Chapter 1 Introduction
4.2 GIS Based RE Potential Calculation for North India
4.2.3 Representation of RE Information in NIMRT Model
4.2 GIS Based RE Potential Calculation for North India 71
0.0 0.2 0.4 0.6
01−J AN−H01
02−FEB−H0103−MAR−H0104−APR−H0105−MA Y−H01
06−JUN−H0107−JUL−H0108−A UG−H01
09−SEP−H0110−OCT−H0111−NO V−H01
12−DEC−H0112−DEC−H24 Time Slice
Capacity Factor
A) Class 1 solar CF
0.0 0.2 0.4 0.6 0.8
01−J AN−H01
02−FEB−H0103−MAR−H0104−APR−H0105−MA Y−H01
06−JUN−H0107−JUL−H0108−A UG−H01
09−SEP−H0110−OCT−H0111−NO V−H01
12−DEC−H0112−DEC−H24 Time Slice
Capacity Factor
B) Class 1 wind CF
New_Plants Existing_Plants
Figure 4.8Time slice capacity factors of existing and new PV plants for class 1 solar and wind classes in RJ
Figures 4.9 outlines the RE capacity potential available in every region for various annual capacity factor ranges. For that, potential of each grid-cell is plotted with respect to its annual capacity factor. The grid-cells are faceted into regions and colored by class. For solar, it can be seen that, RJ has more number of grid cells corresponding to higher classes. The grid-cells in RJ which correspond to higher solar class, also have high capacity potential.
The range of solar CF values is approx. 17%–19.5% in RJ. For wind, again RJ has maximum capacity potential of higher wind classes, followed by UU. The wind CF values of RJ vary between 10%–31%. Variation of class wise grid-cell capacity potential and CF values is further summarized and illustrated in Figure 4.10.
Solar and wind time slice wise CF calculation is automated by programs written in R, as the number of grid-cells is large. The program loops through each grid-cell’s data files, calculates hourly CF values, and finally aggregates them to time slice definitions. The program can be readily extended/ applied to any geographical region depending on data availability.
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PB RJ UT UU
DL HP HR JK
10 20 30 10 20 30 10 20 30 10 20 30
0 10 20
0 10 20
Percentage (Capacity Factor)
GW
Win_Class
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cl01 cl02 cl03 cl04 cl05 cl06 cl07 cl08 cl09 cl10
Class Wise Wind Annual Capacity Factor Vs Capacity Potential
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PB RJ UT UU
DL HP HR JK
14 16 18 14 16 18 14 16 18 14 16 18
0 100 200
0 100 200
Percentage (Capacity Factor)
GW
Sol_Class
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cl01 cl02 cl03 cl04 cl05 cl06 cl07 cl09 cl10
Class Wise Solar Annual Capacity Factor Vs Capacity Potential
Figure 4.9Region wise RE capacity potential vs capacity factors
4.2 GIS Based RE Potential Calculation for North India 73 the generation (CF), capacity potential (GW) and other techno-economic conditions over long-term.
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15 16 17 18 19
DL HP HR JK PB RJ UT UU
State
Percentage
Solar Annual Capacity Factor
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0 100 200
DL HP HR JK PB RJ UT UU
State
GW
Solar Capacity Potential
DL HP HR JK PB RJ UT UU
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5 10 15 20 25 30
DL HP HR JK PB RJ UT UU
State
Percentage
Wind Annual Capacity Factor
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0 10 20
DL HP HR JK PB RJ UT UU
State
GW
Wind Capacity Potential
DL HP HR JK PB RJ UT UU
Figure 4.10Region and grid-cell wise distribution of solar and wind annual capacity factors Other than RE technologies, dummy commodities and processes are created to report solar and wind curtailment. The availability factors of each RE class are incorporated in NIMRT as fixed bounds,i.e. the generators work with utilization factor in a particular time slice. Depending on system resource, load curve,etc., system may or may not absorb the total RE generation. When the system is unable to absorb the total RE generation, RE technologies produce dummy curtailed solar and wind commodities which are exported to a dummy region through dummy uni-directional export processes. Suitable export prices are specified so that production of these commodities are restricted otherwise.
Within a region, for a particular RE class, time slice specific CF data of a single grid-cell is taken as a representativei.e., time slice wise CF values of every grid-cell corresponding to a class within a region are not considered. The region specific RE capacity bounds are
specified to the model by creating user-constraints pertaining to each RE class. The bound is applied on the sum of existing and future capacity calculated by the model. As total existing RE installation is not substantial, current regional capacity is mapped to the highest RE class of that region for simplicity.
As the land calculated for RE is considered to be common for solar and wind, additional constraint has been applied, so that sum of RE capacity (solar and wind) calculated by the model for a region does not violate the maximum available area for that region. Suitable growth rates are also applied such that rate of yearly change of total solar and wind capacity does not violate any logical numbers. Throughout the planning horizon, generation potential of (annual and time slice wise CF values) a RE class is assumed to be constant.
Chapter 5
Long-Term Scenario Analysis with NIMRT Model
This chapter outlines the results from NIMRT model having intra-regional RE variability information, corresponding to various parametric scenarios. Definitions of scenarios are described first, afterwards, the numerical results are presented. Result related to variation of RE penetration levels, nature of power dispatch, technology capacity, role of inter-regional energy exchange and storage, coal supply, and CO2 emission are discussed. Due to the difference of resource potential, technological suitability, and demand, each region’s (i.e state’s) generation mix is unique. Hence, regional interpretations of results are also drawn.
Due to a large number of cases considered in this study, only those which present interesting observations, are outlined.