Module 5.1
Mitigation Methods and Tools in
the Energy Sector
5.1.1
Purpose of this Module
• To introduce different approaches for GHG mitigation
assessment in the energy sector.
• To review the benefits and drawbacks of different
approaches.
• To introduce various software tools that may be useful
for GHG mitigation analysis.
• To provide participants with information to help them
choose an appropriate tool for their own assessments.
• NB: will NOT provide in-depth training in the use of any
one tool.
• Separate, in-depth training will be likely required for any
tools selected.
5.1.2
Module 5.1:
Energy Sector Mitigation Methods
a. Approaches for Energy Sector Mitigation
Modeling
b. Review of Modeling Tools
c. MARKAL
d. ENPEP-BALANCE
e. LEAP
f. RETScreen
g. Conclusions
5.1.3
Module 5.1
a) Approaches for Energy Sector
Mitigation Modeling
5.1.4
Some Background…
• Decision 17/CP.8, para 38:
– Based on national circumstances, NA1
Parties are encouraged to use whatever
methods are available and appropriate in
order to formulate and prioritize programmes
containing measures to mitigate climate
change and that this should be done within
the framework of sustainable development
objectives, which should include social,
economic and environmental factors.
5.1.5
Approaches for Energy Sector
Mitigation Assessment
Top-down
Bottom-up
•
Use aggregated economic data
•
•
Assess costs/benefits through
impact on output, income, GDP
Implicitly capture administrative,
implementation and other costs.
Assume efficient markets, and no
“efficiency gap”
•
•
•
•
•
•
Capture intersectoral feedbacks
and interactions
Commonly used to assess impact
of carbon taxes and fiscal policies
Not well suited for examining
technology-specific policies.
•
•
•
•
Use detailed data on fuels,
technologies and policies
Assess costs/benefits of individual
technologies and policies
Can explicitly include administration
and program costs
Don’t assume efficient markets,
overcoming market barriers can
offer cost-effective energy savings
Capture interactions among
projects and policies
Commonly used to assess costs
and benefits of projects and
programs
5.1.6
Top-Down Assessments (1)
• Examine general impact on economy of GHG mitigation.
• Important where GHG mitigation activities will cause substantial
changes to an economy.
• Typically examine variables such as GDP, employment, imports,
exports, public finances, etc.
• Assume competitive equilibrium and optimizing behavior in
consumers and producers.
• Should also consider role of informal sector, which may be important
in many non-Annex 1 countries.
• Can be used in conjunction with bottom-up approaches to help
check consistency.
– E.g. energy sector investment requirements from a bottom-up energy
model used in macroeconomic assessment to iteratively check the GDP
forecasts driving the energy model.
5.1.7
Top-Down Assessments (2)
•
Types of top-down approaches:
1.
2.
3.
•
•
•
Simplified macroeconomic assessment: seeks consistency
between sectoral forecasts and informs baseline scenarios.
Input-output: captures intersectoral feedbacks but not structural
changes in economies (assume no shifts between sectors).
Computable general equilibrium: captures structural changes,
assume market clearing.
2 & 3 require more expertise and more data, which
may not be available in many non-Annex 1 countries.
All models are abstractions. Assumptions may not
reflect real-world market conditions.
Macroeconomic models tend to be country-specific.
Off-the-shelf software not typically available.
5.1.8
Bottom-Up Models (Energy Sector)
• Optimization Models
e.g. MARKAL
• Iterative Equilibrium/Simulation Models
e.g. ENPEP
• Hybrid Models
e.g. MARKAL-MACRO
• Accounting Frameworks
e.g. LEAP
5.1.9
Models for Mitigation Analysis in
the UNFCCC Context
• UNFCCC Guidelines do not specify which approach is
appropriate for national communications on mitigation.
• Both Top-Down and Bottom-up models can yield useful
insights on mitigation.
– Top-down models are most useful for studying broad
macroeconomic and fiscal policies for mitigation such as carbon
or other environmental taxes.
– Bottom-up models are most useful for studying options that have
specific sectoral and technological implications.
• The lack of off-the-shelf top-down models, the greater
availability of physical, sectoral and technological data,
and the focus on identifying potential projects has meant
that most mitigation modeling has so far focused on
bottom-up approaches.
5.1.10
Module 5.1b
Types of Bottom-Up Models
5.1.11
Optimization Models
•
Use mathematical programming to identify configurations of energy systems that
minimize the total cost of providing energy services.
–
–
•
•
Useful energy services forecast exogenously.
Select among technologies based on their relative costs.
–
–
–
•
•
•
•
•
•
Cost-minimization is performed within constraints (e.g. limits on CO2 emissions, technology
availability, foreign exchange, etc.). Constraints also ensure balance of supply and demand.
May optimize over all time periods (perfect foresight) or year-on-year (myopic).
Dual solution yields estimates of energy prices.
Can yield extreme “knife edge” solutions (model allocates all market share to cheapest
technology – even if only slightly cheaper)
Must be constrained to yield “reasonable” results: by using “hurdle” rates, by disaggregating
demands into more homogenous groups, or by manually constraining market allocations.
Typically assume perfect competition and that energy cost is only factor in technology
choice.
Especially useful where many technical options need to be analyzed and future costs
are well known.
Cost-minimization assumptions may be inappropriate for simulating “most likely”
evolution of real-world energy systems in a baseline scenario.
Data intensive
Calculations are complex making approach hard to apply where expertise is limited.
Examples: MARKAL/TIMES
5.1.12
Iterative Equilibrium/Simulation Models
• Simulates behavior of energy consumers and producers
under various signals (e.g. price, income levels) and
constraints (e.g. limits on rate of stock replacement).
• Easier to include non-price factors in analysis compared
to optimizing models.
• Balances demand and supply by calculating marketclearing prices.
• Prices and quantities are adjusted endogenously using
iterative calculations to seek equilibrium prices.
• Behavioral relationships can be controversial and hard to
parameterize. Crucial parameters are highly abstracted
or poorly known, especially in countries where time
series data is lacking.
• Example: ENPEP-BALANCE
5.1.13
Hybrid Models
• Maximizes present value of utility of a representative
consumer.
• Goes beyond energy system optimization to examine
macroeconomic impacts of energy system on the wider
economy.
• Changes in the energy system can feed-back to effect
macroeconomic growth and structure.
• A production function allows for substitution among
capital, labor and different forms of energy.
• Useful energy demands are endogenous to the model.
• Example: MARKAL-MACRO
5.1.14
Accounting Frameworks
• Account for flows of energy in a system based on simple
engineering relationships (e.g. conservation of energy).
• Rather than simulating decisions of energy consumers and
producers, user explicitly accounts for outcomes of those
decisions (e.g. as market penetration rates, energy service
demands).
• Simple, transparent, intuitive & easy to parameterize.
• Evaluation and comparison of policies are largely performed
externally by the analyst: framework serves primarily as a
sophisticated calculator.
• Framework ensures physical consistency but not economic
consistency.
• Example: LEAP
5.1.15
Types and Sources of Data
Category
Types of Data
Macroeconomic Variables
Sectoral driving variables GDP/value added, population, household size
More detailed driving
variables
Common Data Sources
National statistics and plans; macroeconomic studies; World Bank, GDP data, UN
Population data, World Resources Institute.
Physical production for energy intensive materials; transportation requirements (pass- Macroeconomic studies; national sectoral studies, household surveys, UN FAO Agrostat
km/year); agricultural production and irrigated area; commercial floor space, etc.
database; etc.
Energy Demand
Sector and subsector totals Fuel use by sector/subsector
End-use and technology
characteristics
Response to price and
income changes
Energy Supply
Technical characteristics
Energy prices
Energy supply plans
Energy resources
Technology Options
Costs and performance
Penetration rates
National energy statistics, national energy balance, energy sector yearbooks (oil, electricity,
coal, etc.), International Energy Agency statistics.
Energy consumption by end-use and device: e.g. new vs. existing building stock; vehicle Local energy studies; surveys and audits; studies in similar countries; general rules of thumb
stock; breakdown by type, vintage, and efficiencies; or simpler breakdowns.
from end-use literature.
Price and income elasticities
Econometric analyses of time-series or cross-sectional data.
Capital and O&M costs, performance, efficiencies, capacity factors, etc.
New capacity on-line dates, costs, characteristics.
Estimated recoverable reserves of fossil fuels; estimated costs and potential for
renewable resources
Local data, project engineering estimates, EPRI Technical Assessment Guide,
Local utility or Govt projections. IEA World Energy Outlook and fuel price projections.
National or electric utility plans & projections; other energy sector industries.
Local energy studies; World Energy Council Survey of Energy Resources.
Capital and O&M costs, performance (efficiencies, unit intensities, capacity factors, etc.) Local energy studies and project engineering estimates; technology suppliers; other
mitigation studies,
Percent of new or existing stock replaced per year; overall limits to achievable potential Extrapolation of trends & expert judgment, optimizing or simulation models.
Administrative and program For efficiency investment, often expressed in cost per unit energy saved.
costs
Kg GHG emitted per unit of energy consumed, produced, or transported.
Emission Factors
Local and international studies.
National inventory assessments; IPCC Revised Inventory Guidelines (IPCC, 1996);
CORINAIR; CO2DB, GEMIS, AIR CHIEF; IPCC Technology Characterization Inventory (US
DOE, 1993); TED
Projected Costs of GHG Mitigation
• Repetto and Austin (WRI, 1997) compared
the results from bottom-up and top-down
modeling exercises
• Their analysis clearly illustrates the extent to
which results depend critically on a handful of
key assumptions
5.1.17
Predicted Impacts of CO2 Abatement on U.S. GDP in 2020:
162 Projections from 16 models
2
% Change in GDP
0
CO2 Abatement
20
40
60
80
100
-2
-4
-6
-8
-10
-12
Adapted from Repetto and Austin, WRI, 1997
5.1.18
Changing Assumptions Takes Results
From Costs to Benefits
6
% Change in GDP
4
2
0
% CO2 Abatement
10
20
30
40
50
60
-2
-4
-6
-8
Climate change damages averted
Air pollution damages averted
Revenues recycled efficiently
Joint Implementation
Increased energy and product substitution
Efficient economic responses
Non-carbon backstop fuel available
Worst Case Assumptions
Adapted from Repetto and Austin, WRI, 1997
5.1.19
Module 5.1c
Review of Modeling Tools
5.1.20
Criteria for Inclusion of Tools
in this Review
Tools must be:
– widely applied in a variety of international
settings,
– thoroughly tested and generally found to be
credible,
– actively being developed and professionally
supported,
– primarily designed for integrated energy and
GHG mitigation analysis, or screening of
energy sector technologies.
5.1.21
Included Tools
• LEAP
– Long-range Energy Alternatives Planning system
– Primary Developer: Stockholm Environment Institute
• ENPEP
– Energy and Power Evaluation Program
– Primary Developers: Argonne National Laboratory and the International
Atomic Energy Authority (IAEA)
• MARKAL and MARKAL-MACRO
– MARKet Allocation model
– Primary Developers: IEA/ETSAP
• RETSCREEN
– Renewable Energy Technology Screening
– Primary Developers: Natural Resources Canada
• All are integrated scenario modeling tools except RETSCREEN,
which screens renewable and CHP technologies.
• Modeling can also use spreadsheets and/or other tools.
• Full Disclosure: Dr. Heaps is the developer of LEAP: reviewed here.
5.1.22
Included Tools Compared (1)
LEAP
Characteristic
Developer
Stockholm Environment
Institute
Home page
www.energycommunity.org
Scope
Integrated energy
and GHG scenarios
Methodology
- Model type
- Soution algorithm
MARKAL
ENPEP (BALANCE)
RETSCREEN
MARKAL-MACRO
IEA/ETSAP
Natural Resources Canada
www.dis.anl.gov
www.etsap.org
www.retscreen.net
Integrated energy
and GHG scenarios
Integrated energy
and GHG scenarios
Integrated energyeconomy and GHG
scenarios
Optimization
Linear programming
Accounting
Hybrid
Non-linear programming Accounting
Perfect or myopic
Argonne/IAEA
Equilibrium simulation
Accounting & spreadsheet-like modeling
Iteration
Accounting
Screening of renewable
and CHP projects
n/a
n/a
myopic
Perfect or myopic
Geographic applicability
Local, national, regional, global
Local, national, regional,
global
Local, national, regional, global
Local
Data requirements
Low-medium
Medium-high
Medium-high
Technology specific
Default data included
TED Database with costs,
performace and emission
IPCC Emission factors
factors (inc. IPCC factors).
Coming soon: national energy
& GHG baselines.
None
Extensive defaults: weather
data, products, costs, etc.
Time Horizon
User Controlled. Annual results
User Controlled,
Typically reporting for 5 or 10 year time periods
Primarily static analysis
- Foresight
Up to 75 years. Annual
results
Included Tools Compared (2)
Characteristic
LEAP
MARKAL/MARKAL-MACRO
ENPEP (BALANCE)
RETSCREEN
Expertise required
Medium
High
High
Low
Level of effort required
Low-Medium
High
High
Low
How Intuitive? (matching
analyst's mental model)
High
Low
Medium
High
Reporting capabilities
Advanced
Basic
Basic
Excel
Data management capabilities
Advanced
Basic
Basic
Excel
Software requirements
Windows
Windows
Windows, GAMS, solver & interface
Excel
Software cost:
Free to NGO, Govt and
researchers in non-OECD
countries.
Free to NGO, Govt
and researchers.
$8,500-$15,000
(including GAMS, solver & interface)
Free
Typical training required
& cost
On request: 5 days/$5000
Also regular international
workshops.
5 days
$10,000
8 days
$30,000-$40,000
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
Phone or email
$500-$2500 for one year.
Email or web forum
Free limited support.
Reference materials
Manual & training materials
free on web site
Manual available
to registered users
Manual available
to registered users.
Manuals free
on web site
Languages
English, French, Spanish,
Portuguese, Chinese
English
English
Multiple
Module 5.1d
MARKAL
5.1.25
MARKAL and MARKAL-MACRO
• Developed International Energy Agency, Energy Technology
Systems Analysis Programme (IEA/ETSAP).
• Generates energy, economic, engineering, and environmental
equilibrium models.
• Models are represented as Reference Energy Systems (RES),
which describe an entire energy system from resource
extraction, through energy transformation and end-use devices,
to the demand for useful energy services.
• Calculates the quantity and prices of each commodity that
maximize either the utility (MARKAL-MACRO) or the
producer/consumer surplus (MARKAL) over the planning
horizon, thereby minimizing totally energy system cost.
• Note: TIMES: “The Integrated MARKAL-EFOM System” is
gradually expected to replace MARKAL and MARKAL-MACRO.
5.1.26
Assessing Energy, Economy, Environment &
Trade Interactions
Energy Economy
Availability of technologies
Constraints
on Import
and Mining
of Energy
Capital Needs &
Technology Deployment
Demand
for
MARKAL
Energy
Services
Energy
Consumption
Economy
and
Society
Ecological effects
Emissions
Environment
5.1.27
What Does MARKAL Do?
• Identifies least-cost solutions for energy system planning.
• Evaluates options within the context of the entire energy/materials
system by:
–
–
–
–
balancing all supply/demand requirements,
ensuring proper process/operation,
monitoring capital stock turnover, and
adhering to environmental & policy restrictions.
• Selects technologies based on life-cycle costs of competing
alternatives.
• Provides estimates of:
–
–
–
–
–
–
energy/material prices;
demand activity;
technology and fuel mixes;
marginal value of individual technologies to the energy system;
GHG and other emission levels, and
mitigation and control costs.
5.1.28
What Aspects of Mitigation Assessment
Can MARKAL Support?
•
•
•
•
•
•
•
•
•
Macroeconomic policies (e.g. carbon taxes)
Transportation
Energy demand
Energy conversion and supply
Energy sector emissions
Non-energy sector industrial process emissions
Solid waste management
Geological sequestration
Value of carbon rights
5.1.29
MARKAL-MACRO
•
•
•
•
•
•
MARKAL-MACRO (M-M) is an extension of the MARKAL model that
simultaneously solves the energy and economic systems.
M-M merges the “bottom-up” engineering and “top-down”
macroeconomic approaches.
M-M has price responsive demands (i.e., determined endogenously),
as does MARKAL-Elastic Demands, while MARKAL does not (i.e.,
demands are exogenously defined).
M-M maximizes consumer welfare over the solution period, optimizes
aggregate investment in the economy and provides least cost energy
system configurations to meet endogenously determined demands.
Energy service costs, energy service demands, and energy prices are
determined simultaneously during optimization.
Relative energy costs determine types and levels of substitution
between energy carriers and technologies.
5.1.30
MARKAL-ED: Producer/Consumer Equilibrium
for each Commodity w/ Technology Detail
5.1.31
MARKAL Requirements
•
•
•
•
Windows PC with 512 MB RAM.
MARKAL/TIMES source code (written in GAMS)
GAMS modeling language and a Solver
Data Management and Reporting User Interface
– Two available: ANSWER and VEDA
• Cost of software: US $8,500-$15,000 depending on
institutional arrangements.
5.1.32
The ANSWER
User Interface
5.1.33
MARKAL Applications
•
•
•
•
•
•
•
•
•
•
•
•
International Energy Agency (IEA): technology detail for the World Energy Outlook
scenarios.
U.S. DOE/SAGE: an analytic framework for the International Energy Outlook.
European Union: 25 state European model: examines externalities and life cycle
assessment issues.
Six New England States: Analysis of Clean Air Act goals and support for climate
change commitments.
USAID: establishing a common framework for assessing demand-side management.
IEA/ETSAP partner institutions: supporting their national governments planning
(Canada, UK, Italy, U.S. DOE & EPA)
China and India: examining reform and energy sector evolution to meet economic
development goals, and developing multi-region national models.
APEC: cost-effective levels of renewable generation in 4 APEC economies.
ASEAN: 8 countries participating in a AusAID sponsored energy planning initiative
Three Central America countries: baselines and opportunities within the realm of
Climate Change.
Bolivia: GHG reduction strategies, including modeling of forestation as a carbon
reduction option.
South Africa: National energy and environmental planning.
5.1.34
MARKAL Data Requirements
• Useful Energy Demands, and own price elasticities for
MED or demand decoupling factors for MACRO
• Costs
– Resource, investment, fixed, variable, fuel delivery, hurdle rates
• Technology Profiles
– Fuels in/out, efficiency, availability
– Resource supply steps, cumulative resources limits, installed
capacity, new investment
• Environmental Impacts
– Unit emissions per resource, technology, investment
• System and other parameters
– Discount rate, seasonal/day-night fractions, electric reserve
margin
5.1.35
MARKAL Support & Training
• Technical support offered by phone and email.
• Cost is US $500-$2500 depending on
institutional arrangements.
• Training is offered through ETSAP and its
partners in different parts of the world.
• A minimum of 2 trainings of 4 days each are
recommended, with follow-up support included.
• Cost is US $15,000-$40,000 plus expenses.
5.1.36
For more information on
MARKAL/TIMES
•
•
•
•
•
•
•
Gary Goldstein
International Resources Group
Sag Harbor, New York, 11963, USA
Phone: +1 (631) 725-1869
Fax: +1 (631) 725-1869
Email: ggoldstein@irgltd.com
www.etsap.org
5.1.37
Module 5.1e
ENPEP-BALANCE
5.1.38
ENPEP
• The Energy and Power Evaluation Program (ENPEP) is a set of ten
integrated energy, environmental, and economic analysis tools.
• Here the focus is on one tool, BALANCE, which is most frequently
used for the integrated assessment of energy and GHG emissions.
• BALANCE is a market-based simulation that determines how
various segments of the energy system may respond to changes in
energy prices and demands.
• BALANCE consists of a system of simultaneous linear and nonlinear
relationships that specify the transformation of energy quantities and
energy prices through the various stages of energy production,
processing, and use.
• BALANCE also calculates emissions of GHGs and local air
pollutants.
• BALANCE can be run in combination with other detailed ENPEP
tools, such as MAED and WASP.
5.1.39
BALANCE Approach
• BALANCE matches the demand for energy with
available resources and technologies.
• The user creates an energy network that traces the flow
of energy from primary resources to useful energy
demands.
• Networks are constructed graphically using various
nodes and links.
• Nodes represent resources, conversion processes,
energy demands, and economic processes.
• Links connect the nodes and transfer information among
nodes.
5.1.40
Nodes and Links in BALANCE
5.1.41
BALANCE User Interface
5.1.42
BALANCE Market Share Simulation
• A logit function estimates the market share of
supply alternatives.
• Market share is sensitive to a commodity’s
price relative to the price of alternatives.
• Other constraints (e.g., capacity limits),
government policies (taxes, subsidies, etc.),
and the ability of markets to respond to price
signals can also be modeled.
• Consumer preferences can also be included
via a “premium multiplier” variable.
• Simultaneously finds the intersection of
supply and demand curves for all energy
supply forms and all energy uses in the
energy network.
• Equilibrium is reached when the model finds
the set of market clearing prices and
quantities.
• The objective is not to minimize costs, but
rather, to simulate the response of
consumers and producers to changes in
energy prices and demand levels and to
determine the resulting market equilibrium
and its evolution over time.
5.1.43
BALANCE CALCULATIONS
5.1.44
Other ENPEP Modules
• MACRO-E: feedbacks between the energy sector
and the wider economy.
• MAED: a bottom-up energy demand model.
• LOAD: hourly electric loads and generates load
duration curves for use in other ENPEP modules.
• PC-VALORAGUA: optimal generating strategy for
mixed hydro-thermal electric power systems.
• WASP: least-cost electric generation expansion
paths.
• GTMax: marketing and system operational issues
in deregulated energy markets.
• ICARUS: reliability and economic performance of
alternative electric generation expansion paths.
• IMPACTS: physical and economic damages from
air pollution (now part of BALANCE).
• DAM: a decision analysis tool used to analyze
tradeoffs between technical, economic, and
environmental concerns.
5.1.45
ENPEP Applications
• ENPEP has been used extensively in Africa,
Asia, Europe and North and South America for a
variety of integrated energy analyses.
• Numerous countries used ENPEP to help
prepare GHG mitigation assessments as part of
their national communications to the UNFCCC.
• Numerous ENPEP applications are described at
the ENPEP web site, in most cases with links to
related reports.
5.1.46
BALANCE Support & Training
• Technical support offered by phone, email, or
on-line.
• Basic support is free; premium support
packages available for up to US $10,000 per
year.
• Training is offered by the developers on-site or
at ANL.
• Since 1978, ANL has trained over 1300 experts
from over 80 countries.
• Minimum of 5 days training is recommend.
• Cost is US $10,000 plus expenses.
5.1.47
For more information on ENPEP:
• Guenter Conzelmann
• Center for Energy, Economic, and Environmental
Systems Analysis (CEEESA), Argonne National
Laboratory (ANL)
• 9700 South Cass Avenue, Argonne, IL 60439, USA
• Phone: +1 (630) 252-7173
• Fax: +1 (630) 252-6073
• Email: guenter@anl.gov
• http://www.dis.anl.gov/ceeesa/programs/enpepwin.html
5.1.48
Module 5.1f
LEAP: Long-range Energy
Alternatives Planning System
5.1.49
Long-range Energy
Alternatives Planning System

An integrated energy-environment, scenario-based modeling system.

Based on simple physical accounting and simulation modeling approaches.

Flexible and intuitive data management and advanced reporting.

Scope: demand, transformation, resource extraction, GHG emissions and
local air pollutants, full system social cost-benefit analysis, non-energy sector
sources and sinks.

Annual time-step, unlimited number of years.

Methodology: physical accounting for energy demand and supply via a
variety of methodologies.
– Optional specialized methodologies for modeling of certain sectors/issues. E.g.
stock/turnover modeling for transport analyses.

Links to MS-Office (Excel, Word and PowerPoint).

Low initial data requirements (for example costs not required for simplest
energy and GHG assessment). Many aspects optional.
5.1.50
Compared to
ENPEP and MARKAL
 Unlike ENPEP and MARKAL, LEAP does not require the user to
subscribe to a particular view of how an energy system behaves (e.g.
least cost optimization, market-clearing equilibrium).
 Instead LEAP is based on relatively simple physical energy and
environmental accounting principles.
 Thus all of the basic calculations in LEAP are non-controversial and can
be easily verified, making the system highly transparent.
 Instead of the model endogenously calculating market shares of
devices, in LEAP the user must tell the software how those shares will
evolve in each scenario.
 Thus instead of using a complex tool that tells you “what’s best”, the
approach in LEAP is to use a relatively simple tool that makes it quick
and easy for the user to explore the implications (cost, GHGs, etc.) of
different hypothetical scenarios.
5.1.51
LEAP User Interface: Analysis View
Expressions in LEAP
• Basic non-controversial energy-environment accounting
relationships are built-in to LEAP.
• Data are specified using spreadsheet-like expressions.
• Expressions can be simple static values or they can be time-series
functions that describe how variables change over time in different
scenarios.
• Expressions can also be used to create relationships between
variables: allowing for engineering, econometric or simulation
models.
• Expressions can also be used to create live links to Excel
spreadsheets: allowing LEAP to function as an overall organizing
and integrating framework for separate spreadsheet analyses.
5.1.53
Expression Examples
•
Growth(3.2%)
Exponential growth after the base year.
•
Interp(2000, 40, 2010, 65, 2020, 80)
Interpolates between specified data points.
•
Step(2000, 300, 2005, 500, 2020, 700)
Discrete changes in particular years.
•
GrowthAs(Income,e)
Future years calculated from rate of growth in
variable “Income” and an elasticity variable, “e”.
•
Interp(c:\sample.xls,Importrange)
Interpolate based on values in range
“importrange” from sheet “sample.xls”
5.1.54
Scenarios in LEAP
•
•
•
•
•
•
Scenarios are story-lines about how an energy system might evolve over time.
Can be used for analysis of alternative policy assumptions and for sensitivity
analysis.
In LEAP, the Scenario Manager is used to create a hierarchy of scenarios.
Typically users create one baseline scenario, and one or more scenarios used
to screen individual policies or measure.
These policy scenarios are then combined to form overall integrated mitigation
scenarios, which examine the interactions between measures.
Default expressions are inherited from
one scenario to another, thus
minimizing data entry and allowing
common assumptions to be edited in
one place.
On screen, expressions are color
coded to show which have been
entered explicitly in a scenario (blue),
which are inherited from a parent
scenario (black), and which are
inherited from another region (purple).
5.1.55
A Simple Demand Data Structure
Households
(8 million)
Urban
(30%)
Electrified
(100%)
Lighting
(100%)
Existing (80%, 400 kWh/yr)
Efficient (20%, 300kWh/yr)
Refrigeration
(80%)
Rural
(70%)
Electrified
(20%)
Cooking
(100%)
Other
(50%)
Non-Electrified
(80%)
• 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.
Results Reporting in LEAP
GIS/Mapping of Results
Transformation Analysis
• 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.
• Exogenous and/or endogenous capacity expansion.
Endogenous capacity added in scenarios to maintain
planning reserve margin.
• Optional system load data, & choice of methods for
simulation of dispatch to meet peak power requirements.
• Calculates imports, exports and primary resource
requirements.
• Tracks costs and environmental loadings.
5.1.59
LEAP Transformation
Module
Auxiliary Fuel Use
Output
Fuel
Process
(efficiency)
Output
Fuel
Process
(efficiency)
Module
Dispatch
Output
Fuel
Process
(efficiency)
Output
Fuel
Process
(efficiency)
Output
Fuel
Process
(efficiency)
Feedstock Fuel
Feedstock Fuel
Feedstock Fuel
Feedstock Fuel
Feedstock Fuel
Feedstock Fuel
Feedstock Fuel
Feedstock Fuel
Feedstock Fuel
Feedstock Fuel
Auxiliary Fuel Use
Co-Product
Fuel (e.g Heat)
5.1.60
Load-Duration Curve and
System Dispatch in LEAP
100
95
Peak Load
Plants
90
85
Percent of Peak Load
80
75
70
65
Intermediate
Load Plants
60
55
50
45
40
35
30
Baseload
Plants
25
20
Capacity (MW) * MCF
15
10
5
0
0
500
1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 5,500 6,000 6,500 7,000 7,500 8,000 8,500
Hours Sorted from Highest to
Lowest Demand
5.1.61
Typical Data Requirements
for
Typical
Data
Requirements
LEAP/Bottom-up Analyses
Macroeconomic Variables
Sectoral driving variables
More detailed driving variables
GDP/value added, population, household size
Production of energy intensive materials (tonnes or $ steel);
transport needs (pass-km, tonne-km); income distribution, etc.
Energy Demand Data
Sector and subsector totals
End-use and technology
characteristics by sector/subsector
Price and income response (optional)
Fuel use by sector/subsector
a) Usage breakdown by end-use/device: new vs. existing
buildings; vehicle stock by type, vintage; or simpler breakdowns;
b) Technology cost and performance
Price and income elasticities
Energy Supply Data
Characteristics of energy supply,
transport, and conversion facilities
Energy supply plans
Energy resources and prices
Capital and O&M costs, performance (efficiencies, capacity
factors, etc.)
New capacity on-line dates, costs, characteristics;
Reserves of fossil fuels; potential for renewable resources
Technology Options
Technology costs and performance
Penetration rates
Administrative and program costs
Emission Factors
Capital and O&M costs, foreign exchange, performance
(efficiency, unit usage, capacity factor, etc.)
Percent of new or existing stock replaced per year
Emissions per unit energy consumed, produced, or transported.
NB: data requirements vary greatly depending on type of analysis.
5.1.62
TED: The Technology and
Environmental Database
Fields
Information
Pages
Technologies
Technology
Data
Cost
Data
Environmental Notes
Reference
Impacts
and
s
Demand
Conversion
Database Contents
Supply:
Extraction
Resource
Transmission &
Distribution
5.1.63
LEAP Selected Applications
•
•
•
•
•
•
•
•
•
•
•
•
•
Greenhouse Gas Mitigation Studies: Argentina, Bolivia, Cambodia, Ecuador, El Salvador,
Lebanon, Mali, Mongolia, Korea, Senegal, Tanzania, Vietnam and many others through US
and Danish Country Studies Programs and as part of UNFCCC national communications.
USA: Greenhouse Gas Emissions Mitigation studies in California, Washington, Oregon and
Rhode Island.
U.S. DOE: ongoing project to construct a global end-use oriented energy model.
Energy and Carbon Scenarios: Chinese Energy Research Institute (ERI) and U.S. DOE.
U.S. Light Duty Vehicle Energy Use and Emissions: Various U.S. transportation NGOs.
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.
U.N. Millennium Project: Costs of meeting a parallel millennium development goal (MDG)
for energy.
Integrated Resource Planning: Brazil, Malaysia, Indonesia, Ghana, South Africa.
City Level Energy Strategies: Cape Town South Africa.
Transportation Studies: Texas (Tellus) and 7 Asian Cities (AIT).
Sulfur Abatement Scenarios for China: Chinese EPA/UNEP.
Rural Wood Energy Planning in South Asia: FAO.
5.1.64
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.
Demand
(costs of saved energy,
device costs, other non-fuel
costs)
Transformation
(Capital and O&M costs)
Primary Resource Costs
or
Delivered Fuel Costs
Environmental
Externality Costs
5.1.65
LEAP Support & Training
• Technical support offered by phone, email and
web forum.
• Free to registered users.
• Minimum of 5 days training is recommended
• On-site training is offered by the developers
(SEI) and regional partners.
• Cost is US $5,000 plus expenses.
• Regular regional trainings also being organized.
Cost to attend is minimal, but participants must
cover travel expenses.
5.1.66
• Four year initiative (2003-2006) sponsored by the Govt. of the
Netherlands to build capacity and foster a community among
developing country energy analysts working on sustainability issues.
• Managed by the Stockholm Environment Institute in collaboration
with regional partners in Africa, Europe and Latin America.
• Open to everyone at no charge.
• Activities:
–
–
–
–
–
Regional training workshops (Africa, Latin America, Planned in Asia).
Community web site
Technical support for Southern energy analysts
LEAP development & maintenance
Semi-annual newsletter
• http://www.energycommunity.org
5.1.67
For more information on LEAP
•
•
•
•
•
•
•
Dr. Charles Heaps
Stockholm Environment Institute – Boston Center
11 Arlington Street, Boston, MA, 02116, USA
Phone: +1 (617) 266 8090
Fax: +1 (617) 266 8303
Email: leap@tellus.org
http://www.energycommunity.org
5.1.68
Module 5.1g
RETScreen
5.1.69
RETScreen
• Evaluates the energy production, life-cycle costs and
GHG emissions reductions from renewable energy and
energy efficient technologies.
• Intended primarily for project-level analysis
(screening/feasibility), not for national-level integrated
analyses.
• Does allow options to be compared to a counter-factual
situation, but this is primarily a static comparison.
• Complements other tools reviewed here.
– Can be used for screening of options before inclusion in
integrated assessments, or for detailed project-level
assessments. Can help develop the technical, cost and
performance variables required in other models.
5.1.70
RETScreen Modules
• Structured as a set of separate modules, each with a
common look and approach.
• Each module is developed in Microsoft Excel
• Modules include:
–
–
–
–
–
–
–
–
–
–
Wind energy
Small hydro
Photovoltaics
Combined heat & power
Biomass heating
Solar air heating
Solar water heating
Passive solar heating
Ground-source heat pumps
Energy efficiency measures (coming soon)
5.1.71
RETScreen Interface
5.1.72
RETScreen Data Requirements
• Data requirements are those needed for a technical and financial
assessment of any clean energy project.
• This includes location data, meteorological data, equipment data,
cost data, and financial data.
• RETScreen includes both meteorological and product cost and
performance databases which help determine the amount of
clean energy that can be delivered (or saved) by a project, and
help calculate parameters such as heating loads.
• The weather database has data from 4,720 meteorological
stations around the world.
• The product database is linked online to continuously updated
data.
5.1.73
RETScreen Support & Training
• Free support is available via email or a web-based
forum.
• Because RETScreen is developed in Excel, training
requirements are minimal.
• Users with little experience of the technologies being
analyzed, will need to study the introductory training
materials available for free on the website
• Free training materials include: slides, teacher’s notes,
e-textbooks, online manual, case studies.
• An online distance-learning course is also freely
available to all registered users.
• A network of trainers conducts other training events,
which are posted on the RETScreen Website.
5.1.74
RETScreen Applications
•
•
RETScreen has > 65,000 users in 207 countries around the world.
Some examples are:
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
Canada, Archemy Consulting, Solar/wind electric - Solar thermal, 21 kW
Canada, DGV Engineering Services, Small hydro, 35 MW
Canada, WindShare, Wind energy, 750 kW
Australia, Power and Water, Photovoltaics & Wind energy, 890 kW & 50 kW
Brazil, Negawatt, Small hydro, 4 MW
Czech Republic, Hydrohrom, Small hydro, 2 MW
France, Electricité de France, Small hydro & wind energy, 27 MW & 7 MW
Ireland, Sustainable Energy Authority, Wind energy, 100 MW
India, IT Power India, Photovoltaics & Small hydro, 89 kW & 1 MW
Italy, Seriana Servizi, Biomass power, 48 MW
Nicaragua, Comisión Nacional de Energía, Mini hydro, 12 MW
Russia, SKIF-TECH., Earth energy, 320 kW
Romania, SPERIN, Wind & solar thermal, 8.4 MW & 80 m2
Senegal, ASERA, Wind energy & Photovoltaics, 9 kW & 5 kW
United States, Artha Renewable Energy, Solar water heating, 560 m2
5.1.75
For more information on RETScreen
• RETScreen Customer Support
• Natural Resources Canada
• 1615 Boulevard Lionel-Boulet, Varennes,
QC, J3X1S6, Canada
• Phone: +1 (450) 652-4621
• Fax: +1 (450) 652-5177
• Email: rets@nrcan.gc.ca
• http://www.retscreen.net
5.1.76
Module 5.1h
Conclusions
5.1.77
Conclusions
• MARKAL is a good choice if:
– Already have MARKAL modeling experience.
– Technical and statistical data are relatively plentiful.
– A large number of complex and interacting
technology options need to be assessed.
– Assessment team is familiar with concepts of
optimization.
– Assumptions of optimizing models are reasonable in
the study context.
– Assessment will be conducted over a relatively long
time frame (e.g. one year) and able to invest
considerable human resources in the assessment.
– Cost of software & support is acceptable.
5.1.78
Conclusions (2)
• ENPEP-BALANCE is a good choice in similar situations to
MARKAL:
– particularly if there is need to take a market-simulation approach, and
optimization assumptions are not appropriate,
• LEAP is a good choice if:
–
–
–
–
–
Data is less plentiful.
Team has less modeling expertise.
Time frame for analysis is relatively short.
Inherent assumptions of MARKAL/ENPEP are not appropriate.
Assessment will focus on both technology choice and other mitigation
options.
• RETScreen, is complementary to all of the integrated/national level
tools.
• Country-specific approaches, using spreadsheets or other models
may make sense for many Parties.
5.1.79
Further Reading
• Sathaye, J. and Meyers, S. 1995. Greenhouse Gas Mitigation
Assessment: A Guidebook; Kluwer.
http://ies.lbl.gov/iespubs/iesgpubs.html
• Halsnaes, K.; Callaway, J.M.; Meyer, H.J. 1999. Economics of
Greenhouse Gas Limitations: Methodological Guidelines. UNEP
Collaborating Centre on Energy and Environment, Denmark.
http://uneprisoe.org/EconomicsGHG/MethGuidelines.pdf
• Swisher, J.; Januzzi, G.; Redlinger, R.Y. 1997. Tools and Methods
for Integrated Resource Planning. UNEP Collaborating Centre on
Energy and Environment, Denmark.
http://www.uneprisoe.org/IRPManual/IRPmanual.pdf
• Heaps, C. 2005. User Guide for LEAP 2005. SEI-Boston.
http://forums.seib.org/leap
5.1.80
Possible Topics for Discussion
• What additional information do you need
to allow you to decide on a modeling
approach?
• How well do the existing models fit the
needs of your national communications
assessments?
• How can training needs best be addressed
in your country?
5.1.81