System for the Analysis of Global Energy SAGE: Electric Sector Enhancement

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System for the Analysis of Global Energy (SAGE)
Electric Sector Enhancement
John J. Conti Acting Director Office of
Integrated Analysis and Forecasting Prepared for
the Energy Technology System Analysis Program
(ETSAP) Florence, Italy November 24, 2004
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SAGE Background

• A MARKAL-based energy model utilizing the VEDA
  software for model creation and results analysis.
• Produces integrated energy projections through
  2025 for 15 regions of the world.
• EIAs International Energy Outlooks have been
  based on SAGE model results since 2003.
• Uses a time-stepped approach to address foresight
  and other modeling issues (i.e., learning, market
  sharing, etc).
• Each year a number of model enhancements are
  scheduled (some are even implemented).
• One key improvement this year has been a
  reformulated electric sector.

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Electricity Sector Enhancement

• Purpose to increase model flexibility in
  choosing between alternative fuel/technology
  combinations in response to various changes in
  inputs to address alternative policies, energy
  market conditions, or technological
  characteristics.
• Allow model to economically choose to build and
  operate appropriate mix of electricity
  technologies
• Maintain selected external constraints to allow
  for regional differences and assumed must run/out
  of merit order generation and capacity selection
  (e.g., renewable portfolio standards, etc.)
• Allows for the appropriate economic valuation of
  carbon allowances in electricity sector and
  flexibility to react to carbon shadow prices or
  constraints.

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Elements Modified

• Improved the technological characterization of
  existing and new capacities.
• Better representation of peaking, intermediate,
  and baseload demand

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Concerns with Previous SAGE Electricity Model
Formulation

• Model builds and operates large amounts of
  baseload technologies
• Constraints required to force proportional
  generation by other fuels
• Model dispatch and capacity choice decision were
  not based on the technological specifications,
  capital cost parameters, and fuel costs

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Concerns with Previous Model Formulation (cont.)

• Annual electricity demand is segmented into 6
  periods
• Summer, Winter, and Intermediate
• Day and Night
• Electricity capacity expansion and dispatch
  decisions meet demand varying by the above 6
  seasonal/diurnal segmentations.
• The 6 segment load demand curve representation is
  too flat to adequately capture the comparative
  economics of peaking and intermediate technologies

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New SAGE Electricity Model Formulation

• Changed seasonal/time of day framework to load
  duration curve concept
• Developed mapping of typical end use load for
  each end use to aggregate load.
• By region, selected dominant end use load as
  basis for establishing time slots for load
  duration curve slices (e.g., industrial worlds
  peak dominated by cooling load)
• Stayed within default 6 slice limit.

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New SAGE Electricity Model Formulation (continued)

• Demands mapped to load slice based on end use
  demands proportion of load slice
• Load slices are organized around load duration
  curve

Peak time slice
Intermediate time slice
Demand met with Peaking capacity
Demand met with Intermediate
Base load
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Original vs. New SAGE Electricity Load Curves
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Model Results Summary

• Peaking facilities (such as turbines) are built
  to operate in the 1st (i.e. peak) slice
• Intermediate facilities (such as combined cycle)
  are built to operate primarily in 2nd slice.
• Baseload facilities (such as coal or combined
  cycle) are built to operate in the four baseload
  slices.
• First, we will look at the capacity expansion
  decision, followed by the dispatch decision.
  This is based on the U.S. region for the year
  2025.

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Electric Sector Capacity
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Electric Sector Capacity by Segment
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Comparison of Total Generation
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Generation by Segment
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Electricity Reformulation Benefits

• Appropriate model capacity expansion and dispatch
  is not forced via additional constraints.
• Improved model application for policy analysis
• Electricity price forecast more reasonable
• Electric sector results respond to changes in
  endogenous variables such as relative fuel prices.

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Other SAGE Activities

• In progress
• Improved market share algorithm
• Enhanced discount, interest, investment hurdle
  rate methodology
• Formalized version control, issue tracking, and
  software installation.
• Planned
• Kyoto Scenario Analysis