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Power Development Plan 2015 on air quality from 2016 to 2036 using - - PowerPoint PPT Presentation

Oct 09, 2023 197 likes •534 views

Analysis of the effects of the Thai Power Development Plan 2015 on air quality from 2016 to 2036 using GAINS and CAMx Jared Allard, Michael Alleyne, Daniel Day, Robert Gourley, Thao Pham, Thanonphat Boonman, Sebastien Bonnet, Savitri Garivait

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SLIDE 1

Analysis of the effects of the Thai Power Development Plan 2015 on air quality from 2016 to 2036 using GAINS and CAMx

Jared Allard, Michael Alleyne, Daniel Day, Robert Gourley, Thao Pham, Thanonphat Boonman, Sebastien Bonnet, Savitri Garivait University of North Carolina at Chapel Hill, KMUTT Joint Graduate School of Energy and Environment

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SLIDE 2

Abstract: Air pollution is a serious issue that affects many parts of the world, Southeast Asia in particular. Nitrogen dioxide, particulate matter, sulfur oxides, and other emissions have negative impacts on human health as well as overall environmental quality. The major sources in Thailand are open burning and fossil fuel combustion, both in vehicles and power generation. Given increasing actual and projected GDP growth, subsequent increases in energy consumption are inevitable. The power generation system must grow and expand as well to meet changes in demand from industrial, commercial, and residential

  • customers. The Ministry of Energy of Thailand has published the Power Development Plan 2015 (PDP

2015) to outline policies and goals of the growing power generation and transmission systems throughout the nation. Notably, the plan involves increasing the use of coal-fired generation. Using both the Greenhouse Gas and Air Pollution Interactions and Synergies Model (GAINS) and the Comprehensive Air Quality Model with Extensions (CAMx), we have compared two different emissions scenarios: one with standard emission control technology, and another with maximum emission controls. The effectiveness of emission control technology varied by region and pollutant. The greatest increase in air quality was located around Rayongprovince of the eastern region. For PM10 in the north, however, emission control technologies did little to increase air quality because the main source of pollutant, biomass burning, was left unabaited. This forecast of air quality can show possible impacts from future air quality in Thailand and regions that may benefit from added emission control technology in the future.

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 3

Methodology

Analysis of Thai PDP Collection of fuel share Scenario development GAINS (emissions) CAMx (concentrations) Interpretation

  • f results

Draw conclusions Future Study

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 4

Thai Power Development Plan 2015

  • Published in 2015, projected growth of

power sector to 2036

  • Highlights changes in fuel share (coal,

natural gas, etc.)

  • 3 mains goals

– Increase energy security – Maintain reasonable cost to consumer – Reduce Greenhouse Gas emissions

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 5

PDP 2015 Revision

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

Year PDP2010 Rev3 PDP2015 Change Peak (MW) Energy (GWh) Peak (MW) Energy (GWh) Peak Energy % % 2016 32,000 211,000 30,000 198,000

  • 6
  • 6

2026 46,000 305,000 41,000 268,000

  • 11
  • 12

2030 52,000 347,000 44,000 292,000

  • 15
  • 16

2036

  • 50,000

326,000

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SLIDE 6

PDP net capacity

Generating Capacity contributors during 2015-2036 (MW)

~~Existing capacity as of December 2014 38,000 ~~New capacity during 2015-2036 57,000 ~~Retired Capacity during 2015-2036

  • 25,000

~~Total Capacity in 2036 70,000 ~~New capacity during 2015-2036 57,000

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 7

Breakdown of capacity to be added during 2015-2036 (Total=57,000 MW)

~~New capacity during 2015-2036 57,000

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 8

GAINS model

Raw fuel data Analyze ETP and TCAP project Input scenarios into GAINS Ran model for specified time scale Obtained emissions

  • utput

Analyze and format to GAINS input file Developed emission controls (CLE & MFR) Applied fuel data and controls to make GAINS inputs On all pollutant species in all GAINS regions between 2016 and 2036 on five year intervals Used as input to CAMx model

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 9

GAINS Regions

NHIG BMR NEPL CVAL SPEN

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 10

Input Fuel Data to GAINS

20 40 60 80 100 120 140 160 180

Petajoules

2016 Fuel Consumption

BMR CVAL NHIG NEPL SPEN 100 200 300 400 500 Natural Gas Diesel Fuel Oil Biomass/Renewable Hard Coal Brown Coal

Petajoules

2036 Fuel Consumption

BMR CVAL NHIG NEPL SPEN 100 200 300 400 500

Petajoules

2036 Fuel Consumption

BMR CVAL NHIG NEPL SPEN 180 20 40 60 80 80 100 120 140 160 180

Petajoules

2016 Fuel Consumption

BMR CVAL NHIG NEPL SPEN mass/Renewable Hard Coal BMR BMR Co C Brown Coal IG IG wnNEPL NEPL

2036 Fuel Consumption pt o

BMR CVAL NHIG NEPL SPEN 100 200 300 400 500 Natural Gas Diesel Fuel Oil Biom

Petajoules

036 ue Co su

100 2 00

jo jo

300 400 4 500

Petajoules

2036 Fuel Consumption 2

BMR atura atura CVAL s NHIG D NEPL l SPEN 20 40 60 80 100 120 140 160 180 Natural Gas Diesel Fuel Oil Biomass/Renewable Hard Coal Brown Coal

Petajoules

2016 Fuel Consumption

BMR CVAL NHIG NEPL SPEN

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 11

Scenario Development

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 12

Scenario Development - CLE

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 13

Scenario Development - MFR

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 14

GAINS Results - NOx

30 60 90 120 150 180 210 240 270 300 330 Bangkok Metropolitan Central Valley Northeastern Plateau Northern Highlands Southern Peninsula

kt NOx-eq GAINS Region

NOx Emissions by GAINS Region

2036 MFR 2036 CLE 2016 CLE

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 15

GAINS Results – SO2

30 60 90 120 150 180 210 Bangkok Metropolitan Central Valley Northeastern Plateau Northern Highlands Southern Peninsula

kt SO2 GAINS Region

SO2 Emissions by GAINS Region

2036 MFR 2036 CLE 2016 CLE

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 16

GAINS Results – PM10

30 60 90 120 150 180 Bangkok Metropolitan Central Valley Northeastern Plateau Northern Highlands Southern Peninsula

kt PM10 GAINS Region

PM10 Emissions by GAINS Region

2036 MFR 2036 CLE 2016 CLE

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 17

CAMx model

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 18

CAMx Input Requirements

  • Emissions Inventory

– GIS and spatial allocation – Chemical speciation

  • Meteorological Model

– WRF

  • Boundary and Initial Conditions

– MEGAN-MACC and GFEDs database

  • Shipping emissions survey

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 19

CAMx monitoring stations

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 20

CAMx Results - NOx

0.0 3.0 6.0 9.0 12.0 15.0 18.0 21.0 24.0 Phra Nakhon Si Ayudhya Saraburi Rayong Chonburi Lampang Nakhon Sawan Surat Thani Concentration (ppb) Monitoring Stations

NOx Concentrations March Average

2016CLE 2036CLE 2036MFR Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 21

CAMx Results - NOx

0.0 3.0 6.0 9.0 12.0 15.0 18.0 21.0 24.0 Phra Nakhon Si Ayudhya Saraburi Rayong Chonburi Lampang Nakhon Sawan Surat Thani Concentration (ppb) Monitoring Stations

NOx Concentrations August Average

2016CLE 2036CLE 2036MFR Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 22

CAMx Results – SO2

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 Phra Nakhon Si Ayudhya Saraburi Rayong Chonburi Lampang Nakhon Sawan Surat Thani Concentrations (ppb) Monitoring Stations

SO2 Concentrations March Average

2016CLE 2036CLE 2036MFR Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 23

CAMx Results – SO2

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 Phra Nakhon Si Ayudhya Saraburi Rayong Chonburi Lampang Nakhon Sawan Surat Thani Concentration (ppb) Monitoring Station

SO2 Concentrations August Average

2016CLE 2036CLE 2036MFR Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 24

Scenario Cost Comparison

200 400 600 800 1000 1200 1400 1600 BMR CVAL NHIG NEPL SPEN Total

Dollars (2016 Dollars)

Scenario Cost Comparison

CLE MFR

MFR Total Cost – $1.4 Billion Thai GDP 2015 - $395 Billion

50 100 150 200 250 300 350 NOx CLE NOx MFR SO2 CLE SO2 MFR PM10 CLE PM10 MFR

Kilo-tonnes per year

Emissions Comparison of CLE and MFR in 2036

BMR CVAL NHIG NEPL SPEN

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 25

Human health impacts

  • Determined using WHO standards

–Thai standards are out of date –Intended for international use

  • Most pollutants had small improvements
  • SO2 witnessed most improvement
  • PM10 overshadowed by biomass burning

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 26

Concentrations vs. standards

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 Phra Nakhon Si Ayudhya Saraburi Rayong Chonburi Lampang Nakhon Sawan Surat Thani Concentration (ppb) Monitoring Station

SO2 Concentrations March Average

2016CLE 2036CLE 2036MFR WHO SO2 Air Quality Standard Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 27

Concentrations vs. standards

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 Phra Nakhon Si Ayudhya Saraburi Rayong Chonburi Lampang Nakhon Sawan Surat Thani Concentration (ppb) Monitoring Station

SO2 Concentrations August Average

2016CLE 2036CLE 2036MFR WHO SO2 Air Quality Standard Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 28

Discussion

CLE MFR SO2 Emissions: 2036 March

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 29

Discussion

CLE 2036 MFR 2036

No Risk Risk to Health

PM10 Air Quality Standard Comparison

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 30

Conclusions

  • NEPL and NHIG power plants emit the

most PM10 out of all GAINS regions

  • Biomass dwarfed coal in the NHIG and the

NEPL GAINS regions

  • Little difference observed between the CLE

2036 and the MFR 2036 for the SPEN region

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 31

Conclusions

Wind Trajectory from TCAP ECLIPSE Meteorology Scenario

Methodology PDP GAINS Fuel Data Scenario Development GAINS Results CAMx CAMx Results Discussion Conclusion

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SLIDE 32

Future Study

  • Solve CAMx pollution modelling discrepancy in

the SPEN region

  • Conduct regional modelling with a resolution

better than 12km by 12km

  • Use BenMAP to predict DALY from

concentrations

  • Designing scenarios with wider ranges of

emissions control technologies

  • Interchanging fuel types for optimal air quality