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Working with TIMES and Monte Carlo in a Policy Setting

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Working with TIMES and Monte Carlo in a Policy Setting

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Working with TIMES and Monte Carlo in a Policy Setting

  1. 1. Working with TIMES and Monte Carlo in a Policy Setting 72TH SEMI-ANNUAL ETSAP MEETING, ETH Zürich, Switzerland, December 11, 2017 Kristoffer S. Andersen, Advisor, Danish Energy Agency and PhD student, Technical University of Denmark December 18, 2017 Page 1
  2. 2. IntERACT Integrated Energy and Economic Tool December 18, 2017 Page 2 General Equilibrium Feedback Technological Explicitness Behavioural Realism TIMES-DK CGE Developers:
  3. 3. Agenda December 18, 2017 Page 3 1. Why consider uncertainty 2. Implementing uncertainty in the IntERACT model 3. Working with uncertainty in a policy setting
  4. 4. IntERACT: Two optimizing criterias 1. Determine least cost low carbon transition pathways 2. Minimize costs of pathway uncertainty related to:  Investment cost  Fuel and emission prices  Policy  Behavior December 18, 2017 Page 4 Cost Pathway A Pathway B
  5. 5. Why we consider uncertainty Externally 1. Means of quantifying the uncertainty associated with a policy proposal 2. Facilitates dialogue with stakeholders as it provide additional insight into a complex model (opening the black box) Internally 1. A means of testing the model and identifying possible weakness in assumption and model structure 2. Gives a higher degree of confidence in the model December 18, 2017 Page 5
  6. 6. Agenda December 18, 2017 Page 6 1. Why consider uncertainty 2. Implementing uncertainty in the IntERACT model 3. Working with uncertainty in a policy setting
  7. 7. Reconciling Engineers and Economists December 18, 2017 Page 7 TIMES-DK Energy System Model CGE General Equilibrium Model Change in demand for energy services Price of energy services Energy subsidies and taxes Change in capital intensity TIMES-DK • Optimizes Danish energy system towards 2050 • 12 Economic sectors • Power and district heat sector • Residential sector • Transport sector • Electricity exchange with neighbouring countries • 32 time slices CGE model • 20 economic sector • One household • Government • Foreign trade Soft-link • 12 Economic sectors • Power and district heating sect • Residential sector
  8. 8. Household Heat Services in TIMES-DK IntERACT TIMES-DK 8 Heat services are measured as Mm2 in the model The building are split in before and after 1972 and in multi-story (multi storey+non-detached) and detached (detached+farm houses) In the current version we do not assume any rebound effect on heat demand from chaning the price of heat service
  9. 9. Industry structure i TIMES-DK 18. december 2017 Side 9 12 Economic sectors o Agriculture, forestry, fishing, gravel & stone o Food, beverages, tobacco industry o Chemical industry (excl manufacture of basic metals) o Metals, machinery and transport equipment industry o Cement and bricks, glass and ceramics o Other commodity production o Wholesale and retail trade o Private service industries (incl support for transportation and postal activities) o Public services industries o Construction o Other utilities o Motor vehicles - purchase and repair Energy services demand o High temperature (>150°C) o Medium temperature (<150 °C) o Room heat o Electric motors and cooling o Light and IT o Tractor services etc (agriculture sector only) o Fork lifts Conversion technologies o Sector and energy service specific Savings potentials o Sector and energy service specific Fuel input o Electricity o District heating o Coal o Natural gas o Diesel o Fuel oil o Solid biomass o Biogas o Biofuels Taxes and subsidies o Sector and energy service specific Capacity constraints o Sector and energy service specific Sector specific demand drivers from CGE model
  10. 10. Baseline (iteration) IntERACT cockpit (MS Excel) Defining baseline and alternative scenarios Top-down assumptions: Growth assumptions, elasticities, macro closure. Bottom-up assumptions: ChooseTIMES scenario CGE productivity indicies 2012-2050 Alternative scenarios (iteration) TIMES -> CGE Electricity and district heat prices, fuel mix, subsidies and taxes (%TIMESscenario%.gdx) TIMES -> CGE Electricity and district heat production and prices Fuel use supply sector (%TIMESbaseline%.gdx) TIMES-DK Baseline scenario 2010-2050 CGE alternative Alternative scenarios 2012-2050 TIMES-DK Alternative scenario 2010-2050 CGE reference Baseline scenario (2012-2050) Report (2012-2050) IntERACT: Iteration routine 10 CGE->TIMES Adjusted demand projection (%ZZ-CGE_Linking%.dd) CGE->TIMES Adjusted demand projection (%ZZ-CGE_Linking%.dd)
  11. 11. IntERACT: Monte Carlo routine 1. Define the proces and/or commodity sets for which uncertainty is to be considered 2. Define the uncertainty distribution(s) in R 3. Start loop in R, where each loop-iteration draws samples from the uncertainty distribution(s). i. Write the draws into a dd-file used by TIMES ii. Run IntERACT iteration routine inside R i. Baseline ii. Policy iii. Save relevant output in R 4. Look at results 18-12-2017 11
  12. 12. ACT_EFF(REG,ALLYEAR,PRC,"ACT",ALL_TS)$MonteCarloPRC(PRC) = ACT_EFF(REG,ALLYEAR,PRC,"ACT",ALL_TS)*0.97 1) Define MonteCarloPRC(PRC) 2) Define Uncertainty Distribution 3) Run R loop, draw from distribution and write the draw into dd-file Save relevant output in R
  13. 13. Agenda December 18, 2017 Page 13 1. Why consider uncertainty 2. Implementing uncertainty in the IntERACT model 3. Working with uncertainty in a policy setting
  14. 14. Working with uncertainty in a policy setting  Reduce tax on electricity for heating in order to incentivize the adoption of heat-pumps for room-heat in households and industry.  How does uncertainty with to the cost effectiveness of heat pump and market price of electricity affect the adaption of heat pumps in IntERACT? December 18, 2017 Page 14
  15. 15. Working with uncertainty (timeline) September Early October Late October November # of sensitivity Iterations 100 100 50 50 # of sensitivity input parameters 1 1 2 2 # of scenarios 2 4 3 3 # of IntERACT iterations by each scenario 3 3 5 5 # of IntERACT model iterations 600 1200 750 750 # of hours 10 20 12.5 7.5 December 18, 2017 Page 15
  16. 16. Household demand response from a reduction in tax on electricity for heat (PJ) December 18, 2017 Page 16 Preliminary results based on IntERACT v. 1.0.0 Biomass Electricity District heat Natural gas Olie Solar heat
  17. 17. Household and industry electricity demand response following different levels of reduction in tax on electricity for heat (PJ) December 18, 2017 Page 17 IntERACT v. 1.0.0 Biomass Electricity District heat Natural gas Olie Solar heat Household Industry Preliminary results based on IntERACT v. 1.0.4
  18. 18. Take ways December 18, 2017 Page 18 1. Sensitivity analysis on TIMES can be implemented fairly easy in R, which allows for a high degree of flexibility in both in input variation and visualsation of results. 2. Using uncertainty analysis has proven to be a key tool both internally for model testing and externally for quantifying policy uncertainty. 3. Further work focus on how to develop and refine the use of sensitivity analysis in the IntERACT model.

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