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.
  
• SEIs 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

1. Our descendents are important
2. Uncertainty is inescapable
3. 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

1. Our descendents are important
2. Uncertainty is inescapable
3. 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
Insurance Planning for the Worst

• People care a lot about unlikely worst cases
• Insurance is not based on average outcomes
• Probability of a residential fire in 1 year is lt
  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.

13
Three ideas about climate economics

1. Our descendents are important
2. Uncertainty is inescapable
3. 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
Energy Sector Assessment Models

• 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
• Dont 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

• 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.

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
Optimization Models (2)

• Pros
• Powerful consistent approach for a common type
  of analysis called Backcasting. E.g. What will be
  the costs of meeting a certain policy goal?
• Especially useful where many options exist. E.g.
  What is the least cost combination of
  efficiency, fuel switching, pollution trading,
  scrubbers and low sulfur coal for meeting a SOx
  emissions cap?
• Cons
• Questionable fundamental assumption of perfect
  competition (e.g., no monopolistic practices, no
  market power, no subsidies, all markets in
  equilibrium).
• Not well suited to simulating how systems behave
  in the real world.
• Assumes energy cost is only factor in technology
  choice. Is a Ferrari the same as a Ford? Tends
  to yield extreme allocations, unless carefully
  constrained.
• Not well suited to examining policy options that
  go beyond technology choice, or hard-to-cost
  options. E.g. To reduce CO2 you can either (a)
  use a large hybrid car, or (b) drive a smaller
  car.
• Relatively complex, opaque and data intensive
  hard to apply for less expert users, so less
  useful in capacity building efforts.

22
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.

23
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?

24
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).

25
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.

26
Tools Compared (1)
27
Tools Compared (2)
28
Part 2 An Introduction to LEAP
29
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

30
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.

31
LEAP Calculation Flows
32
Selected LEAP Studies

• APEC Energy Demand and Supply Outlook (2006)
• Chinas Sustainable Energy Future (2003)
• Americas 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)

33
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

34
LEAP Users Map
35
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

36
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_at_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.

37
Typical Data Requirements
38

• An online community with
• discussion support forums
• online libraries and newsletters
• downloadable software
• training and reference materials
• gt 4000 members in 164 countries.
• www.energycommunity.org

39

• 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.

40
LEAP Main Screen
41
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.

42
(No Transcript)
43
(No Transcript)
44
(No Transcript)
45
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)

46
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 branches are 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.

47
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.

48
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.

49
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.

50
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!

51
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.
  

52
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).

53
The Scenario Manager
54
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

55
Demand Modeling Methodologies

• Final Energy Analysis e a ? i
• Where eenergy demand, aactivity level, ifinal
  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 uuseful 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.

56
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.

57
A Simple Demand Data Structure

• The tree is the main data structure used for
  organizing data and models, and for reviewing
  results.
• Icons indicate the types of data (e.g.,
  categories, technologies, fuels and environmental
  effects).
• Users can edit the tree on-screen using standard
  editing functions (copy, paste, drag drop)
• Structure can be detailed and end-use oriented,
  or highly aggregate (e.g. sector by fuel).
• Detail can be varied from sector to sector.

58
Transformation Analysis in LEAP

• Analysis of energy conversion, transmission and
  distribution, and resource extraction.
• Demand-driven engineering-based simulation.
• Basic hierarchy modules (sectors), each
  containing one or more processes. Each process
  can have one or more feedstock fuels and one or
  more auxiliary fuels.
• Allows for simulation of both capacity expansion
  and process dispatch.
• Calculates imports, exports and primary resource
  requirements.
• Tracks costs and environmental loadings.

59
Standard Transformation Module
60
Simple Transformation Module
61
Electric Generation Simulation

• Two Issues to consider
• Capacity Expansion How much capacity to build
  and when? (MW)
• Dispatch Once built, how should the plants be
  operated? (MW-Hr)

62
Capacity Expansion

• Two ways to specify current and future capacity
• Exogenous Capacity User specifies current and
  future capacity of plants including retirements.
• Endogenous Capacity User specifies types of
  plants to be built but LEAP decides when to add
  plants to maintain a specified planning reserve
  margin.
• NB LEAP is not an optimizing model the
  resulting scenarios may not be a least cost
  optimal strategy.

63
Two Dispatch Modes

• Mode 1 Historical LEAP simply dispatches plants
  based on historical generation.
• Mode 2 Simulation plants dispatched based on
  various dispatch rules ranging from very simple
  ( of total generation) to more sophisticated
  (dispatch by merit order or in order of running
  costs)
• Set the First Simulation Year variable for each
  process to determine when to use historical mode
  and when to use simulation mode.
• You can mix modes and dispatch rules in
  neighboring processes. (e.g. dispatch wind by
  percentage to meet a renewable portfolio
  standard, but dispatch other processes by merit
  order).

64
Understanding Dispatch
65
Electric Generation Dispatch

• Plants are dispatched to meet both total demand
  (in MWh) as well as the instantaneous peak demand
  which varies by hour, day and season.
• User can exogenously specify a load-duration
  curve and LEAP will dispatch plants by merit
  order.
• Alternatively, load shapes be specified for each
  demand device so that the overall system load is
  calculated endogenously. Thus the effect of DSM
  policies on the overall load shape can then be
  explored in scenarios.
• Plant dispatch can also then be varied by season
  (e.g. to reflect how hydro dispatch may vary
  between wet and dry seasons).

66
Hourly Demand Curve

• Hour-by-hour load curve
• Power demand in each hour of the year
• Area Power (kW) x time (1 hour) Energy (kWh)

67
Load Duration Curve

• Rearrange hourly demand curve
• Hours on x-axis is of hours/year that demand is
  greater than or equal to a particular value

68
Load-Duration Curve and System Dispatch in LEAP
69
(No Transcript)
70
Electric Dispatch Calculationsfor an Exogenous
Load Curve
71
Electric Dispatch Calculationsfor an Endogenous
Load Curve
72
Transformation Modules with Feedback Flows
73
Oil Refining Simulation

• Uses the same basic module structure as for
  Electric Generation, but generally have a single
  input fuel (crude) and multiple output fuels
  (gasoline, diesel, kerosene, LPG, fuel oil ,
  etc.)
• Outputs produced in specified proportions, and
  the whole module is run to the point where
  demands for priority products are met (assuming
  module has sufficient capacity).
• Other products are considered by-products and may
  or may not be produced in sufficient quantities.
• User sets simulation rules to tell what LEAP to
  do in situations of surpluses (export or waste)
  and deficits (import or ignore).

74
Simple Refinery Simulation Example
75
Emissions Accounting

• Emission factors for any greenhouse gas or local
  air pollutant can be entered in LEAP and used to
  calculated emissions loadings for any scenario.
• Factors can be specified in any physical unit and
  can be denominated by units of either energy
  consumption or production (e.g. kg/ton of coal)
  or distance driven for transport factors (e.g.
  grams/mile).
• Emission factors can also be specified in terms
  of the chemical composition of fuels (e.g.
  sulfur) so that factors can be corrected if fuel
  composition is different from the default in the
  area of study (e.g. if a country has high sulfur
  coal).
• LEAP can use emission factors entered in the
  accompanying TED database which includes all of
  the default IPCC GHG emission factors.
• Emission results can be shown for individual
  pollutants or summed across all greenhouse gases
  in terms of the overall Global Warming Potentials
  (GWPs).

76
TED The Technology and Environmental Database
77
Social Cost-Benefit Analysis in LEAP

• Societal perspective of costs and benefits (i.e.
  economic not financial analysis).
• Avoids double-counting by drawing consistent
  boundary around analysis (e.g. whole system
  including.
• Cost-benefit analysis calculates the Net Present
  Value (NPV) of the differences in costs between
  two scenarios.
• NPV sums all costs in all years of the study
  discounted to a common base year.
• Optionally includes externality costs.

78
Simple Cost-Benefit Analysis Example

• Two scenarios for meeting future growth in
  electricity lighting demand
• Base Case
• Demand future demand met by cheap incandescent
  bulbs.
• Transformation growth in demand met by new
  fossil fired generating capacity.
• Alternative Case
• Demand DSM programs increase the penetration of
  efficient (but more expensive) fluorescent
  lighting.
• Transformation Slower growth in electricity
  consumption and investments to reduce
  transmission distribution losses mean that less
  generating capacity is required.

79
Simple Cost-Benefit Analysis (cont.)

• The Alternative Case
• uses more expensive (but longer lived)
  lightbulbs.
• Result depends on costs, lifetimes, discount
  rate.
• requires extra capital and OM investment in the
  electricity transmission distribution system.
• Result net cost
• ..requires less generating plants to be
  constructed (less capital and OM costs).
• Result net benefit
• requires less fossil fuel resources to be
  produced or imported.
• Result net benefit
• produces less emissions (less fuel combustion).
• Result net benefit (may not be valued)

80
Energy Balances
An accounting system that describes the flows of
energy through an economy, during a given period.
Non-energy consumption (e.g. petrochemical
feedstock, fertilizers)
Transformation Sectors Losses and Consumption
Imports
Exports
Total Final Energy Use in Consuming Sectors
Net Changes in Stocks
Total Primary Energy Produced
81
Sample IEA Energy Balance
Breakdown by Sector and Activities
Breakdown by Energy Source
82
Energy Balances in LEAP

• Results automatically formatted as standard
  energy balance tables in Energy Balance View.
• Balances can be viewed for any year, scenario and
  region in different units.
• Balance columns can be switched between fuels,
  fuel groupings, years, and regions.
• Balance rows are Demand sectors and
  Transformation modules. Optionally can display
  demand subsectors.
• Display in any energy unit.
• Balance can also be shown in chart or energy flow
  diagram formats.

83
LEAP Energy Balance Table
84
LEAP Energy Balance Diagram
85
Multi-Regional Analysis

• LEAP supports multi-region analyses.
• Regions appear as an extra data dimension.
• Each region shares a similar basic tree structure
  although tree branches can be selectively hidden
  in different regions.
• All results can be summed and displayed across
  regions or aggregated into groups of regions
• Forthcoming LEAP 2007 will support
  inter-regional trade calculations so that import
  requirements for some regions will drive
  production and exports in other regions.

86
Showing Results for a Multi-Region Data Set in
LEAP
87
The Application Programming Interface (API)

• LEAPs API is a standard COM Automation Server
• Other programs can control LEAP changing data
  values, calculating results, and exporting them
  to Excel or other applications.  
• For example, a script could iteratively run LEAP
  multiple times revising input assumptions for
  goal-seeking applications.
• LEAP has a built-in script editor that can be
  used to edit, interactively debug and run scripts
  that use its API.
• LEAP uses Microsoft's ActiveScript technology
  which supports in Visual Basic and JavaScript.

88
LEAP Terminology

• Area the system being studied (e.g. country or
  region).
• Current Accounts the data describing the Base
  Year (first year) of the study period.
• Scenario one consistent set of assumptions about
  the future, starting from the Current Accounts.
  LEAP can have any number of scenarios. Typically
  a study consists of one baseline scenarios (e.g.
  business as usual) plus various counter-factual
  policy scenarios.
• Tree the main organizational data structure in
  LEAP a visual tree similar to the one used in
  Windows Explorer.
• Branch an item on the tree branches can be
  organizing categories, technologies, modules,
  processes, fuels and independent driver
  variables, etc.
• Views The LEAP software is structured as a
  series of different views onto an energy
  system.
• Variable data at a branch. Each branch may have
  multiple variables. Types of variables depend on
  the type of branch, and its properties. In LEAP,
  Variables are displayed as tabs in the Analysis
  view.
• Disaggregation the process of analyzing energy
  consumption by breaking down total demand into
  the various sectors, subsectors, end-uses and
  devices that consume energy.
• Expression a mathematical formula that specifies
  the values of a variable over time at a given
  branch and for a given scenario. Expressions can
  be simple values, or mathematical formula that
  yield different results in different years.

89
When you have a problem

• Post message on LEAP discussion at
  www.energycommunity.org or email leap_at_sei-us.org
• Be as Specific as Possible!
• Include
• Error message (if any)
• Did problem happen during installation or when
  running LEAP?
• What were you doing and what part of LEAP were
  you using when problem occurred?
• Is the problem reproducible and what steps do I
  need to take do that?
• Operating system version (2000, XP, Vista, etc.)
  and language
• Version of LEAP (check Help About)
• If possible include the LEAP.LOG file and attach
  the problem data set as a zip file.

90
Coming Developments

• New optimization methodologies for power sector
  planning. Currently being developed in
  collaboration with the IAEA the IAEAs MESSAGE
  system will be linked to LEAP to provide basic
  optimizing capabilities.
• New national starter level data sets one per
  country available for free download from the
  COMMEND web site (available late 2008)
• Additional translations.
• Let us know YOUR priorities/wishes!

91
Saturation and Share

• Saturation Similar to a market penetration. When
  using this unit all values must be between 0 and
  100, but neighboring values need NOT sum to
  100. For example, 100 of households may use and
  electric stove and 20 may also use a gas stove.
• Share Use this unit to tell LEAP that all
  immediately neighboring branches must sum to
  100. For example, the sum of urban and rural
  percentages should equal 100. In calculations,
  if branches do not sum to 100 LEAP will halt the
  calculations and show an error message.
• When there is only one branch either saturation
  or share can be used.

92
Transport Stock-Turnover Modeling

• In earlier activity level analysis we were always
  dealing with the average characteristics of all
  vehicles on the road (averaged across new and
  old).
• In a stock-turnover analysis we want to reflect
  the different characteristics of of vehicles of
  different ages (vintages).
• Vehicle characteristics will change as vehicles
  get older (emissions profiles, km driven, fuel
  economy, etc.)
• We also want to reflect how transport policies
  affecting new vehicles (e.g. new fuel economy
  standards and emissions standards) will have a
  gradual impact as older vehicles are retired and
  newer vehicles are purchased. So we need to
  model how long vehicles survive on the road.
• Ability to examine fuel switching and
  multi-fueled vehicles independently of transport
  stock turnover,

93
Transport Stock-Turnover Modeling

• Energy calculated as follows
• e s ? m / fe
• Where s number of vehicles (stock), m
  vehicle distance, fe fuel economy
• (NB fuel economy can be defined as either l/100
  km or MPG)
• Emissions can be specified per unit of energy
  consumed or per unit of distance driven (which
  reflects how vehicle emissions are generally
  regulated).

94
Two Dynamics to Consider

• Two dynamics to consider
• How characteristics of new vehicles might evolve
  (e.g. due to new regulations).These changes are
  specified from year to year using LEAPs standard
  expressions (interp, growth, etc.)
• How characteristics of existing vehicles change
  as they get older (so need to keep track of
  number of vehicles of each vintage).These
  changes are specified by vehicle age (vintage)
  from new to old (0, 1, 2, years, etc.) using a
  special lifecycle profile screen.

95
Lifecycle Profiles

• Describe how vehicle characteristics change as
  they get older.
• Used to describe
• Emissions degradation
• Mileage degradation
• Fuel economy degradation
• Survival of vehicles
• Typically start from value of 100 (the
  characteristic of a new vehicle).
• Can be specified using data values, or an
  exponential curve or imported from Excel.

96
Three Typical Approaches for Demand Modeling in
LEAP

• Bottom-Up/End-Use
• Top-down/Econometric
• Decoupled Models

97
Bottom-Up/End-Use Modeling

• Detailed engineering-based accounting for all the
  various sectors/subsectors/end-uses/devices that
  consume energy.
• Pros
• Provides a fundamental understanding of why
  energy is used in an economy thus is probably
  the best approach for thinking about potential
  long-term transitions.
• The best approach for capturing impacts of
  structural shifts and from technology-based
  policies such as energy efficiency.
• Cons
• Data intensive.
• Highly reliant on expertise of analyst for many
  trends and assumptions.
• Hard to capture impacts of fiscal policies (e.g.
  Carbon tax).

98
Top-down/Econometric Modeling

• A more aggregate approach often with energy
  consumption broken down only into sectors and
  fuels.
• Less data intensive but relies on having good
  historical time-series data.
• Consumption trends forecast into future using
  simple historical trends or aggregate econometric
  relationships (GDP, fuel prices, etc.)
• Pros
• Can capture relatively short run impacts of
  fiscal policies (e.g. C tax)
• Cons
• Not well suited to long-range scenarios since the
  exogenous variables (e.g. prices) are themselves
  so poorly known. Not well suited for examining
  technology-based policies.

99
Decoupled Modeling

• A hybrid approach baseline scenario is forecast
  using top-down approach. Alternative scenarios
  are modeled as policy measures that reduce energy
  consumption over time.
• In LEAP, these are entered as negative wedges
  of consumption subtracted from baseline energy
  use in each sector.
• Pros
• Less data intensive than end-use approach, but
  able to capture technology-based policies (unlike
  top-down approach).
• Cons
• Not a full end-use model, so does not give
  insights into how energy system structure might
  change in long-run. Limited to situations where
  measures are a small vs. baseline.

100
Key Assumptions

• Key Assumption Variables are used for creating
  additional user-defined variables such as
  macroeconomic, demographic and other time-series
  variables.
• Can hold exogenous variables (input assumptions)
  and can also be used to calculate intermediate
  results using LEAPs expressions. 
• You can also add your own User Variables which
  are visible in the Demand, Transformation and
  Resource branches, and Indicator Variables which
  are used to calculate additional results after
  all other LEAP calculations are complete.  

101
Indicators

• Optional additional branches in the tree used to
  calculate user-defined results variables.
• Just like Key Assumptions, they are not used
  directly in LEAP's calculations.  
• Unlike Key Assumptions, Indicators are calculated
  after all other LEAP calculations are complete,
  so they can include direct non-lagged references
  to all other data and results variables.  
• Can make use of a series of Indicator Functions
  that calculate normalized comparisons between
  regions and scenarios, (e.g. scores, rankings,
  ratios, etc.).

102
Three Ways to Import from Excel

• Copy a range of data from Excel (Ctrl-V) and then
  paste into a LEAP expression (Ctrl-V). If the
  range has two rows or two columns and includes
  years in the first row/column, then LEAP will
  automatically create an Interp expression for
  those years/values. If there is a single
  row/column, LEAP will prompt you for the years.

• Use the Time-Series Wizard to import data or
  create a dynamic link to a named range in an
  Excel sheet. If importing as a dynamic link,
  LEAP will automatically be updated whenever the
  spreadsheet is changed and saved.

• Use Analysis Menu Import from Excel Export to
  Excel functions to
• Export a blank Excel template containing the LEAP
  data structures and all variables.
• Add your own data to this spreadsheet.
• Import this spreadsheet into LEAP. LEAP will
  automatically import scaling factors, units, data
  and expressions.