# Presentation: 548092

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

**Phillip Tseng, SMG, and Dave Costello, EMEU**
**Energy Information Administration**
**October 28, 2004**

**Notes:**
Good morning, members of ASA committee, Mr. Caruso, EIA colleagues, ladies and gentlemen.

Today I will talk about the regional electricity model for the new regional short term model.  Before I start, I have to say that the work is the result of team work.  It involves tremendous amount of work on data collection, analysis, methodology development, and programming.  

I would also like to thank Lawrence Stroud and Ann Whitfield for their help in preparing the slides.

## Topics

**Objectives of the Electricity Model**
**Electricity Regions**
**Dispatching algorithm**
**Data requirements**
**System requirements**
**Model outputs**
**An example of 9-region trade**

## Objectives of the Electricity Model

- 	Provide a 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**

**Notes:**
Currently,  the Short term integrated forecasting system (STIFS) is a national  model. There is no regional representation.

The current model is demand driven.  In other words, the model will project demand for electricity and assume that generation will be there to meet the demand. 

The new representation include a regional electric power supply module, which provides additional capability in answering the key questions that policy markers frequently ask.

## Electricity Regions

**Mid Atlantic less New York**
**East North Central**
**West North Central**
**South Atlantic less Florida**
**East South Central**
**West South Central less Texas**
**New England **

**Mountain****	**
**Pacific less **
**10.****	****California**
**11.****	****Florida**
**12.****	****New York**
**13.****	****Texas**

## RSTEM 13 Electricity Demand and Supply Regions

## Dispatching Algorithm

**Single region dispatching  **
**Regional dispatching with trade ****	****	****and transmission constraint**
**Western Interconnection**
**Eastern Interconnection**
**Texas**** **

## Single Region Dispatching

- Coal
- Gas
- Oil

- Hydro
- Power

- Biomass
- Geothermal
- Solar
- Wind

- Electricity
- Supply

- Electricity
- Demand

- Residential
- Commercial
- Industrial
- Transport./Other

- Market
- clearing
- price and quantity

- Predetermined generation schedule

- Cost based dispatching

## Demand Determines Dispatching and Marginal Cost

- D1

- P1

**Notes:**
The single region solution algorithm is based on hourly demand and supply.

First, the monthly demand is converted to an average daily 24-hour load curve.  

For each hour, the supply is identified at the point where hourly load is satisfied.

This example ignore regional trade.  If we allow regional trade, demand may not equal supply within each region.

Please note the x-axis shows the marginal cost of supply.  It is a bit unconventional for economists but it is a convenient way to illustrate the dispatching algorithm.

## Regional Dispatching without Transmission Constraints 

- 	A 3-region trade module will have:
- A hourly load curve, which is the sum of three load curves and the 3-region supply curve, which  includes all the generators in these three regions.
- The aggregated regional demand and supply will determine required dispatching.
- The model uses region and technology codes to identify dispatching by region, technology, and fuel use.

## A Three Region Dispatching Algorithm

- Region 2
- Supply

- Region 3
- Supply

- Regional Electricity
- Supply

- Regional Electricity
- Demand

- Region 1 Demand

- Market
- clearing
- price and quantity

- Region 2
- Demand

- Region 3
- Demand

## Regional Dispatching with Transmission Constraints

- Solutions from the regional trade model provides hourly import and export of power
- For importing regions, transmission capacity estimates will be used as constraints on hourly imports. If a constraint is binding, hourly power imports will be reduced to the level of estimated transmission capacity.
- Indigenous power supply will be used to supply the reduced imports.
- Aggregate demand for the trading block will be adjusted downward to reflect the impact of any binding constraint.  Aggregate supply, which will include only the exporting regions, is used to determine dispatching.

## Data requirements - 1

- 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

## Data requirements - 2

- Hourly load curves:
- State/regional average and hourly temperature
- State/regional hourly electricity load
- 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)

## System Requirements

** ****Hardware**
- PC with 1.8 GHz CPU and 10 Gigabytes of storage space
**Software**
- Eviews 5
**Solution time**
- 20-25 seconds for each month (24 hour load)
- Total run time is about 10 minutes for 24 month.

## Model Outputs

- Power supply by technology and by regions
- Fossil fuel consumption by fuel and by regions
- Marginal cost of power supply
- Imports and exports of power by region
- Ability to assess the effects of demand on consumption of oil and gas
- Ability to assess the effects of fuel switching on demand for oil and gas

- Examples of Model Outputs

## Effects of a %5 Demand Reduction on Use of Natural Gas

## Slide 18

| Table 1:   Hypothetical Fuel Switching in July: Response to an Increase in the Relative Price of Natural Gas |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  |  |  |  |  |  |
| Region | oil consumption/trillion btu |  | gas consumption/trillion btu |  |  |
|  | Reference Case | Higher Gas Price Case | Reference Case | Higher Gas Price Case | Percent 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% |

## Demand and Generation for the Eastern Interconnection

## An Example of the effects of transmission constraints on 

- regional marginal costs

**Notes:**
Figure on the left compares mc without and with  transmission constraints.

If constraints are binding, demand for power of the trading block will be reduced, and prices will be lower.

For regions experiencing constraints, marginal costs will be higher because cheaper  imported power is no longer available and more expensive power from indigenous power plants will be used.

The impacts are significant for region 5.

It is important to note that this model is not fully calibrated and the results are for illustration only.

## Demand, Generation and Supply Curve for 

- Regions in the Eastern Interconnection

## Slide 22

## Slide 23

**Notes:**
Please note the magnitude and difference in scale.  Region three has very large generating capacity and is a major exporting region.

## Slide 24

## Slide 25

## Slide 26

## Slide 27

## Slide 28

## Slide 29

## Questions for the Committee

- The conversion of monthly demand to hourly load involves correlating HDD, CDD, to the hourly temperature, which in turn are used to estimate the hourly load. What is the most appropriate method to develop the load for fuel demand forecasts?
- The solution algorithm ignores potential feed back effects.  What do we lose by not capturing the tertiary effects of prices on dispatching decisions? _[Slide 10](slide10.xml)_