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Wouter Nijs, VITO, Belgium

16/06/2009. Technological choices for achieving the EU-objectives on climate change and renewable energy in Belgium, a sensitivity analysis. Wouter Nijs, VITO, Belgium. The Belgian TIMES model. Energy model Bottom-up Linear Programming model

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Wouter Nijs, VITO, Belgium

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  1. 16/06/2009 Technological choices for achieving the EU-objectives on climate change and renewable energy in Belgium,a sensitivity analysis. Wouter Nijs, VITO, Belgium

  2. The Belgian TIMES model • Energy model • Bottom-up Linear Programming model • Maximise sum of welfare; welfare defined as sum of consumer and producer surplus • All costs are discounted to 2000 with a 4% rate. • Elastic demand • Perfect competition, perfect foresight • Subjected to technical and energy political constraints • Results: • Energy flows • Investments, costs, prices • Emissions • Center for Economic Studies • KULeuven

  3. The Belgian TIMES model • Time horizon: 2000-2050 • End use sectors: • Domestic • Industry • Commercial • Transport • Conversion sector: • Electricity and district heat generation • Supply of Petroleum products • Non-energetic use • Renewable energy sources • Hydrogen • Center for Economic Studies • KULeuven

  4. The Belgian TIMES energy model The premisse = A policy scenario that examines the impact for Belgium of the EU objectives for climate change and renewable energy for 2020 (nuclear decommissioned). LP Problem Max ctx Ax ≤ b x ≥ 0 • Where • ctx is the objective • x is a vector of decision variables, • Ax ≤ b is a set of inequality constraints. • Center for Economic Studies • KULeuven

  5. The research question Direction of increasing objective

  6. The research question Motivation: “The Purpose of Mathematical Programming is Insight, not Numbers.” (Arthur Geoffrion, 1976) “The best solutions to real-world problems are often different solutions than the model solution.” “Knowing what is optimal, should we forget all other technologies ?”

  7. The research question “Analyse the effect of • The assumption on CCS possibilities • Price elasticities on the investments in electricity and transport technologies.” ..with a focus on • The “optimal” technologies • The distance of all other technologies to this optimum

  8. Handling uncertainty • Post-optimality analysis of the normal least cost optimization is used for analysing electricity and transport investments [€/MWel, €/car…] • Alternative methods: • Make scenarios • Modeling to generate alternatives (MGA) • Parametric programming • Mutliobjective optimisation / Tradeoff Analysis / Stochastic Programming • …

  9. Post-optimality analysis “Post-optimality” analysis = feature of LP solver GAMS • “Constraint ranging” • “Objective ranging”: how much the objective coefficient can change without changing the optimal basis Applied on investements (objrng VAR_NCAP): • “Amount by which the investment cost needs to change before the investment choices will change” • For technologies not chosen: “The minimum amount by which OBJ will change when forcing an investment”

  10. Post-optimality analysis Original problem Problem with one cost modification Invest in Wind Invest in Wind Invest in Gas Invest in Gas In this example, the change in investment cost is higher than indicated by the range of post-optimality analysis

  11. Post-optimality analysis • Advantages: • A robust sensitivity analysis • No extra model runs • Very quick to predict effect of change in investment cost • Could be standardised • Challenges: • “Ceteris paribus”, only change one parameter at a time; no interdependencies (example electric car vs way of producing electricity) • Only marginal changes

  12. Ranking Investments 333 1000 €/kW  = 667 €/kW

  13. Ranking investments Post optimality  Investment cost Profitability Index • PI = _____________________ = _______________________ PV of initial investment INVCOST • The “distance to optimum“ = PI – 100% • PI < 100%: project creates less value than capital cost • PI > 100% : “price of constraint” • PI = [85% to 115%] = Near optimal technology PV of future cash flow INVCOST – Reduced cost

  14. Scenarios • No climate policy • EU2020 NOCCS: 13% renewables in final energy, CO2 ceiling conform non-ETS target (-15%) and CO2 price. • EU2020: idem with CCS

  15. Results: No climate policy + EU2020 no CCS Car transport technologies, 2020 and 2040

  16. Results: EU2020 with CCS, elasticity -0.3 and 0 Car transport technologies, 2020 and 2040

  17. Results: No climate policy Electricity technologies, 2020 and 2040

  18. Results: EU2020 no CCS Electricity technologies, 2020 and 2040

  19. Results: EU2020 with CCS Electricity technologies, 2020 and 2040

  20. Results: EU2020 with CCS, inelastic Electricity technologies, 2020 and 2040

  21. Conclusions • Ranking gives extra information on technology options • The effect of a cost increase/decrease can be quickly estimated • Transport technologies: • Do not differ a lot, except H2 and electric cars • H2 and electric cars closer to optimum through cost reduction • Impact of EU2020, CCS and price elasticity on ranking is small • Electricity technologies: • CCS is of major importance in the ranking of technologies; not to forget is CCS on gas power plants • A low price elasticity does favor wind and solar, but not CCS • Nuclear has a PI of more than 500% in all EU2020 scenario’s • Wind is a winning technology in all EU2020 scenarios • Same is true for PV if cost reduction continues

  22. Thank you ! Contact wouter.nijs@vito.be

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