The EIA Short-Term Regional Electricity Model: Capabilities and Data
Requirements

Phillip Tseng, SMG, and Dave Costello, EMEU, EIA

October 2004

# Abstract

The United States Energy Information Administration (EIA) is developing
a thirteen-region electricity demand and supply model in response to
questions on regional energy issues from high-level decision makers. One
of the important features of the new modeling system is transparency; it
must provide tractable results and insights that stakeholders can easily
understand. The electricity module of the Regional Short Term Energy
Model serves that function and provides a vital link to the new
integrated regional energy system in answering several key
region-specific questions on:

-   Winter heating fuel market

-   Natural gas market

-   Summer gasoline market

-   Summer electricity market

The objective of this paper is to document the structure of the regional
electricity model, identify data requirements, and demonstrate the
model's capability in providing users an understanding of issues facing
the current electricity market. The model will be used for generating
routine short-term forecasts. However, the model can also be used as an
analytical tool to provide useful insights into the electric power
market itself and into the principal interactions between electricity
supply and fossil fuel markets. The interactions and linkages between
the electricity market, the natural gas market, and heating fuel market
will be described explicitly. In addition, an illustration of the
potential modeling applications will be presented. The demonstration
will show how the model can help policy makers in their decisions on
deployment of technologies that may be cost effective socially but not
privately.

# 1. Introduction

# 

This paper documents the new regional electricity model for the Regional
Short Term Energy Model (RSTEM). A regional modeling approach to
addressing regional energy issues is more appropriate than a strictly
national-level approach even though it is a very challenging task to
compile a regional demand and supply database. This new modeling
framework can provide more specific outputs that reflect changes in
demand, electricity generating capacity, and fuel markets.

This paper is as an interim report that will be presented at the October
Energy Committee meeting of the American Statistical Association. The
goal is to receive comments and constructive inputs from the committee
before the model is finalized and calibrated for use in actual
production of the Short Term Energy Outlook.

![](media/image2.wmf)

Section 2 of this paper describes the model structure. Section 3 reports
the data requirements. Section 4 presents a few examples to illustrate
the capability of the model. Data on power supply will probably be used
for the projection of the new outlook. Data on the demand curve and the
hourly load curves are still being developed. Simulation results are for
illustrative purpose only.

# 2. The Regional Electricity Model

## 

## The regional electricity model is a partial equilibrium model of the U.S. electricity market. The model includes nine Census regions and breaks out four important states as special cases. It includes demand from four end-use sectors and supply of electric power from about 10,000 power generators. The model is solved monthly to provide an outlook of the electricity market over the 24 to 36 months subsequent to the RSTEO publication date, and it tracks exogenously changes in electricity generating capacity over the forecasting period. 

## ![](media/image1.wmf)Figure 1 shows the 13 regions included in the model: 

## 

## New England, 

## Mid Atlantic less New York, 

## E. North Central, 

## W. North Central, 

## South Atlantic less Florida, 

## E. South Central, 

## West South Central less Texas, 

## Mountain, 

## Pacific less California, 

## California, 

## Florida, 

## New York, 

## Texas. 

These thirteen regions are grouped into three blocks: The Eastern
Connections, which includes regions 1 through 7, and regions 11 and 12,
the Western Interconnection, which includes regions 8, 9, and 10, and
Texas.

Figure 2 shows the elements of electricity demand and supply in a single
region without trade. The demand side of the electricity model includes
four end-use sectors: residential, commercial, industrial, and other.
The supply side includes power generators for oil, gas, coal, nuclear,
hydro, biomass, geothermal, wind, and solar.

Projected monthly demand for electricity in each end-use sector is
determined by heating degree days (HDD), cooling degree days (CDD),
gross regional product, and electricity prices for each sector. Monthly
demand projections are converted to 16 daily time slices to capture the
variations in daily demand patterns. These time slices provide more
detailed load information than the NEMS because the short-term model
puts more emphasis on the peak load demand and its impacts on fuel use.

There are 8 three-hour load periods considered for weekdays and 8
three-hour load periods for weekend (and holiday) days. The conversion
of monthly demand derived from regional demand forecasts to 16
three-hour load periods is based on historical hourly load data, hourly
temperature, average daily temperature, and the historical relationship
between HDD, CDD, and average daily temperature. Appendix A describes
more of the conversion process.

On the supply side, the EIA plant file from EIA's Office of Integrated
Analysis and Forecasting (OIAF) has data on power generators by state,
by technology, and by fuel. The database also includes information on
heat rates, fixed and variable O&M costs, capacity factors, identifiers
for dual fired generators, and NOx and SO2 emission coefficients. For
each generator, variable cost is computed using the heat rate, capacity
factor, O&M cost, and fuel cost, which are derived from the Form
EIA-423. For dual-fired units, such as oil and gas, a generator is
treated as an oil-fired unit for the month if the price of residual fuel
or diesel fuel is lower than natural gas and vice versa. One exception
in the computation of variable costs is for power plants identified as
"must-runs". For these plants, the variable costs include only the O&M
costs to ensure that these power plants are cost effective and will
always be used in a cost-based dispatching algorithm. For each
generator, generation is computed based on capacity and a capacity
factor. The supply curve is constructed in two steps. First the plants
are sorted in terms of variable costs (and "must-runs") from lowest to
highest. Cumulative generation is calculated summing the plant capacity
with its associated plant factor along the cost curve. To derive a
supply curve, variable costs are sorted in ascending order and the model
computes cumulative generation starting from the one with the lowest
variable cost.

Hydropower and solar power are two exceptions. For hydro, there are
multipurpose dams designed to meet requirements for irrigation,
navigation, generation, and flood control. Dispatching from those plants
will be treated as base load and will be subtracted from the load curve.
For solar power, dispatching and generation are based on capacity and
the average number of daylight hours. The capacity factor serves as a
proxy for the number of hours that a solar plant can produce power.
Estimated generation from solar power will be subtracted from the load
curve during the daylight hours between 10:00 AM and 6:00 PM.

*[Single region dispatching and fuel use by type.]{.underline}* Figure 3
shows how hourly load determines the supply and marginal cost of
electricity. The vertical lines represent the hourly load of a
hypothetical day and the supply of power. The horizontal axis shows
marginal costs of power supply. The line chart depicts the relationship
between cumulative generation and the variable costs. For demand level
D1, the corresponding supply is P1, and the marginal cost is determined
by the intersection of the line D1-P1 and the supply curve. Given the
dispatching level and variable costs, dispatching by technology and by
fuel type can be identified. The model accumulates fuel consumption for
each time slice to get projected average daily fuel use and power
generation.

![](media/image1.wmf)Note that in this model electricity price plays a
role in determining the monthly demand for electricity. However, the
hourly load curves are treated as exogenous in the dispatching module
and are used to determine dispatching only. They are conditional upon
the weather conditions and temperature elasticities. The outputs of the
dispatching module include generation by technology and fuel use. The
fuel use data will be passed to the fuel demand module to solve for
market prices of natural gas, coal, and petroleum products.

## 

## 

## *[Regional Trade]{.underline}*. The electricity model includes several modules to assess the effects of regional trade on regional dispatching, net trade flows, regional equilibrium price at the generator gate, and regional fuel use by type. The solution algorithm for a multi-region market is similar to a single region approach. For example, a 3-region trade module will have an hourly load curve, which is the sum of three load curves and the 3-region supply curve includes all the generators in these three regions. The 3-region demand and supply will determine dispatching required in meeting the demand. The model uses region codes to identify dispatching by technology and fuel use by type for each region. The final version of the model would most likely include two major trading blocks. The West and the East. Texas will be treated as a stand along region without trade. This arrangement reflects the three separate transmission interconnections.

Transmission capacity constraints between regions within a trading block
will be imposed to reflect physical constraints on the flow of power
between regions. The data will be from the NERC report. Specific data
source will be provided later.

It is worth noting that the components of the electricity model of the
RSTEM is modular and can easily be expanded to include more
disaggregated representations to address state level questions for
possible demand, supply, and power flow analysis.

[3. Data requirements and assumptions]{.underline}

The Short-Term Energy Outlook relies heavily on short-term historical
data and near-term economic forecasts. This section lists the data
sources that will be used for the electricity model. The model runs on
Eviews. Preliminary runs show that run time for a 13-region model with
trade and transmission constraints solves in about 20 seconds for each
time period (24 hours). Total run time is estimated to be about 8 to 10
minutes.

Demand data and economic variables:

-   Monthly data on electricity sales/demand by state aggregated to
    regional demand

-   Monthly electricity retail prices

-   Quarterly state/regional income and retail sales

-   Heating degree-days

-   Cooling degree-days

-   Projected state/regional income and retail sales from Global Insight

-   Households

-   Employment

Hourly load curves:

-   State/regional average and hourly temperature

-   State/regional hourly electricity load

Note that load and temperature data are not homogenous within the same
state or independent system operator (ISO). A proper approach to map the
monthly demand to hourly load curves will benefit greatly from the ASA
committee. We would like to conduct more future research on the subject
and make future improvement in the RSTEM.

Electricity supply:

-   Electricity generator by technology, and by fuel type.

    -   Oil, gas, coal, nuclear, hydro, biomass, wind, solar

-   Heat rate

-   Fuel prices by region

-   Operating and Maintenance costs (fixed and variable)

-   Primary and secondary fuel for dual fuel generators

-   Transmission interconnection constraints (future research)

-   Imports (future research)

# 

# 

# 4. Applications of the model in Forecasting and Analysis 

At the spring, 2004, ASA Energy Committee meeting questions arose
regarding the objectives of the regional modeling framework. In this
section, a few examples will be discussed in great detail to highlight
the capability of the new model as a forecasting and analytical tool.
One advantage of the new modeling framework is its capability in tracing
the links between the electricity market and other fuel markets. One
advantage of the new modeling framework is the capability to show the
relationship between electricity consumption and utility fuel use
according to a dispatch algorithm and the subsequent link to the fuel
markets and renewable supply resources.

*[Understand the fuel use pattern in the power generation
sector.]{.underline}*

Regional demand for electricity and the hourly load curve for a typical
day determine regional dispatching requirements. In the near term, power
generation will come from existing power plants. Therefore, plants with
lower variable costs would most likely be deployed first. Exceptions to
this rule include combined heat and power (CHP) and units in areas where
transmission line capacity is a limiting factor for least-cost
considerations. The marginal cost based dispatching algorithm for the
RSTEM provides a good accounting of generation by technologies and by
fuel types. A list of technologies and fossil fuels is shown in figure
2.

In recent years prices of crude oil and natural gas have reached new
highs (in nominal dollars). Increased demand for power and the addition
of new gas combined-cycle power plants play a significant role in the
use of natural gas. Dispatching patterns of gas-fired units (base load
and peak load) can have profound impacts on natural gas consumption,
storage, and prices. Prices of crude oil and natural gas can change
dispatching decisions for many dual-fuel units. To a smaller extent, the
dispatching of oil-fired and gas-fired units could change with fuel
prices as well.

The effects of fuel switching on fuel use depend on the shape of hourly
load curves for each region. In regions where peak load demand is high
and there are many dual-fired units, the potential for switching will
also be high. This is demonstrated by simulating the dispatch model
(note: it is still in testing mode). Table 1 illustrates the changes in
natural gas consumption in July when the price of natural gas is raised
above the price of diesel fuel. In the reference case, diesel fuel
prices are higher than the natural gas price. As a result, duel-fired
units select gas to power generators. Given the load curve used for July
in the model, use of oil could increase by about 180 trillion Btu and
gas will decrease by about the same amount if the relative price of
diesel fuel becomes more favorable.

Note that the simulation results reported in Table 1 assume no regional
trade and are for demonstration only. For regions with high peak load
demand, the results will overstate the use of peak load generators
relative to the results expected when regional trade is incorporated in
the sensitivity run.

  -------------- ---------------------- ----------- ---------------------- ----------- -------------
  Table 1:                                                                             
  Hypothetical                                                                         
  Fuel Switching                                                                       
  in July:                                                                             

  Response to An                                                                       
  Increase in                                                                          
  the Relative                                                                         
  Price of                                                                             
  Natural Gas                                                                          

  Region         oil                                gas                                
                 consumption/trillion               consumption/trillion               
                 btu                                btu                                

                 Reference Case         Higher Gas  Reference Case         Higher Gas  Percent
                                        Price Case                         Price Case  Change in Oil
                                                                                       Consumption
                                                                                       from
                                                                                       Reference
                                                                                       Case ((col
                                                                                       3/col 2)-1)

                                                                                       

  1              12                     26          28                     13          116.67%

  2              10                     26          51                     35          160.00%

  3              4                      13          48                     39          225.00%

  4              14                     26          27                     15          85.71%

  5              22                     37          84                     68          68.18%

  6              1                      2           42                     41          100.00%

  7              0                      3           56                     53          cc

  8              1                      6           29                     24          500.00%

  9              0                      2           5                      3           cc

  10             3                      10          115                    108         233.33%

  11             36                     70          77                     43          94.44%

  12             24                     69          67                     22          187.50%

  13             2                      20          198                    180         900.00%

                                                                                       

  Total          129                    310         827                    644         140.31%
  -------------- ---------------------- ----------- ---------------------- ----------- -------------

*[Effects of summer fuel use on winter heating fuel prices]{.underline}*

Natural gas plays a major role in meeting space heating needs. Table 2
shows residential energy use for space heating in 2001 as reported in
the Residential Energy Consumption Survey. In the Northeast, natural gas
consumption was 0.71 quadrillion Btu (quads) and fuel oil consumption
was 0.58 quads. Natural gas dominates in the Midwest, South and West
where consumption was 1.39, 0.70, and 0.52 quads, respectively.

Table 2: Residential Sector Energy Use for Space Heating in 2001

  ------------------ ------------ ----------- ---------- --------- ---------
  Fuel use (quads)   Total U.S.   Census                           
                                  Region                           

                                  Northeast   Midwest    South     West

  Natural Gas        3.32         0.71        1.39       0.70      0.52

  Fuel Oil           0.58         0.45        0.06       0.05      0.01

  Electricity        0.39         0.05        0.06       0.20      0.08

  LPG                0.28         0.03        0.12       0.10      0.04

  Kerosene           0.04         0.02        \*         0.02      0.01
  ------------------ ------------ ----------- ---------- --------- ---------

Source: RECS 2001

Higher demand for natural gas in the summer can reduce gas destined for
injection into underground storage. This could reduce availability and
raise natural gas prices in the winter. There are many dual-fired power
generators in the electric power sector. They are responsive to changes
in fuel prices and the demand for electricity. The electricity module of
the RSTEM tracks the use of natural gas and fuel oil. It provides a
useful accounting framework and tool to evaluate and analyze the overall
natural gas market, gas storage, and prices over a period of 24 months.

Figure 4 shows the effects of a 5% reduction in electricity demand in
July on natural gas use for power generation in region 2 East North
Central, 3 Middle Atlantic, and 12 New York In a three-region trade
analysis, there is almost no change in the use of natural gas in the
first 10 hours of a hypothetical July day. The biggest impacts are
between 11^th^ and 20^th^ hour. The reduction in natural gas use for the
month in these three regions would be about 17 trillion Btu or 0.017
quads. Given the level of supply, changes in demand can be easily
converted to additions to storage.

Summer demand for gasoline has similar effects on the production, and
storage of heating fuel. Petroleum refiners maximize gasoline production
in the summer. After the summer

driving season, refiners change operations to maximize production of
diesel fuel. Summer demand for diesel fuel for power generation would
lower availability of heating fuel in the winter in the northeast.

![](media/image1.wmf)

### Electricity demand, supply, and regional trade

The electricity model has one supply and demand representation for each
of the 13 regions. Differences in regional demands, the general shapes
of hourly load curves, time zones, and costs of generation, create
opportunities for trade. To facilitate trade analysis, load curves for
all time zones are converted to an Eastern time-zone basis so that
analysis of electricity trade between different time zones with
different peak load demand can be conducted. As an example, Figure 5
shows the results of a three-region trade for East-North Central(region
3) , Mid- Atlantic excluding New York (region 2), and New York (region
12).

Model results show that Region 12 will import electricity from other
regions because its own generating capacity cannot meet demand. Under
normal conditions, Region 12 will not use all of its generating capacity
because of high generating costs for peak load units. Region 3 has
excess generating capacity and will be an exporter of power. It is worth
noting in this sensitivity run that Region 12 exports electricity from
mid-night to about 6 AM, while the major exporter Region 3 imports small
amounts of power during these hours. The example demonstrates that the
model is flexible enough to capture the power flow on the basis of
marginal cost of generation and the shape of load curve.

![](media/image1.wmf)

This test run does not consider imports from Canada to New York.
Electricity imports will be handled exogenously in the current version
of RSTEM.

For each region, the monthly model solution includes net trade flow,
regional generation, and regional equilibrium prices, and utility fuel
consumption by type. Note that the trade is based on hourly load curves
and region specific supply curves. Therefore, it is possible to identify
potential transmission bottlenecks with the NERC interregional
transmission report.

Analysis of the role of energy technologies and dispatching decisions

The model structure can be used to simulate the effects of technologies
such as distributed generation for CHP and PV on dispatching and fuel
consumption. For example, if a market experiences transmission
constraints and cannot import enough power to meet demand economically,
deployment policies such as installing distributed generation
technologies (DG) or photovoltaic (PV) may reduce peak load demand and
fuel use. In the case of PV, if it reduces peak load demand in the
summer and backs out enough demand for natural gas, the price of gas may
decline in the heating season. (Numerically, PV costs will have to come
down significantly to make this option feasible).

## 

The modeling framework can also be used to assess the effects of
dispatching decisions for hydropower on the regional energy system. If
dispatching decisions can be made that shave the demand for natural gas,
it may be possible to change regional demand and underground storage
patterns, which in turn may have impacts on winter fuel prices.

## 

## [Future Work]{.underline}

In the next few month analysts at EIA will work on model calibration.
This process will be designed to check for data consistency. For each
region/state, historical data will be used to compare model results in
generation by fuel type and fuel use. Insights from this process will be
documented to help enhancement of future modeling and data collection
efforts.

## Questions for the Committee

The methodology used to map HDD, CDD, to the hourly temperature, which
in turn are used to estimate the hourly load, is critical to the
dispatching algorithm in solving for generation by technology and the
use of fossil fuel. Please provide comments on the appropriate
statistical methods for the estimation of the load curves.