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Energy Models

Energy Models. 86025_11. Overview. What is a Model?. A stylized, formalized representation of a system to probe its responsiveness. Classification of Energy Models. Energy systems boundaries (energy sector vs. economy, demand vs. supply, (final) energy demand vs. IRM)

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Energy Models

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  1. Energy Models 86025_11 Arnulf Grubler

  2. Overview Arnulf Grubler

  3. What is a Model? A stylized, formalized representation of a systemto probe its responsiveness Arnulf Grubler

  4. Classification of Energy Models • Energy systems boundaries (energy sector vs. economy, demand vs. supply, (final) energy demand vs. IRM) • Aggregation level (“top-down” vs “bottom-up”) • Science perspectives: Natural (climate), Economics (typical T-D, demand), Engineering (typical B-U, supply),Social science (typical B-U, demand)Integrated Assessment Models (all of above) Arnulf Grubler

  5. System Boundaries in Models • Demand (final vs. intermediary) • Supply (end-use vs. energy sector) • Energy systemeconomyemissions impacts feedbacks(?) • Aggregation level:“top-down”“bottom-up” Arnulf Grubler

  6. Energy Systems Boundaries Supply Demand Arnulf Grubler

  7. (Component) Models of Energy Demand • Bottom-up (MEDEE, LEAP, WEM)focus on quantitiessimulation (activitiesdemand) and/or econometric (income, price demand)many demand and fuel categories • Top-down (ETA-MACRO, DICE, RICE)focus on price-quantity relationships (cf econometric B-U models) and feedbacks to economy (equilibrium): higher energy costs = less consumption (GDP); T-D because offew demand and fuel categories • Hybrids (linked models, solved iteratively, (e.g. IIASA-WEC, IIASA-GGI) Arnulf Grubler

  8. (Component) Models of Energy Supply • Bottom-up (MESSAGE, MARKAL) • Top-down (ETA-MACRO, GREEN) • Varying degrees of:technology detailemissions (species)regional and sectorial detail • Increasing integration (coupling to demand and macro-economic models) Arnulf Grubler

  9. Energy Models: Commonalities of Supply and Demand Perspectives • Optimization (minimize supply costs, maximize “utility of consumption”) • Forward looking (perfect information&foresight,no uncertainty) • Intertemporal choice (discounting) • Single agent (social planner) • “Backstop” technology • Exogenous changedemand (productivity, GDP growth)technology improvements (costs, AEII) Arnulf Grubler

  10. Energy – Economy – Environment: Systems Boundaries of 3 ModelsMESSAGE, ETA-MACRO, DICE MESSAGE Taxes Emissions Impacts Damages(monetized) ΔETA-MACRO and MESSAGE: Degree of technology detail Arnulf Grubler

  11. Top-Down -- Ex. DICE Arnulf Grubler

  12. A Simple “Top-down” Energy Demand Model Arnulf Grubler

  13. Bill Nordhaus’ DICE Model: Overview - (AEEI) + Solow Avoided damage Remaining damage Arnulf Grubler

  14. Bill Nordhaus’ DICE Model: Illustrative Result “do nothing”, i.e. ignore climate change “optimal solution”balancing costs (abatement)vs avoided costs (damages) keep climate constant (no further change) Arnulf Grubler

  15. DICE Model - Analytically Resolved (99% of all solutions by 2100). Source: A. Smirnov, IIASA, 2006 abatement costs damage costs Arnulf Grubler

  16. DICE – Assumptions Determining Results • Modeling paradigm:-- utility maximization (akin cost minimization)-- perfect foresight (akinno uncertainty)-- social planner (when-where flexibility, strict separation of equity and efficiency) • Abatement cost and damage functions,calibrated as %GWP vs. GMTC (°C) • Discount rate (for inter-temporal choice, 5%)matters for damages (long-term) vs abatement costs (short-term) • No discontinuities (catastrophes) Arnulf Grubler

  17. Attainability Domain of DICE with original Optimality Point 2100 Source: Smirnov, 2006

  18. DICE Attainability Domain and Isolinesof Objective Function Surface Percent of max. of objective function.Note the large “indifference” area Source: Smirnov, 2006

  19. Attainability Domain, Objective Function, and Thermohaline Collapse Risk Surfaces Risk Surface of Thermohaline collapse(years of exposure 1990-2100)climate sensitivity = 3 ºC Source: Smirnov, 2007

  20. Attainability Domain, Objective Function, and Thermohaline Collapse Risk Surfaces Risk Surface of Thermohaline collapse(years of exposure 1990-2100)climate sensitivity = 3.5 ºC Source: Smirnov, 2007

  21. Attainability Domain, Objective Function, and Thermohaline Collapse Risk Surfaces Risk Surface of Thermohaline collapse(years of exposure 1990-2100)climate sensitivity = 4 ºC Source: Smirnov, 2007

  22. More Nordhaus and Boyer, Warming the World:Economic Models of Global Warming, MIT Press, Cambridge, Mass, 2000. Online documentation and .xls and GAMS versions of model : http://www.econ.yale.edu/~nordhaus/homepage/dicemodels.htm Arnulf Grubler

  23. Bottom up – Ex. MESSAGE Arnulf Grubler

  24. Structure of a typical “Bottom-up” model • Demand categories (ex- or endogeneous): time vectors, e.g. industrial high- and low-temperature heat, specific electricity,... • Supply technologies (energy sector and end-use): time vectors of process characteristics, energy inputs/outputs, costs, emissions,….. • Resource “supply curves” (costs vs quantities) • Constraints:physical: balances, load curvesmodeling: e.g. build-up ratesscenarios: e.g. climate (emissions) targets Arnulf Grubler

  25. Example MESSAGE(Model of Energy Supply Systems Alternatives & their General Environmental Impacts) Model structure: • Time frame (horizon, steps) • Load regions (demand/supply regions) • Energy levels (primary to final) • Energy forms (fuels) Model variables: • Technologies (conversion): main model entities • Resources (supply curves modeling scacity) • Demands (exogenous GDP, efficiency, and lifestyles) • Constraints (restrictions, e.g. CO2 emissions):ultimately determine solution (ex. TECH, RES, DEM) Arnulf Grubler

  26. Basic Structure of MESSAGE(recall energy balance sheets!) Energy levels Pro duction Storage Con version Demand Resources Blending Cogen eration Energy forms Technologies Arnulf Grubler

  27. A Reference Energy System of a B-U Model (MESSAGE) 2000 Additional by 2020 Arnulf Grubler

  28. Representation of Technologies • Installed capacity (capital vintage structure) • Efficiency (1st Law conversion efficiency) • Costs • Investment • Fixed O&M • Variable O&M • Availability factor • Plant life (years) • Emissions per unit activity (output) 0≥coefficient≤1 Arnulf Grubler

  29. Linear Programming Production inputs (e.g. Capital, Labor) x1 cx1+d<C Resource constraintse.g. capital and labor x1 < L Demand constraintsupply≥demand c1x1+c2x2min ax1+bx2>D Cost function minimized x2 Source: Strubegger, 2004.

  30. Linear Programming x1 cx1+d<C x1 < L ax1+bx2>D c1x1+c2x2min x2 Solution Space (Simplex) Optimum Solution at Simplex Corner(defined by constraints & objective function) Source: Strubegger, 2004.

  31. More Eric V. Denardo, The Science of Decision Making. A Problem-based Approach Using Excel. Wiley, 2002.Good introduction and CD with excel macros and solvers.(see Arnulf or Denardo at ENG for a browse copy) Arnulf Grubler

  32. SummaryT-D and B-U Models Arnulf Grubler

  33. Top-down vs. Bottom-up: Different Questions and Answers • T-D: “How much a given energy price (environmental tax) increase will reduce demand (emissions) and consumption (GDP growth)?” • B-U: “How can a given energy demand (emission reduction target) be achieved with minimal (energy systems) costs?” Arnulf Grubler

  34. US – Mitigation Costs Arnulf Grubler

  35. Top-down vs. Bottom-up: Strengths and Weaknesses • Top-down (equilibrium):+ transparency, simplicity, data availability+ prices & quantities equilibrate- ignores (externalizes) major structural changes (dematerialization, lifestyles, TC) • Bottom-up (status-quo):+ detail, clear decision rules- main drivers remain exogenous (demand, technology change, resources)- quality does not matter- invisible costs:? Arnulf Grubler

  36. More e.g. IPCC TAR(intro and summary and implications on CC mitigation costs) http://www.grida.no/climate/ipcc_tar/wg3/310.htm http://www.ipcc.ch/ipccreports/tar/wg3/374.htm Arnulf Grubler

  37. Integrated Assessment Models Arnulf Grubler

  38. IIASA-WEC Global Energy Perspectives:Hybrid IA Model • Top-down, bottom-up combination (soft-linking) • Top-down scenario development (aggregates) • Decomposition into sectorial demands (useful energy level) • Alternative supply scenarios • Iterations to balance prices & quantities (macro-module) • Calculation of emissions (no feedbacks) Arnulf Grubler

  39. IIASA-WEC Integrated Scenario Analysis Arnulf Grubler

  40. IIASA GGI Climate Stabilization Scenarios • Capturing uncertainty: 3 baselines (demand, technology innovation and costs), stabilization targets • Energy, agriculture, forestry sectors and all GHGs • Spatially explicit analysis (11 world regions, ~106 grid cells) • Stabilization targets: Exogenous • Methodology: Inter-temporal cost minimization (global) Arnulf Grubler

  41. GGI IA Framework Spatially explicit scenario drivers: Population, Income, POP and GDP density(land prices)MESSAGE demands Exogenous drivers for CH4 & N2O emissions: N-Fertilizer use, Bovine Livestock Data Sources: Fischer & Tubiello,LUC MESSAGE System Engineering Energy Model Data Sources :Obersteiner & Rokityanskiy, FOR Bottom-up mitigation technologies for non-CO2 emissions, Data Sources:USEPA,EMF-21 Black Carbon and Organic Carbon Emissions Data Sources: Klimont & Kupiano,TAP Data sources: Fischer &Tubiello, LUC Data Sources: Obersteiner & Rokityanskiy, FOR

  42. Biomass Potentials Dynamic GDP maps (to 2100) Dynamic population density (to 2100) Downscaling Development of bioenergy potentials (to 2100) Consistency of land-price, urban areas, net primary productivity, biomass potentials (spatially explicit)

  43. Scenario Characteristics (World, 2000-2100) *Historical development since 1850 Arnulf Grubler

  44. Emissions & Reduction MeasuresMultiple sectors and stabilization levels Arnulf Grubler

  45. Costs: Energy-sector (left), and Macro-economic (right) vs Baseline and Stabilization Target Uncertainty Arnulf Grubler

  46. Costs of Different Baselines and Stabilization Scenarios Deployment rate of efficiency and low-emission technologies Arnulf Grubler

  47. Emissions and Reductions by Source in the Scenarios(for an illustrative stabilization target of 670 ppmv-equiv) Arnulf Grubler

  48. Emissions & Reduction MeasuresPrincipal technology (clusters) and stabilization targets Improvements incorporated in baselines Emissions reductions due to climate policies Arnulf Grubler

  49. Emission Reduction Measures:Principal technology (clusters) and stabilization targets (0.9 incl. baseline) Arnulf Grubler

  50. More Technological Forecasting and Social Change74(2007) Special Issue Available via ScienceDirect or via: http://www.iiasa.ac.at/Research/GGI/publications/index.html?sb=12 Arnulf Grubler

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