Module 5.1

1. Module 5.1 Mitigation Methods and Tools in the Energy Sector

2. Purpose of this Module • To introduce different approaches for GHG mitigation assessment in the energy sector. • To review the benefits and drawbacks of different approaches. • To introduce various software tools that may be useful for GHG mitigation analysis. • To provide participants with information to help them choose an appropriate tool for their own assessments. • NB: will NOT provide in-depth training in the use of any one tool. • Separate, in-depth training will be likely required for any tools selected.

3. Module 5.1: Energy Sector Mitigation Methods • Approaches for Energy Sector Mitigation Modeling • Review of Modeling Tools • MARKAL • ENPEP-BALANCE • LEAP • RETScreen • Conclusions

4. Module 5.1 a) Approaches for Energy Sector Mitigation Modeling

5. Some Background… • Decision 17/CP.8, para 38: • Based on national circumstances, NA1 Parties are encouraged to use whatever methods are available and appropriate in order to formulate and prioritize programmes containing measures to mitigate climate change and that this should be done within the framework of sustainable development objectives, which should include social, economic and environmental factors.

6. Top-down Use aggregated economic data Assess costs/benefits through impact on output, income, GDP Implicitly capture administrative, implementation and other costs. Assume efficient markets, and no “efficiency gap” Capture intersectoral feedbacks and interactions Commonly used to assess impact of carbon taxes and fiscal policies Not well suited for examining technology-specific policies. Bottom-up Use detailed data on fuels, technologies and policies Assess costs/benefits of individual technologies and policies Can explicitly include administration and program costs Don’t assume efficient markets, overcoming market barriers can offer cost-effective energy savings Capture interactions among projects and policies Commonly used to assess costs and benefits of projects and programs Approaches for Energy Sector Mitigation Assessment

7. Top-Down Assessments (1) • Examine general impact on economy of GHG mitigation. • Important where GHG mitigation activities will cause substantial changes to an economy. • Typically examine variables such as GDP, employment, imports, exports, public finances, etc. • Assume competitive equilibrium and optimizing behavior in consumers and producers. • Should also consider role of informal sector, which may be important in many non-Annex 1 countries. • Can be used in conjunction with bottom-up approaches to help check consistency. • E.g. energy sector investment requirements from a bottom-up energy model used in macroeconomic assessment to iteratively check the GDP forecasts driving the energy model.

8. Top-Down Assessments (2) • Types of top-down approaches: • Simplified macroeconomic assessment: seeks consistency between sectoral forecasts and informs baseline scenarios. • Input-output: captures intersectoral feedbacks but not structural changes in economies (assume no shifts between sectors). • Computable general equilibrium: captures structural changes, assume market clearing. • 2 & 3 require more expertise and more data, which may not be available in many non-Annex 1 countries. • All models are abstractions. Assumptions may not reflect real-world market conditions. • Macroeconomic models tend to be country-specific. Off-the-shelf software not typically available.

9. Bottom-Up Models (Energy Sector) • Optimization Models e.g. MARKAL • Iterative Equilibrium/Simulation Models e.g. ENPEP • Hybrid Modelse.g. MARKAL-MACRO • Accounting Frameworks e.g. LEAP

10. Models for Mitigation Analysis in the UNFCCC Context • UNFCCC Guidelines do not specify which approach is appropriate for national communications on mitigation. • Both Top-Down and Bottom-up models can yield useful insights on mitigation. • Top-down models are most useful for studying broad macroeconomic and fiscal policies for mitigation such as carbon or other environmental taxes. • Bottom-up models are most useful for studying options that have specific sectoral and technological implications. • The lack of off-the-shelf top-down models, the greater availability of physical, sectoral and technological data, and the focus on identifying potential projects has meant that most mitigation modeling has so far focused on bottom-up approaches.

11. Module 5.1b Types of Bottom-Up Models

12. Optimization Models • Use mathematical programming to identify configurations of energy systems that minimize the total cost of providing energy services. • Cost-minimization is performed within constraints (e.g. limits on CO2 emissions, technology availability, foreign exchange, etc.). Constraints also ensure balance of supply and demand. • May optimize over all time periods (perfect foresight) or year-on-year (myopic). • Useful energy services forecast exogenously. • Select among technologies based on their relative costs. • Dual solution yields estimates of energy prices. • Can yield extreme “knife edge” solutions (model allocates all market share to cheapest technology – even if only slightly cheaper) • Must be constrained to yield “reasonable” results: by using “hurdle” rates, by disaggregating demands into more homogenous groups, or by manually constraining market allocations. • Typically assume perfect competition and that energy cost is only factor in technology choice. • Especially useful where many technical options need to be analyzed and future costs are well known. • Cost-minimization assumptions may be inappropriate for simulating “most likely” evolution of real-world energy systems in a baseline scenario. • Data intensive • Calculations are complex making approach hard to apply where expertise is limited. • Examples: MARKAL/TIMES

13. Iterative Equilibrium/Simulation Models • Simulates behavior of energy consumers and producers under various signals (e.g. price, income levels) and constraints (e.g. limits on rate of stock replacement). • Easier to include non-price factors in analysis compared to optimizing models. • Balances demand and supply by calculating market-clearing prices. • Prices and quantities are adjusted endogenously using iterative calculations to seek equilibrium prices. • Behavioral relationships can be controversial and hard to parameterize. Crucial parameters are highly abstracted or poorly known, especially in countries where time series data is lacking. • Example: ENPEP-BALANCE

14. Hybrid Models • Maximizes present value of utility of a representative consumer. • Goes beyond energy system optimization to examine macroeconomic impacts of energy system on the wider economy. • Changes in the energy system can feed-back to effect macroeconomic growth and structure. • A production function allows for substitution among capital, labor and different forms of energy. • Useful energy demands are endogenous to the model. • Example: MARKAL-MACRO

15. Accounting Frameworks • Account for flows of energy in a system based on simple engineering relationships (e.g. conservation of energy). • Rather than simulating decisions of energy consumers and producers, user explicitly accounts for outcomes of those decisions (e.g. as market penetration rates, energy service demands). • Simple, transparent, intuitive & easy to parameterize. • Evaluation and comparison of policies are largely performed externally by the analyst: framework serves primarily as a sophisticated calculator. • Framework ensures physical consistency but not economic consistency. • Example: LEAP

16. Types and Sources of Data

17. Projected Costs of GHG Mitigation • Repetto and Austin (WRI, 1997) compared the results from bottom-up and top-down modeling exercises • Their analysis clearly illustrates the extent to which results depend critically on a handful of key assumptions

18. 2 CO2 Abatement 20 40 60 80 100 0 -2 -4 % Change in GDP -6 -8 -10 -12 Predicted Impacts of CO2 Abatement on U.S. GDP in 2020:162 Projections from 16 models Adapted from Repetto and Austin, WRI, 1997

19. 6 4 2 % CO2 Abatement 0 40 10 20 30 50 60 % Change in GDP -2 Climate change damages averted Air pollution damages averted -4 Revenues recycled efficiently Joint Implementation -6 Increased energy and product substitution Efficient economic responses -8 Non-carbon backstop fuel available Worst Case Assumptions Adapted from Repetto and Austin, WRI, 1997 Changing Assumptions Takes ResultsFrom Costs to Benefits

20. Module 5.1c Review of Modeling Tools

21. Criteria for Inclusion of Tools in this Review Tools must be: • widely applied in a variety of international settings, • thoroughly tested and generally found to be credible, • actively being developed and professionally supported, • primarily designed for integrated energy and GHG mitigation analysis, or screening of energy sector technologies.

22. Included Tools • LEAP • Long-range Energy Alternatives Planning system • Primary Developer: Stockholm Environment Institute • ENPEP • Energy and Power Evaluation Program • Primary Developers: Argonne National Laboratory and the International Atomic Energy Authority (IAEA) • MARKAL and MARKAL-MACRO • MARKet Allocation model • Primary Developers: IEA/ETSAP • RETSCREEN • Renewable Energy Technology Screening • Primary Developers: Natural Resources Canada • All are integrated scenario modeling tools except RETSCREEN, which screens renewable and CHP technologies. • Modeling can also use spreadsheets and/or other tools. • Full Disclosure: Dr. Heaps is the developer of LEAP: reviewed here.

23. Included Tools Compared (1)

24. Included Tools Compared (2)

25. Module 5.1d MARKAL

26. MARKAL and MARKAL-MACRO • Developed International Energy Agency, Energy Technology Systems Analysis Programme (IEA/ETSAP). • Generates energy, economic, engineering, and environmental equilibrium models. • Models are represented as Reference Energy Systems (RES), which describe an entire energy system from resource extraction, through energy transformation and end-use devices, to the demand for useful energy services. • Calculates the quantity and prices of each commodity that maximize either the utility (MARKAL-MACRO) or the producer/consumer surplus (MARKAL) over the planning horizon, thereby minimizing totally energy system cost. • Note: TIMES: “The Integrated MARKAL-EFOM System” is gradually expected to replace MARKAL and MARKAL-MACRO.

27. Energy Economy Capital Needs & Technology Deployment Availability of technologies Constraints on Import and Mining of Energy Demand for Economy MARKAL and Energy Society Services Energy Consumption Ecological effects Emissions Environment Assessing Energy, Economy, Environment & Trade Interactions

28. What Does MARKAL Do? • Identifies least-cost solutions for energy system planning. • Evaluates options within the context of the entire energy/materials system by: • balancing all supply/demand requirements, • ensuring proper process/operation, • monitoring capital stock turnover, and • adhering to environmental & policy restrictions. • Selects technologies based on life-cycle costs of competing alternatives. • Provides estimates of: • energy/material prices; • demand activity; • technology and fuel mixes; • marginal value of individual technologies to the energy system; • GHG and other emission levels, and • mitigation and control costs.

29. What Aspects of Mitigation Assessment Can MARKAL Support? • Macroeconomic policies (e.g. carbon taxes) • Transportation • Energy demand • Energy conversion and supply • Energy sector emissions • Non-energy sector industrial process emissions • Solid waste management • Geological sequestration • Value of carbon rights

30. MARKAL-MACRO • MARKAL-MACRO (M-M) is an extension of the MARKAL model that simultaneously solves the energy and economic systems. • M-M merges the “bottom-up” engineering and “top-down” macroeconomic approaches. • M-M has price responsive demands (i.e., determined endogenously), as does MARKAL-Elastic Demands, while MARKAL does not (i.e., demands are exogenously defined). • M-M maximizes consumer welfare over the solution period, optimizes aggregate investment in the economy and provides least cost energy system configurations to meet endogenously determined demands. • Energy service costs, energy service demands, and energy prices are determined simultaneously during optimization. • Relative energy costs determine types and levels of substitution between energy carriers and technologies.

31. MARKAL-ED: Producer/Consumer Equilibrium for each Commodity w/ Technology Detail

32. MARKAL Requirements • Windows PC with 512 MB RAM. • MARKAL/TIMES source code (written in GAMS) • GAMS modeling language and a Solver • Data Management and Reporting User Interface • Two available: ANSWER and VEDA • Cost of software: US $8,500-$15,000 depending on institutional arrangements.

33. The ANSWER User Interface

34. MARKAL Applications • International Energy Agency (IEA): technology detail for the World Energy Outlook scenarios. • U.S. DOE/SAGE: an analytic framework for the International Energy Outlook. • European Union: 25 state European model: examines externalities and life cycle assessment issues. • Six New England States: Analysis of Clean Air Act goals and support for climate change commitments. • USAID: establishing a common framework for assessing demand-side management. • IEA/ETSAP partner institutions: supporting their national governments planning (Canada, UK, Italy, U.S. DOE & EPA) • China and India: examining reform and energy sector evolution to meet economic development goals, and developing multi-region national models. • APEC: cost-effective levels of renewable generation in 4 APEC economies. • ASEAN: 8 countries participating in a AusAID sponsored energy planning initiative • Three Central America countries: baselines and opportunities within the realm of Climate Change. • Bolivia: GHG reduction strategies, including modeling of forestation as a carbon reduction option. • South Africa: National energy and environmental planning.

35. MARKAL Data Requirements • Useful Energy Demands, and own price elasticities for MED or demand decoupling factors for MACRO • Costs • Resource, investment, fixed, variable, fuel delivery, hurdle rates • Technology Profiles • Fuels in/out, efficiency, availability • Resource supply steps, cumulative resources limits, installed capacity, new investment • Environmental Impacts • Unit emissions per resource, technology, investment • System and other parameters • Discount rate, seasonal/day-night fractions, electric reserve margin

36. MARKAL Support & Training • Technical support offered by phone and email. • Cost is US $500-$2500 depending on institutional arrangements. • Training is offered through ETSAP and its partners in different parts of the world. • A minimum of 2 trainings of 4 days each are recommended, with follow-up support included. • Cost is US $15,000-$40,000 plus expenses.

37. For more information on MARKAL/TIMES • Gary Goldstein • International Resources Group • Sag Harbor, New York, 11963, USA • Phone: +1 (631) 725-1869 • Fax: +1 (631) 725-1869 • Email: ggoldstein@irgltd.com • www.etsap.org

38. Module 5.1e ENPEP-BALANCE

39. ENPEP • The Energy and Power Evaluation Program (ENPEP) is a set of ten integrated energy, environmental, and economic analysis tools. • Here the focus is on one tool, BALANCE, which is most frequently used for the integrated assessment of energy and GHG emissions. • BALANCE is a market-based simulation that determines how various segments of the energy system may respond to changes in energy prices and demands. • BALANCE consists of a system of simultaneous linear and nonlinear relationships that specify the transformation of energy quantities and energy prices through the various stages of energy production, processing, and use. • BALANCE also calculates emissions of GHGs and local air pollutants. • BALANCE can be run in combination with other detailed ENPEP tools, such as MAED and WASP.

40. BALANCE Approach • BALANCE matches the demand for energy with available resources and technologies.  • The user creates an energy network that traces the flow of energy from primary resources to useful energy demands. • Networks are constructed graphically using various nodes and links. • Nodes represent resources, conversion processes, energy demands, and economic processes. • Links connect the nodes and transfer information among nodes.

41. Nodes and Links in BALANCE

42. BALANCE User Interface

43. BALANCE Market Share Simulation • A logit function estimates the market share of supply alternatives. • Market share is sensitive to a commodity’s price relative to the price of alternatives. • Other constraints (e.g., capacity limits), government policies (taxes, subsidies, etc.), and the ability of markets to respond to price signals can also be modeled. • Consumer preferences can also be included via a “premium multiplier” variable. • Simultaneously finds the intersection of supply and demand curves for all energy supply forms and all energy uses in the energy network. • Equilibrium is reached when the model finds the set of market clearing prices and quantities. • The objective is not to minimize costs, but rather, to simulate the response of consumers and producers to changes in energy prices and demand levels and to determine the resulting market equilibrium and its evolution over time.

44. BALANCE CALCULATIONS

45. Other ENPEP Modules • MACRO-E: feedbacks between the energy sector and the wider economy. • MAED: a bottom-up energy demand model.   • LOAD: hourly electric loads and generates load duration curves for use in other ENPEP modules. • PC-VALORAGUA: optimal generating strategy for mixed hydro-thermal electric power systems.   • WASP: least-cost electric generation expansion paths.  • GTMax: marketing and system operational issues in deregulated energy markets.   • ICARUS: reliability and economic performance of alternative electric generation expansion paths. • IMPACTS: physical and economic damages from air pollution (now part of BALANCE). • DAM: a decision analysis tool used to analyze tradeoffs between technical, economic, and environmental concerns.

46. ENPEP Applications • ENPEP has been used extensively in Africa, Asia, Europe and North and South America for a variety of integrated energy analyses. • Numerous countries used ENPEP to help prepare GHG mitigation assessments as part of their national communications to the UNFCCC. • Numerous ENPEP applications are described at the ENPEP web site, in most cases with links to related reports.

47. BALANCE Support & Training • Technical support offered by phone, email, or on-line. • Basic support is free; premium support packages available for up to US $10,000 per year. • Training is offered by the developers on-site or at ANL. • Since 1978, ANL has trained over 1300 experts from over 80 countries. • Minimum of 5 days training is recommend. • Cost is US $10,000 plus expenses.

48. For more information on ENPEP: • Guenter Conzelmann • Center for Energy, Economic, and Environmental Systems Analysis (CEEESA), Argonne National Laboratory (ANL) • 9700 South Cass Avenue, Argonne, IL 60439, USA • Phone: +1 (630) 252-7173 • Fax: +1 (630) 252-6073 • Email: guenter@anl.gov • http://www.dis.anl.gov/ceeesa/programs/enpepwin.html

49. Module 5.1f LEAP: Long-range Energy Alternatives Planning System

50. Long-range Energy Alternatives Planning System • An integrated energy-environment, scenario-based modeling system. • Based on simple physical accounting and simulation modeling approaches. • Flexible and intuitive data management and advanced reporting. • Scope: demand, transformation, resource extraction, GHG emissions and local air pollutants, full system social cost-benefit analysis, non-energy sector sources and sinks. • Annual time-step, unlimited number of years. • Methodology: physical accounting for energy demand and supply via a variety of methodologies. • Optional specialized methodologies for modeling of certain sectors/issues. E.g. stock/turnover modeling for transport analyses. • Links to MS-Office (Excel, Word and PowerPoint). • Low initial data requirements (for example costs not required for simplest energy and GHG assessment). Many aspects optional.