Geographical Information System basedRenewable Energy Integration Planning:

Quantifying Solar Energy Potential in North IndiaPartha Das∗, Jyotirmay Mathur∗, Rohit Bhakar∗, Amit Kanudia†

∗Centre for Energy and EnvironmentMalaviya National Institute of Technology Jaipur, Rajasthan-302017, India

†KanORS-EMRNSEZ, Noida, Uttar Pradesh-201305, India

Abstract—This paper demonstrates the utility of Geograph-ical Information System (GIS) as a useful tool for renewableenergy (RE) integration planning. The focus of this articleis twofold. Firstly, it demonstrates that application of GIScan improve current resource potential estimation practices.Secondly, it shows the utility of GIS in developing high granularRE related data sets for planning purpose, when they are notavailable in desired spatial resolution. Solar energy potential(capacity, and annual capacity factor (CF)) for all districts inNorthern Indian grid has been quantified in this regard usingGIS tools using open source data sets. The total potential of PVcapacity in North India is found to be significantly higher thanthe official estimates due to the difference in land suitabilityassumptions. It has also been observed that official values havebeen overestimated in some states due to non-consideration ofterrain suitability (e.g. slope, elevation).

I. INTRODUCTION

One of the major challenge associated with variable RE(e.g. solar and wind) integration planning is their locationspecificity. Geographical variability of RE resource intensityhas a direct impact on power system operation, economics,and planning; creating significant challenges towards theirsuccessful grid integration. Therefore, it is essential thatnational level system design studies should adopt a highspatial resolution to derive RE capacity targets.

Long-term planning of energy system requires informationof capacity as well as annual generation potential (CF) ofpower producing technologies. Traditional RE potential esti-mation practices in various countries do not consider detailedgeographical analysis. For example, the national potential ofsolar capacity (750 GW) in India has been estimated usinga uniform assumption of 3% wasteland availability [1]. TheRE potential estimates provided by different agencies arenot often available at a higher granular level beyond states.Finally, different RE potential estimation is often undertakenby independent authorities, leading to double considerationof the same land for different technologies e.g. solar, thewind, and biomass.

Traditional system planning activities in India and similarcountries adopt coarse spatial definition primarily due toinherent methodological limitations and unavailability ofhighly granular data. These planning exercises often considerthe whole country as a single region though there areinstances of breaking it into parts. But, the spatial granularityin these approaches often does not go beyond states. Ina vast country like India, this coarse spatial definition is

not suitable to address the geographical variability of REgeneration and capacity potential. Planning with this coarsedata in aggregated modelling framework has several negativeconsequences. In large states like Rajasthan, the cost ofRE generation, as well as integration, varies significantlydue to variation of resource intensity and infrastructure(e.g. grid) availability. Not considering these issues leadsto an impractical estimation of RE expansion targets andunrealistic overall system portfolio.

GIS is a useful and efficient platform for RE potentialanalysis. GIS tools and methods has been widely used world-wide to quantify geographical as well as technical potentialof RE sources, selecting suitable location for installation,and environmental impact assessment [2], [3], [4], [5]. InIndia, there has been wide application of GIS in nationaland regional RE potential estimation (e.g. biomass [6], wind[7], [8], [9], and solar [10]). Different planning activitiesin India relies on official estimates of RE potential whichare often not available in high spatial granularity. But littleattempt has been made to utilize the GIS facility to developdata for high resolution planning activities.

This article demonstrates how GIS tools can utilize opensource data sets to develop high-resolution RE related infor-mation for use in energy system planning when the data isnot available in desired spatial granularity. Freely availableGIS data sets are used to develop district wise solar PVcapacity potential and annual CF for all north Indian statesand union territories. Only large scale grid connected instal-lation has been considered, excluding rooftop or communityscale isolated plants. The methodology can be scaled up andcan be applied for any single/ hybrid RE resource potentialcalculation. The following section describes the data andmethodology used in the study. Section three discusses theresults, and finally, Section four concludes.

II. DATA AND METHODOLOGY

The current work utilizes open source GIS data layersrelated to solar average GHI (Global Horizontal Irradiance),average annual PV generation, different exclusion areas, andterrain condition (table I).

Protected areas, road, rail, urban areas, water bodies areexcluded from land availability calculation with suitablebuffers (road 500 meters, rail 500 meters, protected areas1000 meter, water bodies 500 meters, etc.). For terrain

Exclusion Areas

Suitable Areas

Erase

Road Buffer

Rail Buffer

Urban Area Buffer

Protected Area Buffer

Water body Buffer

Elevation

Terrain Slope

Land Cover

India GHI Raster

India PV Out Raster

India District Administrativ

e Layer

Suitable Area for PV Installation

Mean GHI Per District

Mean PV Gen Per District

Annual Capacity Factor per

district

Summarization to Districts

District Wise available area District Wise PV capacity

potential District wise average GHI District wise average annual

generation District wise average capacity

factor

Fig. 1. Overall Methodology of Geospatial Analysis

(a) Suitable Area (b) Restricted AreaFig. 2. Suitable and Excluded Areas for Solar PV Installation

suitability, slope more than 10 degrees and elevation of 2000meter are not considered. Only bare and sparsely vegetatedareas are taken as the suitable land cover type. The map ofappropriate area and exclusion areas are illustrated in figure2.

TABLE IGIS DATA LAYERS

GIS Data SourceAdministrative Boundary Database of Global Administrative AreasProtected Areas The World Database on Protected AreasLand Cover Gloecover 2009 V2.3Road SEDAC NASAUrban Areas Natural EarthRail Natural EarthDigital Elevation Model The United States Geological SurveyWaterbody Global Lakes and Wetlands DatabaseSolar GHI Global Solar AtlasSolar PV Output Global Solar Atlas

Model builder facility of ArcGIS software has been uti-lized to develop a tool for the overall geospatial analysisand data aggregation. The methodology has been outlinedin figure 1. The exclusion layers are merged and dissolvedto form a single layer of exclusion areas. The slope of theterrain is calculated from digital elevation model. Raster datarelated to altitude, slope and land cover is reclassified to se-lect only the areas with desired condition for PV installation.The exclusion areas are erased from the suitable area layerand aggregated to district boundaries. Raster data of annualGHI, and PV generation (Kwh/Kw) is also summarized todistricts. The annual CF is calculated by the formula ‘AnnualPV generation’/8760. The capacity potential is calculated bythe assumption of 4 Acre/MW.

III. RESULTS

From the analysis, district wise (north Indian States)annual average CF, and solar PV capacity potential has beenquantified. District wise potential has been aggregated tostates and compared with the official estimate (table II) [1].

TABLE IISTATE WISE SOLAR PV POTENTIAL

State GIS Estimate (GW) Official Estimate (GW)Chandigarh 0.00 0.00Haryana 5.57 4.56Himachal Pradesh 0.26 33.84Jammu and Kashmir 0.24 111.05NCT of Delhi 0.06 2.05Punjab 0.74 2.81Rajasthan 2656.32 142.31Uttar Pradesh 41.29 22.83Uttarakhand 0.61 16.8Total 2705.09 336.25

District wise distribution of annual CF and capacity po-tential is outline in maps (figure 3, and 4).

As the capacity potential of Rajasthan is significant,district wise annual average GHI, and capacity potential arefurther highlighted in figure 5 and 6 respectively.

IV. DISCUSSION AND CONCLUSIONS

The purpose of this article is not to quantify real REpotential but to demonstrate the role of GIS as a usefultool for long-term RE integration planning purpose. As

Fig. 3. District Wise Distribution of Capacity Potential North India

Fig. 4. District Wise Distribution of Annual CF North India

Fig. 5. District Wise Distribution of Annual Average GHI in Rajasthan

official data related to RE potential is often ‘static’, it cannotbe scaled up or down to desired spatial resolution. GISprovides a platform to quantify realistic RE potential andcost and supports planning activities at national and regionallevel. The capacity potential reported in this article is thegeographic potential rather than technical one. It does notimpose restriction on the availability of road, transmissionlines, etc. and assume that these infrastructural facilitieswould be developed in future. It also not considers the futurechange of land use (urbanization, waste to agricultural landconversion, and hybrid RE generators, etc.). This method-ology can further be used to consider these effects and

Ajmer

Alwar

Banswara

Baran

Barmer

Bharatpur

Bhilwara

Bikaner

Bundi

Chittaurgarh

Churu

Dausa

Dhaulpur

Dungarpur

Ganganagar

Hanumangarh

Jaipur

Jaisalmer

Jalor

Jhalawar

Jhunjhunun

Jodhpur

Karauli

Kota

Nagaur

Pali

Pratapgarh

Rajsamand

Sawai Madhopur

Sikar

Sirohi

Tonk

Udaipur

0

300

600

900

PV Potential (GW)

Dis

tric

ts

Fig. 6. District Wise PV Capacity Potential in Rajasthan

calculate the actual cost of grid integration of RE resources.This information then can be transferred to planning modelsfor long-term designing of system portfolio.

ACKNOWLEDGMENT

The research work is supported by the Ministry of Newand Renewable Energy of the Government of India underthe National Renewable Energy Fellowship Programme.

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