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Addressing RE Intermittency and Operation Aspects of Generating Units in Long-term System Planning of Indian Power Sector

IEA-ETSAP
IEA-ETSAP

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

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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
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
2
▪ 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
▪ 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
3
Challenges in system planning with high RE
▪ 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
4
Indian power sector TIMES (IPST) model: General settings
Fig: Regional load dispatch centres
Northern Region
Western Region
Southern Region
Eastern Region
North-eastern Region
▪ 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
5
Model description
▪ 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
6
Intra-regional solar and wind variability

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Addressing RE Intermittency and Operation Aspects of Generating Units in Long-term System Planning of Indian Power Sector

  • 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 2 ▪ 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 3 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 4 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 5 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 6 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 7
  • 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: 8
  • 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. 9
  • 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 10 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 11
  • 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. 12
  • 13. Spatial distribution of solar and wind capacity in 2040 13 (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) 14
  • 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) 15
  • 16. Thank you for your attention! Anjali Jain 2017ren9505@mnit.ac.in Centre for Energy and Environment, Malaviya National Institute of Technology Jaipur India