World Energy Projection System (WEPS): 

Electricity Module

Independent Statistics & Analysis 

www.eia.gov

U.S. Department of Energy

Washington, DC 20585

Last Updated 2021
U.S. Energy Information Administration   |   World Energy Projection System: Electricity Module i

This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and

analytical agency within the U.S. Department of Energy. By law, EIA’s data, analyses, and forecasts are

independent of approval by any other officer or employee of the United States Government. The views

in this report therefore should not be construed as representing those of the U.S. Department of Energy

or other federal agencies.
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Table of Contents 

1. Introduction ..............................................................................................................................................2

2. Objective Function ....................................................................................................................................2

3. Key Constraints .........................................................................................................................................2

3.1 Summary of key constraints .............................................................................................................. 2

3.2 Discussion of key constraints............................................................................................................. 3

3.3 Optional constraints .......................................................................................................................... 3

3.4 Renewables penetration limits.......................................................................................................... 4

4. Interaction with Other WEPS Modules..................................................................................................... 5

5. Energy System Structure........................................................................................................................... 5

6. Marginal Prices and Cost‐of‐Service Regimes........................................................................................... 7

7. Endogenous Technology Learning ............................................................................................................ 8

8. Module Data Sources................................................................................................................................8

8.1 Existing generation stock................................................................................................................... 8

8.2 Operating and maintenance costs..................................................................................................... 8

8.3 Wind and solar potential ................................................................................................................... 8

8.4 Hydro potential and generation profile............................................................................................. 9

8.5 Inter‐regional transmission capacity ............................................................................................... 11

8.6 Historical generation and consumption .......................................................................................... 11

Table of Figures 

Figure 1. Energy system structure and WEPS linkages for each module region .......................................... 6
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1. Introduction 

The International Electricity Market Module (IEMM) projects the following key quantities:

 Electricity generating capacity, including additions and retirements

 Electricity generation

 Electricity added and removed from storage  

 Electricity sold and purchased

 Electricity delivered to consumers

 Fuel consumed in electricity generation

 Carbon dioxide emissions

 Electricity prices

The module projects these quantities for each of the WEPS regions by year and time‐slice.  A time‐slice is

a time period, specified by EIA analysts, as a period of the day during a particular month or season (for

example, January from 7:00 p.m. to 9:00 p.m.). In some cases, some of the quantities listed above are

also broken out by other characteristics such as the type of technology used to generate the electricity.  

The module projects these quantities by minimizing an objective function, subject to several constraints.

The objective function represents the total cost to electricity suppliers of meeting all of the electricity

demands, by year and time‐slice, as projected by the WEPS demand modules. Examples of constraints

include limits on fuel‐specific electricity generating equipment, policy constraints (for example,

renewable portfolio standards), and emissions caps. The IEMM uses linear programming to perform the

constrained optimization.

2. Objective Function 

To minimize the total system cost of meeting electricity demands projected by WEPS demand modules,

the IEMM breaks the cost into several components:  

 Capital costs for investment in new electricity generating capacity—these costs are annualized

over the generating unit’s economic life at either the system discount rate or a technology‐

specific discount rate estimated by EIA analysts

 Fixed and variable annual Operation and Maintenance (O&M) costs of electricity generating

equipment

 Costs for fuel consumed, including purchases at regional fuel prices and any delivery costs

identified by EIA analysts

 Projected taxes and subsidies minus the salvage value of generators with remaining economic

life

3. Key Constraints 

3.1 Summary of key constraints

The cost minimization is subject to constraints that impose physical, operational, and policy restrictions

on the electricity supply costs. These constraints account for:

 Retrofits and retirements of electricity generating equipment, based on equipment age
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 Fuel‐specific equipment efficiencies and fuel shares for fuel‐flexible generating units

 Equipment availability

 Reserve capacity requirements, which require suppliers to maintain sufficient generating

capacity to exceed peak demand by a specified amount

 Commodity balance and demand satisfaction

 Projected policy constraints, including renewable portfolio requirements, emissions limits, and

minimum and maximum addition and operation of generating units by type

3.2 Discussion of key constraints

This section provides more information on the first five constraints listed above.

 Equipment retrofits and retirements.  When computing the available capacity for a certain time

period, the module accounts for the capacity resulting from all investments up to that specified

period. Some of these capacity investments may have been made before the initial period but

the equipment is still in operating condition and other capacity investments may have been

projected at or after the initial period, up to and including the period in question. Investments in

a particular technology increase its projected installed capacity for the duration of the technical

life of the technology. When the technology is retired, the projected capacity for this technology

decreases.

 Equipment efficiencies and fuel shares.  These constraints specify that the amount of electricity

produced (output) is in fixed proportion to the sum of the fuels used in electricity generation

(inputs). For example, we may have a fuel‐flexible power plant that can burn different

combinations of fuels, but the total amount of electricity generated must be proportional to the

sum of the quantities of fuel used.  

 Use of available capacity.  For each time period, the module may project the use of some or all

of the installed electricity generating capacity, according to the technology‐specific availability

factor, which defines a maximum capacity usage.  

 Reserve capacity requirements.  This constraint requires that the total capacity of all processes

producing electricity in each year, region, and time‐slice must exceed the average demand in the

time‐slice where peak demand occurs (for example, a daytime hour in summer) by a certain

percentage.

 Commodity balance and demand satisfaction.  In each year and time‐slice, the production of

electricity within each region, plus any imports from other regions, must balance the amount of

electricity consumed in the region and exported to other regions.

3.3 Optional constraints

In addition to the core constraints discussed above, EIA analysts may add bounds to module variables.

This section describes the major additional bounds and constraints that we added to IEMM.  

A bound is an upper, lower, or fixed limit on a single module variable. For example, bounds can be

imposed on:

 Capacity, investment, generation, fuel consumption, or emissions from any technology (for

example, to project the effects of operational or policy limits on technology investment and

behavior in each region)

 Total production, consumption, or net level of any commodity (for example, to set emissions

caps)
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EIA analysts may include constraints specifying more complex relationships between variables. Common

applications of such constraints include:  

 Imposing bounds on selected technologies/commodities for a group of regions (for example, a

common emission cap on a group of regions that are allowed to trade)

 Imposing renewable portfolio, clean energy standards, carbon emissions intensity, and other

policies based on relationships between fractions of generation by different plant types or

between emissions and generation  

 Creating banking schemes for cap and trade programs

 Imposing build‐rate penalties (for example, any capacity created beyond x GW per year will cost

an additional $y / GW)

Constraints that relate variables across periods or time‐slices can be used to:

 Impose a limit on the rate of growth (or decay) in capacity/activity of a new technology type

from one period to the next

 Limit the ramping rate—the percentage increase or decrease in the utilization level of a thermal

power plant from one time‐slice to the next

3.4 Renewables penetration limits

IEMM uses the bound and constraint facilities to construct two sets of limitations to the penetration of

variable renewable (VRE) generation. The need for these limits arises because VRE has inherently

granular temporal and spatial dependencies. No matter how detailed the time and space resolution of a

global module such as IEMM, it will always be insufficient to capture these dependencies fully.

Specifically, we wish to address two challenges:

 Limiting the use of country‐level wind and solar penetration in large, possibly multi‐country,

regions

 Limiting the ability of variable resources to meet average loads in model time‐slices

IEMM contains country‐level wind and solar potentials, but it projects demand at a regional level. In

multi‐country regions, as well as in large countries, resources may not be located near loads and

transmission capacity may not exist from resources to load. We do not want the module to satisfy an

unrealistic portion of regional load with renewables that may be located in a small, and potentially

distant, portion of the region.  

To provide a generic structure to control the penetration of these technologies, IEMM allows each

country to produce a maximum level of generation from wind‐plus‐solar before it has to incur a grid

extension cost. To allow generation beyond that limit, we provided three levels of grid extension

investment—notionally representing short distance, medium distance, and long distance transmission—

each with assumed bounds and investment costs.  

The bounds for the initial maximum generation and the three levels of grid extension are initially driven

by projected country‐level electricity demand, which is based on a logistic curve fitted to minimize the

sum of squared errors of per capita consumption from historical values and population projections.  EIA

analysts may add similar constraints to prevent the IEMM from projecting over utilization of
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hydroelectric resources in countries with large hydroelectric potential that don’t have the corresponding

electricity transmission investment.  

IEMM time‐slices are designed to capture two important types of temporal variation in load and

generation:  season and time of day. They do not capture day‐to‐day variation within seasons; rather,

they average over such variation. When penetrations of VRE sources become large relative to total load,

such averaging may run the risk of implicitly assuming free storage between days, or even hours, within

the same time‐slice.  

To avoid such an outcome, we implemented constraints that limit the maximum projected share of total

electricity production in each region and time‐slice from VRE sources. Output from storage technologies

is included in the denominator of the constraint, resulting in a requirement for storage investment and

operation when VRE penetrations become very high. The maximum VRE share is currently set at 65%.  

4. Interaction with Other WEPS Modules 

The IEMM receives regional annual sectoral electricity demands and power sector fuel prices from other

WEPS modules. IEMM also receives nuclear capacity and generation projections from the WEPS nuclear

module. For each of the four WEPS end‐use sectors (residential, commercial, industrial, and

transportation), the IEMM provides to other WEPS modules the projections for

 Regional electricity capacity and generation by fuel and technology

 Power sector fuel consumption

 Wholesale and retail prices  

IEMM provides an economic projection of global electricity generating capacity and dispatch. IEMM

tracks existing equipment by region and age (or vintage), along with analyst‐specified planned capacity

builds, and evaluates options to build new capacity and retire existing capacity. At the same time, it

optimizes the dispatch, subject to technology‐ and region‐specific dispatch limitations and policy

requirements. It also tracks existing inter‐regional transmission capacity and can project new

transmission capacity. The IEMM projects electricity demand by end‐use sector and time‐slice using

sector‐specific data provided by other WEPS modules. The module uses a linear programming approach

to find the lowest cost capacity‐plus‐dispatch solution.  

5. Energy System Structure 

The IEMM energy system structure within each WEPS region is depicted in Error! Reference source not

found., along with the information flows between IEMM and other WEPS modules.  
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Figure 1. Energy system structure and WEPS linkages for each module region

Stepped fuel supply curves: IEMM receives regional delivered power sector fuel prices for coal, natural

gas, and oil products from the WEPS supply modules. It also retains a delivered quantity from a previous

run of the WEPS system. IEMM constructs a stepped supply curve, shaped by analyst‐provided

elasticities, around these initial points. These supply curves allow some supply‐price adjustment to help

speed convergence of the WEPS system. IEMM also receives projected fuel‐based emissions factors

from the WEPS emissions module.

Renewable potential and cost: The IEMM knowledgebase contains detailed country‐level potentials for

onshore and offshore wind, photovoltaic, and hydroelectric capacity. Wind potentials are segmented by

resource class, distance from transmission, and for offshore wind, depth. Each country‐level segment

has its own cost, resulting in a detailed global wind supply curve. PV potential is similarly segmented by

resource class within each country. Hydroelectric capacity is specified by a three‐step cost supply curve.  

We developed hourly wind and solar generation profiles for each country using geospatial solar and

wind speed data. The hourly data are aggregated into model time‐slices. Country‐level availability

factors constrain hydroelectricity production at the annual and seasonal levels, based on historical

seasonal generation, where available, or seasonal precipitation data. Moderate (20%) additional

seasonal flexibility is provided to represent the capacity for reservoirs to allow seasonal shifting of

production.
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Energy storage:  Electricity storage devices move electricity from one model time‐slice to another,

generally with a cost and/or efficiency penalty. As of IEO2021, the storage device in IEMM is assumed to

be a battery storage system. Pumped hydro storage is not currently modeled in IEMM. New battery

storage investment options may be represented in the module at an assumed capital cost and efficiency.  

The base capital cost is taken from U.S. battery storage cost assumptions and declines as a result of

learning‐by‐doing (Section 7. Endogenous Technology Learning). Battery storage efficiency is assumed to

be 85%.

Combined heat and power:  Heat‐demand driven combined heat‐and‐power (CHP) capacity and

generation are modeled in IEMM starting in IEO2021. Three separate end‐use modules in WEPS,

Residential, Commercial, and Industrial, calculate annual total heat demand and the fraction of heat

demand to be satisfied by CHP at the regional level. The part of total heat demand to be satisfied by CHP

is passed to IEMM.   

The total CHP heat requirements are then transformed into seasonal requirements based on standard

patterns found in historical sectoral heat consumption data. IEMM assumes that the industrial CHP

demand remains constant throughout the year because industrial facilities tend to operate at a similar

level year round.  The residential and commercial heat demand varies by season, based on historical

heat consumption shares. The sectoral CHP heat requirement, that is, the sum of Residential,

Commercial, and Industrial, by season, is then aggregated into a total seasonal heat requirement.  

Proxies within the IEMM Module Instance Generator (MIG) can further transform each sectoral CHP

requirement from a WEPS region to the country level if sub‐regional modeling is enabled for the region

inside IEMM.  In this case, the sub‐regional sectoral seasonal requirements are aggregated to be met in

the country level. Later, the results obtained using the constraints described in the following paragraphs

are summed based on the region definitions in WEPS.

IEMM assumes that the CHP heat requirement is provided economically in individual seasons in each

region, subject to constraints on two variables: the CHP heat‐to‐power ratio and the share of CHP

generation by different fuels (coal, liquid, natural gas, and biomass).  

Both the CHP heat‐to‐power ratio and the CHP fuel mix constraints are defined at the WEPS region level,

based on historical data. The CHP heat‐to‐power ratio is assumed to be 0.25 if the historical CHP heat‐

to‐power ratio is not available. For each region, the same region‐specific CHP heat‐to‐power ratio is

assigned to each of the fuel technologies (coal, oil, natural gas, and biomass).

6. Marginal Prices and Cost-of-Service Regimes 

IEMM sends capacity, generation, and fuel consumption projections to the main WEPS system for each

region and technology type. In addition to these outputs, IEMM produces:  

 Marginal prices (shadow prices) of generation by region and time‐slice

 Average cost of electricity generation by region

 The marginal price on meeting reserve capacity requirements by region
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The WEPS system uses these inputs to produce electricity generation prices under cost‐of‐service and

competitive pricing regulatory regimes.  

Under cost‐of‐service regimes, the generation price is calculated as the average cost of generation,

which is simply the sum of all system costs (including capital, operating, fuel, and policy compliance

costs) divided by total generation. Under competitive pricing regimes, the retail price is the sum of an

energy component based on the marginal price of generation and a capacity component based on the

shadow price on the reserve requirement constraint. EIA analysts specify the ratio of cost‐of‐service and

competitive pricing, along with end‐use sector markups representing transmission and distribution

adders, taxes, and subsidies.  

7. Endogenous Technology Learning 

Starting in IEO2021, IEMM uses Endogenous Technology Learning (ETL) to project future technology

capital costs based on cumulative technology investment (learning‐by‐doing effects). The capital cost

projections are estimated between module iterations based on cumulative investment and learning

rates specified by EIA analysts.

8. Module Data Sources 

8.1 Existing generation stock

Existing power generation capacity combined with additional capacity under construction, by country

and state, is based on the data from the Platts UDI World Electric Power Plants Database (WEPP).

Planned builds may be adjusted by EIA analysts to reflect any supplemental information on national

plans not captured by WEPP.

8.2 Operating and maintenance costs

Country‐level operating and maintenance (O&M) costs are based on the 2010 and 2015 IEA Generation

Cost studies, which provide national O&M cost estimates for new power plants. Because of incomplete

coverage, we divided countries into five reporting peer groups, and we used data from the Bureau of

Labor Statistics on international wage differentials to establish which countries were most closely

aligned in labor costs. On this basis, we assigned non‐reporting countries to a peer group paired with

countries that did report. We used calculations based on the two‐capacity‐factor scenarios IEA reported

to distinguish fixed and variable O&M. We used data from our recently commissioned Sargent & Lundy

study on operating costs of U.S. generation to develop ratios for large versus small power plants. This

ratio was applied to the base costs for all countries.

8.3 Wind and solar potential

Wind resource data by country come from a resource assessment performed at the National Renewable

Energy Laboratory (NREL), based on the National Center for Atmospheric Research's (NCAR) Climate

Four Dimensional Data Assimilation (CFDDA) mesoscale climate database. Resources are defined by

country and resource quality. We further differentiated onshore supply curves by distance to nearest

large load or power plant and offshore supply curves by distance to shore and water depth. Resources

are calculated from hourly wind velocity vectors at a 40 kilometer (km) grid at 90 meter hub heights.
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Output is derated for outages and wake losses to obtain the net capacity factor. For onshore resources,

protected, urban, and high‐elevation areas are fully excluded, and certain land cover types are

fractionally excluded. For offshore resources, areas within 5 nautical miles of or farther than 100

nautical miles from shore are excluded. Protected marine areas are also excluded. Marine areas are

assigned to countries based on exclusive economic zones; unassigned or disputed areas are excluded.

The estimated solar potential is obtained from NREL and represents an annual average of the daily total

solar resource averaged over surface cells of 10 km resolution at the country level. The data are based

on the State University of New York, Albany satellite radiation model, which uses several factors to

calculate the hourly total insolation (sun and sky) falling on a horizontal surface output over a 12‐year

period (1998–2009):

 Hourly radiance images from geostationary weather satellites

 Daily snow cover data

 Monthly averages of atmospheric water vapor, trace gases, and the amount of aerosols in the

atmosphere

The values returned are the kilowatthours per square meter per day (kWh/m2

/day) available to fixed,

flat‐plate systems titled toward the equator at an angle equal to the latitude. The total solar resources

within each country are then organized by class, starting at 3 kWh/m2

/day or less and increasing in 0.5

kWh/m2

/day increments, with the highest solar class being 6 kWh/m2/day or more. Each solar class in

each country is then converted into total potential solar energy available per year as a function of land

area per solar class, conversion efficiency (10%), and number of days per year (365).

8.4 Hydro potential and generation profile

Hydropower potential and installed cost are based on an article published in Gernaat et al. (2017)1 that

only evaluated data for river power plants (or conventional hydropower). Currently, countries with

specific cost and potential include  

 Angola

 Argentina

 Australia

 Bolivia

 Brazil

 Cameroon

 Canada

 Central African Republic

 Chile

 China

 Colombia

 Congo (Brazzaville)

 Democratic Republic of Congo

 Egypt

                                                            

1 Gernaat, D. E. H. J., Bogaart, P. W., Van Vuuren, D. P., Biemans, H., and Niessink, R. “High‐resolution assessment of global

technical and economic hydropower potential,” Nature Energy, Vol 2 (October 2017), p. 821‐828.
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 Ethiopia

 France

 Gabon

 India

 Indonesia

 Italy

 Japan

 Madagascar

 Malaysia

 Mexico

 Mozambique

 Myanmar

 Nepal

 New Zealand

 Pakistan

 Papua New Guinea

 Peru

 Russia

 Sudan

 Tanzania

 Turkey

 Uganda

 United States

 Venezuela

 Vietnam

 Zambia

Countries with no specific installed cost information use regional costs, which are based on data from

the International Renewable Energy Agency (IRENA, Renewable Power Generation Costs in 2017) and

Oak Ridge National Laboratory (ORNL).

The annual availability factor for hydropower generation for each country is an average capacity factor,

calculated from historical (2000–2015) capacity and generation data from EIA International Energy

Statistics. Monthly capacity factor is based on historical hydropower generation for:  

 OECD countries

 Argentina

 Brazil

 China

 Colombia

 India

 Russia

 Vietnam

For other countries with no historical hydro generation, we use monthly share of precipitation as the

proxy for monthly capacity factor.  
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8.5 Inter‐regional transmission capacity

Inter‐country transmission capacity data are derived from ENTSO‐E data for European countries. For

other major electricity trading countries, historical trades are derived from the UN Comtrade database

and converted into an implied capacity.

8.6 Historical generation and consumption

We used the International Energy Agency’s historical data on electricity generation and fuel

consumption by country, fuel, and major unit type and electricity consumption by sector for:  

 Calibrating  

 Deriving sector load shapes

 Projecting country‐level load to set renewable bounds