A Tool for Energy Planning and GHG Mitigation Assessment

1. A Tool for Energy Planning and GHG Mitigation Assessment Charlie Heaps, Ph.D. Director, U.S. Center Stockholm Environment Institute

2. Stockholm Environment Institute • An independent international research organization focusing on the issue of sustainable development. • Headquarters in Stockholm, Sweden with centers in the US, UK (York & Oxford), Estonia, and Bangkok. • SEI’s is interdisciplinary in nature: drawing upon engineering, economics, ecology, ethics, operations research, international relations and software design. • SEI conducts applied scientific research: bringing science to policy makers. • Main program areas: climate & energy, water resources & ecological sanitation, atmospheric pollution, risk, livelihoods & vulnerability, sustainable futures. • Apx. 150 staff (21 in the US Center). • Funders include the Swedish and US Governments, multilateral agencies (UNDP, UNEP, UNFCCC. World Bank, foundations (Google, Energy Foundation, etc.)and national & local governments. • SEI-US, the US Center of SEI, is an independent non-profit research institute affiliated with Tufts University in Massachusetts. • Web sites: www.sei-us.org and www.sei.se

3. Part 1: Some Thoughts on Energy Planning

4. Why Energy Planning is Important • General goal: matching supply to demand at reasonable cost. • Energy is an area of the economy where a long-term perspective and active planning and policy-making are vital. • A major driver of emissions and climate change. • A major cause of other environmental impacts • A major economic cost (and vulnerability) and a vital basic need. • A major area of economic vulnerability (energy security) • Tendency toward “natural monopoly” or significant “market power”. • Long life of energy equipment (cars 15-25 years; power plants ~ 50 years; housing ~100 years; urban development has implications for centuries). • Traditional energy policy analyses (e.g. least cost “optimal” planning) are vital but are nor well adapted to the challenges coming in the next few decades: • where social choices may be as important as technical fix • where robust planning rather than optimal solutions are needed • Forecasting with any certainty has proven very difficult.

5. 2008

6. Three ideas about climate economics • Our descendents are important • Uncertainty is inescapable • Some costs are better than others

7. Why do discount rates matter? • A higher discount rate makes it harder to “see” future costs • How much should we pay to prevent $1000 of damages 100 years from now? • Present value of $1000 in 2107… • At 1.5%: $226 • At 3%: $52 • At 6%: $3 • Thus, economic analysis supports active climate mitigation policy with 1.5% discount rate – but not at 3% or 6%!

8. Choosing a discount rate • Market interest rates? • Appropriate for short/medium-term private investments • Need not apply to long-term public policy • Will future generations be richer and need less help? • If they are poorer, will they need more help? • Pure impatience: if all generations are equally wealthy, should we discount the future? • Is your grandchild less valuable than your child, because he/she will be born a generation later? • If both are equally valuable, the “pure impatience” component of discounting should be zero.

9. Three ideas about climate economics • Our descendents are important • Uncertainty is inescapable • Some costs are better than others

10. Average or Worst Case Outcomes? • Traditional economic analysis is based on average predictions • Sea level rise: without catastrophic loss of ice sheets is likely to be less than 1 meter forecast in this century (IPCC 2007) • Even this poses problems for low-lying areas (Bangladesh) • But the most important fears about climate change are often based on worst-case possibilities • Complete loss of the Greenland (or West Antarctic) ice sheet would cause 7 meters of sea level rise. • Catastrophic impacts on most coastal cities, communities. • Will the Greenland ice sheet melt? • Complete melting is unlikely in this century. • But it becomes less unlikely as temperatures rise. • Average: some problems this century • Worst case: increasing probability of catastrophic outcomes.

11. On average, sea walls are not needed..

12. People care a lot about unlikely “worst cases” Insurance is not based on average outcomes Probability of a residential fire in 1 year is < 1% Probability that healthy young parents will die in a year is much less than 1% But people buy fire insurance and life insurance. Insurance is not justified as an economic investment. It is better on average to put your money in a good bank. Probability of enough warming to guarantee loss of Greenland ice sheet is much greater than 1%. Insurance: Planning for the Worst

13. Three ideas about climate economics • Our descendents are important • Uncertainty is inescapable • Some costs are better than others

14. Problems with Conventional Cost-Benefit Analysis • Economic models of climate change are based on conventional cost-benefit analysis: Benefits must exceed costs in order to endorse a policy. • But many benefits cannot meaningfully be measured in dollars (the value of a human life, the extinction of a species, loss of natural systems etc.) • And what do we mean by “costs”? • Pure physical losses (storm damages) • Investment in different industries than we had planned on? • Building sea walls creates jobs (but is essentially a defensive measure) • Letting storms destroy property does not create jobs. • Investing in energy efficiency, helps reduce damages AND also helps make the an economy more productive.

15. Conclusions • Need to focus on multiple goals of energy policy: climate, development, security and not be “lead by the nose” into the future based on blind faith in markets. • Need to identify robust policies – not optimal policies • …Cost effective scenario planning.

16. Why Use a Model? • Reflects complex systems in an understandable format. • Helps to organize large amounts of data. • Provides a consistent framework for testing hypotheses. • Helps to communicate assumptions and beliefs among decision makers and between decision makers and stakeholders. • Helps make decisions more transparent and therefore more open to scrutiny.

17. 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. 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 Energy Sector Assessment Models

18. Top-Down Models • Examine general impact on economy of energy policies. • Typically examine variables such as GDP, employment, imports, exports, public finances, etc. • Assume competitive equilibrium and rational behavior in consumers and producers. • Tend to be country-specific. Off-the-shelf software not typically available. • 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 check the GDP forecasts driving the energy model.

19. Bottom-up Energy Policy Models • Optimization Models • Typically used to identify least-cost configurations of energy systems based on various constraints (e.g. a CO2 emissions target) • Selects among technologies based on their relative costs. • Simulation Models • Simulate behavior of consumers and producers under various signals (e.g. prices, incomes, policies). May not be “optimal” behavior. • Typically uses iterative approach to find market clearing demand-supply equilibrium. • Energy prices are endogenous. • Accounting Frameworks • Rather than simulate the behavior of a system in which outcomes are unknown, instead asks user to explicitly specify outcomes. • Main function of these tools is to manage data and results. • Hybrids Models combining elements of each approach.

20. Optimization Models • Typically uses linear programming to identify energy systems that provide the least cost means of providing an exogenously specified demand for energy services. • Optimization is performed under constraints (e.g. technology availability, supply = demand, emissions, etc.) • Model chooses between technologies based on the costs of delivering energy services.

21. Simulation Models • Simulate behavior of energy consumers and producers under various signals (e.g. price, income levels, limits on rate of stock turnover). • Pros: • Not limited by assumption of “optimal” behavior. • Do not assume energy is the only factor affecting technology choice (e.g. market share algorithms may be based on both price and quality of energy service). • Cons: • Tend also to be complex and data intensive. • Behavioral relationships can be controversial and hard to parameterize. • Future forecasts can be sensitive to starting conditions and parameters.

22. Accounting Frameworks (1) • Physical description of energy system, costs & environmental impacts optional. • Rather than simulating decisions of energy consumers and producers, modeler explicitly accounts for outcomes of decisions • So instead of calculating market share based on prices and other variables, Accounting Frameworks simply examine the implications of a scenario that achieves a certain market share. • Explores the resource, environment and social cost implications of alternative future “what if” energy scenarios. • Example: “What will be the costs, emissions reductions and fuel savings if we invest in more energy efficiency & renewables vs. investing in new power plants?”

23. Accounting Frameworks (2) • Pros: • Simple, transparent & flexible, lower data requirements • Does not assume perfect competition. • Capable of examining issues that go beyond technology choice or are hard to cost. • Especially useful in capacity building applications. • Cons: • Does not automatically identify least-cost systems: less suitable where systems are complex and a least cost solution is needed. • Does not automatically yield price-consistent solutions (e.g. demand forecast may be inconsistent with projected supply configuration).

24. Models vs. Decision Support Systems • Model methodology is only one (albeit important) issue for analysts, planners and decision makers. • They also require the full range of assistance provided by modern decision support systems including: data and scenario management, reporting, units conversion, documentation, and online help and support. • Some modern tools such as LEAP focus as much on these aspects as on the modeling methodology.

25. Part 2: An Introduction to LEAP

26. Long-range Energy Alternatives Planning System • A software tool for energy planning and climate mitigation scenario analysis. • Emphasizes ease-of-use, and intuitive and transparent modeling and data management techniques. • Originally designed for use in developing countries & distributed free to developing country organizations. • Growing number of users in OECD countries. • Hundreds of users in over 160 countries worldwide. • Widely applied by government energy and environmental agencies, in academia (for teaching energy and climate policy) in research institutions, in consulting companies and increasingly in energy utilities. • Recently chosen for use by 85 developing countries for use in their national climate mitigation studies. • www.energycommunity.org

27. Key Characteristics • An integrated energy-environment, scenario-based modeling system. • Based on simple and transparent accounting and simulation modeling approaches. • Broad scope: demand, transformation, resource extraction, GHG & local air pollutant emissions, social cost-benefit analysis, non-energy sector sources and sinks. • Used for Forecasting, energy planning, GHG mitigation assessment, emissions inventories, transport modeling. • Not a model of a particular system, but a tool for modeling different energy systems. • Support for multiple methodologies such as transport stock-turnover modeling, electric sector load forecasting and capacity expansion and econometric and simulation models. • Standard energy and emissions accounting built-in. User can also create their own econometric and simulation models using spreadsheet-like math expressions. • Low initial data requirements: most aspects optional. • Includes a Technology and Environmental Database (TED) containing costs, performance and emissions factors of energy technologies, plus IPCC default emission factors. • Links to MS-Office (Excel, Word and PowerPoint). • Local, national, regional and global applicability. • Medium to long-term time frame, annual time-step, unlimited number of years. • Downloadable data sets under development for most countries.

28. LEAP Calculation Flows

29. Selected LEAP Studies • APEC Energy Demand and Supply Outlook (2006) • China’s Sustainable Energy Future (2003) • America’s Energy Choices (1991) • Toward a Fossil Free Energy Future: The Next Energy Transition (1992) • Prospectiva Energetica de America Latina y el Caribe (2005) • Implementing Renewable Energy Options in South Africa (2007)

30. More LEAP Applications • USA: Greenhouse gas emissions mitigation in California, Washington, Oregon and Rhode Island. • Lawrence Berkeley Nat Labs: constructing a global end-use oriented energy model. • Energy and Carbon Scenarios: Chinese Energy Research Institute (ERI) and LBNL. • Transport Energy Use and Emissions: Various U.S. transportation NGOs (UCS, ACEEE, SEI) and seven Asian Cities (AIT). • Greenhouse Gas Mitigation Studies: 85 countries are using LEAP for their UNFCCC National Communications.SEI is assisting the UN to support countries in this process. APERC Energy Outlook: Energy forecasts for each APEC economy. • East Asia Energy Futures Project: Study of energy security issues in East Asian countries including the Koreas, China, Mongolia, Russia, Japan. • Integrated Resource Planning: Brazil, Malaysia, Indonesia, Ghana, South Africa. • Integrated Environmental Strategies: U.S. EPA initiative that engages developing countries in addressing both local environmental concerns and associated global greenhouse gas emissions. • City Level Energy Strategies: South Africa. • Sulfur Abatement Scenarios for China: Chinese EPA/UNEP. • More at: www.energycommunity.org

31. Minimum Hardware & Software Requirements • Windows 2000, NT, XP, Vista. • Not compatible with Windows 95 or 98 • Not directly compatible with Apple MACs, but can be used if MAC is dual booted with Windows. • 400 Mhz Pentium PC • 1024 x 768 screen resolution. • 128 MB RAM • Optional: Internet connection, Microsoft Office

32. LEAP: Status and Dissemination • Available at no charge to non-profit, academic and governmental institutions based in developing countries. • Download from: www.energycommunity.org • Technical support from web site or leap@sei-us.org • User name and password required to fully enable software. Available on completion of license agreement. • Most users will need training: available through SEI or regional partner organizations. • Check LEAP web site for news of training workshops.

33. Typical Data Requirements

34. A n international initiative sponsored by the Governments of Sweden and the Netherlands to build capacity and foster a community among analysts working on energy and sustainability issues. Managed by SEI in collaboration with regional partners in Africa, Europe and Latin America. Open to all at no charge. Activities: Annual regional training workshops in Africa & Latin America. The COMMEND web site Technical support for energy analysts in developing countries. Development, maintenance and technical support for LEAP software.

35. LEAP: Main Screen

36. View Bar • Analysis View: where you create data structures, enter data, and construct models and scenarios. • Results View: where you examine the outcomes of scenarios as charts and tables. • Diagram View: “Reference Energy System” diagram showing flows of energy in the area. • Energy Balance: standard table showing energy production/consumption in a particular year. • Summary View: cost-benefit comparisons of scenarios and other customized tabular reports. • Overviews: where you group together multiple “favorite” charts for presentation purposes. • TED: Technology and Environmental Database – technology characteristics, costs, and environmental impacts of apx. 1000 energy technologies. • Notes: where you document and reference your data and models.

39. The Tree • The main data structure used for organizing data and models, and reviewing results • Icons indicate types of data (e.g., categories, technologies, fuels and effects) • User can edit data structure. • Supports standard editing functions (copying, pasting, drag & drop of groups of branches)

40. Tree Branches • Category branches are used mainly for organizing the other branches into hierarchical data structures. • End-Use branches indicate situations where energy intensities are specified for an aggregate end-use, rather than with a specific fuel or device.  Primarily used when conducting useful energy analysis. • Technology branches are used to represent final energy consuming devices, and hence when choosing this type of branch you will also need to select the fuel consumed.  The three basic demand analysis methodologies are represented by three different icons: • Activity Level Analysis, in which energy consumption is calculated as the product of an activity level and an annual energy intensity (energy use per unit of activity). • Stock Analysis, in which energy consumption is calculated by analyzing the current and projected future stocks of energy-using devices, and the annual energy intensity of each device. • Transport Analysis, in which energy consumption is calculated as the product of the number of vehicles, the annual average distance traveled per vehicle and the fuel economy of the vehicles. • Key Assumptions branchesare used to indicate independent variables (demographic, macroeconomic, etc.) • In the Transformation tree, fuel branches indicate the feedstock, auxiliary and output fuels for each Transformation module. In the Resource tree, they indicate primary resources and secondary fuels produced, imported and exported in your area . • Effect branches indicate places where environmental loadings (emissions) are calculated.

41. Modeling at Two levels • Basic physical accounting calculations handled internally within software (stock turnover, energy demand and supply, electric dispatch and capacity expansion, resource requirements, costing, pollutant emissions, etc.). • Additional modeling can be added by the user (e.g. user might specify market penetration as a function of prices, income level and policy variables). • Users can specify spreadsheet-like expressions that define data and models, describing how variables change over time in scenarios: • Expressions can range from simple numeric values to complex mathematical formulae. Each can make use of • math functions, • values of other variables, • functions for specifying how a variable changes over time, or • links to external spreadsheets.

42. Top-Level Tree Categories • Key Assumptions: independent variables (demographic, macroeconomic, etc.) • Demand: energy demand analysis (including transport analyses). • Statistical Differences: the differences between final consumption values and energy demands. • Transformation: analysis of energy conversion, extraction, transmission and distribution. Organized into different modules, processes and output fuels. • Stock Changes: the supply of primary energy from stocks. Negative values indicate an increase in stocks. • Resources: the availability of primary resources (indigenous and imports) including fossil reserves and renewable resources. • Non-energy sector effects: inventories and scenarios for non-energy related effects.

43. Expressions • Similar to expressions in spreadsheets. • Used to specify the value of variables. • Expressions can be numerical values, or a formula that yields different results in each year. • Can use many built-in functions, or refer to the values of other variables. • Can be linked to Excel spreadsheets. • Inherited from one scenario to another.

44. Some Expression Examples • Simple Number • Calculates a constant value in all scenario years. • Simple Formula • Example: “0.1 * 5970” • Growth Rate • Example: “Growth(3.2%)” • Calculates exponential growth over time. • Interpolation Function • Example: “Interp(2000, 40, 2010, 65, 2020, 80)” • Calculates gradual change between data values • Step Function • Example: “Step(2000, 300, 2005, 500, 2020, 700)” • Calculates discrete changes in particular years • GrowthAs • Example: “GrowthAs(Income,elasticity) • Calculates future years using the base year value of the current branch and the rate of growth in another branch. • Many others!

45. Four Ways to Edit an Expression: • Type to directly edit the expression. • Select a common function from a selection box. • Use the Time-Series Wizard to enter time-series functions (Interp, Step, etc. and to link to Excel) • Use the Expression builder to make an expression by dragging-and-dropping functions and variables.

46. Scenarios in LEAP • Consistent story-lines of how an energy system might evolve over time. Can be used for policy assumption and sensitivity analysis. • Inheritance allows you to create hierarchies of scenarios that inherit default expressions from their parent scenario. All scenarios inherit from Current Accounts minimizing data entry and allowing common assumptions to be edited in one place. • Multiple inheritance allows scenarios to inherit expressions from more than one parent scenario. Allows combining of measures to create integrated scenarios. • The Scenario Manager is used to organize scenarios and specify inheritance. • Expressions are color coded to show which expressions have been entered explicitly in a scenario (blue), and which are inherited from a parent scenario (black) or from another region (purple).

47. The Scenario Manager

48. Demand Analysis in LEAP • Analysis of energy consumption and associated costs and emissions in an area. • Demands organized into a flexible hierarchical tree structure. • Typically organized by sector, subsector, end-use and device. • Supports multiple methodologies: • End-use analysis: energy = activity level x energy intensity • Econometric forecasts • Stock-turnover modeling

49. Demand Modeling Methodologies • Final Energy Analysis: e = a  i • Where e=energy demand, a=activity level, i=final energy intensity (energy consumed per unit of activity) • Example: energy demand in the cement industry can be projected based on tons of cement produced and energy used per ton. Each can change in the future. • Useful Energy Analysis: e = a  (u / n) • Where u=useful energy intensity, n = efficiency • Example: energy demand in buildings will change in future as more buildings are constructed [+a]; incomes increase and so people heat and cool buildings more [+u]; or building insulation improves [-u]; or as people switch from less efficient oil boilers to electricity or natural gas [+n].

50. Demand Modeling Methodologies (2) • Transport Stock Turnover Analysis: e = s  m / fe • Where: s= number of vehicles (stock), m = vehicle distance, fe = fuel economy • Allows modeling of vehicle stock turnover. • Also allows pollutant emissions to be modeled as function of vehicle distance. • Example: model impact of new vehicle fuel economy or emissions standards.