Charlie-Seksan LEAP Slides

8/17/2019 Charlie-Seksan LEAP Slides

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A Tool for Energy Planningand GHG Mitigation

AssessmentSeksan Udomsri - KTHCharles Heaps - SEI

Stockholm Environment Institute


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Charles Heaps, Ph.D.

Stockholm Environment InstituteU.S. Center 11 Curtis AvenueSomerville, MA 02144, USA

Tel: +1 (617) 627-3786Fax: +1 (617) 449-9603

Web: www.sei-us.orgEmail: [email protected]

An Independent Research Affiliate of Tufts University

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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.• Main program areas: climate & energy, water resources & ecological

sanitation, atmospheric pollution, risk, livelihoods & vulnerability,

sustainable futures.• Apx. 150 staff (20 in the U.S.).• Funders include the Swedish Government, multilateral agencies,

foundations and national & local governments.• SEI-US is an independent non-profit research institute affiliated with Tufts

University in Massachusetts.

• Web sites: www.sei-us.org and www.sei.se

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Today’s Talk• Part 1: Energy Planning, GHG Mitigation

Assessment and Energy Modeling• Part 2: LEAP Overview

• Part 3: LEAP Demonstration

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Part 1: Energy Planning, GHGMitigation Assessment and

Energy Modeling


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Why Energy Planning isImportant

• 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. – Tendency toward “natural monopoly” for delivery of some energy forms

(electricity power lines, gas pipes, etc.) and often significant “marketpower” of major energy companies. – Long life of energy equipment (cars have ~15 year life; power plants ~

50 years; housing ~100 years; urban development has implications forcenturies).

• Energy planning can therefore have potentially a huge impact onsocieties.

• Forecasting with any certainty has proven very difficult.• Traditional energy policy analyses (e.g. least cost “optimal”

planning) may not be well suited to the coming climate challenge,where social choice may be as important as technical fix, and whererobust planning rather than optimal solutions are needed. 6


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Cost-benefit analysis: pros and cons

• Cost-benefit analysis is now seen as the standard economicmethod for policy analysis – Add up all costs of a policy, and all benefits

– Approve the policy if benefits are greater

• Cost-benefit analysis is a powerful tool – but not the right tool forevery job

• Strong simplifying assumptions make it powerful – but also limit itsapplicability – All costs and benefits must have prices

– Total costs to society are compared to total benefits, regardless ofwho pays or who gains

– When future costs or benefits are uncertain, an average or most likelyvalue is used


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Worst case or average?• Economic analysis often relies on average forecasts – Sea level rise: without catastrophic loss of ice sheets, less

than 1 meter forecast for this century – Will be hard on low-lying areas (Bangladesh, Miami, Venice)

• The greatest fears about climate change are oftenbased on worst-case possibilities

– Complete loss of the Greenland (or West Antarctic) ice sheetwould cause 7 meters of sea level rise

– Catastrophic impacts on most coastal cities, communities

• Will the Greenland ice sheet melt? – Complete melting is still unlikely

– But it becomes less unlikely as temperatures rise

– Average: no problem this century

– Worst case: increasing cause for worry


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Things that won’t happen (soon)


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Why buy insurance?• People care a lot about unlikely “worst cases”

– How much time do you leave to get to the airport?

– Airport security is all about worst case possibilities

• Insurance is not based on average outcomes – The average (US) house has a fire every 250 years (0.4%

probability per year of a residential fire)• But most people have fire insurance – Probability of death next year is less than 1% until age 61; under

0.2% until 40 (US data)• But most young parents have life insurance

• Probability of enough warming to guarantee loss ofGreenland ice sheet is much greater than 1% – Should we buy insurance for the planet?


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On average, sea walls are not needed


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• Uncertainty and catastrophic risk are decisive – Climate policy is insurance against low-probability (but not

impossible) catastrophic events

– By comparison, the “most likely” outcome is irrelevant

• Climate catastrophes are now at least as likely as risks(fire, death) we buy insurance against – Exact probabilities are unknown, but become more likely as

the climate changes• Cost-benefit analysis offers to guard against the risk of

spending “too much” on renewable energy, etc. – This is a very different (less urgent) problem

• The real economic question: what is the least-cost wayto ensure that we prevent global catastrophe?


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IRP vs. Traditional UtilityPlanning

• Traditional energy planning – Focus on demand growth projections – Expansion planning to determine available resources and when

they are needed – Production cost analysis to rank supply options by cost – Calculation of required revenues and rates

• Integrated Resource Planning (IRP) – Meet demand for energy services instead of energy

• Focus on Demand Side Management (DSM) and efficiencyprograms

– Include externalites in decision making• Emissions costs• Social costs

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Traditional Utility PlanningProcess:

Expand supply resources to meet anticipated energy demand growth.

Objectives:

high reliability (wide reserve margins)least cost expansion planning

Results:

Rapid capacity expansionPromotion of demand growthLittle consideration of the necessity for energy efficiency

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Integrated ResourcePlanning

Process:

Integrated assessment of supply and demand-side options in order to

meet the projected demand for energy services.Objectives:

Least total cost (economic + social + environmental)

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Forecasts can be Wrong…

Source: Smil, 2003

Successive 10 year forecasts of U.S. Summer peak electricity demand issued bythe North American Electric Reliability Council (1974-1983)

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Forecasts or Scenarios?Energy Forecasts Energy Scenarios

What is likely? What could be?Under what assumptions?

Approach Rational focus on analysis andoutcomes

Focus on process. strategy andlearning

Objective To develop the most likely pathwayand characterize uncertainty

To develop a number of insightfulpathways that explore uncertainties

Methods Analytical models and driver variables Qualitative stories, quantified andevaluated by models

Treatments of uncertainty Probabilistic methods, statistics andtransparency of assumptions

Exploration of critical uncertainties,and separation of predetermined anduncertain elements in crafting stories

Important Actors Reliance on experts, state andnational planning agencies

Group facilitators, strategists,problem-solvers

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?

?

Where is society going?

forecast

backcast

Where do we want to go?How do we get there?

Forecasting & Backcasting

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Mitigation Assessments• A specific application of Integrated Planning.

• Designed to: – To provide policy makers with an evaluation of technologies and

practices that can mitigate climate change and also contribute tonational development objectives.

– Help us to understand the costs of avoiding climate disruption. – Identify potential project/programme investments.


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Steps in a Mitigation Assessment• Define Time Frame (typically long run)

• Define Scope (energy demand & supply, agriculture, land-use,forestry, solid waste, geological sequestration).

• Define participants and key stakeholders (policy makers, scientificcommunity, NGOs).

• Define desired results.

• Select methodologies consistent with data and expertise availability.

• Standardize key parameters (base year, end year, discount rate,etc.)

• Define project boundaries (consistent with approach used todevelop emissions inventories)

• Define scenarios (typically at least two: “baseline” and “mitigation”)


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Structure of a MitigationAssessment

Source: UNEP Economics of Greenhouse Gas Limitations Guidelines (1999)


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Steps of a MitigationAssessment

Depends on goals, scope & sectors, but has common steps:

1. Collect data.2. Assemble base year/historical data on activities,

technologies, practices and emission factors.3. Calibrate base year to standardized statistics such as

national energy balance or emissions inventory.4. Prepare baseline scenario(s).5. Screen mitigation options.6. Prepare mitigation scenario(s) and sensitivity analyses.

7. Assess impacts (social, economic, environmental).8. Develop Mitigation Strategy.9. Prepare reports.


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Timeframe for Assessments• Ideally, should be long-term to reflect economic lifetime

and potential for stock turnover of major technologies(e.g. 30-40 years in the energy sector).

• But development of long-term projections are verydifficult, especially in developing countries, due touncertainties over future development and limitedstatistical data.

• Nearer term assessments (10-20 years) based onnational plans and sectoral assessments are more

practical for most developing countries.• These nearer term assessments could usefully becomplemented by more aggregate assessments oflonger-term trends.


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Key Study Parameters• Base year of study

• Time horizon• System boundaries

• Costing perspective (societal or market)• Discount rate

• Treatment of avoided emissions:

– Should they be discounted?


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Discounting Over LongTimeframes

• Discounting states that money (excluding inflation) is worth moretoday than tomorrow

• There is criticism of discounting because it devalues thefuture worth of things

0%

10%

20%

30%

40%

50%

60%

70%

80%90%

100%

0 20 40 60 80 100

Future Year

3%10%

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Sustainability and Discounting• Sustainability - "Meeting the needs of the present generation without

compromising the ability of future generations to meet their needs.” -

Brundtland Report• Intergenerational equity and sustainability would seem to indicate

the use of a 0% discount rate – Costs and benefits that occur today or in the future should not be valued

differently.

• Discounting leads to outcomes that may not be equitable – Future costs will be lower than present costs and distant future costs

are close to zero in present terms. – Thus, future costs (e.g. climate change damage, impacts of nuclear

waste) may be undervalued compared with avoiding present costs. – However, substitution of capital implies that money saved today w.ill

grow and can be used to mitigate or adapt to future damages

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Data Collection• Specific data requirements depend on scope and

objectives of study.• Depending on methodology, may need to collect data

only for a base year, but longer historical time seriesdata generally provides a better context and may be

required for econometric analyses.• Decide on level of data disaggregation: avoid temptationto be “data driven”.

• Primary focus should be collation of secondary data, but

some primary data collection may be required andassumptions/judgment will be needed to fill data gaps.


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Key Participants• The development of mitigation assessments will require close cooperation

among a wide range of stakeholders.

• Energy, environment and finance ministries will all likely need to be

involved. Some tasks may be undertaken by outside consultants or theacademic community.

• Expert skills required include: statisticians, energy policy experts, engineers,modelers, statisticians & technical writers.

• However, mitigation assessments are not simply technocratic exercises:they involve much broader judgments about how mitigation activities can fitinto national development priorities.

• Thus, the context for defining mitigation priorities will in large part dependon the process by which priorities are expressed in each country (e.g.

whether priorities are set by the Government alone or in consultation withother stakeholders such as NGOs, industries, the scientific community, etc.)


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Steps of a Mitigation

AssessmentDepends on goals, scope & sectors, but has common steps:



national energy balance or emissions inventory.4. Prepare baseline scenario(s).5. Screen mitigation options.6. Prepare mitigation scenario(s) and sensitivity analyses.7. Assess impacts (social, economic, environmental).8. Develop Mitigation Strategy.9. Prepare reports.


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Baseline Scenarios• Plausible and consistent description of how a system might evolve into the future

in the absence of explicit new GHG mitigation policies.

• Assessments will typically require one or more baseline scenarios: thecounterfactuals against which mitigation measures will be evaluated.

• Critical to a mitigation assessment since mitigation measures are largely judgedon the basis of the incremental costs and benefits relative to the baseline.• Should not be considered a forecast of what will happen in the future, since the

future is inherently unpredictable and depends, in part, on planning and theadoption of policies.

• Highly uncertain over the long run and may be controversial. For example,

should a baseline assume that the Millennium Development Goals will actually bemet, and if so what does this imply for the energy systems of the poorestcountries?

• Ideally, multiple baselines should be constructed to reflect uncertainties(sensitivity analysis). Each baseline requires separate mitigation analyses.

• Baselines should not be simple extrapolations of current trends: they shouldconsider likely evolution of activities that effect emissions and sinks including:

– Macroeconomic and demographic trends.

– Structural shifts in the economy

– Evolution of technologies and practices, (saturation effects, likely adoption of

efficient technologies).


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Screening Mitigation Options• Enables a rough assessment of the potential feasibility of options.• Particularly important when using bottom-up methodologies in which a wide

range of technologies and policies need to be considered.• May include a quantitative assessment of the mitigation potential (T CO2)

and cost of saved carbon ($/TC) of each option. May also include qualitativefactors.

• One approach is to prepare a matrix and assign scores or rankings tooptions in order to identify those options that need to be included in the

more in depth analysis.• Gives the opportunity to explicitly consider a comprehensive set of optionswhile reducing the level of effort required in the later more in-depthmitigation analysis.

• Reduces likelihood of overlooking important options.• Screening criteria should be consistent with overall framing of mitigation

scenario.


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Possible Screening Criteria• Potential for large impact on greenhouse gases (GHGs)• Consistency with national development goals

• Consistency with national environmental goals, such as: – emissions reduction of local air pollutants – effect on biodiversity – soil conservation – watershed management – indoor air quality, etc.

• Potential effectiveness of implementation policies• Sustainability of an option• Data availability for evaluation• Institutional considerations such as:

– Institutional capacity needed (data collection, monitoring, enforcement,

permitting, etc.) – Political Feasibility – Replicability (adaptability to different geographical, socio-economic-cultural,

legal, and regulatory settings)• Other sector-specific criteria


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Screening MatrixExamples of Criteria Mitigation Option 1 Option 2 Option 3

Mitigation Potential Tonnes CO2,

score or rankin low mediumDirect Costs $/Tonne, C/B ratio, score or ranking

Indirect Costs

- Increase in domestic employment Score or ranking

- Decrease in import payments Score or ranking

Consistency with Development Goals

- Potential for wealth generation Score or ranking

- Consistency with MDGs Score or ranking

Consistency with Environmental Goals

-Potential for reducing air, water and other pollution Score or ranking

Long term sustainability of option Score or ranking

Data

-Availability Score or ranking -Quality Score or ranking

Feasibility (political, social, technical) Score or ranking


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Screening with Cost Curves• A technique for screening

and ranking GHG mitigationoptions.

• Plot GHG reduction fromsuccessive mitigation options(e.g. tonnes of CO2 avoided)against cost per unit of GHGreduction (e.g. $/tonne).

• Area under curve yields totalcost of avoided emissions.• Need to consider

interdependencies among

Source: Sathaye & Meyers. Greenhouse Gas Mitigation Assessment: A Guidebook (1995)


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Three Approaches toDeveloping Cost Curves

• Partial approach• Retrospective systems approach

• Integrated approach


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The Partial Approach• Each technology is evaluated separately

and compared to some referencetechnology.

• Overall emissions reductions and costsare created by combining options whileassuming no interaction between options.


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The Retrospective System

Approach• Step 1: Independent ranking of options (like partial

approach)

• Step 2: Include most cost effective option in a scenarioand then recalculate costs and emission reductions forall other options.

• Step 3: Include next option and recalculate.• Continue until cost curve meets mitigation objectives.

• Takes into account interdependencies between eachoption and previous options on the curve.

• May not account for impacts that more expensiveoptions have on cheaper options already chosen.


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Integrated Approach• Requires an integrated model that can chose marginal

options based on their cost per emission reduction.• Automatically develops least cost curves within technical

parameters and model constraints.

• Fully accounts for interdependencies among options.• Powerful but complex modeling process – also may bedifficult to equate reductions with specific options (i.e.points on cost curve are due to some interaction of

options). – This is important when considering who pays for an option (or who gets

the rewards from a CDM investment).

– Need to trade-off accuracy vs. complexity. The complexity of assessingoptions when dealing with theoretical counterfactuals makes for high


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Mitigation Scenarios• Reflect a future in which explicit policies and measures

are adopted to reduce the sources (or enhance thesinks) of GHGs.

• Mitigation scenarios should take into account thecommon but differentiated responsibilities of the Parties

and the specific national and regional developmentpriorities, objectives and circumstances.

• Mitigation scenarios should not simply reflect currentplans. Instead they should assess what would behypothetically achievable based on the goals of thescenario.


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Steps in Constructing

Mitigation Scenarios

• Establish framing.• Create option portfolios (identify synergistic and/or mutually

exclusive options & double counting), estimate penetrationrates.

• Construct integrated scenarios using chosen modelingmethodology.

• Calculate overall costs, benefits and GHG mitigation potential.


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Mitigation ScenariosScenario frameworks may include:

– An emission reduction target…• relative to the baseline,• relative to emissions in some historical year, or

• Relative to some indicator such as CO2/capita or CO2/$

– All options up to a certain cost per unit of emissionsreduction (equivalent to a carbon tax).

– “No regrets” (cost-effective options only).

– Specific options or technologies: included based onperceived technical and/or political feasibility.


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Part 2: Modeling Methods anda LEAP Overview

se o o e s n ga on


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se o o e s ga o

Assessments: Why Use aModel?

• Reflects complex systems in anunderstandable form.

• Helps to organize large amounts of data.• Provides a consistent framework for

testing hypotheses.

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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 theGDP forecasts driving the energy model.

Energy Sector Assessment


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Energy Sector Assessment

ModelsTop-down

• Use aggregated economic data

• Assess costs/benefits throughimpact 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 administrationand program costs

• Don’t assume efficient markets,overcoming market barriers canoffer cost-effective energy savings

• Capture interactions among

projects and policies• Commonly used to assess costs

and benefits of projects andprograms


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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.

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Optimization Models (1)• 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 their lifecycle

costs.• Least-cost solution also yields estimates of energy prices (the

“dual” solution).

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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 costcombination of efficiency, fuel switching, pollution trading, scrubbers and lowsulfur 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 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 largehybrid 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.

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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 to be complex and data intensive. – Behavioral relationships can be controversial and hard to

parameterize.

– Future forecasts can be sensitive to starting conditions andparameters.

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Accounting Frameworks (1)• Physical description of energy system, costs & environmental

impacts optional.

• Rather than simulating decisions of energy consumers andproducers, modeler explicitly accounts for outcomes of decisions

• So instead of calculating market share based on prices and other variables, Accounting Frameworks simply examine theimplications of a scenario that achieves a certain market share.

• Explores the resource, environment and social cost implications ofalternative future “what if” energy scenarios.

• Example: “What will be the costs, emissions reductions and fuelsavings if we invest in more energy efficiency & renewables vs.investing in new power plants?”

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Accounting Frameworks (2)• Pros:

– Simple, transparent & flexible, lower data requirements

– Does not assume perfect competition.

– Capable of examining issues that go beyond technologychoice 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 supplyconfiguration).

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Hybrid Models• Many current generation models combine

elements of optimization, simulation andaccounting: – LEAP operates at two levels: basic accounting

relationships are built-in and users can add theirown models on top.

– The U.S. National Energy Modeling System (NEMS)includes optimization modules for the electricity

sector, along with simulation approaches for eachdemand sector, all packaged together into a generalequilibrium system.

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Models vs


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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 providedby modern decision support systems including: data andscenario 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.

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Tools Compared (1)


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Tools Compared (1)Characteristic LEAP ENPEP (BALANCE) MARKAL MARKAL-MACRO RETSCREEN

Developer Stockholm Environment

Institute Argonne/IAEA Natural Resources Canada

Home page www.energycommunity.org www.dis.anl.gov www.retscreen.net

ScopeIntegrated energy

and GHG scenarios

Integrated energy

and GHG scenarios

Integrated energy

and GHG scenarios

Integrated energy-

economy and GHG

scenarios

Screening of renewable

and CHP projects

Methodology

- Model type Accounting & spreadsheet-like Equilibrium simulation Optimization Hybrid Accounting

- Soution algorithm Accounting Iteration Linear programming Non-linear programming Accounting

- Foresight n/a myopic Perfect or myopic Perfect or myopic n/a

Geographic applicability Local, national, regional, globalLocal, national, regional,

globalLocal

Data requirements Low-medium Medium-high Technology specific

Default data included

TED Database with costs,

performace and emission

factors (inc. IPCC factors).

Coming soon: national energy

& GHG baselines.

IPCC Emission factorsExtensive defaults: weather

data, products, costs, etc.

Time Horizon User Controlled. Annual resultsUp to 75 years. Annual

resultsPrimarily static analysis

IEA/ETSAP

Medium-high

None

Local, national, regional, global

User Controlled,

Typically reporting for 5 or 10 year time periods

www.etsap.org

Tools Compared (2)


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Tools Compared (2)Characteristic LEAP ENPEP (BALANCE) RETSCREEN

Expertise required Medium High Low

Level of effort required Low-Medium High Low

How Intuitive? (matching

analyst's mental model)

High Low High

Reporting capabilities Advanced Basic Excel

Data management capabilities Advanced Basic Excel

Software requirements Windows Windows Excel

Software cost:Free to NGO, Govt andresearchers in non-OECD

countries.

Free to NGO, Govtand researchers.

Free

Typical training required

& cost

On request: 5 days/$5000

Also regular international

workshops.

5 days

$10,000

Minimal

Free distance learning &

global network of trainers

Technical support

& Cost:

Phone, email or web forum

Free limited support.

Phone or email

$10,000 for 80 hours

Email or web forum

Free limited support.

Reference materialsManual & training materials

free on web site

Manual available

to registered users

Manuals free

on web site

LanguagesEnglish, French, Spanish,

Portuguese, ChineseEnglish Multiple

Medium

High

High

MARKAL/MARKAL-MACRO

Manual available

to registered users.

Phone or email

$500-$2500 for one year.

English

8 days

$30,000-$40,000

$8,500-$15,000(including GAMS, solver & interface)

Windows, GAMS, solver & interface

Basic

Basic

L E


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• A software tool for energy planning and climate mitigation scenarioanalysis.

• Emphasizes ease-of-use, and intuitive and transparent modelingand data management techniques.

• Originally designed for use in developing countries & distributed freeto developing country organizations.

• Growing number of users in OECD countries.

• Many hundreds of users in over 150 countries.• Widely applied by government energy and environmental agencies,in academia (for teaching energy and climate policy) in researchinstitutions and increasingly in energy utilities.

• Recently chosen for use by 85 developing countries for use in theirnational climate mitigation studies.

• www.energycommunity.org

Long-range Energy

Alternatives Planning System

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Key


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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.• Low initial data requirements: most aspects optional.• Includes a Technology and Environmental Database 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.

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LEAP Calculation Flows


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Demographics Macro-

Economics

Demand

Analysis

Transformation

Analysis

Statistical

Differences

StockChanges

Resource

Analysis

I n t e gr a t e d C o s t -B

en ef i t A n al y s i s

E n v i r o n m e

n t a l L o a d i n g s

( P o l l u t a n

t E m i s s i o n s )

Non-Energy Sector

Emissions Analysis

Environmental

Externalities

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Selected LEAP Studies


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Selected LEAP Studies• APEC Energy

Demand and SupplyOutlook (2006)

• China’s SustainableEnergy Future(2003)

• America’s EnergyChoices (1991)

• Toward a FossilFree Energy Future:The Next EnergyTransition (1992)

• ProspectivaEnergetica de

America Latina y elCaribe (2005)

• ImplementingRenewable EnergyOptions in SouthAfrica (2007)

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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) andLBNL.

• Transport Energy Use and Emissions: Various U.S. transportation NGOs (UCS,ACEEE, SEI) and seven Asian Cities (AIT).

• Greenhouse Gas Mit igation Studies: 85 countries are using LEAP for their

UNFCCC National Communications. SEI is assisting the UN to support countries inthis process. APERC Energy Outlook: Energy forecasts for each APEC economy.

• East Asia Energy Futures Project: Study of energy security issues in East Asiancountries 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 globalgreenhouse gas emissions.

• City Level Energy Strategies: South Africa.• Sulfur Abatement Scenarios for China: Chinese EPA/UNEP.

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LEAP Users Map


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p

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Minimum Hardware & Software


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Requirements

• Windows 98, 2000, NT, XP, Vista.

• 400 Mhz Pentium PC, 1024 x 768 screen resolution.

• 64 MB RAM

• Internet Explorer 4.0 or later

• Optional: Internet connection, Microsoft Office

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Status and Dissemination• Available at no charge to non-profit, academic and

governmental institutions based in developing

countries.• Download from http://www.energycommunity.org

• Technical support from web site or [email protected]

• User name and password required to fully enablesoftware. Available on completion of licenseagreement.

• Most users will need training: available through SEI orregional partner organizations.

• Check LEAP web site for news of training workshops.

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Typical Data Requirementsac ro ec o n o m c a r a es

S e c t o r a l d r i v i n g v a r i a b l e s GDP/value added, population, household sizeM o r e d e t a i l ed d r i v i n g v a r i a b l e s Production of energy intensive materials (tonnes or $ steel);

transport needs (pass-km, tonne-km); income distribution, etc.E n e r g y D e m a n d D a t aS e c to r a n d s u b s e c t o r t o t al s Fuel use by sector/subsectorE n d - u s e an d t e c h n o l o g yc h a r a c t e r i s t i c s b y s e c t o r / s u b s e c t o r

a) Usage breakdown by end-use/device: new vs. existingbuildings; vehicle stock by type, vintage; or simpler breakdowns;b) Technology cost and performance

P r ic e a n d i n c o m e r e s p o n s e ( o p t io n a l ) Price and income elasticities

E n e r g y S u p p l y D a t aC h a r a c t e r i s t ic s o f e n e r g y s u p p l y ,t r a n s p o r t , a n d c o n v e r s i o n f a c i l it i es

Capital and O&M costs, performance (efficiencies, capacityfactors, etc.)

E n e r g y s u p p l y p l a n s New capacity on-line dates, costs, characteristics;E n e r g y r e s o u r c e s a n d p r i c es Reserves of fossil fuels; potential for renewable resources

T ec h n o l o g y O p t io n sT e c h n o l o g y c o s t s an d p e r f o r m a n c e Capital and O&M costs, foreign exchange, performance

(efficiency, unit usage, capacity factor, etc.)P e n e t r a ti o n r a t es Percent of new or existing stock replaced per year

A d m in is t ra t iv e an d p ro g r am c o s ts

E m i s s i o n F ac t o r s Emissions per unit energy consumed, produced, or transported.

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• An online community with: – discussion & support forums – online libraries and newsletters – downloadable software – training and reference materials

• > 2500 members in 150countries.

• www.energycommunity.org

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Main Screen


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ewBar


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• Analysis View: where you create data structures, enter data, and constructmodels and scenarios.

• Results View: where you examine the outcomes of scenarios as charts andtables.

• Diagram View: “Reference Energy System” diagram showing flows of energyin the area.

• Energy Balance: standard table showing energy production/consumption in aparticular year.

• Summary View: cost-benefit comparisons of scenarios and other customized

tabular reports.

• Overviews: where you group together multiple “favorite” charts for presentationpurposes.

• 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.

Bar

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The Tree


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• The main data structureused for organizing dataand models, and reviewing

results• Icons indicate types of data(e.g., categories,technologies, fuels and

effects)• User can edit data

structure.

• Supports standard editingfunctions (copying, pasting,drag & drop of groups ofbranches)

Tree Branches


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• Category branches are used mainly for organizing the other branches into hierarchical datastructures.

• End-Use branches indicate situations where energy intensities are specified for anaggregate end-use, rather than with a specific fuel or device. Primarily used whenconducting useful energy analysis.

• Technology branches are used to represent final energy consuming devices, and hencewhen choosing this type of branch you will also need to select the fuel consumed. The threebasic demand analysis methodologies are represented by three different icons:

– Activity Level Analysis, in which energy consumption is calculated as the product ofan activity level and an annual energy intensity (energy use per unit of activity).

– Stock Analysis, in which energy consumption is calculated by analyzing the currentand projected future stocks of energy-using devices, and the annual energy intensity ofeach device.

– Transport Analysis, in which energy consumption is calculated as the product of thenumber of vehicles, the annual average distance traveled per vehicle and the fueleconomy 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 fuelsfor each Transformation module. In the Resource tree, they indicate primary resources andsecondary fuels produced, imported and exported in your area .

• Effect branches indicate places where environmental loadings (emissions) are calculated.74


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Modeling at Two levels1. Basic physical accounting calculations handled internally withinsoftware (stock turnover, energy demand and supply, electricdispatch and capacity expansion, resource requirements, costing,

pollutant emissions, etc.).2. Additional modeling can be added by the user (e.g. user might

specify market penetration as a function of prices, income leveland 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 of1. math functions,2. values of other variables,

3. functions for specifying how a variable changes over time, or 4. links to external spreadsheets.

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Top-Level Tree Categories


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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 valuesand energy demands.

• Transformation: analysis of energy conversion, extraction, transmissionand 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-energyrelated effects.

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Expressions• Similar to expressions in spreadsheets.

• Used to specify the value of variables. Expressions can be

numerical values, or formulae that yield different results in eachscenario year.

• Can use many built-in functions, or refer to the values of othervariables.

• Can be linked to Excel spreadsheets.• Inherited from one scenario to another.

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• 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 currentbranch and the rate of growth in another branch.

• Many others!

Some Expression Examples

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Four Ways to Edit an Expression:


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y p

• Type to directly edit the

expression.• Select a common function from aselection box.

• Use the Time-Series Wizard toenter time-series functions (Interp,Step, etc. and to link to Excel)

• Use the Expression builder tomake an expression by dragging-and-dropping functions and

variables.

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Scenarios in LEAP• Consistent story-lines of how an energy system might evolve overtime. 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 inheritfrom Current Accounts minimizing data entry and allowing commonassumptions to be edited in one place.

• Multiple inheritance allows scenarios to inherit expressions frommore than one parent scenario. Allows combining of measures to

create integrated scenarios.• The Scenario Manager is used to organize scenarios and specifyinheritance.

• Expressions are color coded to show which expressions have beenentered explicitly in a scenario (blue), and which are inherited from a

parent scenario (black) or from another region (purple).

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The Scenario Manager


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g

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Demand Analysis in LEAP


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y

• Analysis of energy consumption and associatedcosts and emissions in an area.

• Demands organized into a flexible hierarchicaltree structure.

• Typically organized by sector, subsector, end-use and device.

• Supports multiple methodologies:

– End-use analysis: energy = activity level x energyintensity

– Econometric forecasts

– Stock-turnover modeling82

Demand Modeling Methodologies


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g g

1. 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 usedper ton. Each can change in the future.

2. Useful Energy Analysis: e = a × (u / n) – Where u=useful energy intensity, n = efficiency

– Example: energy demand in buildings will change in future asmore buildings are constructed [+a]; incomes increase and so

people heat and cool buildings more [+u]; or building insulationimproves [-u]; or as people switch from less efficient oil boilersto electricity or natural gas [+n].

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Demand Modeling Methodologies


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(2)3. 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 bemodeled as function of vehicle distance.

• Example: model impact of new vehicle fueleconomy or emissions standards.

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A Simple Demand Data Structure


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Households(8 million)

Cooking

(100%)

Refrigeration

(80%)

Lighting(100%)

Exist ing (80%, 400 kWh/yr)Urban

(30%)

Rural(70%)

Effic ient (20%, 300kWh/yr)

Other

(50%)

Electrified

(100%)

Electrified(20%)

Non-Electrified

(80%)

• The tree is the main data structure used for organizing dataand 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 highlyaggregate (e.g. sector by fuel).

• Detail can be varied from sector to sector.

Transformation Analysis in


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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 fuelsand 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.

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Standard TransformationModule Auxiliary Fuel Use


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Output

Fuel

Output

Fuel

OutputFuel

Output

Fuel

Module

Dispatch

Process

(efficiency)

Co-Product

Fuel (e.g Heat)

Feedstock Fuel

Feedstock Fuel

Process

(efficiency)

Feedstock Fuel

Feedstock Fuel

Process

(efficiency)

Feedstock Fuel

Feedstock Fuel

Process(efficiency)

Feedstock Fuel

Feedstock Fuel

Process

(efficiency)

Feedstock Fuel

Feedstock Fuel

Output

Fuel

Auxiliary Fuel Use

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Simple Transformation Module


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Transformation Modules with Feedbacks


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Electric Generation Simulation


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• Examines both Capacity Expansion (MW) and PlantDispatch (MWh).

– Exogenous Capacity: User specifies current and possible futurecapacity of plants (MW)

– Endogenous Capacity: User specifies types of plants to be builtbut LEAP decides when to add plants to maintain a specified

planning reserve margin.• Two Modes of Dispatch simulation:

– Mode 1: Historical: LEAP simply dispatches plants based onhistorical generation.

– Mode 2: Simulation: plants dispatched based on variousdispatcxh rules ranging from very simple (% of total generation)to quite sophisticated (dispatch in order of running costs)

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Electric Generation (2)


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• Plants are dispatch to meet both total demand (in MWh)as well as the instantaneous peak demand which varies

from 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 demanddevice so that the overall system load is calculatedendogenously. Thus the effect of DSM policies on theoverall load shape can then be explored in scenarios.

• Plant dispatch can also then be varied by season (e.g. toreflect how hydro dispatch may vary between wet anddry seasons).

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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)

1 2 3 4 5 6 7 8 9 10 ÉÉ ÉÉ ÉÉ 8759 8760

hour number in year

L d D ti C


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1 2 3 4 5 6 7 8 9 10 ÉÉ ÉÉ ÉÉ 8759 8760

hour number in year

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

Load-Duration Curve and

S t Di t h i LEAP


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System Dispatch in LEAP

Intermediate

Load Plants

Baseload

Plants

Peak Load

Plants

Capacity (MW) * MCF

Hours Sorted from Highest to

Lowest Demand

8,5008,0007,5007,0006,5006,0005,5005,0004,5004,0003,5003,0002,5002,0001,5001,0005000

P e r c e n t o f P e a k

L o a d

100

95

90

85

80

75

70

65

60

55

50

4540

35

30

25

20

1510

5

0

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Oil Refining Simulation


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• Uses the same basic module structure as for ElectricGeneration, 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 thewhole module is run to the point where demands for“priority products” are met (assuming module hassufficient 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). 95

Si l R fi Si l ti E l


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Simple Refinery Simulation Example

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Emissions Accounting


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• Emission factors for any greenhouse gas or local air pollutant can beentered in LEAP and used to calculated emissions loadings for anyscenario.

• Factors can be specified in any physical unit and can bedenominated 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 chemicalcomposition of fuels (e.g. sulfur) so that factors can be corrected iffuel 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 TEDdatabase which includes all of the default IPCC GHG emissionfactors.

• Emission results can be shown for individual pollutants or summedacross all greenhouse gases in terms of the overall Global Warming

Potentials GWPs .97

TED:The Technology and Environmental

Database


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Database

Technologies

Demand

Conversion

Transmission &Distribution

Supply:Extraction

Information Pages Technology Data CostData EnvironmentalImpacts

Fields

Database Contents

NotesReference

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Social Cost-

Benefit Analysis in

Demand

(costs of saved energy,d i t th f l


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Benefit Analysis inLEAP• Societal perspective of costs

and benefits (i.e. economic notfinancial analysis).

• Avoids double-counting bydrawing consistent boundaryaround analysis (e.g. whole

system including.• Cost-benefit analysis calculates

the Net Present Value (NPV) ofthe differences in costs betweentwo scenarios.

• NPV sums all costs in all yearsof the study discounted to acommon base year.

• Optionally includes externalitycosts.

( gydevice costs, other non-fuel

costs)

Transformation

(Capital and O&M costs)

Primary Resource Costsor

Delivered Fuel Costs

Environmental

Externality Costs99

Simple Cost-Benefit Analysis

Example


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ExampleTwo scenarios for meeting future growth in electricity lighting demand:

1. Base Case – Demand: future demand met by cheap incandescent bulbs.

– Transformation: growth in demand met by new fossil firedgenerating capacity.

2. Alternative Case

– Demand: DSM programs increase the penetration of efficient(but more expensive) fluorescent lighting.

– Transformation: Slower growth in electricity consumption andinvestments to reduce transmission & distribution losses meanthat less generating capacity is required.

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Simple Cost-Benefit Analysis (cont.)


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• The Alternative Case…

• …uses more expensive (but longer lived) lightbulbs.

• Result: depends on costs, lifetimes, & discount rate.• …requires extra capital and O&M investment in the electricitytransmission & distribution system.

• Result: net cost

• ..requires less generating plants to be constructed (less capital andO&M 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)

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Energy BalancesAn accounting system that describes the flows of energy through an


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Net Changesin Stocks

P+ I − X = L +C F +C

NE + DS

Non-energy consumption(e.g. petrochemicalfeedstock, fertilizers)Imports

Exports

Transformation SectorsLosses and Consumption

Total PrimaryEnergy Produced

Total Final EnergyUse in ConsumingSectors

An accounting system that describes the flows of energy through aneconomy, during a given period.

Sample IEA Energy BalanceBreakdo n b


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Breakdown bySector andActivities

Breakdownby EnergySource

Energy Balances in LEAP


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• Results automatically formatted as standard energybalance tables in Energy Balance View.

• Balances can be viewed for any year, scenario andregion in different units.

• Balance columns can be switched between fuels, fuelgroupings, years, and regions.

• Balance rows are Demand sectors and Transformationmodules.

• Display in any energy unit.

• Balance can also be shown in chart or energy flowdiagram formats.

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LEAP Energy Balance Table


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LEAP Energy Balance Diagram


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Multi-Region Analysis


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• LEAP supports multi-region analyses.

• Regions appear as an extra data dimension.

• Each region shares a similar basic tree structurealthough tree branches can be selectivelyhidden in different regions.

• All results can be summed and displayed acrossregions or aggregated into groups of regions

• Forthcoming: LEAP 2007 will support inter-regional trade calculations so that importrequirements for some regions will driveproduction and exports in other regions. 107

Showing Results for a Multi-

Region Data Set in LEAP


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Region Data Set in LEAP

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The Application Programming

Interface (API)


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Interface (API)• LEAP’s API is a standard COM Automation Server • Other programs can control LEAP: changing data

values, calculating results, and exporting them to Excelor other applications.

• For example, a script could iteratively run LEAP multipletimes revising input assumptions for goal-seekingapplications.

• 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 whichsupports in Visual Basic and JavaScript.

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LEAP Terminology• Area: the system being studied (e.g. country or region).


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• 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 theCurrent 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 oneused 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 ofvariables 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 downtotal demand into the various sectors, subsectors, end-uses and devices thatconsume energy.

• Expression: a mathematical formula that specifies the values of a variable over timeat a given branch and for a given scenario. Expressions can be simple values, or 110

When you have a problem…• Post message on LEAP forum at


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• Post message on LEAP forum atwww.energycommunity.org or email [email protected]

• 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 usingwhen problem occurred?

– Is the problem reproducible and what steps do I need to take dothat?

– 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 problemdata set as a zip file. 111


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Part 3: LEAP Demonstration

Exercises• Two sessions on Wednesday (26th) at 13-17 (group A) and Thursday


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(27th) at 8:00-12.00 (group B) in Trotter (Trötter, Brinellväg 64).

• You will work in groups of 2-3 per PC.

• Each session is a 2 part exercise: – Part 1: A screening exercise: using Excel to do simple “screening” of

various GHG mitigation options.• You will be provided with a partly completed Excel spreadsheet that you will

need to complete.

• Work in groups to develop a screening matrix. – Part 2: Preparing an integrated mitigation assessment using LEAP (and

options assessed in part 1)

• Lab evaluation: this will be described in the lab.

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• An online initiative designed tofoster a community among analystsworking on energy and

sustainability issues.• Managed by SEI in collaborationwith regional partners in Africa,Europe and Latin America.

• Open to all at no charge.• Activities:

– Annual regional training workshopsin Africa & Latin America.

– The COMMEND web site – Technical support for energy

analysts in developing countries. – Development, maintenance and

tech support for SEI’s LEAPsoftware.

– Semi-annual newsletter(reCOMMEND)

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