have important impacts on welfare and growth. But quantifying those impacts is difficult because utility-level data on power outages are rarely available and not always reliable. This paper introduces a new method of tracking power outages from outer space. This measure identifies outage-prone areas by detecting excess fluctuations in light outputs. To develop these measures, the study processed the complete historical archive of sub-orbital Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime imagery captured over South Asia on every night since 1993. The analysis computes annual estimates of the Power Supply Irregularity index for all 600,000 villages in India from 1993 to 2013. The Power Supply Irregularity index measures are consistent with ground-based measures of power supply reliability from the Indian Human Development Survey, and with feeder-level outage data from one of the largest utilities in India. The study’s methods open new opportunities to study the determinants of power outages as well as their impacts on welfare.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fzhang1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Whose Power Gets Cut? Using High-Frequency Satellite Images to Measure Power Supply Irregularity**<sup>**1**</sup>

Brian Min University of Michigan Zachary O’Keeffe University of Michigan Fan Zhang World Bank

# **Key words:  Power outages, load shedding, nighttime lights, remote sensing, DMSP, India**

1 This paper is part of a broader analytical effort to assess the cost of power sector distortions in South Asia conducted by Office of the Chief Economist of the South Asia Region. The authors are grateful to Chris Elvidge, Kim Baugh, Yue Li and Virgilio Galdo for providing access to the data. We thank Martin Rama and Kwawu Mensan Gaba for helpful comments on the methodology of the study. Priyamvada Trivedi and Fatima Najeeb provided excellent research assistance. Financial support from the Partnership for South Asia Trust Fund is greatly appreciated.

1
# **I. Introduction**

Almost 1.2 billion people in the world live without electricity. A billion more only have an unreliable and intermittent supply. Maintenance failures, transmission congestion, and inadequate generation all contribute to frequent and long hours of blackouts in the developing world. According to World Bank Enterprise Surveys, firms in a third of developing countries experience at least 20 hours of power outages in a typical month. The situation is even worse in South Asia, where businesses report almost one outage a day, lasting on average 5.3 hours each.

Power outages impose sizable costs on households, firms, and the environment. Lack of access to reliable electricity is a major barrier to economic advancement for underserved populations (Samad and Zhang, 2016 and 2017). Unreliable power supply also negatively affects firms’ productivity, resulting in lost output and revenue (Reinikka and Svensson 2002; Alby, Dethier et al. 2011; Allcott, CollardWexler et al. 2014, Andersen and Dalgaard, 2013, Grainger and Zhang, 2017a, b). Blackouts also lead to environmental damages when households have to rely on kerosene for lighting and businesses use diesel-based self-generators. Both kerosene lamps and diesel generators are major sources of black carbon, contributing to climate change and degraded air quality.

Although power outages have severe economic and environmental consequences, tracking when and where power outages occur is often difficult. Data on loadshedding are often not reported consistently and are always difficult to acquire. Household surveys based on a Multi-Tier Framework developed by the World Bank offer an alternative way to monitor quality of power supply. But household surveys are costly to implement, representative at a highly-aggregated level, and only reflect a snapshot in time due to limited frequency.

In this paper, we introduce a new method of using high-frequency satellite imagery of nightly lights data to detect near real-time power outages at a highly disaggregated level. This measure identifies outage-prone areas by detecting excess fluctuations in light outputs. To develop this measure, we process the complete historical archive of sub-orbital DMSP-OLS nighttime imagery captured over South Asia on every night since 1993, including 5 terabytes of data encompassing some 30,000 visible band images. Our approach is a departure from prior research. Most earlier analysis of nighttime lights data uses annual composite images which describe the average brightness of a locality over a calendar year. While annual composites provide a useful estimate of average brightness, they cannot be used to assess volatility of brightness which indicates disruptions to power supply. To the 2
best of our knowledge, our study is the first to explore nightly variation in light output to detect the stability of electricity provision.

<mark>To illustrate the approach, we compute annual estimates of the Power Supply Irregularity (PSI) index for all 600,000 villages in India from 1993 to 2013. We test the validity of the computed PSI using ground-based measures of power supply reliability from Indian Human Development Survey and feeder-level outage data from Maharashtra’s largest utility. We find that PSI measures are significantly positively correlated with the duration and frequency of outages reported in the ground-truth data.</mark> <mark>Our methods open new opportunities to study the determinants of power outages as well as their impact on welfare. A key strength of our method is that it can be applied to any specific location of interest using the same autonomously collected data. Moreover, since the coefficient of variation is a normalized measure of variability, values on the PSI index can be directly compared across regions. The method also demonstrates the possibility of using near real-time satellite data to monitor power outages and identify problem areas.</mark> The rest of the paper proceeds as follows: Section II describes the significance of power outages in the developing world. Section III presents the new approach to measuring power supply reliability based upon an analysis of a time series of nighttime satellite imagery. Section IV discusses ground-truth validation of our new power supply irregularity index. Section V presents power outage estimates across India using the new measure. Section VI concludes the paper.

# **II. Power Outages in the Developing World**

For many in the developing world, access to electricity is uneven and inconsistent, characterized by frequent and long hours of power outages. Many countries now engage in planned load shedding because of a systemic shortage of supply relative to rapidly increasing demand for electrical power. For example, the World Bank reports that the average Nigerian firm suffers 25 power outages in a typical month, with more than one outage a day reported in many other countries like Albania, Kosovo, Nepal, and the Republic of Yemen. In Bangladesh, firms reported over 100 power outages a month (World Bank Enterprise Surveys).

In India, firms report almost one outage every other day, with an average duration of 2 hours. The situation is almost certainly worse for Indian households. According to the Indian Human Development Survey, 57% of electrified households reported 3
at least 4 hours of power cuts per day. Moreover, in 2012 fewer homes benefited from uninterrupted power supply than in 2005 (12% compared to 17%), suggesting that outages are becoming more common, not less.

Blackouts can occur for many reasons. Electricity is sent from power generating plants to a country’s users through the grid, an intricate network of wires interconnected by transformers and substations. Sometimes the faults are due to downed wires or short circuits, as from a fallen tree during a storm. More commonly in the developing world, when demand exceeds supply or the capacity of a portion of the grid, outages are intentionally imposed to reduce loads and protect the sensitive infrastructure of the grid.

Unlike many other services, electricity is a dynamic system that must be balanced in real time. Every unit of electricity generated must be used at the very same moment. There are no cost effective ways to store electricity, and if too much electricity is generated without an accompanying load, the excess energy can lead to system failure. Not only do wires heat up when placed under extreme loads, but transformers, whose job it is to convert the very high voltages used in transmission into a level more safely used by consumers, are placed under enormous stress. When too much power is drawn, transformers and other critical equipment can blow, with costly consequences.

As a result, utility officials must constantly monitor and predict changing demand as it fluctuates throughout the day and across seasons, and manage supply to ensure the right number of power plants are in operation. When demand exceeds the supply capacity, power companies must preemptively disable the supply of electricity to portions of the grid. These intentional blackouts — usually called load shedding or rostering — often cut off electricity to users when they need power the most.

Official estimates in India indicate that there has been a consistent deficit in electricity supply relative to estimated demand, at least for the last three decades. Figure 1 plots the total supply of electricity in India against estimated demand. In 1984, India experienced an overall shortage of 6.7%, peaking to 11.6% on the highest demand days. By 2012, even after total electricity supply had increased more than sixfold, the overall shortage had nevertheless increased to 8.7%, with a peak shortfall of 9.0%.

4
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Table 1: Peak Energy Shortages (%) by Region

|Eastern!|2007<br>-1l1l|2008<br>8-86|2009<br>-6<3|2010<br>-50|2011<br>-48|2012<br>-74|2013<br>-18|2014<br>-06|2015<br>-0.6|Avg(07-15)<br>5.1|
|---|---|---|---|---|---|---|---|---|---|---|
|North-Eastern?<br>|-22.7<br>|-25.4<br>|-17.9<br>|-185<br>|-7.2<br>|<br>-67<br>|<br>-5.4<br>|-129<br>|-8.0<br>|<br>-13.8<br>|
|Northern?<br>|-9.1<br>|-10.7<br>|-15.4<br>|-8.9<br>|-7.8<br>|-8.9<br>|-6.9<br>|-8.3<br>|-7.1<br>|-9.2<br>|
|Southern*<br>|90<br>|-94<br>|-9.7<br>|-64<br>|-14.4<br>|-185<br>|-76<br>|-52<br>|0.4<br>|-9.0<br>|
|Western|-23.2|-19.0|-17.7|-14.7|-13.8|-15|-24|-23|-0.9|-10.6|
|AllIndia|-166|-11.9|-12.7|-98|-106|-9.0|-4.5|-4.7|-3.2|-9.2|

1 Bihar, Damodar Valley Corporation, Jharkhand, Orissa, West Bengal, Sikkim, Andaman & Nicobar 2 Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura 3 Chandigarh, Delhi, Haryana, Himachal Pradesh, Jammu & Kashmir, Punjab, Rajasthan, Uttar Pradesh

* Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, Telangana, Puducherry, Lakshadweep > Chhatisgarh, Gujarat, Madhya Pradesh, Maharashtra, Goa, Daman & Diu, Dadra and Nagar Haveli Source: Central Electricity Authority, Growth of Electricity Sector in India from 1947-2015.
illuminated when the power is working but appear dark when there is no power. Across a time series of imagery, areas prone to power outages should have higher variability in light output than areas with no outages.

Most prior research on nighttime light has relied on analysis of annual composite images. While annual composites provide a useful estimate of average brightness across the globe, they depict overly prolonged timespans, smooth away substantial variation in light output over the calendar year, and do not enable precise evaluation of discrete interventions like new village electrification projects, extensions of the power grid into new areas, nor short-term disruptions to the supply of electricity as a result of load shedding or maintenance failures. In this analysis, we return to the original raw nightly data to better study changes in the level of light output, as well as information on the variability of light output, which is a useful indicator of power supply irregularity.

The primary assumption of the approach, building upon a growing body of research (Elvidge et al. 1997b; De Souza Filho 2004; Doll et al. 2006; Sutton et al. 2007; Kiran Chand et al. 2009; Ghosh et al. 2010; Henderson et al. 2012; Min 2015), is that villages that benefit from electrical power appear brighter at night than unelectrified villages because of the use of outdoor lighting and leakage from indoor lighting sources. This relationship has been documented via ground-truthing studies comparing survey-based measures of electricity use to nighttime output in villages in Senegal, Mali, and Vietnam (Min et al. 2013; Min and Gaba 2014).

In partnership with NOAA’s National Centers for Environmental Information (NCEI), we acquired the complete archive of raw DMSP-OLS visual band imagery captured over India from 1993 to 2013. The archive comprises more than 30,000 highresolution image strips captured over nearly 8,000 nights. Each image strip represents a single pass on DMSP’s sun-synchronous, near-polar orbit with a swath width of about 3,000 km. Because India has an east-west width of about 3,000 km, image strips from two or three overpasses were often required to capture all locations on the Indian subcontinent.

The primary data of interest is the visible (VIS) band data, which records light output in a bandpass covering wavelengths from about 0.4 to 1.0 μm, which overlaps the wavelengths most commonly detectable to the human eye. Brightness on the visible band is recorded on a 6-bit scale, with relative brightness (digital number) values from 0 to 63.

Once the data were acquired and organized, the next step was to extract relevant brightness values from the image products. Since energy access and quality 7
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Examining the time series data of SR-VIS brightness values reveals both how villages vary in their average brightness level, as well as the variability of the brightness values over time. Figure 3 plots the brightness of the village of Sheorajpur in Allahabad district on every night from 1993 to 2013. Electrification of Sheorajpur as part of India’s national electrification program, that is Rajiv Gandhi Grameen Vidyutikaran Yojana (RGGVY) program, was completed on 30 June 2007. Light output in the village did not increase much until about 2010, suggesting a lengthy period before electrical power was widely available and in use by residents of the village. The bars indicate one standard deviation above and below the mean of all statistically recalibrated visible band brightness observations in each year. The increase in the size of the bars in recent years shows that variability in light output has increased. Power outages are an obvious reason why a village would appear brightly lit on one night but far dimmer on another. We use this information on the variability of light output over a village to construct a new Power Supply Irregularity (PSI) index.

We define PSI as the unexplained variability in light output relative to the predicted variability of light output for a village, calculated using data over a time period of interest. The excess variability is calculated as the residual of a regression using data from all villages that predicts the standard deviation of light output given the brightness of each village. To fit the curvilinear relationship between standard deviation and brightness in the DMSP data, we estimate a regression model including several polynomial terms. More details can be found in the Appendix.

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# **IV. Validation of PSI against Ground-Based Data on Outages**

## **A. Validation in Maharashtra**

To evaluate the reliability and consistency of our new measure, we also compare PSI against officially reported power system reliability data from Maharashtra’s largest power utility, Maharashtra State Electricity Distribution Co. Ltd, or MahaDiscom. The company provides information on its website on the reliability of its feeder network. The most widely used measure of reliability is SAIFI, or System Average Interruption Frequency Index, which describes the average number of interruptions experienced by a customer over a prescribed period. MahaDiscom reports SAIFI for “circles,” which overlap closely with districts in each year over time. However, MahaDiscom’s SAIFI reports are not a perfect measure of outages because MahaDiscom does **_not_** include planned load shedding in their measure, only unplanned outages. Nevertheless, we would expect districts with higher SAIFI levels to be associated with higher PSI.

MahaDiscom provides power reliability indices at the circle level by month from 2009 to 2013. Circles are the largest geographic unit MahaDiscom uses, which roughly correspond to districts. Because PSI is calculated annually, annual averages of the reliability indices were calculated. In the few cases where multiple circles are contained in a district, their values were averaged. Because PSI is calculated for rural villages only, urban circles were omitted from the analysis. There are 33 district-circle matches, and five years of data. There are no data from MahaDiscom for two districts in 2010. The total _N_ for the regressions is thus 163.

We focus on the System Average Interruption Frequency Index (SAIFI), which is the average number of sustained interruptions a customer would experience in a given period of time. It is (total number of customer interruptions)/(total number of customers). It does not include planned outages (e.g., load shedding), or outages due to natural disasters.

While we expect SAIFI to correlate positively with PSI despite the fact that it does not encompass all outages, there are other factors associated with light output patterns. For example, agricultural electricity usage is less likely to result in light output than residential or industrial use. To control for these confounders, we gather district estimates from the 2011 Indian Census on: logged population; logged area; % illiterate; % work in agriculture; % forested; % population rural; and % area rural. We use these because they might impact actual electricity use (e.g., poverty, as approximated by the literacy rate and proportion of people working in 13
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# **Table 2: PSI and power supply reliability in Maharashtra districts**

DV is PSI measured in each district-year, 2009–13

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**ln(SAIFI)**|**0.348****|**0.224'**|**0.369****|**0.239'**|
||**(0.114)**|**(0.125)**|**(0.125)**|**(0.139)**|
|ln(population)||-0.124||-0.122|
|||(0.199)||(0.202)|
|ln(area)||0.189||0.187|
|||(0.190)||(0.193)|
|% population illiterate||0.009'||0.009'|
|||(0.005)||(0.005)|
|% workers in agriculture||-0.004||-0.004|
|||(0.005)||(0.005)|
|% area forest||-0.001||-0.001|
|||(0.003)||(0.003)|
|Year FE|No|No|Yes|Yes|
|intercept|0.133*|0.340|0.082|0.271|
||(0.061)|(1.706)|(0.066)|(1.735)|
|_N_|163|163|163|163|

Robust standard errors clustered on district in parentheses. ' p < .1; * p < .05; ** p < .01.

# **B. Validation against IHDS Survey Data**

The second source of ground-truth data is the Indian Human Development Survey (IHDS), which was jointly carried out by University of Maryland and the National Council of Applied Economic Research (NCAER) in New Delhi. The survey covers all of India’s key states and union territories except Andaman and Nicobar Islands and Lakshadweep. The first round of the survey was carried out mostly in 2005 and collected information on 41,554 households. The second one, conducted mostly in 2012, interviewed a total of 42,152 households, including 83 percent of the original households. The survey asks a wide range of questions related to households’ energy consumption patterns including electrification status and daily average duration of electricity outages. While the survey includes both urban and rural households, we focus on the rural sample for reasons described in the previous section.

We calculate district-level average PSI and district-level average duration of power outages reported by rural households. We also obtain data on total population, total area, % of agriculture employment and forest area all at the district level from the 2011 Indian Census because these variables could also affect PSI values extracted from satellite images. Table 3 summarizes regression results using district-level PSI 15
as the dependent variable. The results show that average duration of power outages reported in IHDS are significantly and positively correlated with PSI.

**Table 3 Correlation between PSI and duration of power outages reported by**

**IHDS**

||(1)|(2)|
|---|---|---|
|outage_IHDS|0.0358**|0.0503***|
||(0.0140)|(0.0143)|
|Forest||0.00266<br>(0.00535)|
|Area||0.0000160<br>(0.0000102)|
|Population||-0.0000920**<br>(0.0000364)|
|Emp_Agriculture||0.00304<br>(0.00360)|
|N|217|206|
|adj. R-sq|0.025|0.077|

Note: The dependent variable is PSI. Standard errors clustered at the district level are reported in parentheses.

Figure 7 depicts the probability density distribution of PSI and the outage variable based on kernel density estimation. It shows that both variables generally follow a normal distribution.

16
**Figure 7: Probability density distribution of PSI and power outages reported by IHDS**

<!-- Start of picture text --> Kernel density estimate Kernel density estimate<br>-2 0 2 4 5 10 15 20 25<br>psi outage<br>kernel = epanechnikov, bandwidth = 0.1009 kernel = epanechnikov, bandwidth = 0.7083<br>.2<br>1<br>.8<br>.15<br>.6<br>.1<br>Density Density<br>.4<br>.05<br>.2<br>0 0<br><!-- End of picture text -->

# **V. Variations in Power Outages across India**

Table 4 ranks all states by the mean level of PSI in 2013. In this section, we describe anecdotal evidence which comports with these rankings. Power supply reliability as measured by PSI was highest in West Bengal, a state that has been recognized for its progress in reforming its power sector.<sup>4</sup> In 2013, it recorded a power surplus for the first time (though much of it was driven by a drop in industrial demand). Speaking to a reporter, the principal secretary for West Bengal’s department of power, Malay Kumar De, said, “Political interference at the local level is totally absent here. We don’t have a power rationing or shortage situation.”<sup>5</sup> Areas with severe power reliability problems have also received media attention. For example, a news article on the ongoing load shedding situation in Madhya Pradesh identified particularly poor service in Harda and Hoshangabad districts which had been receiving only 10 and 13 hours of power a day, respectively.<sup>6</sup> These

> 4 World Bank. 2009. _West Bengal Power Sector Reforms: Lessons Learnt and Unfinished Agenda._ Washington, DC. <https://openknowledge.worldbank.org/handle/10986/12375>

> 5 “Power thieves prosper in India’s patronage-based democracy.” _Washington Post_ . 4 October 2012.

> 6 “No end to state’s power woes.” _Times of India_ . 27 August 2014.

17
districts had the second and third highest PSI levels in 2013 among all districts in the country.

While the table of state level averages is informative, the map in Figure 9 reveals that in many parts of the country, there is even more local and regional variation within states than across-state variation.

**Table 4: Power Supply Irregularity (PSI) index, 2013 state-wise rankings (higher values indicate more outages)**

|Rank|State|Mean PSI13|Std. dev PSI13|Village<br>observations|
|---|---|---|---|---|
|1|Mizoram|1.955|1.989|729|
|2|Manipur|1.124|1.595|2,199|
|3|Meghalaya|0.835|1.569|5,782|
|4|Jammu And Kashmir|0.411|0.538|6,417|
|5|Uttrakhand|0.257|0.626|15,761|
|6|Maharashtra|0.257|1.200|41,095|
|7|Karnataka|0.248|1.179|27,481|
|8|Nagaland|0.234|1.032|1,278|
|9|Himachal Pradesh|0.214|0.674|17,495|
|10|Arunachal Pradesh|0.209|0.708|3,863|
|11|Madhya Pradesh|0.175|1.354|52,117|
|12|Tamil Nadu|0.132|0.866|15,215|
|13|Tripura|0.129|1.108|858|
|14|Uttar Pradesh|0.128|0.887|97,942|
|15|Orissa|0.048|1.143|47,529|
|16|Bihar|0.023|1.149|39,031|
|17|Jharkhand|-0.039|0.891|29,354|
|18|Gujarat|-0.198|0.846|18,066|
|19|Andhra Pradesh|-0.231|0.906|28,108|
|20|Kerala|-0.261|0.728|1,364|
|21|Punjab|-0.271|0.720|12,278|
|22|Chhattisgarh|-0.273|1.046|20,286|
|23|Assam|-0.336|0.834|25,121|
|24|Rajasthan|-0.369|0.659|39,753|
|25|Haryana|-0.439|0.771|6,764|
|26|West Bengal|-0.472|0.716|37,954|
||ALL INDIA|0.000|1.029|594,818|

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<!-- Start of picture text --> Andhra Pradesh Assam Bihar Chhattisgarh<br>is<br>1.0<br>0.50.0 (l (|<br>Gujarat Jharkhand Karnataka Kerala<br>is<br><!-- End of picture text -->

<!-- Start of picture text -->
1.0<br>0.5<br>5 1.; Madhya Pradesh Maharashtra Orissa Punjab<br>1.0<br><!-- End of picture text -->

<!-- Start of picture text --> 0.0<br>Rajasthan Tamil Nadu Uttar Pradesh West Bengal<br>1.5<br>1.0<br>0.5<br>-2.5 0.0 2.5 -2.5 0.0 25 -2.5 0.0 25 -2.5 0.0 25<br>standardized log-modulus PSI<br><!-- End of picture text -->
<!-- Start of picture text --> A Parity PSI Index, 2013<br>More outages<br>; |<br>Fewer outages<br><!-- End of picture text -->

<!-- Start of picture text --> {- s<br><!-- End of picture text --> <!-- Start of picture text --> Jharknand<br><!-- End of picture text -->
# **VI. Conclusion**

In this paper, we develop a new method to monitor the quality of power supply using high-frequency satellite imagery of nightly lights data. We define PSI as the excessive variability in light output relative to the predicted variability in light output. Variability is measured as the deviation from the mean brightness of a locality observed from a lengthy time series of nighttime satellite imagery. To test the validity of the method, we compute the PSI index for all 600,000 villages in India each year from 1993 to 2013. We find that power outages predicted by the PSI index are consistent with those reported by alternative ground-based measures of power supply reliability in India.

Our method of monitoring PSI opens new opportunities for policy research, power system planning, and citizen engagement. First, previous analyses of the impact of power outages have often relied on shortage data at the district or state level. Our research shows that PSI varies significantly across villages. Using the PSI index, researchers can capture the spatial granularity in supply disruption to better estimate the effects of power outages on welfare and growth. Second, the method developed in this study can potentially be used to facilitate near real-time monitoring of power supply disruption by utilities and government agencies. Power system planners can rely on this information to identify outage-prone areas in a cost-effective way. Finally, when information on PSI is made available to consumers, it could help consumers to better engage with utilities to improve the quality of power supply.

In the future, we plan to expand the approach to other parts of the world and to develop a global database of the PSI index for other countries across time.

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