# Presentation: 908721

## Modeling Regional Electricity Generation

**Phillip Tseng**
**Statistics and methods Group**
**Energy Information Administration**
**April 2007**

- *This is a working document prepared by the Energy Information Administration (EIA) in order to solicit advice and
- comments on statistical matters from the American Statistical Association Committee on Energy Statistics. This topic
- will be discussed at EIA's spring 2007 meeting with the Committee to be held April 19 and 20, 2007

**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.

## Overview

**Review of past effort**
**Objectives of this modeling effort**
**Data Sources**
**Challenges**
**Methodology**
**Preliminary findings**
**Questions on fuel switching for the Committee**

## Past Effort

**	**
**Started regional electricity modeling about 2 years ago. **
**The objective was to increase regional details to the short term projections and analysis**
**The methodology used variable costs to represent supply curves, required longer solution time, and was very difficult to achieve satisfactory solution**
**Lost a valuable colleague **

## Objectives

**	****Develop a short-term regional electricity model capable of performing two functions:**
**Project power generation from power plants burning fossil fuels**
**Conduct scenario analysis and provide insights into potential power flow problems **
**	**
**	****We focus on fossil plants because in the short run dispatching pattern of other types of power plants are less responsive to changes in demand.**

## Data Sourcesby state/region

- Power control area
- Daily Hourly Load

- Generation capacity
- by fuel type
- EIA-860
- Annual

- Sales and revenue by
- end-use sector
- EIA-826
- Monthly (billing)

- Net generation
- by generator/plant
- EIA-906
- Monthly (calendar)

- Demand curves
- (24-hour load)

- Supply curves
- (By fuel type)

**Notes:**
The challenge of this project, at the outset, is developing a model capable of linking all  EIA electricity data sources and making it operational.

## Electricity Regions/Sub-regions

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

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

## RSTEM 13 Electricity Demand and Supply Regions

## Challenges

**Develop a methodology to integrate these data with different characteristics**
**Provide a reasonable modeling framework to project generations of fossil plants**
**Provide an analytical framework capable of answering what if questions**
**Use Eviews to solve and maintain the model with ease**

**Notes:**
The challenge of this project, at the outset, is developing a model capable of linking all  EIA electricity data sources and making it operational.

## Dispatching Methodology - 1

**Use most up-to-date data (year 2005) to project generation for the next 24 months**
**Power plants owner should dispatch least cost generators first then move to the next lowest cost units as demand increases**
**Actual dispatching decisions may depend on the ****least system cost**** instead of least variable cost because of spatial considerations such as:**
**costs and accessibility of transmission lines,**
** ****distance to load centers, **
**transmission and distribution losses, **
**and availability of fuels.**

## Dispatching Methodology - 2

**The construction of short-run supply curves for coal, natural gas, residual fuel, and diesel fuel, uses observed dispatching patterns instead of costs due to lack of detailed data.**
**Fossil plants are sorted by utilization rates and assigned 10 bin numbers**
**Two functional forms are selected to fit the supply data (cumulative capacity); one for each of the  4 types of fossil plants (coal, gas, diesel , and residual fuel)**
**For each demand level, adjusted for non-fossil plants, generation is determined by equating demand to the combined quadratic supply curve.**

## Bin Assignment by Fuelsfor Texas (capacity in megawatt)

- 		Capacity by bin numbers		Cumulative capacity
- 	Bin	Coal	Diesel	Resid	NG	Coal	Diesel	Resid	NG
- 	1	4623	1	0	2319	4623	1	0	2319
- 	2	3944	2	0	5667	8567	3	0	7986
- 	3	6670	3	0	6647	15237	6	0	14633
- 	4	2764	4	0	6945	18001	10	0	21577
- 	5	0	6	0	5313	18001	16	0	26890
- 	6	0	9	0	5333	18001	25	0	32223
- 	7	1880	12	0	3052	19881	37	0	35275
- 	8	0	14	0	8714	19881	61	0	43989
- 	9	0	15	0	4816	19881	66	0	48805
- 	10	0	16	0	14461	19881	82	0	63265

## Adjust Availability Factors of Coal Plants

**	****Most operators of coal plants reported use of small amounts of gas, diesel, or residual fuel. It implies that coal plants are regularly shut down for ash removal or routine maintenance. For model calibration, we applied three factors to three regions:**
**Eastern region: 0.93**
**Western region: 0.94**
**Texas: 0.96**

## Fit the Curves

**For coal the functional form is:**
**Log(coal) = C(0) + C(1) * (1/bin) **
**For Gas, diesel, and residual fuel, the functional form is:**
**Log(XXX) = C(0) + C(1) * bin + C(2) * Bin^2**
**Natural log is used to avoid negative values.**

## Texas Coal Supply Curve  from July 2005 Data(right axis is log of capacity, horizontal axis is bin #)

## Texas Gas Supply Curve  from July 2005 Data

## Texas Diesel Fuel Supply Curve  estimated with adjusted July 2005 Data

## Convert Monthly Sales to Load Curves

- O

## Subtract non-fossil generation from  load curves

**For nuclear, renewable energy, and other non-utility fossil generation, we divide the generations by 24 and subtract them from the hourly loads.  For solar and wind, we can change the adjustment to the load curve easily.**
**For California, we assume hydro generation is proportional to the 24-hour load curve**
**For hydro storage, we divide the number by 6 and subtract from load 1 through 6. (note hydro storage is reported as negative)**

## Solve for Generation

- oil

- gas

- coal

- demand

- Total fossil supply

- Bin #

- Generation

**Solve for ****Generation**

## Preliminary Findings

**The 2005 monthly dispatching curves worked well for coal and gas generators in most months**
**The out of sample test perform satisfactorily for historical 2004 and preliminary 2006 data**
**The model is capable of capturing seasonal fluctuations**
**Shape of load curves (max and min, kurtosis) may affect dispatching of oil-based generators.**

## Eastern Region Coal and Gas Generation(mwh/day)

## Western Region Coal and Gas Generation(mwh/day)

## Texas Coal and Gas Generation(mwh/day)

## Performance of the Model - 1

**	****We use two measures to evaluate the performance of the model**
**Mean absolute percentage error**
**Twelve month percentage difference**

## Performance of the Model – 2(mean absolute percentage error)

|  | East |  | West
 |  | Texas |  |
| --- | --- | --- | --- | --- | --- | --- |
|  | Coal | Gas | Coal | Gas | Coal | Gas |
| 2005 | 2.58% | 4.15% | 1.92% | 2.88% | 2.39% | 2.10% |
| 2004/5 | 3.06% | 7.44% | 2.44% | 3.51% | 3.32% | 3.04% |

## Performance of the Model – 3(12 month percentage difference)

|  | East |  | West
 |  | Texas |  |
| --- | --- | --- | --- | --- | --- | --- |
|  | Coal | Gas | Coal | Gas | Coal | Gas |
| 2005 | 2.35% | 4.06% | 0.62% | -0.83% | -0.23% | 0.24% |

## Modeling Issues

**The electricity dispatching model does not do a good job in capturing dispatching of oil-based generators.**
**The dispatching curves assume system costs do not change from one year to the next. It needs an adjustment factor to consider oil and gas price changes:**
**More generation will be from gas units when gas is cheaper**
**For dual fueled generators, some units may switch from gas to oil when gas is cheaper **

## Questions for the Committee

**	****A few sub-regions in the East have generators that can switch from gas to oil or vice versa. The adopted modeling methodology needs to estimate a switching variable to capture the effects of gas and oil prices on gas and oil use**
**Is it appropriate to use sub-regional generation data and fuel prices to estimate the effects of prices on fuel switching?**

## Simulation and Contingency Analysis

**Simulate the effects of power outage on generation and inter-regional power flow**
**Simulate the effects of gas supply shortages on regional generation**
**Simulate the effects of emissions constraints on generation (we can truncate the supply curve)**
**These simulations can be performed by changing the capacity matrix and re-estimating the supply curves**

## Sub-regional Demand and total generation in the Western Region (meg watts per day)

**Notes:**
Natural gas uses scale on the right

## Insights on Transmission Problems

**A well-calibrated model can show load, generation, and imports/exports in each sub-region**
**The simulated hourly indigenous generation and imports can provide valuable information on supply capability and possible bottlenecks on imports**
**We use California as an example to demonstrate a potential application of the model in this area.**

## Slide 32

**Notes:**
Natural gas uses scale on the right

## Slide 33

**Notes:**
Natural gas uses scale on the right

## Simulation and Contingency Analysis

**Note that every capacity matrix is derived from EIA-860 and EIA-906 and can be traced back via plant ID.**
**Each capacity matrix has a corresponding fuel use matrix and emissions matrix, which can be used for contingency and emissions analysis.**
**These types of analysis can be performed iteratively by running the model, assessing the fuel use and emissions, and adjusting the capacity matrix and power supply curves.**

## 
Thanks for your Attention

**Thanks for your Attention**