Addressing RE Intermittency and Operation Aspects of Generating Units in Long-term System Planning of Indian Power Sector Anjali Jain, Malaviya National Institute of Technology, India

1. Addressing RE Intermittency and Operation Aspects
of Generating Units in Long-term System Planning
of Indian Power Sector
Anjali Jain, Dr. Rohit Bhakar, Prof. Jyotirmay Mathur
Centre for Energy and Environment,
Malaviya National Institute of Technology Jaipur, India

2. Introduction
Fig: Installed Capacities in GW
▪ INDC Targets of India –
1) Reduction in CO2 emission intensity of its
GDP by 30% - 35% from 2005 level by 2030
2) 40% capacity share of non-fossil fuel
generation by 2030
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▪ A huge potential of solar and wind energy
resources available
▪ All planning models are at developing stage
▪ Current installed capacity of India: 365 GW
▪ Driven by concerns of energy security and
global warming, country is accelerating
towards a renewable energy (RE) future
Coal +
Lignite
199.594
Gas
24.99
Large-
hydro
45.69
Nuclear
6.78
Small-
hydro
4.712
Biomass
9.93
Solar
35.30
Wind
37.94
RES
87.89

3. ▪ Traditional planning approaches use low level of spatial, temporal and technical
details to avoid associated computation
▪ Planning model with low spatial resolution fail to capture intra-regional
intermittency of renewable energy sources (RESs) in large geographical regions
▪ Low-temporal definition does not facilitate inclusion of seasonal/diurnal variation
of RE sources that demand additional system flexibility
▪ Neglecting techno-economic operational parameters may significantly alter the
generation portfolio and results in a sub-optimal capacity mix
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Challenges in system planning with high RE

4. ▪ Spatial resolution: 5 Regions - NR, ER, NER, WR, SR
▪ Calibration years: 2015-2019
▪ Planning years: 2020-2040
▪ Timeslices: 288
Annual
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
H01 H02 H03 - - - H22 H23 H24
Annual
Seasonal
Daynite
Fig: Timeslice level
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Indian power sector TIMES (IPST) model: General settings
Fig: Regional load dispatch centres
Northern Region
Western Region
Southern Region
Eastern Region
North-eastern Region

5. ▪ Existing generation technologies: Coal, Lignite, Gas, Nuclear, Large-hydro,
RE (Small-hydro, Wind, Solar, Biomass)
▪ New generation technologies: Coal, Gas, Large-hydro, RE
▪ Discount rate: 10%
▪ Techno-economic parameters: Fixed and variable operating cost, availability factor,
efficiency, start year of plant, plant life and investment cost for new technologies
▪ Solar and Wind energy: 1) Class wise categorization based on annual capacity factor
2) Timeslice wise capacity factor
▪ Hydro power plants: Region wise seasonal availability factor
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Model description

6. ▪ Assessment of total land availability and capacity potential of solar and wind plants for each
1x1 degree grid cell – GIS based study
▪ Categorization of grid cells in 10 different classes based on annual availability factor
Fig: Wind classesFig: Solar classes
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Intra-regional solar and wind variability

7. Demand projection and inter-regional trading
▪ Exogenous projection of annual electricity consumption
▪ Drivers: Past electricity consumption and GDP
▪ Regional electricity consumption: estimated based on historical share of regions
▪ AT&C losses: 21.04% in 2017, assumed to reduce to 9.2% by 2040
▪ Inter-regional links are represented by existing inter-regional transmission capacities to
facilitate bi-directional trading - Total 7 links
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8. Scenario and hypothesis
1) RE capacity targets: Interim target of 175 GW RE by 2022,
100 GW RE addition between 2023 and 2027
2) Carbon tax: an increasing price on CO2 emission
No new coal plants between 2023-2027 (except proposed or under commissioning)
Fig: Carbon tax year wise
a) Without Operational Constraint
b) With Operational Constraint (Unit
commitment – UC)
Considered cases:
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9. Case 1 (C1): Without operational constraints
Fig: Scenario 1 (RE capacity targets)
Fig: Scenario 2 (Carbon tax)
▪ With 175 GW of RES in 2022,
the remaining total installed
capacity - 304 GW, comprising
47.7 GW Hydro, 215.8 GW coal,
25.4 GW Gas, 5.51 GW Lignite,
and 9.6 GW nuclear.
▪ Total installed capacity-
803.44 GW in 2030 and 1412.80
GW in 2040.
▪ Solar and wind generation
share - 36.38 % in 2030 and
45.02 % in 2040.
▪ Total installed capacity-
740.92 GW in 2030 and 1483.98
GW in 2040.
▪ Solar and wind generation
share - 32.75 % in 2030 and
49.55 % in 2040.
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10. ▪ Indian power system is dominated by inflexible coal plants
▪ Without UC parameters, model treats inflexible generation to be highly flexible
▪ With UC parameters, model increases investment in flexibility resources
Fig: Timeslice wise dispatch in 2040
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Case 2 (C2): With operational constraints

11. Comparative analysis of different cases
Case 2-a (C2a) - Inclusion of minimum stable generation in C1
Case 2-b (C2b) - Inclusion of ramp rate in C2a
Case 2-c (C2c) - Inclusion of startup and shutdown cost in C2b
Case 2-d (C2d)- Inclusion of partial load efficiency in C2c
Particulars
Scenario 1
C1 C2a C2b C2c C2d
Solar capacity (GW) 635.5 435.3 433.2 361.2 362.6
Solar generation share (%) 25 17.3 17.2 14.3 14.34
Wind capacity (GW) 277.3 276 276 306.4 302.7
Wind generation share (%) 19.6 23 23 26.4 26.2
Storage capacity (GW) 77.5 104 103.6 96.5 101.7
Non-fossil fuel generation share (%) 58.6 50.4 50.4 48.8 48.7
Coal capacity (GW) 318.3 306.4 306.4 305.7 304.8
Average CUF of coal plants 0.58 0.72 0.72 0.75 0.75
Carbon emission (Mt) 1386 1706 1708 1778 1860
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12. Final results with all UC constraints
Fig: Scenario 1 (RE capacity targets)
Fig: Scenario 2 (Carbon tax)
▪ Total installed capacity-
691.31 GW in 2030 and 1156.95
GW in 2040.
▪ Capacity share of solar and
wind – 47.24 % in 2030 and
57.51 % in 2040.
▪ Total storage capacity- 16.25
GW in 2030 and 101.72 GW in
2040.
▪ Total installed capacity-
692.41 GW in 2030 and 1255.42
GW in 2040.
▪ Capacity share of solar and
wind – 46.16 % in 2030 and
61.31 % in 2040.
▪ Total storage capacity - 25.54
GW in 2030 and 148.72 GW in
2040.
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13. Spatial distribution of solar and wind capacity in 2040
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(a) Solar energy (a) Wind energy

14. CO2 emissions and emission intensity
Fig: Yearly CO2 emissions and emission intensity
Fig: Regional CO2 emission intensity (a) year 2015 (b) scenario 1 - year 2040 (c) scenario 2 - year 2040
▪ Total CO2 emission in 2030:
1270.73 Mt in scenario 1 and
1191.03 Mt in scenario 2
▪ Total CO2 emission in 2040:
1860.31 Mt in scenario 1 and
1685.41 Mt in scenario 2
(a) (b) (c)
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15. Conclusions
▪ Explicitly targeting renewable generation may not fully ensure power sector
decarbonization as fossil-fuel generation will continue to contribute to CO2 emissions
▪ An instrument like carbon tax can expedite decarbonization by promoting investments
in green technologies and improving efficiency of existing thermal power plants
▪ Methodology to classify RE sources in different classes, as opposed to increasing the
resolution of planning model, reduces computational complexity
▪ For a power system dominated by inflexible generation, incorporating all UC
constraints is vital to ensure adequate quantification of flexibility resources and analyze
emission related polices
▪ Computational efforts can be further reduced by removing ramping constraints (if
flexibility is not a concern)
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16. Thank you for your attention!
Anjali Jain
2017ren9505@mnit.ac.in
Centre for Energy and Environment,
Malaviya National Institute of
Technology Jaipur
India