NREL is a national laboratory of the U.S. Department of Energy

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Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Variability of Power from LargeScale Solar Photovoltaic 

Scenarios in the State of Gujarat

Preprint

B. Parsons

Evergreen Renewable Consulting

M. Hummon, J. Cochran, and B. Stoltenberg 

National Renewable Energy Laboratory

P. Batra

Central Electricity Authority

B. Mehta and D. Patel

Gujarat Energy Transmission Corporation Ltd.

To be presented at the Renewable Energy World Conference and 

Expo--India

New Delhi, India

May 5-7, 2014

Conference Paper

NREL/CP-7A40-61555

April 2014
NOTICE

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Acknowledgements

This conference paper draws on results of an analysis documented in a report published by the 

U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) titled Variability 

of Photovoltaic Power in the State of Gujarat Using High Resolution Solar Data

(http://www.nrel.gov/docs/fy14osti/60991.pdf). For this work, the authors are greatly indebted to 

thoughtful reviews and comments from Surendar Kumar Negi, Gujarat Energy Transmission 

Corporation Ltd.; Elena Berger, U.S. Department of Energy; Shannon Cowlin, Asian 

Development Bank; Anish De, Mercados Energy Markets India Private Limited; Anjan Bose, 

Washington State University; Ranjit Deshmukh of University of California, Berkeley, and 

Lawrence Berkeley National Laboratory; and Doug Arent, Jeffrey Logan, Kara Clark, Nate 

Blair, Andrew Weekley, Anthony Lopez, Jie Zhang, and Trieu Mai of NREL. The authors also 

wish to thank Sarah Booth and Melissa Butheau (NREL) for providing valuable input and 

comments during the analysis and production process. Any errors or omissions are solely the 

responsibility of the authors. 

This analysis was funded by the U.S. Department of Energy’s Office of Energy Efficiency & 

Renewable Energy and developed in coordination with India’s Ministry of New and Renewable 

Energy under the U.S.-India Energy Dialogue’s New Technology and Renewable Energy 

Working Group. 

Abstract 

India has ambitious goals for high utilization of variable renewable power from wind and solar, 

and deployment has been proceeding at a rapid pace. The western state of Gujarat currently has 

the largest amount of solar generation of any Indian state, with over 855 megawatts direct current 

(MWDC) installed as of late 2013. Combined with over 3,240 megawatts (MW) of wind, variable 

generation renewables comprise nearly 18% of the electric-generating capacity in the state. A 

new 10-kilometer (km) gridded solar radiation data set capturing historic hourly insolation values 

for 2002-2011 is available for India. We apply an established method for downscaling hourly 

irradiance data to estimate one-minute irradiance values at potential photovoltaic (PV) power 

production locations for one year, 2006. 

The objectives of this report are to characterize the intra-hour variability of existing and planned 

photovoltaic solar power generation in the state of Gujarat, and compare five possible expansion 

scenarios of solar generation that reflect a range of geographic diversity. Each expansion 

scenario postulates 500-1,000 MW of additional solar capacity, added to the 1.9 gigawatts of 

existing and planned baseline. The report statistically analyzes one year’s worth of solar power 

variability data, applied to both the baseline and expansion scenarios, to evaluate diurnal and 

seasonal power fluctuations, different timescales of variability (e.g., from one to 15 minutes), the 

magnitude of variability (both total megawatts and relative to installed solar capacity), and the 

extent to which the variability can be anticipated in advance. The paper also examines how 

Gujarat Energy Transmission Corporation (GETCO) and the Gujarat State Load Dispatch Centre 

(SLDC) could make use of the solar variability profiles in grid operations and planning. 
2 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

1 Introduction

India has ambitious goals for high utilization of variable renewable power from wind and solar, 

and deployment has been proceeding at a rapid pace. The western state of Gujarat currently has 

the largest amount of solar capacity of any Indian state, with over 855 MWDC

1

 among plants 

above 1 MWDC in size. Combined with over 3,240 MW of wind, variable generation renewables 

comprise nearly 18% of the electric-generating capacity in the state. The Central Electricity 

Authority has projected these wind and solar capacities will more than double by 2017.2

 

With high penetration levels of wind and solar, system operators must have access to additional 

resources that can help balance the net-load variability (load minus wind and solar output) and 

carry adequate reserves to respond to the combination of load and variable generation forecast 

errors. To assess the adequacy of balancing resources, and to evaluate operational practices to 

access these resources, system operators and planners typically perform grid integration studies. 

Key to informative analysis is accurate representationspatially and temporallyof the power 

variability and uncertainty of solar and wind generation. This paper focuses on the solar 

characteristics needed for a grid integration analysis, which would also include information on 

load, wind, and conventional generation. 

2 Solar Data 

A new 10- km gridded solar radiation data set capturing historic hourly insolation values for 

2002-2011 is available for India.3

 The data set containing global horizontal irradiance (GHI) and 

direct normal irradiance, is derived from satellite images from the Meteosat satellite using the 

semiempirical model developed by Perez et al.4 The data set captures impacts of earth/solar 

geometry and localized atmospheric effects, including clouds, aerosols, humidity, and decreased 

visibility. The data is mapped into a 10 km by 10 km grid on the Earth’s surface. 

The authors apply an established method for downscaling hourly irradiance data to one-minute 

estimated irradiance values. This study uses the sub-hour irradiance algorithm (SIA), developed 

for the U.S. Western Wind and Solar Integration Study Phase 2.5 The algorithm generates 

synthetic GHI values at an interval of one minute, for a specific location, using satellite-derived, 

hourly irradiance values for the nearest grid cell to that location and grid cells within 40 km. The 

input to SIA, spatial irradiance data surrounding the location of interest, make the output of SIA 

dependent on the specific location, season, and time of day by classifying the likely temporal 

variability from the spatial distribution of irradiance variability. During each hour, the observed 

GHI value for the grid cell of interest and the surrounding grid cells is related, via probability 

distributions, to one of five temporal cloud coverage classifications. An algorithm for each cloud 

 

1 Direct current (DC) ratings refer to the capacity of the photovoltaic panels under standard conditions: 1,000 

watts/square meter, 25°C. Alternate current (AC) ratings refer to the peak power output of the inverter, which 

includes system losses and DC to AC conversion losses.

2 Large-Scale Grid Integration of Renewable Energy Sources—Way Forward. Central Electricity Authority, 2013. 

www.cea.nic.in/reports/powersystems/large_scale_grid_integ.pdf. 3 “India Solar Resource Data: Hourly.” National Renewable Energy Laboratory, 2013. 

http://www.nrel.gov/docs/fy14osti/61121.pdf, http://rredc.nrel.gov/solar/new_data/India/nearestcell.cgi. 4 Perez, R. et al. "A New Operational Satellite-to-Irradiance Model." Solar Energy (73:5), 2012; pp. 307-317. 5 Lew, D. et al. Western Wind and Solar Integration Study Phase 2. NREL/ TP-5500-55588. Golden, CO: National 

Renewable Energy Laboratory, 2013. www.nrel.gov/docs/fy13osti/55588.pdf. 
3 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

coverage classification was designed to reproduce the variability statistics of that class in the 

form of a time series with one-minute time steps.

3 Deployment Scenarios 

The state of Gujarat has a high annual average solar resource of 5.5 to 6.0 kilowatt-hours 

(kWh)/square meter/day over the entire state. This uniformity implies solar power plants may be 

optimally located based on land use compatibility, proximity to load centers, and transmission 

lines.

3.1 Baseline Scenario

A “baseline” scenario totaling 1.9 gigawatts direct current GWDC was defined to include existing 

and expected capacities and locations, based on information from GETCO (see Figures 1 and 

2).6 

Figure 1. Existing solar plants in Gujarat

 

6 All systems modeled for Gujarat, India, were assumed to be mounted at a 17° tilt and oriented due south (azimuth 

of 180°). 
4 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 2. Expected solar plants in Gujarat 

The modeled power outputs for a single 5-MW plant and the full baseline (existing plus expected 

plants) for two days during the monsoon (top) and dry (bottom) seasons, are plotted in Figure 3. 

These results illustrate why taking output from a single plant and simply scaling up power output 

to model larger deployment capacity values would misrepresent the variability by disregarding 

the smoothing value of geographic diversity. 
5 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 3. Clear sky and synthetic PV power output, one-minute resolution, for (left) single, 

randomly selected site from the baseline PV scenario, and (right) aggregation of all the baseline 

PV sites on July 23, 2006, and Oct. 12, 2006

To anticipate how variability might change if Gujarat continues to pursue renewable energy 

growth targets, the authors created solar profiles for five potential future expansion scenarios 

reflecting different locations for PV plants. Three of the sites for future scenarios (Charanka, 

Kutchh, and over the Narmada Canal) were suggested by GETCO staff. The other two scenarios 

were selected to examine and reflect broader geographic diversity: solar capacity allocations 

across seven central station locations throughout the state and a distributed rooftop scenario 

across 16 cities. The latter was selected to reflect the growing interest in India for rooftop PV.7

The remainder of this section will describe the five expansion scenarios in greater detail. 

3.2 Charanka Expansion Scenario 

The “Charanka” scenario expands the baseline scenario by adding 1,000 MWDC at the Charanka 

Solar Park. Figure 4 shows the location of the Charanka scenario (yellow) along with the 

baseline (existing and expected) scenario (orange), overlayed on the Power Map of Gujarat.

8 The 

Charanka Solar Park was initially started in 2010 and will be located on more than 20 squarekilometers (km2

) near Charanka in the Patan district in north Gujarat. The solar park was 

envisioned to be the site of 500 MWDC of installed solar capacity and is expected to be 

completed by the end of 2014. GETCO is supporting the project with infrastructure upgrades, 

including a 400-kilovolt transmission line, to evacuate the solar power produced.9

 This 500 

MWDC capacity is reflected in the baseline scenario. The addition of 1,000 MWDC in the 

 

7 “Gujarat Prepares New Solar Rooftop Policy.” PV Magazine, Sept. 9, 2013. www.pvmagazine.com/news/details/beitrag/gujarat-prepares-new-solar-rooftop-policy_100012661/#axzz2t8RFTuNM. 8 The power map reflects infrastructure as of March 2013. More recent maps can be found at 

http://getco.co.in/getco_new/pages/power%20map.php. 9 “Report on Green Energy Corridors: Transmission Plan for Envisaged Renewable Capacity.” Power Grid 

Corporation of India Ltd., July 2012. www.forumofregulators.gov.in/Data/study/Report-Green-Energy-Tr.-

corridor.pdf. 
6 

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expansion scenario would result in a total of 1,500 MWDC of solar capacity at the Charanka 

Solar Park. 

This is the only scenario that includes solar expansion at an existing site. While future solar 

growth might occur at other existing sites, the Charanka scenario was chosen to capture the most 

centralized expansion and is useful in illustrating the impact of concentrated deployment on 

variability. 

Figure 4. Charanka scenario is 1.0 gigawatts of additional capacity at Charanka Solar Park (yellow) 

with the baseline scenario (orange)

3.3 Seven Utility Photovoltaic Locations Expansion Scenario

The “seven utility PV locations scenario” expands the baseline scenario by adding approximately 

143 MWDC at each of seven locations throughout western Gujarat. Figure 5 shows the locations 

of the seven PV plants, which are based on grid cells with the highest annual GHI intensity and 

at least 4 km2

 (using 40 MWDC/km2) of developable land.10 Developable land within grid cells is

also tested for proximities, including: 5 km from roads11 and 3 km from transmission.12 If two 

adjacent grid-cells qualify, the next best grid cell is used in order to diversify the locations of the 

seven utility PV plants. 

 

10 The excluded area is used to inform PV siting in the expansion scenarios, and comprises forests, permanently 

flooded areas, regularly flooded areas, urban areas, water bodies, slope >5%, and protected areas as defined in 

“Global Land Cover Characterization: Eurasia Version 1.” U.S. Geological Society, 2008. Version 1: 

http://edc2.usgs.gov/glcc/glcc.php and “ProtectedPlanet.net” United Nations Environment Programme and 

International Union for the Conservation of Nature, 2014. www.protectedplanet.net/. 11 “OpenStreetMap.” OpenStreetMap contributors, undated. Accessed Jan. 7, 2013: 

http://www.openstreetmap.org/#map=5/51.500/-0.100. 12 Shah, M.N. “Power Map of Gujarat” sent in personal communication to Stoltenberg, B., National Renewable 

Energy Laboratory. GETCO, May 27, 2013. The map was labeled as Updated March 31, 2013. 
7 

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Figure 5. Seven utility PV locations scenario (yellow) each with 143 MW PV plants with the 

baseline scenario (orange)

3.4 Kutchh Region Expansion Scenario

The “Kutchh region” scenario is six utility PV plants spread through the Kutchh region, as 

shown in Table 1. The allocation of capacity throughout Kutchh was performed under the 

guidance of GETCO.13 Grid cell selection within an area was performed using the same 

guidelines of annual GHI intensity and developable land use restrictions.

Table 1. Allocation of Capacity for Utility PV Plants in the Kutchh Region

Area Capacity (MWDC)

Bhachau and Shivlakha 200

Anjar 200

Varsana 200

Nakhatrana 150

Abdasa 150

Nani Khakhar 100

 

13 Kalsaria JE, D.H. Email to Stoltenberg, B., National Renewable Energy Laboratory. GETCO, Sept. 30, 2013. 
8 

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Figure 6 shows the grid cells selected for the Kutchh scenario and the baseline scenario.

Figure 6. Kutchh scenario (yellow) consists of six utility PV plants ranging from 100 to 200 MW 

with the baseline scenario (orange)

3.5 Narmada Canal Expansion Scenario 

The “Narmada Canal” scenario is based on recent reports that fixed PV panels are being 

mounted over the canal.14 The selection of grid cells for this scenario was formed by finding the 

grid cells that spatially intersect the canal and calculating the PV capacity available within each 

grid cell, assuming 1.3 MWDC per kilometer of canal. The grid cells are sorted by annual GHI 

intensity, and are selected in order of highest resource quality until the cumulative sum is 500 

MWDC. 

Figure 7 shows the grid cells selected along the Narmada Canal. The average DC capacity per 

grid cell is 31 MW.

 

14 “Gujarat’s Canal-Top Solar Power Plant: 10 Must-Know Facts.” The Economic Times, April 10, 2013. 

http://economictimes.indiatimes.com/slideshows/infrastructure/gujarats-canal-top-solar-power-plant-10-must-knowfacts/slideshow/19472958.cms. 
9 

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Figure 7. Narmada Canal scenario (yellow) with 500 MW distributed along the Narmada Canal in 

Gujarat with the baseline scenario (orange)

3.6 Sixteen Cities: Rooftop PV Expansion Scenario 

The “16 cities: rooftop PV” scenario consists of 62.5 MWDC of rooftop PV in 16 large cities in 

Gujarat, for a total of 1,000 MWDC. The cities are randomly selected from a spatial data set that 

identified large cities.15 Rooftop PV is assumed to be evenly distributed over the entire grid cell. 

Figure 8 shows the locations of the 16 cities on top of the baseline scenario. Grid issues for 

rooftop deployment include distribution system concerns related to safety and electric effects. 

Specific local feeder impacts depend on interconnection voltage, protection schemes, inverter 

characteristics, and other factors of feeder system design. For the purpose of this paper, only the 

aggregate power ramping effects are examined, with the view of characterizing changes in the 

bulk electric system operational needs. This scenario captures the impact of the most widely 

dispersed geographic deployment.

 

15 “ESRI Data and Maps 9.3: World Populated Places.” ESRI, 2005. Cities: Vadodara, Dhrangadhra, Patan, 

Visnagar, Gandhi Dam, Dahod, Nadiad, Anand, Jamnagar, Botad, Bhavnagar, Porbandar, Kundla, Ukal, Mahuva, 

and Veraval. www.esri.com/. 
10 

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Figure 8. Sixteen cities: rooftop PV scenario (yellow) consists of 62.5 MW in each of 16 cities with 

the baseline scenario (orange)

4 Solar Power Variability Results

The primary objective of this analysis is to characterize and contrast the intra-hour variability of 

these six PV generation scenarios. The report statistically analyzes power production based on 

2006 irradiance data,16 applied to both the baseline and expansion scenarios, to evaluate diurnal 

and seasonal power fluctuations, different timescales of variability (e.g., from one to 15 

minutes), the magnitude of variability (both total megawatts and relative to installed solar 

capacity), and the extent to which the variability can be anticipated in advance. The paper also 

examines how GETCO and the Gujarat SLDC could make use of the solar variability profiles in 

operations and planning. 

One factor inherent to grid balancing challenges associated with increased solar deployment is 

the ramp rate, which is the sustained rate of power increase or decrease over time.17 Solar power 

ramps result from both the daily solar path and cloud patterns that decrease the incident solar 

radiation on the PV panels. Quantifying total megawatts per minute ramp rates allows system 

operators and planners to assess balancing options. 

This analysis quantifies the relatively simple concept that the total magnitude of solar power 

ramping goes up with increased solar capacity. Simply put, total ramping in the baseline scenario 

of 1.9 GWDC is less than total ramping in the baseline plus expansion scenario of 2.4 GWDC, 

which is less than the four other baseline plus expansion scenarios totaling 2.9 GWDC. The 

dominant cause of this correlation is ramping due to sunrise and sunset, which occurs over a 

short, predictable period of time. During the monsoon season, clouds reduce the sunrise and 

 

16 A single year of sub-hour data captures the seasonal and time of day variability; however multiple study years 

would be needed to understand the long-term economics of a PV plant at a particular location.

17 This change in power over time is typically expressed in MW over some time scale of interest, for example five 

minutes.
11 

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sunset ramp rates, and decrease the peak solar output by 20%-35%. Figure 9 illustrates these 

results by showing the aggregate power output from the baseline plus each expansion scenario in 

monsoon (left) and dry (right) seasons..This analysis also demonstrates that aggregate power 

ramps from solar expansion scenarios are less dramatic as the geographic spread of deployment 

(spatial diversity) is increased.

Figure 9. Aggregate power output of baseline plus expansion scenarios on July 23, 2006 

(monsoon), and Oct. 12, 2006 (dry season)

The more complex effects of geographic diversity from alternative solar expansion scenarios are 

better assessed by normalizing ramp rates to the total nameplate rating. As Figure 10a illustrates, 

the five-minute ramps normalized as a percentage of installed solar capacity are relatively 

constant across the baseline and expansion scenarios during the dry season, but vary based on 

time of day. The smallest ramps occur during the midday of the dry season, with the sun high in 

the sky and very few clouds. While observable, the ramp rate differences between scenarios are 

small because the East-West and North-South impacts on solar path are not large within 

Gujarat’s boundaries. 
12 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 10. The dry season (a) and monsoon season (b) 95th-percentile five-minute ramp (percent of 

scenario installed DC solar capacity) during each time of day: sunrise, morning, midday, 

afternoon, and sunset

The monsoon season (Figure 10b) shows a different time of day pattern than the dry seasonthe 

largest ramps occur during midday, when there are many clouds that change the solar power 

production. The effect of geographic diversity becomes more noticeable when cloud coverage 

contributes to solar ramping. In this case, geographically diverse locations, such as baseline plus 

utility PV at seven locations or rooftop PV, experience lower absolute ramps than similarly sized 

scenarios. The baseline plus Charanka scenario (2.9 GWDC), which concentrates new solar 

generation in a single location, has 30% and 7% greater ramps in the monsoon and dry seasons, 

respectively, over the baseline plus rooftop PV in 16 cities (2.9 GWDC). These increases may be 

significant if added requirements for load and conventional generation to provide balancing 

services are costly.

In contrast to the 95th percentile five-minute ramps, which present the extreme ramps over five 

minutes, the average “unpredicted” one-minute ramp is characterized by how similar the system 

is from one five-minute period to the next. The calculation of “unpredicted” one-minute ramps 

used is illustrated in Figure 11. The “unpredicted” ramp is the difference between the projected 

and actual one-minute solar power data (Steps 2 and 3 of Figure 11).18 This is calculated in the 

clear power index (cpi) space to minimize the effect of the zenith angle of the sun. The 

unpredicted ramps are expressed as a change in solar power megawatts per minute (Step 4 in 

Figure 11).

 

18 The calculation and interpretation of unpredicted one-minute ramps is the subject of ongoing research.
13 

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Figure 11. Illustration of the calculation of unpredicted one-minute solar power variability

The average unpredicted down (decrease in solar power) and up (increase in solar power) oneminute ramps are shown in Figure 12. The data is divided into dry and monsoon seasons because 

the monsoon season has nearly twice the absolute magnitude of unpredicted one-minute solar 

power ramps. The baseline scenario is shown on the far left, and the incremental addition of 

ramps from the expansion scenario is shown as a darker set of blocks below/above the baseline 

down/up ramps. The Narmada Canal scenario has a smaller magnitude of one-minute ramps 

because the expansion scenario is 500 MW, compared to the 1,000 MW in the other expansion 

scenarios. Across all of the remaining baseline plus expansion scenarios, the additional 

unpredicted one-minute ramps seem to be driven more by total installed capacity than 

geographic diversity. These results also demonstrate that dry season variability conditions driven 

by solar path alone persist for longer periods than monsoon season variability conditions where 

clouds impact solar output. In other words, it is harder to predict short-term variability in the 

monsoon season than in the dry season. 

The NREL report titled Variability of Photovoltaic Power in the State of Gujarat Using High 

Resolution Solar Data (http://www.nrel.gov/docs/fy14osti/60991.pdf) contains additional 

analysis of interest to grid operators, including comparisons of the large relative variability from 

single solar power plants to the lower relative variability for broader, larger megawatt 

deployment scenarios (further illustrating the geographic diversity smoothing effect), a 

comparison of five-minute and 15-minute ramp distributions (which could be useful for 

informing ancillary service duration needs), and further “heat map” and distributional 

representations of the diurnal and seasonal variability characteristics for the baseline and 

expansion scenarios. Interested readers are directed to the full report. 
14 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 12. Average minute-to-minute change in solar production, normalized by expected clear 

sky solar production for the baseline scenario plus each expansion scenario. Darker tones reflect 

the incremental addition of ramps from the expansion scenarios.

5 Actions to Address the Variability of Solar Generation 

The variability of solar generation, as described in the previous section, can be managed through 

changes to system operations and planning, a number of which are already being adopted in 

Gujarat. The changes most relevant to Gujarat span the areas of grid reinforcement, system 

operations, and market reforms. 

5.1 Grid Reinforcement

One potential constraint to increasing solar generation is the capacity of the grid. A strong grid 

serves two key purposes: 1) power can be physically evacuated from areas with high solar 

resources to where the electricity is needed and 2) system operators can access a broader range of 

flexible resources, including conventional generation, to balance the variability of net load 

(which is electricity demand minus the electricity supplied by wind and solar). 

To identify grid infrastructure priorities necessary to meet renewable energy targets in the 12th

five-year plan, the central transmission utility, the Power Grid Corporation of India Ltd., 

conducted a “green corridors” study.19 The engineering study optimizes inter- and intra-state 

transmission capacity expansion based on likely areas of renewable energy development as 

identified by state agencies in seven states, including Gujarat, with input from central agencies, 

such as the Ministry of New and Renewable Energy, Central Electricity Regulatory Commission, 

 

19 “Report on Green Energy Corridors: Transmission Plan for Envisaged Renewable Capacity.” Power Grid 

Corporation of India Ltd., July 2012. www.forumofregulators.gov.in/Data/study/Report-Green-Energy-Tr.-

corridor.pdf. 
15 

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Central Electricity Authority, and Power System Operation Corporation. The proposed 

infrastructure is designed to both move electricity between states and help resource-rich states 

better absorb renewable energy within the state, while maintaining both frequency and voltage 

stability.20 

The green corridors report identified several infrastructure improvements for Gujarat, including 

increased interstate transmission capacity to better connect with the northern region and 

evacuate power from the Charanka Solar Park, intrastate lines to better integrate wind and solar 

generation internally, and dynamic volt-ampere reactive compensators and other equipment 

needed to maintain voltage. This transmission will facilitate access to solar power for out-of-state 

customers, currently a critical constraint for renewable energy capacity expansion. Additional 

transmission capacity connecting to flexible resources will also assist system operators with

managing solar ramping. 

Potential follow-on analysis to this paper, as part of a larger grid integration study for Gujarat, 

includes evaluating the impact of grid upgrades on the ability of Gujarat SLDC to manage solar 

ramps. Such an analysis would support an optimized cost-benefit assessment of transmission and 

generation investments to better evaluate scenario expansion options explored in this paper. 

5.2 Systems Operations 

In addition to increased transmission capacity, changes to system operations—through advanced 

forecasting, improved scheduling, and ancillary services—can also help manage the variability 

of solar generation. Many of these changes will be implemented as part of the new Renewable 

Energy Management Centres (REMC), the first of which is under construction in Gujarat. The 

green corridor report proposed the REMCs be colocated in load dispatch centers in order to 

address some of the challenges to system operators in managing variable resources. Table 2 

summarizes challenges to wind and solar integration that the proposed REMC would address. 

Challenge REMC Functions

Low visibility of renewable energy 

forecasting, especially for solar

Forecast renewable energy generation at timescales ranging from 

hour-ahead to month-ahead, including at solar power generation 

plants

Low availability of real-time generation data Provide real-time tracking of renewable energy generation and 

system conditions, including geospatial visualization

Limited ability to automatically control 

generation output, such as through a 

supervisory control and data acquisition

system or automatic generation control 

Automated coordination with load dispatch centers to manage 

variability of generators to control output, when necessary

Table 2. Challenges to renewable energy grid integration addressed by the proposed REMCs

 

20 Although the transmission corridors are being planned, commercial arrangements to secure the grid upgrades have 

yet to be implemented.
16 

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5.2.1 Advanced Forecasting 

The load dispatch center in Gujarat receives forecasting data (day ahead and real time) for all 

solar plants 5 MW and above, and for wind generators that are 10 MW or greater and connected 

to a pooling station.21,22 The REMC in Gujarat will allow the creation of forecasts based on 

weather sensors being installed at wind locations of at least 50 MW, which will provide data on 

speed, direction of wind flow, and temperature, among other factors. Because persistence 

forecasts for the next 15-minute time period are fairly accurate (for wind generally, and for solar 

in the dry season), this real-time power production data will be particularly helpful to the SLDC 

in evaluating upcoming scheduling requirements. 

With better visibility of forecasted and real renewable energy generation, system operators will 

be better equipped to manage ramping. For example, in the baseline plus Charanka scenario, the 

modeling in this study shows how solar variability changes over the course of a year, and that the 

monsoon months of June through August, with its cloud coverage, create the greatest variability 

in solar ramps. The REMC will be designed to predict specific storm events, allowing system 

operators to prepare for sudden changes in output. The analysis in this paper demonstrates that 

dry season variability conditions (one-minute interval changes in solar power output) persist for 

longer periods than monsoon season variability conditions. In other words, it is harder to predict 

short-term variability in the monsoon season than in the dry season. Although the practice and 

science of very short-term solar forecasting is evolving, the forecasting objective of the REMC 

will likely not address the unpredicted one-minute ramps. An ancillary service, such as automatic 

generation control, would likely offer the most value to the SLDC in managing unexpected 

ramping. 

Nevertheless, improved forecasting will allow system operators to better anticipate periods of 

ramping and adjust the quantity of operating reserves based on expected system conditions. This 

paper illustrates two components of solar variability. The first is determined by the solar path, 

and plant location and configuration. The associated morning ramp-up and evening ramp-down 

power changes are known in advance, and can therefore be anticipated in day-ahead scheduling. 

The second, cloud-driven component of variability becomes better anticipated through 

forecasting, and is diminished by the geographic diversity of broad deployment scenarios. For 

example, this study shows that reserves needed to manage midday solar variability are likely to 

be minimal in October, when skies are largely clear, and greatest in July, when solar variability 

is less predictable. 

5.2.2 Improved Scheduling

Unlike some other states in India, Gujarat has a strong base of resources to meet demand and 

hold reserves. The adequacy of resources provides Gujarat more in-state options to balance wind 

 

21 Wind plants commissioned on or after May 3, 2010, and covered under the Renewable Regulatory Fund also must 

provide forecasting data.

22 Forecasting protocols in India continue to evolve. For example, in March 2014, the CERC temporarily suspended 

penalties for failing to provide an accurate wind forecast, citing feedback from industry on the difficulty of 

predicting within the required 30 percent band for day ahead (www.bloomberg.com/news/2014-03-07/india-putswind-forecasting-on-hold-on-inaccurate-results.html). 
17 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

and solar variability. Access to this flexibility, however, is limited if schedules are fixed dayahead and offer little ability to make intraday adjustments based on improved forecasts. 

One mechanism to access flexibility is exercising direct control. Under the Energy Act of 2003, 

the SLDC functioned primarily as a coordinator rather than a controller of resources. The Indian 

Electricity Grid Code of 2010 now permits direct management of identified resources through 

automatic generation controls. However, in practice, in Gujarat and all other Indian states, use of 

governors has been limited, in part due to the prevalence of large frequency swings. With the 

progressive narrowing of the frequency band, the use of automatic generation controls has 

become a possibility. As noted in the description of the REMC functions, the REMC will be 

designed to feed forecast and generation information into the SLDC scheduling system. This 

allows the SLDC to use real-time telemetry, such as through software like a supervisory control 

and data acquisition system, which is used to transfer data and control equipment remotely, to 

directly control the output of wind and solar generators. 

Thermal plants offer another source of flexibilityelsewhere, coal has demonstrated the ability 

to cycle on and off up to twice a day.23 Coal-based generators in India—both publicly and 

privately ownedare wary of schedule revisions, given the maintenance requirements and wear 

and tear associated with cycling. However, these costs (e.g., wear and tear while ramping and 

providing ancillary services), along with information on operating parameters (e.g., fuel 

availability, ramp rate, minimum generation point), can be incorporated in economic dispatch 

when revising intraday schedules. Gujarat is already using coal as a source of flexibility to help 

balance variability. The state has secured agreement from NTPC Ltd. to vary coal generation 

under Gujarat’s contractual control (500 MW), from 60% to 100% of unit capacity.24 Expanding 

the use of thermal plants to meet intraday scheduling revisions will provide greater options for 

Gujarat to balance variability and address uncertainty.

Automation of scheduling at state load dispatch centers will also help system operators balance 

ramp events. Currently, many scheduling transactions are not automated—SLDCs can require 

several hours to clear requests from state distribution utilities and over a day from captive 

(industry-owned) power plants. This manual process, in turn, impacts the speed of scheduling at 

regional and national load dispatch centers. Once all scheduling is conducted online, as already 

occurs in Gujarat, state-level clearance can be automated, which in turn, will increase for all 

states the liquidity and access to resources to help balance net load.

5.2.3 Ancillary Services 

The development of ancillary services would be one of the most significant changes to system 

operations, with significant potential to improve options and reduce the costs of managing 

variability and uncertainty. Ancillary services would compensate for the provision of reserves, 

tailored to different time scales, and voltage control. This package is under consideration by the 

 

23 Cochran, J.; Lew, D.; Kumar, N. “Flexible Coal: Evolution from Baseload to Peaking Plant.” 21st Century Power 

Partnership. NREL/BR-6A20-60575. Golden, CO: National Renewable Energy Laboratory, 2013. 

http://www.nrel.gov/docs/fy14osti/60575.pdf. 24 Large-Scale Grid Integration of Renewable Energy Sources—Way Forward. Central Electricity Authority, 2013. 

www.cea.nic.in/reports/powersystems/large_scale_grid_integ.pdf.
18 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Central Electricity Regulatory Commission,25 and would likely replace system operators’ 

existing tool for balancingthe unscheduled interchange mechanism.26 Although Gujarat has a 

greater reserve margin for peak demand relative to other states (500-1,000 MW, provided by 

conventional generation), ancillary services could be structured to ensure adequate spinning (hot) 

reserves, which are currently in insufficient supply.27 

The type, magnitude, and timing of ancillary services can be tailored to meet the projected 

variability and uncertainty of renewable energy. The analysis in Section 4 illustrates the types of 

ancillary services of most importance to solar variability. For example, in the monsoon season, 

automatic generation control would be valuable to address the unexpected minute-to-minute 

variations due to cloud coverage. The analysis in Section 4 also illustrates potential ranges, for 

example, in the magnitude of solar output change over five-minute periods. The study finds that 

greater than 88 MW ramps per five minutes occurs less than 0.10% of the time for all 

scenariosthis type of information could serve as input for reserve capacity calculations. 

Varying the timing of ancillary servicessuch as dynamic reserves that are targeted to ramping 

needs specific to time of day and yearcan reduce the cost of integrating variable renewable 

energy. This paper demonstrates the change in variability and uncertainty over the course of the 

day and across a year. When solar variability is combined with wind and load data, including 

forecast errors for all three, the net variability can be calculated, and an evaluation can be made 

on how to define and schedule reserves, and whether the capacity to provide these reserves is 

sufficient among existing generators. 

Finally, this paper also demonstrates the impact of geographic diversity on reserve management, 

and quantifies the differences in cloud-driven ramping between extremely centralized 

deployment (Charanka), alternative widely distributed central plants (utility PV at seven 

locations), and even broader distributed rooftop scenarios (rooftop PV in 16 cities). Rooftop PV 

would require fewer reserves to manage solar ramping compared to other expansion scenarios. 

5.3 Market Reforms 

Commercial impediments to renewable generation sales remain one of the greatest challenges in 

Gujarat for scaling up renewable energy. As with other states, utilities in Gujarat must pay a 

feed-in tariff for wind and solar production (average of Indian rupees 3.56/kWh or near 6 U.S 

cents/kWh in June 2012) and must balance excess supply by backing down cheaper generation 

(Indian rupees 2.50-2.70/kWh, near 4 U.S. cents/kWh).28 Other states may be deterred from 

purchasing Gujarat’s renewable energy, for a number of reasons: 

 

25 “Introduction to Ancillary Services in Indian Electricity Market.” Central Electricity Regulatory Commission 

(CERC), April 10, 2013. www.cercind.gov.in/2013/whatsnew/SP13.pdf. Note CERC staff is currently revising this 

paper to evaluate a wider range of options, beyond markets, for SLDCs to procure ancillary services.

26 The unscheduled interchange mechanism prices deviations from schedule such that there is a financial incentive to 

maintain grid frequency. The mechanism limits how much schedules are allowed to deviate.

27 Integrating Variable Renewable Energy with the Grid: Lessons from the Southern Region. Mercados Energy 

Markets India PVT Ltd., November 2012. 

www.shaktifoundation.in/cms/uploadedImages/variable%20re%20grid%20integration.pdf. 28 Large-Scale Grid Integration of Renewable Energy Sources—Way Forward. Central Electricity Authority, 2013. 

www.cea.nic.in/reports/powersystems/large_scale_grid_integ.pdf.
19 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

• States have limited options to balance variability. The state purchasing solar through a 

power purchase agreement (PPA) is also purchasing the balancing requirement.

• Energy purchased through the unscheduled interchange during periods of supply excess, 

such as during the windy monsoon months, is cheaper than establishing PPAs. 

• Obligations for other states to meet renewable energy targets are not enforced.

Changes to market design and participation, such as development of a robust spot market, would 

increase the options for system operators to address solar ramping, providing a centralized 

exchange to balance wind and solar variability. Also, contracts for all generators (e.g., to provide 

compensation for part load, flexible operations, and ancillary services) will likely require 

renegotiation to align with evolving operating and market structures. Wholesale market designs 

for energy and ancillary services can help integrate and make efficient the provision of these 

services, and serve as a framework for contract renegotiation.

Short of having a robust market, improving cooperation across balancing areas can help expand 

access to resources needed for balancing, although this is less of an issue for Gujarat, which has 

adequate in-state resources for balancing. Typically in India, the process for revising the dayahead interstate schedules is inflexible. As forecasts for generation and load increase with 

accuracy close to real-time power delivery, system operators have limited options to revise 

schedules. For example, an intraday market on the power exchanges exists, but with too little 

participation to offer liquidity. The SLDCs have limited options to revise intraday, interstate 

schedules with the regional load dispatch center, for example, to accommodate a bilateral trade 

necessary for balancing. Therefore, SLDCs have three primary options to balance their system in 

real time: adjust schedules of in- and out-of-state resources (such as conventional generation 

based on merit order), shed load (in Gujarat, agricultural feeders are scheduled to be curtailed 

first), and purchase or sell power through the unscheduled interchange mechanism). 

Options to improve interstate trade could include improved intraday market liquidity, sharing of 

reserves (such as under development in the Western U.S. energy imbalance market),29 and 

increased availability to revise interstate schedules.

5.4 Actions to Address the Variability of Solar Generation—Summary

Targeted changes to system operations and planning can account for anticipated impacts on grid 

operations introduced by increased amounts of solar generation, across all scenarios analyzed. 

Because most of the solar ramping is based on known changes due to sunrise and sunset, the 

Gujarat SLDC can schedule its resources accordingly. The greater periods of uncertainty stem 

from cloud-based variability. To address this impact, Gujarat is in the process of strengthening 

its suite of tools to anticipate and mitigate this variability, including stronger grids, geographic 

diversity of PV locations, advanced forecasting (day ahead and real time), improved scheduling, 

introduction of ancillary services, and strengthened markets across balancing areas. 

 

29 Milligan, M.; Clark, K.; King, J.; Kirby, B.; Guo, T.; Liu, G. Examination of Potential Benefits of an Energy 

Imbalance Market in the Western Interconnection. NREL/TP-5500-57115. Golden, CO: National Renewable 

Energy Laboratory, 2013. www.nrel.gov/docs/fy13osti/57115.pdf. 
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

6 Summary, Implications, and Next Steps

This work brings out several salient features that are important when evaluating potential future 

PV generation and its potential grid integration impacts on the power system, including: 

• The diurnal and seasonal power profiles and variability statistics for the existing and 

planned 1.9 gigawatts (GW) of solar are characterized using 2006 historic solar resource 

data. The geographic spread of this baseline is relatively broad. 

• The expansion scenarios of 500 to 1,000 MW increase nearly proportionally the absolute 

magnitude of the solar variability. The differences in geographic density of the expansion 

scenarios affect variability, but when combined with the geographically diverse baseline 

scenario and normalized to the total nameplate direct current capacity, the differences 

among scenarios are small. 

• Results illustrate the power ramp rate differences between a very large plant, several 

spread out central plants, and rooftop distributed deployments. The least geographically 

diverse, baseline plus Charanka scenario, has 30% and 7% greater midday ramps in the 

monsoon and dry seasons, respectively, over the more geographically diverse baseline 

plus rooftop PV in 16 cities scenario. 

• In evaluating expansion plans, system planners need to consider other factors related to 

location, such as access to uncongested transmission capacity, in addition to variability 

differences. If solar deployment across more Indian states is considered, and state-to-state 

grid cooperation is increased, the beneficial impacts of geographic diversity should be 

larger and would be worth reevaluating. 

• During the monsoon season, individual plant variability can be quite large due to cloud 

passage, but the aggregated power across all plants shows less volatility. Due to the broad 

and general nature of the storms, planning and operating the system using a derating of 

the solar capacity may help system operations. Further research could assess whether this 

implication could be applicable to other regions of the world with similar monsoonal 

climatology and where solar power is being deployed or contemplated. 

• The study shows that much of the diurnal solar variability is based on a known solar path, 

illustrating a large amount of the PV-imposed needs for grid flexibility are known in 

advance, which facilitates grid operational day-ahead scheduling. 

• The variability shows strong seasonal characteristics. Generally, absolute variability is 

higher during dry seasons during known sunrise and sunset ramps. Variability relative to 

output is higher during the monsoon season at midday, when the region experiences a 

dramatic decrease in solar power output due to periodic (and relatively unpredictable) 

cloud passage. 

• Diurnal and seasonal variability characteristics can be important to grid practices. Known 

variability implies ramping resources can be confidently quantified and potentially 

scheduled. Unpredictable variability statistics can also be quantified, but may require 

partially loaded quick ramp or quick start/stop resources to respond. 
21 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

6.1 The Need for Similar Wind Resource and Power Profiles

The availability of the new historic solar data set for India was key to motivating this PV 

variability analysis. However, wind power is currently posing more challenges to the Gujarat 

SLDC. During 2012-2013, when Gujarat’s installed wind capacity reached over 3 GW, wind 

power fluctuated more than 500 MW within 69% of the days, while fluctuating more than 1,000 

MW on 16% of the days. Like the solar variability analyzed in this paper, similar analyses of 

subhourly wind variability and different potential future wind power generation scenarios are 

needed to more fully understand the challenges of increasing grid-connected renewable energy. 

The broader context calls for similar evaluation of wind power deployment scenario variability, 

which will be undertaken by the authors later in 2014. 

6.2 Uses of Renewable Power Variability Profiles 

Integration studies of the full integrated electric system, including conventional generator 

capabilities, load variability, operational practice reform, and assessment of system-level 

mitigation measures, will allow system planners and operators to examine and address 

challenges imparted by large-scale variable generation futures. 

Realistic wind and solar power profiles corresponding to potential future deployment scenarios 

help characterize some of the integration challenges. Without these evaluations, vagueness and 

anxiety might otherwise hold back development. With the power profiles, many types of 

analyses can be performed, including intra- and interregional transmission load flow and 

transmission expansion planning studies, evaluation of reactive power requirements and 

harmonics, production cost operational modeling of the integrated grid, and resource adequacybased reliability assessments, including wind and solar capacity credit determinations. 

Performing these types of investigations leads to a better understanding of the challenges to a 

grid with high penetration levels of variable renewable energy. Through production cost 

modeling, both physical and institutional features of the grid can be examined. For example, 

ramp capabilities of conventional generators and possible maintenance cost increases due to 

increased cycling; market structure and dispatch practices; and balancing mechanisms and 

associated ancillary service and reserve procedures could be further analyzed. 

A holistic approach to grid integration analysis will recognize that each element might be 

important for further planning and could serve as the basis to support technology innovation, 

new procurement rules, new transmission interties, among other strategies to integrate wind and 

solar. Operational evaluations have often led to creative solutions. 
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Once the flexibility needs are quantified, a wide variety of mitigation approaches can be 

evaluated. Some effective approaches based on international experience include:30,31

• Improving forecasting in appropriate time frames through better data gathering and 

analysis

• Shortening unit commitment and dispatch intervals and gate closures 

• Alternative market structures and balancing mechanisms (ancillary services and reserves)

• Utilizing smart-grid modernization to improve situational awareness, data flow, and 

improved access to demand control measures (such as is being developed through the 

REMC in Gujarat)

• Increased balancing area cooperation through shared imbalances, reserves, and 

load/resource diversity (for India, this includes national-to-state and state-to-state 

cooperation) 

• Steering future generation additions to encourage increased geographic dispersion of 

renewables and more flexible conventional generation 

• State-of-the-art active power control capabilities on wind and solar plants, such as ramp 

rate control, inertial response, and automatic generation control. 

These types of analyses and evaluations of solutions are predicated on realistic, quantified wind 

and solar power variability profiles, such as what is illustrated in this paper for solar PV 

generation in the state of Gujarat. 

 

30 See, for example, Holttinen, H. et al. Design and Operation of Power Systems with Large Amounts of Wind 

Power. VTT Technology, 2013. www.ieawind.org/task_25/PDF/T75.pdf. 31 Cochran, J., L. Bird, et al. Integrating Variable Renewable Energy in Electric Power Markets: Best Practices 

from International Experience. Golden, CO, National Renewable Energy Laboratory, 2012. 

www.nrel.gov/docs/fy12osti/53732.pdf.