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National Renewable Energy Laboratory 

Innovation for Our Energy Future 

A Method and Case Study for Conference Paper

NREL/CP-500-38153 Estimating the Ramping May 2005 

Capability of a Control Area 

or Balancing Authority and 

Implications for Moderate or 

High Wind Penetration 

Preprint 

B. Kirby 

Oak Ridge National Laboratory 

M. Milligan, Consultant 

National Renewable Energy Laboratory 

To be presented at WINDPOWER 2005 Conference 

and Exhibition 

Denver, Colorado 

May 15–18, 2005 

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A Method and Case Study for Estimating the Ramping Capability of a 

Control Area or Balancing Authority and Implications for Moderate or High 

Wind Penetration 

Brendan Kirby 

Oak Ridge National Laboratory 

P.O. Box 2008, Oak Ridge, TN 37831 

USA 

kirbybj@ornl.gov 

Michael Milligan, Consultant 

National Renewable Energy Laboratory 

National Wind Technology Center 

1617 Cole Blvd., Golden, CO 80401 

USA 

michael_milligan@nrel.gov 

In several regions of the United States there has been a significant increase in wind 

generation capability over the past several years. Increasing and volatile natural gas 

prices have made wind energy even more attractive, and recent renewable portfolio 

standards will also result in additional wind development. As the penetration rate of wind 

capacity increases, grid operators and planners are increasingly concerned about 

accommodating the increased variability that wind contributes to the system. 

In this paper we examine the distinction between regulation, load following, hourly 

energy, and energy imbalance to understand how restructured power systems 

accommodate and value inter-hour ramps. The individual services are defined and the 

underlying cost components for each service are identified. We use data from two 

restructured markets, California and PJM, and from Western Area Power 

Administration’s (WAPA’s) Rocky Mountain control area to determine expected loadfollowing capability in each region. Our approach is to examine the load-following 

capability that currently exists using data from existing generators in the region. We then 

examine the levels of wind penetration that can be accommodated with this capability 

using recently collected wind farm data. We discuss how load-following costs are 

captured in restructured markets, what resources are available to meet these requirements, 

why there are no explicit load-following tariffs, and the societal importance of being able 

to access generator ramping capability. Finally, the implications for wind plants and wind 

integration costs are examined. 

1
Introduction 

Wind power plants are becoming much more common in the United States and around 

the world. As a result of this expansion of wind generation, grid operators, utilities, 

regulators, and customers are increasingly interested in the impact on electricity costs and 

operations. The recent studies that have addressed this issue generally divide wind’s 

impact into several time scales that correspond to operational practice. Because the grid is 

an extremely large machine, the logical framework for analyzing the impacts of wind 

begins with an analysis of the physical impacts. From there, the cost of those impacts can 

be calculated. 

This paper focuses on the load-following time frame, which generally encompasses 

periods ranging from 10 minutes up to a few hours. In this time frame, slow-start thermal 

generation that has already been committed (started) so that sufficient resources are 

available to supply the expected load plus a reserve obligation can be maneuvered to 

accommodate fluctuations in wind and load. Combustion turbines or other fast-start units 

could be started in this time frame, although that capability is not considered in this paper 

(hourly availability data is not public). Our analysis examines the thermal generation 

load-following capability that exists in three control areas based on publicly available 

data. We then examine various wind generation scenarios to determine whether the 

control areas have sufficient load-following capability to accommodate wind. We believe 

that this method, which is not as detailed as a full unit commitment and economic 

dispatch study, is useful in gaining a conceptual understanding of the factors driving 

load-following costs. It may also be useful as a screening tool prior to investing the time 

and effort in a more thorough study. 

Balancing Generation and Load: Regulation, Load 

Following, Energy Markets, and Energy Imbalance 

Load and generation must be continuously balanced on a nearly instantaneous basis in an 

electric power system. This is one of the characteristics that makes supplying electricity 

different from providing any other public good such as natural gas, water, telephone 

service, or air traffic control. It is a physical requirement that does not depend on the 

market structure. How load and generation are balanced does depend, in part, on the 

structure of the electricity markets. One benefit of interconnecting multiple control areas 

is that balancing load and generation within a single control area does not have to be 

perfect. The North American Electric Reliability Council (NERC) has established rules 

governing how well each control area must balance load and generation. Control 

Performance Standards 1 and 2 (CPS1&2) establish statistical limits on how well each 

control area must balance minute-to-minute fluctuations. Inadvertent interchange 

accounts track longer-term differences. In all cases the total system remains in balance 

(otherwise blackouts occur). When one control area fails to balance its load with its 

generation, generation in another control area provides the balance. 

The balancing of aggregate load with aggregate generation is accomplished through 

several services that are distinguished by the time frame over which they operate. 

2
Regulation and load following (which, in competitive spot markets, are provided by the 

intra-hour workings of the real-time energy market) are the two services required to 

continuously balance generation and load under normal conditions (Kirby and Hirst, 

2000). Figure 1 shows the morning ramp-up decomposed into base energy, load 

following, and regulation. Starting at a base energy of 3566 megawatts (MW), the smooth 

load-following ramp (blue) is shown rising to 4035 MW. Regulation (red) consists of the 

rapid fluctuations in load around the underlying trend, shown here on an expanded scale 

to the right with a ±55 MW range. Combined, the three elements serve a total load 

(green) that ranges from 3539 MW to 4079 MW during the 3 hours depicted. 

3000 

3200 

3400 

3600 

3800 

4000 

4200 

0 

30 

60 

90 

) 

ing 

Load and Load Following (MW) 

-60 

-30 

120 

150 

Regulation (MW

Regulation 

Total Load and 

Load Follow

7:00 8:00 9:00 10:00 

Figure 1 Regulation compensates for the minute-to-minute fluctuations in total 

system load. Load following compensates for the inter- and intra-hour ramps. 

In the PJM region, New York, New England, and Ontario, regulation is defined as the 5­

min ramping capability of a generator. In Texas it is a 15-min service, and in Alberta and 

California it is a 10-min service. 

Load following and regulation ensure that, under normal operating conditions, a control 

area is able to balance generation and load. Regulation is the use of on-line generation, 

storage, or load that is equipped with automatic generation control (AGC) and that can 

change output quickly (MW/min) to track the moment-to-moment fluctuations in 

customer loads and to correct for the unintended fluctuations in generation. A typical 

large fossil-fired thermal generator may be able to ramp 1% of its capacity in 1 minute. 

Smaller units and combustion turbines can typically ramp faster. Hydro units typically 

have very fast and accurate ramping capability. Load following is the use of on-line 

generation, storage, or load equipment to track the intra- and inter-hour changes in 

customer loads. The two differ only in the time frame over which they operate. 

3
Control areas are not able and not required to perfectly match generation and load. NERC 

has established the Control Performance Standard (CPS) to determine the amount of 

imbalance that is permissible for reliability purposes. CPS1 measures the relationship 

between the control area’s area control error (ACE)1

 and the interconnection frequency 

on a 1-min average basis. CPS1 values can be either “good” or “bad.” When frequency is 

above its reference value, undergeneration benefits the interconnection by lowering 

frequency and leads to a good CPS1 value. Overgeneration at such times, however, 

would further increase frequency and lead to a bad CPS1 value. CPS1, although recorded 

every minute, is evaluated and reported on an annual basis. NERC sets minimum CPS1 

requirements that each control area must exceed each year. 

CPS2, a monthly performance standard, sets control-area-specific limits on the maximum 

average ACE for every 10-min period. Control areas are permitted to exceed the CPS2 

limit no more than 10% of the time. This 90% requirement means that a control area can 

have no more than 14.4 CPS2 violations per day, on average, during any month. 

Regulation is the most expensive ancillary service, as shown in Table 1. Interestingly, 

control area operators do not need to specifically procure load following; it is obtained 

from the short-term energy market with generators responding to real-time energy prices. 

Regulation, however, requires faster response than can be obtained from units responding 

to market signals alone. Instead, generators (and potentially storage and/or responsive 

load) offer capacity that can be controlled by the system operator’s AGC system to 

balance the power system. 

Table 1 Average ancillary service prices (per megawatt-hour) from several markets. 

New York PJM California ISO-NE Alberta 

Regulation $28.32 

Spinning reserve 3.04 

Supplemental reserve 1.51 

Replacement reserve 1.23 

$38.94 $36.43a $38.80 $35 

— 3.89 — 30 

— 1.57 — 17 

— 0.86 — — 

Note: When available, 2003 prices are presented; otherwise, prices are from 2002. a

 California purchases up and down regulation separately. The combined price is shown here for 

comparison. 

In the remainder of this paper we attempt to shed light on why services that are so similar 

can be valued so differently. More specifically, we look at the generation mix available to 

three control areas to see whether there are reasons that load following should be so 

inexpensive. We also develop a simple method to quantify the ramping capability of a 

control area and discuss the implications for wind energy. 

1

 The area control error is the difference between scheduled and actual net interchange with a bias included 

to help maintain scheduled system frequency. 

4
Analysis Method and Data Sources 

We obtained hourly load and generator data from BaseCase, version 8.0.1. BaseCase is a 

product of Platts. The hourly generation data is available only for units that are subject to 

filing reports to the Environmental Protection Agency (EPA) for the Continuous 

Emissions Monitoring System (CEMS). This includes thermal generators, but hydro and 

nuclear units do not file and are therefore not represented in the database. Certain other 

generators are not required to file with CEMS, including some co-generation and some 

low-emission gas units. For the purposes of this study, the implication is that there is 

some existing generation in the control area that we can’t capture. Therefore, the hourly 

ramping capability that we calculate for the control area will be understated. 

We extracted data for three control areas: PJM, California (CAISO), and WAPA’s Rocky 

Mountain Region. Some generators in the WAPA region also participate in other control 

areas, so our extraction was able to identify and pro-rate the generation metered in the 

control area of interest. Results are presented in Table 2. 

The datasets were all from 2002. Data from WAPA included generation capacity ranging 

from more than 400 MW to less than 1 MW. Total peak load was 3,027 MW. It was 

served from hydro generation with a capacity of 700 MW and 30 thermal generators with 

a capacity of 2,912 MW. Although the hydro system has significant ramping capability, 

the hourly output for hydro plants was not available and it was not possible to include 

that in our estimates of system ramping capability. This lack of data results in the analysis 

methodology seriously under estimating the system ramping capability in some cases. 

Wind impacts are, consequently, overstated. 

CAISO peak load was 42,352 MW. We obtained hourly data from 133 thermal 

generators with a total capacity of 24,232 MW, which are included in our system ramping 

estimates. The 13,100 MW of hydro, 4,600 MW of nuclear, and 3,700 MW of other 

generation is not included in our ramping estimates. 

In PJM we found 55,581 MW of peak load served by 45,517 MW of fossil fired 

generation in 375 units which are included in our ramping estimates for PJM. The 2,500 

MW of hydro, 13,500 MW of nuclear and 600 MW of other capability were not included 

in our ramping calculations. 

This discussion of the datasets’ limitation shows that our estimates of the control areas’ 

ability to ramp are understated, perhaps significantly. The results of our calculations and 

discussion below should therefore be interpreted as a minimum floor on the ramping 

capability that is available from thermal resources, and that capability can be 

complemented by other generation that we were unable to measure. 

5
Table 2 Energy requirements and generation mix for three control areas in 2002. 

CAISO PJM WAPA 

Load 

Peak load (MW) 42,352 55,581 3,027 

Average load (MW) 26,573 31,357 2,173 

Measured Thermal Generation 

Number of generators 133 375 30 

Total capacity (MW) 24,232 45,517 2,912 

Highest coincident output (MW) 17,541 35,009 2,617 

Largest unit capacity (MW) 761 907 410 

Average unit capacity (MW) 182 121 97 

Average unit output (MW) 41 44 64 

Additional Generation 

Hydro (MW) 13,100 2,500 700 

Nuclear (MW) 4,600 13,500 0 

Other (MW) 3,700 600 11 

Determining Individual Generator Ramping Capability 

The first step in determining how much ramping capability is available and how much is 

needed is to determine the ramping capabilities of the individual generators. These 

capabilities are not publicly available, so we determined them by observing each 

generator’s behavior. We analyzed a year of hourly generator output data to determine 

the maximum output, minimum non-zero operating output, and MW/min ramping 

capability for each generator. Generator maximum capability is simply the maximum 

hourly output the generator achieved during the year. Generator minimum capability and 

ramping capability are slightly harder to determine. 

The minimum hourly output recorded in the data may be below the unit’s actual sustained 

minimum operating capability. If the unit was turning on or off during the hour, it would 

have spent part of the time at zero output, part of the time ramping on, and part operating 

stably. To better estimate the generator’s minimum sustainable non-zero operating 

capability, we eliminate hours immediately after startup and immediately before shut 

down. 

Each generator’s ramping capability was determined by observing the maximum change 

in output between any 2 hours during the year. Upward and downward ramping were 

determined separately. As with the determination of the generator’s minimum operating 

capability, hours immediately after startup and immediately before shutdown were 

excluded. 

These estimates of generator capability are conservative. The generators may have 

greater capability that they simply did not have call to use during the year. Also, only 

hour-long ramps can be quantified. A 50-MW combustion turbine with a 20 MW 

minimum operating capability, for example, can be credited with a maximum 0.5 

MW/min ramp rate, for example, regardless of the actual ramp rate capability. This is 

6
because the maximum change in output the unit can achieve is 30 MW and the evaluation 

interval is 60 minutes. The unit might be capable of ramping from 20 MW to 50 MW in 

under 10 minutes, giving better than 3 MW/min ramp rate, but the analysis methodology 

limits the calculated ramp rate to 1/6th that value. Conversely, this method does not 

capture other limitations, such as temporary unit de-ratings or emissions limitations. 

Knowing each generator’s maximum and minimum operating capability and the up and 

down ramping capability allows us to determine the aggregate ramping capability 

available to the control area each hour of the year. System hourly MW/min ramping 

capability is the sum of the ramping capabilities of each generator that is on line that 

hour. Each generator’s hourly ramping capability can be limited, for that hour, by the 

generator’s current output and the maximum or minimum output capability. For example, 

a generator that is capable of 3 MW/min upward ramping would be limited to 0.2 

MW/min if it had a maximum output capability of 200 MW and was operating at 188 

MW during an hour (12 MW maximum ramp up / 60 minutes). 

Table 3 summarizes the generator up and down ramping capabilities for the three control 

areas we studied. The small size (182, 121, and 97 MW for CAISO, PJM, and WAPA 

respectively), and the even smaller operating range of most units limits the calculated 

ramping capability for ramps lasting less than an hour. Both CAISO and PJM have a few 

large units that are also relatively fast. Again, these limitations combined with the 

unavailability of hydro data understates, in some cases significantly, the system ramping 

capability and correspondingly overstates the potential impact of wind generation. 

Table 3 Thermal generator ramping capabilities in MW/min. 

Measured Thermal Generation (MW/min) CAISO PJM WAPA 

Fastest unit MW/min ramp capacity (up/down) 8.6/-7.8 9.1/-8.9 2.4/-2.4 

Average unit MW/min ramp capacity (up/down) 1.6/-1.6 0.8/-0.8 0.6/-0.7 

Total capacity (up/down) 215/-214 291/-306 17/-20 

Total simultaneous capacity (up/down) 168/-175 160/-288 9/-19 

Maximum used capability (up/down) 42/-66 54/-61 3/-6 

Analyzing System Ramping Capabilities and Requirements 

The ramping capability available to the control area is the sum of the individual 

generators’ ramping capabilities. This aggregate capability varies from hour to hour as 

different generators come on and off and as their operating levels vary. Having 

determined the maximum and minimum output along with the ramping capabilities of 

each generator we were able to reexamine the year of load data and determine, for each 

hour, what the control area ramping requirements were and what excess ramping 

capability was available from the thermal generation. We only consider generation 

ramping capability that is in the same direction as the current load requirement. That is, 

up-bound ramping capability is evaluated when the load is ramping up and down-bound 

ramping capability is evaluated when the load is ramping down. 

7
The last three lines of Table 3 present the total control area thermal ramping capabilities. 

As expected, the total capability of all the units exceeds the maximum capability that is 

ever actually available. There are two primary reasons for this. First, all the units are 

never on line at the same time. Second, some of the thermal units are typically operating 

near their full output, so they have limited capability to ramp up. Interestingly, none of 

the control areas use the full thermal ramping capability. 

Ramping Capability and Requirements in PJM, CA, and 

WAPA/RM 

Thermal ramping capabilities typically exceed control area load ramping requirements for 

the three control areas we studied. Figure 2 presents histograms of both the generation 

capabilities and the load requirements. WAPA is a significantly smaller control area with 

lower ramping capabilities and needs. Consequently, the PJM and CAISO curves use a 

different scale than the WAPA curve. Results from all three control areas are plotted 

together, however, to show the similarities in the shapes and in the relative differences 

between the generation capabilities and the load requirements. Load ramping 

requirements from all three control areas are similar (other than the 20-to-1 scale factor 

difference between WAPA and CAISO/PJM). Ramp up capabilities are also similar. 

Ramp down capabilities show greater differences between the control areas. The 

California ISO control area tends to operate with many more generators partially loaded 

for many hours of the year. Generators are poised to move up or down and the generation 

ramping capabilities histogram is fairly symmetric. Both WAPA and PJM have more 

base loaded coal fired generators that tend to operate closer to full load. These systems 

have more ramp down capability from their thermal generators than ramp up capability. 

Still, thermal ramping capability exceeds load ramping requirements. 

The histograms presented in Figure 2 do not show simultaneous requirements and 

capabilities. Figure 3 presents simultaneous load ramping requirements and thermal 

generation ramping capability as a ratio. Thermal ramping capability exceeds load 

requirements, in both the up and down directions, for all but 30, 100, and 260 hours for 

PJM, CAISO, and WAPA respectively. For most hours the thermal ramping capability 

far exceeds the load ramping requirements. The extremely high ratios of capability to 

requirements on the left side of the graph result from times when the load is not ramping 

much and are not overly significant. The excess capability represented for many hours in 

the middle of the graph, when the load is ramping moderately, are more important. The 

control areas never fell short of ramping capability; significant hydro and other ramping 

resources are available to each control area but are not captured in our data. 

8
-400 

-300 

-200 

-100 

0 

100 

200 

0 2000 6000 8000 

0.0 

5.0 

10.0 

4000 

PJM & CA ISO Ramping MW/Min 

-20.0 

-15.0 

-10.0 

-5.0 

WAPA Ramping MW/Min 

PJM Thermal Ramping Capability 

PJM Load Ramping Need 

CA ISO Thermal Ramping Capability 

CA ISO Load Ramping Need 

WAPA Thermal Ramping Capability 

WAPA Load Ramping Need 

Ramp Up 

Ramp Down 

Hours/Year 

Figure 2 Thermal ramping capabilities typically exceed load ramping requirements 

in three control areas studied. 

Figure 3 Thermal ramping capability exceeds load ramping requirements in all 

three control areas more than 97% of the time. 

9
10

Adding Wind Ramping Requirements 

Unfortunately we were unable to obtain wind generation data that correspond to the load 

and generation. However, our main purpose is to demonstrate methods for evaluating the 

ramping that exists in a control area and how that can be applied to help determine 

whether a given size wind plant can be accommodated. We used two data sets to illustrate 

our method. The first came from the Buffalo Ridge Wind Plant in southwest Minnesota 

(Wan, 2001). The second is a hypothetical wind plant composite from Eastern Colorado, 

which was used in the recent RMATS study (RMATS, 2004). That data came from the 

Utility Wind Resource Assessment Program at the National Renewable Energy 

Laboratory. Hourly wind generation was calculated based on a modern 1.5-MW-class 

turbine. The resulting hypothetical wind plant is 1723 MW, representing a very large 

wind penetration for the WAPA control area. 

Having determined the load ramping requirements and the ramping capability available 

from thermal generation, we next examined the impact of adding significant wind. Figure 

4 adds the ramping requirements of the 230-MW Buffalo Ridge wind plant and the 1723-

MW Colorado East hypothetical wind plant to the thermal capabilities and load ramping 

requirements of PJM and CAISO previously presented in Figure 2. Clearly these wind 

plants would not present a significant additional ramping burden to either of these two 

large control areas. 

-400

-300

-200

-100

0

100

200

0 2000 4000 6000 8000

Hours/Year

PJM, CA ISO & Wind Ramping MW/Min

PJM Thermal Ramping Capability

CA ISO Thermal Ramping Capability

PJM Load Ramping Need

CA ISO Load Ramping Need

CO-E 1723 MW Wind Plant Ramping Need

Buffalo Ridge 203 MW Wind Plant Ramping Need

Ramp Up

Ramp Down

Figure 4 Ramping requirements for the 230-MW Buffalo Ridge and 1723-MW 

Colorado East wind plants are small in comparison to the load ramping 

requirements of PJM or CAISO. 
15 

MW/min Ramping Requirement 

10 

5 

0 

-5 

-10 

-15 

0 

idge 

2000 4000 6000 8000 

3027 MW of WAPA Load 

Load + Buffalo R

Load + 25% Wind Penetration 

Load + 1723 MW of Wind 

Ramp Up 

Ramp Down 

Hours/Year 

Figure 5 Adding the 230-MW Buffalo Ridge wind plant does not significantly 

raise the WAPA load ramping requirements, but adding 757 MW or 1723 MW 

of Colorado East wind plant does. 

The impact of additional wind would be expected to be much greater on the smaller 

western control area. Figures 5 and 6 confirm this speculation. The effect of adding the 

hour-to-hour wind ramping requirements to the WAPA hour-to-hour load are presented 

in Figure 5. The 230-MW Buffalo Ridge wind plant does not have a large impact, and the 

orange curve showing the combination of Buffalo Ridge and the WAPA load essentially 

overlays the blue curve showing the WAPA load alone. The 1723-MW Colorado East 

wind plant significantly increases the ramping requirements, as shown by the green 

curve. We also scaled the Colorado East wind plant back to 757 MW (25% of the control 

area peak load) and show the results as the turquoise curve. 

The WAPA thermal generation ramping capability is added to Figure 6. Clearly the 1723- 

MW Colorado East wind plant would present ramping problems for the WAPA thermal 

generation. 

The wind/load requirements are not simultaneous with the thermal generation capabilities 

shown in the Figure 6 histograms. Figure 7 does present simultaneous results. Adding the 

1723-MW Colorado East wind plant to the WAPA load would exceed the thermal 

generation ramping requirements 1655 hours in 2002. Restricting Colorado East to 757 

MW would have reduced the number of hours when thermal generation could not meet 

the ramping requirements to 718 hours. With or without the Buffalo Ridge wind plant, 

thermal generation cannot meet the control area ramping requirements during 242 hours. 

It is important to remember that this analysis does not include the 700 MW of hydro 

generation that WAPA actually uses for ramping. It is also important to note that 

generation was not redispatched or recommitted when wind was added. It is also likely 

that additional ramping capability exists in neighboring control areas, which could 

11
12

increase the ability of the WAPA control area to absorb additional wind. Consequently 

these results are illustrative at best. 

Figure 7 Thermal generation would have had insufficient ramping capability to 

accommodate the hypothetical Colorado East wind farm for 1655 hours in 2002. 

Figure 6 Although significant thermal ramping capability exists in the WAPA 

control area, up bound ramping capability would be challenged by the addition 

of 1723 MW of wind generation. 

-20

-15

-10

-5

0

5

10

15

0 2000 4000 6000 8000

Hours/Year

MW/min Ramping Capability and Load Need

Thermal Generation Capability

3027 MW of WAPA Load

Load + Buffalo Ridge

Load + 25% Wind Penetration

Load + 1723 MW of Wind

Ramp Up

Ramp Down

0%

50%

100%

150%

200%

7000 7500 8000 8500

Hours/Year

Thermal Ramping Capability

Control Area Load

Load + 203 MW Buffalo Ridge Wind

Load + 757 MW Wind (25%)

Load + 1734 MW Wind
Analysis Limitations 

Although we have pointed out some of the limitations of this analysis, we would like to 

reiterate and expand on them here. The limitations can be separated into those involving 

incomplete data and those involving the specific algorithm we have used. 

Because there are many generating resources that do not file with the EPA CEMS, our 

data set is incomplete. Our generator database does not include hydro, which can be a 

significant source of ramping capability. This most likely has the largest impact for 

WAPA, but it is also significant in CA. Certain gas and co-gen plants do not report to the 

CEMS. The relative impact of this omission in our data set is significant. Based on the 

reported measured output of generators in our database, measured generation provided 

57% of the peak load in CA, 86% of the peak load in WAPA, and 63% of the peak load 

in PJM. Although the relative impact in the WAPA control area appears to be much 

smaller than in the other areas, WAPA is less than 10% of the size of the other two 

control areas, so the percentage does not tell the whole story. 

Another data limitation is that the wind data is not from the same year as the load and 

generation data. We are also using wind from geographically remote locations relative to 

some of the control areas. We recognize that this limits the ability to interpret our 

quantitative results, but we believe that this approach can be useful to others. 

The data can’t tell us what the precise ramping capability is for the units we observed. 

Units that can move fast enough to reach rated capacity within an hour can’t be 

accurately measured by our method. There may be units that can ramp quickly, but actual 

operation during 2002 did not call upon those units to ramp near their capability. 

Limitations are also embedded in the approach. The method cannot distinguish between 

units that are committed and those that aren’t. We ignore transmission constraints, and 

local issues such as volt-amps-reactive (VAR) or voltage- support requirements that may 

inhibit the ability of a unit to ramp during certain system conditions. 

The method is not intended to be a substitute for rigorous methods that optimize unit 

commitment and economic dispatch such as the recent studies of wind integration 

impacts on the New York power system (Piwko, 2005) or the Xcel power system 

(Zavadil, 2004). In our study, the added wind generation did not displace existing 

generation; we did not redispatch or (more importantly) recommit any of the generation. 

We did not have enough data about any of the generators to perform such a detailed 

analysis. 

We feel the study still has value, however, because it provides a transparent view into the 

interactions between types of generation and the importance of generator ramping 

capability. This method allows the reader to get a feel for the size of the ramping resource 

that is already available from existing thermal generation. It allows a rough comparison 

of the size of that resource with the ramping needs of system load and wind. 

13
Implications for Wind 

Specific implications for wind generation in the regions studied must be tempered with 

the limitations of the available data sets. One key issue is that, because it was not possible 

to obtain coincident load, generation, and wind data, our quantitative conclusions are 

limited. However, some general conclusions can be drawn. 

First, it is possible to calculate a lower bound to the ramping capability within a control 

area using public databases. Although in our experience some significant capabilities of 

the control area could not be estimated, we are confident that our estimates provide a 

lower bound of the ramping capability in WAPA, CAISO, and PJM. In reality, more 

ramping capability exists than we were able to measure. 

Second, it appears that there is a very large amount of ramping capability during most 

hours of the year in each region we studied. This ramping capability is a natural result of 

the resource mix that has developed in the area. Because each increase or decrease in 

wind generation does not need to be matched one-for-one by another generator, the 

ability of these regions to absorb moderate or even large quantities wind generation 

appears significant for most of the year. 

All three control areas we studied appear to have significant ramping resources available 

from thermal generation that is partially loaded and physically able to respond. PJM and 

CAISO and most ISOs run energy markets that clear several times an hour, providing 

access to the ramping capabilities of the generators active in the energy markets. Control 

areas that do not have access to fluid intra-hour markets still have the physical 

capabilities of the generators but may not have access to that capability simply based on 

the hourly market structure. This lack of access denies the generators the ability to 

position themselves (ramp) to sell as much energy as customers want, forces the control 

area operator to use additional regulating resources instead, and forces consumers to pay 

for the inefficiency. 

There may be significant opportunities for neighboring control areas to assist each other 

in the load-following time frame as well. This is partly a natural consequence of the 

ability of larger control areas to better manage variability, whether caused by load, wind, 

or a combination of both. It is also a consequence of additional capability being 

inherently available from a larger pool of generators. 

Conclusions 

Assessing the ramping capability of a control area with public data presents some 

challenges. Because some data are unreported and because of the shortcomings of our 

method, it is not possible to obtain an accurate measure. However, having said that, we 

think that this type of analysis can be useful in several ways. The estimates provided by 

this approach provide a lower bound on the load-following capability in a control area. 

The approach is transparent, which makes it possible to more easily understand how the 

more complex methods embodied in production simulation models work. The approach 

could easily be extended to include data from non-CEMS-reporting resources. For 

14
entities that have access to such data, a more detailed analysis would be possible and 

would provide a better estimate of the load-following capability of the control area. 

Combining such data with estimates of hourly wind energy production would allow for 

an assessment of the control area’s ability to absorb significant penetrations of wind 

energy. 

Our quantitative results are not complete. However, a few key observations emerge. 

Larger control areas have the ability to handle larger wind power plants. Although this 

may seem obvious, our method allows for a first cut at quantification. Second, the 

approach could easily be expanded to evaluate the ability of combined control areas, or 

institutional arrangements between control areas, to increase the region’s ability to 

integrate larger quantities of wind energy. 

Markets have developed that recognize the high cost and value of regulation. Similar 

markets have not developed for load following. Our analysis indicates that, at least for the 

control areas studied, there is significant ramping capability physically available most of 

the time from thermal generation. While regulation is a specific service a generator sells 

to a control area operator, ramping is what a generator does for free in order to position 

itself to make or discontinue the next energy sale. This points out the critical need to have 

mechanisms to access the ramping capabilities of generators. The ramping generator, the 

power system, and the other system customers benefit when generators have the freedom 

to respond. The alternative is to leave the available ramping capability of generators in 

the energy market idle and instead procure expensive regulation (while denying the 

maneuverable generators energy sales). Creating sub-hourly energy markets is one, but 

not the only, way to obtain access to this capability. 

Our method generally underestimates, and in many cases significantly underestimates, 

control area ramping capability because it excludes hydro, fast start, and other nonreporting resources. It is quite possible, however, that a control area could have a 

generation mix that did not provide sufficient ramping capability to meet the loadfollowing needs. In that case it would be necessary to find ways to pay for ramping (in 

order to draw resources into the market) and to charge users of the service for the costs 

they incur. First, however, it is necessary to determine if the system has excess ramping 

capability that only needs to be accessed. 

References 

Kirby, B., and E. Hirst. 2000. Customer-Specific Metrics for the Regulation and LoadFollowing Ancillary Services, ORNL/CON-474, Oak Ridge National Laboratory, Oak 

Ridge, TN, January. 

Piwko, R., X. Bai, Clark, G. Jordan, N. Miller, J. Zimberlin. 2005. The Effects of 

Integrating Wind Power on Transmission System Planning, Reliability, and Operations, 

Report on Phase 2:System Performance Evaluation, GE Energy, Energy Consulting, 

March. 

15
RMATS, 2004. Rocky Mountain Area Transmission Study. Available at 

http://psc.state.wy.us/htdocs/subregional/home.htm. Accessed May 10, 2005. 

Wan, Y. 2001. Wind Power Plant Monitoring Project Annual Report. 50 pp.; NREL 

Report No. TP-500-30032. National Renewable Energy Laboratory, Golden, CO. 

Zavadil, R., J. King, L. Xiadong, M. Ahlstrom, B. Lee, D. Moon, C. Finley, L. Alnes, L. 

Jones, F. Hudry, M. Monstream, S. Lai, J. Smith. 2004. Xcel Energy and the Minnesota 

Department of Commerce, Wind Integration Study - Final Report, EnerNex Corporation 

and Wind Logics, Inc., September 28. 

16
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4. TITLE AND SUBTITLE 

A Method and Case Study for Estimating the Ramping Capability of 

a Control Area or Balancing Authority and Implications for Moderate 

or High Wind Penetration: Preprint 

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14. ABSTRACT (Maximum 200 Words) 

In several regions of the United States there has been a significant increase in wind generation capability over the 

past several years. As the penetration rate of wind capacity increases, grid operators and planners are increasingly 

concerned about accommodating the increased variability that wind contributes to the system. In this paper we 

examine the distinction between regulation, load following, hourly energy, and energy imbalance to understand how 

restructured power systems accommodate and value inter-hour ramps. We use data from two restructured markets, 

California and PJM, and from Western Area Power Administration’s (WAPA’s) Rocky Mountain control area to 

determine expected load-following capability in each region. Our approach is to examine the load-following capability 

that currently exists using data from existing generators in the region. We then examine the levels of wind penetration 

that can be accommodated with this capability using recently collected wind farm data. We discuss how loadfollowing costs are captured in restructured markets, what resources are available to meet these requirements, why 

there are no explicit load-following tariffs, and the societal importance of being able to access generator ramping 

capability. Finally, the implications for wind plants and wind integration costs are examined. 

15. SUBJECT TERMS 

wind energy; wind plant; wind farm; capacity; grid; regulation; load following; hourly energy; WAPA; PJM 

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