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Cost-Causation-Based Tariffs for 

Wind Ancillary Service Impacts 

Preprint 

B. Kirby 

Oak Ridge National Laboratory

M. Milligan (Consultant) and Y. Wan 

National Renewable Energy Laboratory 

To be presented at WINDPOWER 2006 

Pittsburgh, Pennsylvania 

June 4–7, 2006 

Conference Paper 

NREL/CP-500-40073 

June 2006 
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Cost-Causation-Based Tariffs for Wind Ancillary Service 

Impacts 

Brendan Kirby 

Oak Ridge National Laboratory 

P.O. Box 2008 

Oak Ridge, TN 37831 

USA 

865-576-1768 

kirbybj@ornl.gov 

Michael Milligan, Consultant 

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401 

USA 

303-384-6927 

michael_milligan@nrel.gov 

Yih-huei Wan 

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401 

USA 

303-384-7025 

yih-huei_wan@nrel.gov 

Abstract 

As the use of wind energy increases in the United States, there has been significant 

interest in assessing the integration cost of wind. With higher penetration, these costs 

have been shown to increase but are generally modest relative to the price of wind 

energy. Although specific tariffs for wind generation for ancillary services are 

uncommon, we anticipate that balancing authorities (control areas) and other entities will 

move toward such tariffs. Tariffs for regulation and imbalance services should be costbased, recognize the relevant time scales that correspond with utility operational cycles, 

and properly allocate those costs to those entities that cause the balancing authority to 

incur the costs. In this paper we present methods for separating wind’s impact into 

regulation and load following (imbalance) time scales. We show that approximating these 

impacts with simpler methods can significantly distort cost causation and even cause 

1
confusion between the relevant time scales. Correctly calculating and allocating cost 

impacts provide a market signal that can encourage economic efficiency and avoid 

subsidies. We present results from NREL’s wind data collection program to illustrate 

some of the dangers of linearly scaling wind resource data from small wind plants to 

approximate the wind resource data from large wind plants. Errors in scaling can cause a 

significant over-estimate of wind impacts in the regulation and load following time 

frames and result in cross-subsidies in the resulting tariff. Finally, we provide a 

framework for developing regulation and imbalance tariffs, we outline methods to begin 

examining contingency reserve requirements for wind plants, we provide guidance on the 

important characteristics to consider, and we provide hypothetical cases that the tariff can 

be tested against to determine whether the results are desired. 

Introduction 

In this paper, we discuss general principles of tariffs with the goal of pointing out how 

tariffs can address pricing for services based on cost-causation. Because the use of wind 

energy has expanded significantly over the past few years, some Balancing Authorities 

(BAs) are considering how to develop their own tariffs for wind plant integration. If care 

is not used to develop tariffs that address the specific costs for regulation and imbalance, 

for example, then these tariffs may either over-charge or under-charge wind for its 

impacts. 

Good Tariff Design Should Be Based on Operating 

Principles 

Modern power systems are complex networks of machines. To operate effectively, 

system operation personnel have developed various procedures that ensure the reliable 

operation of the power system. The fundamental task is to maintain balance between 

system loads and generation. Although this is simple in concept, the practice of power 

system operation is quite complicated. Electric loads vary over all time scales, from a few 

cycles up through longer periods such as days, weeks, or months. The task of maintaining 

system balance is divided according to the time scale. Figure 1 is a typical representation 

of the critical time scales. From the operational perspective, the unit commitment time 

scale, which can range from several hours to a few days, represents the time required to 

ensure adequate slow-start generating units are available to provide power. Performing 

the commitment function correctly requires knowledge of future loads, along with other 

relevant variables like the availability of other generating plants. 

The Regulation Time Frame 

Once the unit commitment process is completed, the operator has the ability to control 

this online generation, subject to the various physical and electrical constraints of the 

generators and the balance of system. Some of this control involves automatic generator 

control (AGC) in response to the changing load-resource mix, performed automatically 

by computer. This process typically occurs over several seconds to several minutes, 

depending on the system. AGC units respond in the regulation time scale, and this 

ancillary service is typically quite expensive. In the minute-to-minute regulation time 

frame, loads move in uncorrelated patterns, and the generators that perform the regulation 

2
service do not need to match each individual load change—only the aggregate system 

balance needs to be chased by the regulating units. 

Time (hour of day)

048 12 16 20 24

System Load (MW)

seconds to minutes

Regulation

tens of minutes to hours

Load

Following

day

Scheduling

Days

Unit

Commitment

Figure 1. Representation of critical time scales for system operation 

The Load Following Time Frame 

The next time frame is load following. Changes in load in this time frame are generally 

correlated across electric customers, although there is typically a regulation “wiggle” that 

is superimposed on the correlated movements. The correlation typically occurs during the 

morning load pickup, when many customers increase their electrical demand, and in the 

evening when electrical demand falls off. The system operator must follow load with 

generation that is either already online or generation that can be brought online quickly. 

In either case, the load following unit(s) must have the capability of changing output in 

response to operator instruction and must be able to follow the instruction fairly closely 

(though they do not necessarily have to be on AGC). 

3
The Unit Commitment Time Frame 

Significant time is required to prepare many generators for operation. The unit 

commitment decision, the process of deciding which generators are going to be used on a 

given day, is typically made the day before the operating day based on the load forecast 

and the schedule of available generators. 

The total package of providing load following and regulation, along with voltage support 

(VAR support), contingency reserves, and imbalance service is called ancillary service 

provision. In some U.S. regions, markets cover some ancillary services. In other parts of 

the country, utilities still operate as regulated monopolies and sometimes have access to 

wholesale markets to purchase energy, capacity, and ancillary services. However, this 

latter market is not widespread today in the United States. 

Regardless of whether electricity is provided by a market or regulated monopoly, the key 

performance criterion for power system operation is to ensure that generation and load 

are in balance. This means that the aggregate load must be matched by the aggregate

generation within statistical limits that are prescribed by the North American Electric 

Reliability Council (NERC). This has a critical implication for wind: it is not necessary 

or useful to match each increase or decrease in wind power output by a corresponding 

movement of another generator. Instead, only the aggregate system increases or decreases 

in demand must be matched by changes in generation. This has important implications 

for tariffs related to wind ancillary service provision. The metric used to measure the 

impact of wind on ancillary service requirements for the system cannot be based solely 

on wind generation alone. It must instead be based on the contribution that wind makes to 

the overall level of ancillary services needed to balance the system. 

Economic Efficiency and Ancillary Services 

Because it is critical to operate the power system in a reliable and economic manner, one 

can deduce that society benefits if the economically efficient quantities of these ancillary 

services are provided, whether by regulated entities or markets, at their unique cost of 

service. Charging for these products at the cost of service sends the correct economic 

signal to users, who can then choose whether to use the services based on prices that 

reflect the value of the services to them at a particular, cost-based price. Economic 

efficiency is achieved only if the marginal benefit to society equals marginal cost to 

society. In perfectly competitive markets, the marginal benefit curve coincides with the 

demand curve in the absence of externalities. Similarly, marginal costs coincide with the 

supply curve. Application of these concepts to ancillary service markets implies that the 

economically efficient quantity of the ancillary service should equal its marginal cost. 

The benefit to society includes reliable power system operation, and because ancillary 

services are required for reliable electricity supplies, the benefit of reliability is included 

in overall benefits. 

Using regulation as an example, the left panel of Figure 2 illustrates the consequences of 

too much regulation capacity. The upward sloping red line represents the marginal cost 

(supply) of regulating resources. To obtain more regulation, buyers need to increase the 

price. The downward sloping blue line represents the marginal benefit (demand) for 

4
regulation. The intersection of these curves shows the socially optimal level of regulation. 

However, if more regulation is provided than is optimal, such as represented by the 

vertical green line, then a deadweight loss is incurred by society. This deadweight loss is 

represented by the black triangle and can be interpreted as the economic value that is lost 

due to the oversupply of regulation. Although an oversupply of regulating resources is 

not harmful, from the reliability standpoint it is costly and economically inefficient. 

Regulation

MW-hour

Price

Demand

Supply

Regulation

MW-hour

Price

Demand

Supply

Figure 2. Deadweight loss for (a) too much regulation, (b) insufficient regulation 

 

A similar deadweight loss occurs if there is not enough regulation. In this case, system 

reliability may be compromised. Buyers would be willing to buy more regulation than the 

market or tariffs provide at this inadequate level. But if some type of constraint prevented 

the acquisition of enough regulation, a deadweight loss (represented by the triangle in the 

right panel of Figure 2) would occur. Although these graphs are conceptual, they 

collectively illustrate why it is important to correctly determine the efficient quantity of 

regulating resources. This simple example could easily be extended for other ancillary 

services. 

If ancillary services are provided by a market, one benchmark for the market would be to 

determine if the efficient level of ancillary services is provided. Markets with too many 

or too few ancillary service provisions are not efficient.1 From this discussion we can 

conclude that: 

• There is a real benefit to acquiring sufficient ancillary services. 

• There is a real cost to acquiring excess ancillary services. 

• The objective is to balance aggregate system load with available aggregate 

generation, and it is therefore unnecessary and costly to balance individual loads 

or generators. 

• Markets, and therefore tariffs, should encourage market behavior that results in 

economically efficient outcomes (minimal deadweight loss). 

 

1

 Throughout this discussion, we assume that the cost and benefit curves have been adjusted for risk, so that 

a rational operator with imperfect foresight can prudently acquire ancillary services that may not be needed 

in hindsight, but whose cost is less than what might be incurred if insufficient regulation (or other ancillary 

service) is not available. 

5
Tariffs Should Be Based on Cost-Causation Principles2

These points, however, do not consider how costs should be shared among the various 

users of the products. We adopt the overall principle that tariffs should be based on costcausation, as illustrated in the following more detailed points: 

1. Because maintaining power system reliability is critical, tariffs should base prices 

on costs so that the costs of maintaining reliability can be obvious to users of the 

system and its reliability feature. 

2. Tariffs should be based on cost-causation and the cost of providing the service. 

a. Individuals who cause costs to the system should pay for those costs. 

b. Individuals who mitigate costs to the system should either incur a lower 

cost or be paid for helpful actions. 

c. Complex systems like electric grids produce both joint products and joint 

costs of production that must be allocated among users of the system.3

d. Tariffs should allocate joint production costs on the basis of the use of 

joint products (the cost allocation principle of “relative use”). 

3. Tariffs should not collect revenue if no cost is incurred. 

4. Tariffs should be based on the physical behavior and characteristics of the power 

system. 

a. Recognize the need to balance aggregate system load and aggregate 

system generation. 

b. Recognize that balancing individual loads or resources is not necessary, is 

inconsistent with power system operations, and is very costly. 

5. Tariffs should result in an efficient allocation of resources. 

Although our analysis is based primarily on wind energy, these principles should apply 

equally to all agents in the relevant markets: loads and generators. 

Tariffs can be empirically tested, either through real-world experience or through detailed 

modeling of the grid and the individual costs and behaviors in question. During the 

process of tariff design, hypothetical cases can be established to test each principle in 

question, and the performance of the tariff can be assessed against the principles of tariff 

design above. In a later section of this paper, we propose a series of these tests that we 

call “thought experiments.” Application of these thought experiments can reveal how 

well a tariff matches the principles set forth above. 

There are some broader principles that tariffs should also support. The first is horizontal 

consistency. Horizontal consistency means that if two individuals (loads or generators) 

each cause equal increases in costs, then the tariff should assess each of them the same 

amount. A corollary to this principle is that if two individuals impose similar costs, then 

 

2

 The classic text is Bonbright, James Cummings, “Principles of Public Utility Rates” (1961). 

3

 The classic example is a sheep. A farmer raises a sheep. She sells mutton, hide, and wool. These are joint 

products. She incurs various costs for raising the sheep: feed, medicine, and a shepherd. These are joint 

costs of production. The electric system produces joint products: reliability, energy, capacity, convenient 

system access, ancillary services. The costs for producing these joint products (for fuel, engineers, capital, 

maintenance) must be allocated to the joint products. The most common allocation principle is relative use: 

the more you use, the more you pay. 

6
they should be assessed similar payment amounts. We can extend the principle of 

horizontal consistency in cases in which individuals contribute to cost mitigation. Equal 

cost mitigations or reductions should be matched by either identical reductions in cost 

assessment to the individuals or equal payments to the individuals. If two individuals 

have similar cost mitigation impacts, then their payments should be similar. 

Vertical consistency is the second additional principle. Vertical consistency implies that 

if individual A imposes a larger cost than individual B, then A should pay more than B. 

We can extend the concept of vertical consistency to cases in which two individuals 

mitigate costs in a straightforward manner. 

Horizontal and vertical consistency can be empirically tested, either through real-world 

experience or through detailed modeling of the grid and the individual behaviors in 

question. Application of the tariff to the individual behaviors can determine whether 

horizontal and vertical consistency is achieved by the tariff. 

Inherent Characteristics of Wind 

Tariffs, reliability rules, and market design should include attention to the inherent 

characteristics of all market participants. This does not mean that any participant should 

receive preferential treatment. Most regulatory laws require that “undue” discrimination 

be avoided, not that all discrimination be avoided. Recognition of rational differences, as 

between rate classes for example, is “due” discrimination. Therefore, physical 

capabilities as well as physical limitations should be understood before designing rules. It 

makes little sense, for example, to impose imbalance penalties on wind plants. Wind 

output can generally not be controlled (at least under-generation cannot be controlled). It 

can make sense to allocate imbalance costs to those that create them but penalties, which 

are designed to motivate behavioral change, do no good. 

Aggregation 

For nearly 100 years, since the early days of electric power system development, it has 

been recognized that it is more reliable, easier, and less expensive to serve an aggregation 

of loads rather than supply each individual load separately. Balancing each load’s 

variability with dedicated generation would be too expensive to consider. Instead, loads 

are aggregated and much of their variability cancels, greatly reducing costs. 

This explains why “backing up” wind plants with dedicated generators is never done. 

Using the aggregation of generators available to a system operator is always a more 

economic approach to dealing with the variability of wind output because the operator 

can choose the best option from a large menu of options for handling these tasks. 

Wind generation itself also benefits immensely from aggregation. Individual wind 

turbines are limited in electrical size. Creating wind plants requires aggregating large 

numbers of individual wind turbines. Wind turbines are physically separated. The 

physical nature of the turbulent atmosphere interacting with the wind plant geography 

makes it impossible to synchronize the behavior of large numbers of wind turbines, 

especially over the short time frames that are important for regulation and contingency 

7
reserves. So while the wind might decrease in one part of a wind plant, it might increase 

in strength in another part. This effect is even more likely if wind plants are located 

across broad geographic areas. Aggregating multiple wind plants provides additional 

aggregation benefits. Separating wind plants by a few kilometers eliminates regulation 

correlation, as shown in Figure 3. 

Energy output from multiple wind generators adds linearly to equal the total output of a 

wind farm. The energy output of the wind plant is simply the sum of the outputs of the 

individual turbines. The variability of individual turbines, however, does not add linearly. 

The benefits of aggregation of numbers of wind turbines in the regulation time frame can 

be seen in Table 1, even for a small 11-turbine wind plant. The standard deviation of the 

1-minute output (after removing the load following component) provides a good metric 

for the regulation requirement. If the turbines’ output variability was highly correlated, if 

the same wind turbulence hits each turbine at the exact same time, the variability of the 

array would be 11 times the average variability of each machine (0.648 MW). But this is 

not the case. The total wind plant variability is less than half at 0.315 MW. Interestingly, 

this is 1.5 times the 0.197 MW that would be expected if the individual turbine outputs 

were completely uncorrelated. Note that the ratio of regulation to energy is cut in half by 

aggregating these 11 turbines. This is why it is incorrect to assume (or assess) regulation 

requirements based on simple scaling in modeling and in tariff design. 

0

0.2

0.4

0.6

0.8

1

0 100 200 300 400 500 600

Distance [km]

Correlation Coefficient

4h Average

12-hour 

1h Average

30min Average

5min Average

2h Average

Figure 3. Wind generator variability loses corelation as the distance between machines increases and as 

the time frame of interest decreases (Ernst, Wan, and Kirby 1999) 

Aggregating over larger numbers of turbines and greater geography produces more 

dramatic results (Table 2). Wind plant variability is reduced from 7.5 MW of regulation 

that would be required if all four sections of the example wind plant had to balance 

individually to 4.8 MW for the total wind plant. Aggregating wind plant variability with 

aggregate system load further reduces the amount of regulating reserves that are required 

8
to balance the power system and maintain reliability. The wind plant’s share of the total 

regulation requirement is 0.8 MW. This is the same aggregation benefit and genuine 

savings that led to the original aggregation of loads and the creation of interconnected 

balancing authority areas (control areas). 

Table 1. Regulation variability is greatly reduced through aggregation 

Total 11 Individual Turbines 

Plant Independent Sum Average 

Energy (MW) 2.225 2.225 2.225 .202 

Regulation StDev (MW) .315 .197 .648 .059 

StDev/Energy 14% 9% 30% 30% 

Wind tariffs should recognize aggregation benefits, just the same way that load tariffs 

recognize these benefits. Charging for individual wind farm or wind turbine variability 

greatly overcollects for regulation. It also encourages inefficient balancing on a smaller 

scale, resulting in needless and costly AGC operations. Individuals (wind plants, 

individual loads, and other uncontrolled generators) should be charged for their properly 

allocated share of the total system variability based on the impact of their variability on 

that total. 

Table 2. The wind plant benefits by aggregating the four internal sections and with 

the balancing authority load 

Interconnection Point 

A B C D Total 

Number of turbines 30 39 14 55 138 

MW Rating 23 29 10 41 103 

Stand-alone regulation requirement (MW) 1.8 2.2 1.0 2.5 4.8 

Balancing Authority regulation allocation (MW) –– –– –– –– 0.8 

The implications from these aggregation data are profound. As more electric utilities and 

other entities become involved with wind integration studies that evaluate impacts of 

wind on power systems operations and costs, there is a need to utilize wind generation 

data in these efforts. Wind data can often be obtained from anemometers that are located 

in the proximity of the potential wind plants. Because the primary impacts of wind on the 

power system stem from the increased variability on the grid, any credible analysis must 

be based on a realistic modeling of the wind generation. We can’t stress this point 

enough: Scaling from a single anemometer or small wind plant to a large wind plant 

that could be hundreds of megawatts in size results in an inaccurate representation 

of the wind plant and will overstate the impact of wind on the power system. This 

will lead to overestimates of the ancillary service cost of wind. Such a tariff would clearly 

violate the principle of cost causation and could lead to wind subsidizing other market 

participants’ costs. 

9
Contingency Reserves 

Understanding wind plants’ impacts on contingency reserve requirements is an area that 

needs additional research. Contingency reserves are required to protect the power system 

from the unexpected, sudden, loss of a large generator or transmission facility. When 

such a sudden loss occurs, a series of contingency reserves begins responding 

immediately to restore the generation and load balance. Governors on all generators 

respond autonomously to frequency deviations. Spinning reserves immediately follow 

with non-spinning reserves and replacement or supplemental reserves after that. The 

system operator is typically able to restore market operations or economic dispatch within 

2 hours, often much sooner. When the reserves are restored, the system is back to normal. 

Figure 4 shows both a contingency event and the stacking of reserve response. 

59.90

59.92

59.94

59.96

59.98

60.00

60.02

60.04

5:50 6:00 6:10 6:20 6:30

 TIME (pm)

FREQUENCY 

2600-MW

Generation

Lost AGC RESPONSE

GOVERNOR RESPONSE

-10 0 10 20 30 40 50 60

Minutes

30 Minute Operating Reserve

Contingency

Occurs

10 Minute Spinning 

and 10 Minute NonSynchronized 

Reserve Frequency

Responsive

Reserve

Figure 4. A series of contingency reserves are available to respond to the unexpected loss of a 

generator or transmission line 

How should contingency reserves be allocated to wind plants? This question requires 

further investigation, but the outlines of that investigation can be specified here. The 

nature of wind plant design complicates the evaluation. Wind plants are comprised of 

numerous small generators. Individual generators under 5 MW each do not warrant 

contingency consideration in any balancing authority area. So machine failures are not a 

concern. At the other extreme, failure of a radial transmission line that removes a fewhundred-megawatt wind plant from service clearly is a contingency that requires 

attention. Both of these event types should receive the same treatment as afforded other 

similarly sized and positioned generators. But what about wind-driven events? 

Examining the contingency characteristics of conventional power plants may help clarify 

the issue. We tend to think of contingencies as being based on the cause: something 

breaks and the power plant fails, lightning strikes and the transmission line trips. 

Underlying the various specific causes are the basic characteristics of a contingency: 

• The event is relatively infrequent. 

• The event is large enough to require special treatment. 

• The event occurs too quickly for markets to accommodate. 

The amount of contingency reserves that must be available usually depends on the size of 

the largest generating unit. The minimum size of a contingency event of concern depends 

10
on the size of the reserve sharing pool or balancing authority, but is typically in the 20- to 

200-MW range. 

The event speed is also not rigorously defined. Most contingency events are essentially 

instantaneous, but an event that happens over a few minutes would also be too quick for a 

market response. Guidance concerning what would be too frequent an occurrence to be 

considered a contingency (and should be considered a normal part of the resources’ 

market performance) can be found by looking at the behavior of conventional generators. 

Figure 5 shows the failure rates for 120 coal-fired power plants in the 600 MW to 800 

MW size range for 1996. 

0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16 18 20 >20

Number of Forced Outages per Year

Number of Units

Figure 5. Conventional generators exhibit a range of acceptable outage rates for contingency reserve 

insurance 

Wind-driven events require more study to determine if any have characteristics that are 

similar to those exhibited by conventional power plants and that should be treated as 

contingencies. This is complicated by the fact that wind plants exhibit variability across a 

broad spectrum of size and frequency. Aggregation typically reduces the magnitude of 

swings and lengthens the ramps. Only large, fast, and rare events should be considered 

contingencies. Slower or more frequent variability is better considered as load following 

and regulation. Figure 6 provides an example of events that one might treat as 

contingencies if it was found that they were infrequent enough. Significant statistical 

analysis is required to make this determination. Work is also needed to determine if a 

rapid return to service is typical (similar to transmission line reclosing) and if that is an 

important consideration. 

11
0

20

40

60

80

100

120

12:00 14:00 16:00 18:00 20:00 22:00 0:00

MW

0

5

10

15

20

25

Wind Speed (M/Sec)

D E F G Total Speed

Contingencies?

0

20

40

60

80

100

120

0:00 2:00 4:00 6:00 8:00 10:00 12:00

MW

0

5

10

15

20

25

Wind Speed (M/Sec)

D E F G Total Speed

Contingency?

0

20

40

60

80

100

120

12:00 14:00 16:00 18:00 20:00 22:00 0:00

MW

0

5

10

15

20

25

Wind Speed (M/Sec)

D E F G Total Speed

Contingencies?

0

20

40

60

80

100

120

0:00 2:00 4:00 6:00 8:00 10:00 12:00

MW

0

5

10

15

20

25

Wind Speed (M/Sec)

D E F G Total Speed

Contingency?

Figure 6. Some wind-driven events may be large, fast, and infrequent enough to justify being 

treated as contingencies 

Impact of Wind on Power System Costs 

Several wind integration cost studies have been performed in the United States during the 

past few years and are reviewed in Parsons et al. (2006). The more recent studies have 

benefited from ongoing technical review and increasing sophistication of methods to 

model the wind resource. Because these studies are prospective, evaluating the potential 

impact of various wind generation scenarios that do not yet exist, a significant effort has 

been placed on creating a realistic time series representation of wind generation. The 

overall approach involves a meso-scale meteorological model that recreates the state of 

the atmosphere for the study period. As this model runs, 10-minute wind speed can be 

extracted for as many locations as desired. Current practice indicates that a maximum of 

approximately 30 to 40 MW of wind generation can be represented by one of these 

“virtual” anemometers. A recent integration study for New York state collected data to 

represent more than 100 wind locations, and a recent study in Colorado collected data 

representing more than 1,000 MW of wind using 22 towers to represent 722 MW of wind 

generation. 

Load following impacts have generally been assessed by analyzing the variability of load 

and wind together. This is the closest way to approximate the view of the system from the 

control room. At current wind penetration levels in the United States, the impact of wind 

on this time scale is relatively small. 

Regulation impacts are calculated using statistics from real wind plants and loads. 

Because these data are generally uncorrelated, the influence of wind on the regulation 

required to maintain system balance is small and is generally the same order of 

magnitude as the regulation requirements for load. 

Table 3 shows the impact of wind on ancillary service costs. It is important to note that 

there is not a one-size-fits-all cost impact. This is because systems can vary significantly 

and because the studies have assumed different wind penetration rates. 

12
Table 3. Wind Impact on System Costs 

Date Study Wind 

Capacity 

Penetration (%) 

Regulation Cost 

($/MWh) 

Load 

Following 

Cost 

($/MWh) 

Unit 

Commitment Cost 

($/MWh) 

Gas 

Supply 

Cost 

($/MWh) 

Total 

Operating 

Cost Impact 

($/MWh) 

May 

‘03 

XcelUWIG 3.5 0 0.41 1.44 na 1.85 

Sep 

‘04 

XcelMNDOC 15 0.23 na 4.37 na 4.60 

July 

‘04 

CA RPS 

Phase III 

4 0.46 na na na na 

June 

‘03 

We 

Energies 

4 1.12 0.09 0.69 na 1.90 

June 

‘03 

We 

Energies 

29 1.02 0.15 1.75 na 2.92 

2005 PacifiCorp 20 0 1.6 3.0 na 4.60 

May 

’06 

Xcel-PSCo 10 0.20 na 2.26 1.26 3.72 

May 

‘06 

Xcel-PSCo 15 0.20 na 3.32 1.45 4.97 

Testing a Tariff with Thought Experiments 

Thought experiments provide a means for testing a tariff to ensure that it does what is 

intended and that it does not have undesired consequences. The behavior of the wind 

plants, other generators, loads, and power system components are carefully specified to 

test each tariff attribute of concern. Here we present five thought experiments that can be 

used to test how a regulation tariff assesses a volatile resource like wind. Each thought 

experiment is mapped to at least one of our tariff principles. 

Thought Experiment #1: Perfect Following of a Volatile Schedule 

In formal transactions, both loads and generators forecast their expected behavior and 

establish a schedule for generation or consumption. Regulation tariffs often impose 

penalties if a resource does not follow its schedule. Some tariffs are based exclusively on 

schedule deviations. The reasoning is that the system operator must have a reserve of 

regulating resources available to immediately compensate for unexpected changes in a 

generator or load’s output or consumption. This is true. But does the regulation resource 

requirement go away if the resource follows its schedule perfectly? Figure 7 presents a 

typical system daily load with blocks of generation scheduled to meet that load. If the 

generation follows its schedule perfectly, is there a regulation burden imposed on the 

system? What charge does the tariff impose? 

A regulation tariff that is based exclusively on schedule deviations would impose no 

charge on the block scheduled generator. Indeed, many feel that scheduled imports and 

13
exports impose no regulation burden because the schedule is precisely known, often days 

in advance, and it is typically adhered to. 

The right side of Figure 7 shows that block scheduling imposes severe ramping 

requirements on the system, adding $2.26 to the cost of each MWH delivered through the 

block schedule in this example (based on modeling an example control area). The fact 

that these requirements always happen at the top of the hour and they are known well in 

advance does not reduce the amount of fast response capability the system operator needs 

to have to balance the system and meet CPS 1 & 2 requirements. The tariff needs to 

assess the individual’s impact on total system variability. 

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Figure 7. How does the tariff treat perfect following of a volatile schedule? 

This example tariff would violate principle #2 (cost-causation) because under the 

(unlikely) scenario of a perfect wind forecast, the tariff would not assess any cost to the 

wind generator even though there is a cost of moving the regulating units to mitigate 

variability in the wind output signal. It also violates principle #4, which states that 

individual movements (or in this case schedule deviations) of individuals do not need to 

be matched by a responsive unit – only the aggregate variability of the entire system must 

be compensated. Extrapolating this type of tariff to a case when all schedules and loads 

are known perfectly in advance, the implication is that there is no cost to the system to 

chase the total system variability. This is clearly wrong and would result in distortions in 

the market. 

Thought Experiment #2: Reduced Ramping 

It is tempting to design a regulation tariff that simply quantifies the peak-to-peak 

movements of the generator or load. But this ignores the speed at which the resource 

moves from one power level to another. If the block schedule used in Thought 

Experiment #1 (where the schedules changed abruptly at the top of each hour) is provided 

with 20-minute ramps (where schedules linearly ramp from 10 minutes before the hour to 

10 minutes after the hour), as shown in Figure 8, the regulation costs imposed on the 

power system drop to $0.20 per MWH (again based on modeling an example control 

14
area).4

 Note that the ramp rate scale on the right axis of Figure 8 is one-tenth of that in 

Figure 7. 

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Figure 8. Ramping the block schedule does not impact the energy delivery or forecast accuracy but 

reduces regulation requirements 

This thought experiment violates principle #2, the principle of cost causation. 

Recognition of only the peak-to-peak ramp does not distinguish between the two 

behaviors illustrated here that have significantly different cost impacts. This also violates 

the principle of vertical consistency because there is a significant difference in imposed 

cost that would not be picked up in the tariff. 

Thought Experiment #3: Ramp Rate or First Derivative Metrics 

Another tempting regulation tariff simplification is to measure average ramp rate or the 

average first derivative of the minute-to-minute energy consumption. This can also be 

characterized as a “distance traveled” metric referring to the amount of “movement.” 

This attempts to quantify the amount of ramping or changing of output that a generator 

has to provide. The flaw in this simplification is that behaviors with very different system 

impacts can result in the same measured performance as shown in Figure 9. 

Figure 9 compares the behavior of three hypothetical individuals (loads, wind generators, 

or balancing areas). The minute-to-minute change (“line slope”), integrated over the hour, 

is the same for all three; 60-MW-minutes. Clearly, however, the regulation burdens 

imposed by the three are radically different. In this very simple example, the solid red 

entity requires 1 MW of regulation compensation. The dashed green entity requires 5 

MW. The dotted blue entity requires a total of 60 MW but not of regulation. A sustained 

ramp is a load following requirement that can be, should be, and is (in most locations) 

supplied by moving the base load and intermediate generators. There is no regulation 

burden imposed by the dotted blue ramp. 

Metrics based on average rate of change of an individual violate principle #2 (cost 

causation) and principle #4 (failure to recognize aggregation benefits). 

 

4

 Ten-minute ramps for interchange scheduling are standard in the eastern interconnection. The western 

interconnection benefits in reduced regulation costs from the use of 20-minute ramps. 

15
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MW

Figure 9. These three individuals impose radically different regulation requirements but have the 

same minute-to-minute-change metric performance 

Thought Experiment #4: Equal But Opposite Behavior 

One very powerful feature of thought experiments is that they can be carefully tailored to 

examine specific behavior characteristics. They do not have to be realistic to be useful in 

determining if a tariff will produce desired results. Unrealistic examples can be useful in 

understanding the pieces of complex behavior that are often buried in the intricacies of 

actual operations. 

When designing a regulation tariff it is tempting to assess the generator’s or load’s 

variability in isolation. This ignores the fact that the underlying reliability requirement to 

balance generation and load is imposed on the balancing authority (hence its name) rather 

than on the individuals. Figure 10 shows two mirror image wind plants and a total system 

load. If the wind plants were assessed for their variability in isolation of each other and 

the total system load, they would both receive an identical regulation variability 

assessment. Together they present an absolutely constant output with no regulation 

burden. 

This thought experiment is completely unrealistic, but it illustrates an important point. A 

tariff that cannot recognize complete compensation of one plant for another will not 

recognize more subtle interactions or uncorrelated behavior that, consequently, does not 

add linearly. 

A tariff that does not recognize the impact of equal but opposite behavior would collect 

payment from both of these hypothetical wind plants. However, because their impacts net 

to zero, there would be no cost to the system. This type of tariff would therefore violate 

principle #3 (the principle that if no cost is incurred, the tariff should not collect revenue) 

and principle #4 (the recognition that only the aggregate system variability must be 

compensated for). 

16
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Wind Generator

Mirror Wind Generator

Figure 10. How is equal but opposite behavior treated? 

Thought Experiment # 5: Beneficial Movement 

The last thought experiment asks how the tariff treats movement that is beneficial. 

Regulation tariffs that only assess variability (total range, ramp rate, or adherence to a 

schedule) can penalize a resource that is actually helping reduce the total system 

aggregate variability. Figure 11 presents the measured variability of a number of 

generators and a total system. A tariff that simply charged for variability would penalize 

the AGC Generator that is deliberately balancing the system. Presumably the tariff would 

not be applied to this generator, but the principal remains the same. A generator that 

inherently has favorable response characteristics for whatever reason should not be 

penalized. 

A tariff that assesses a cost based on an individual’s variability in isolation of what the 

system needs would discourage helpful behavior. Because this type of tariff would 

impost a cost when in fact the resource is providing a benefit and limiting costs by 

helping to mitigate system variability, this kind of cost in a tariff clearly violates principle 

#2 (cost causation), principle #3 (imposing a cost instead of paying the generator), and 

principle #4 (does not recognize system balance). 

Although it is not directly testable, we emphasize that any of these tariff examples would 

cause an inefficient level of ancillary service to be acquired. Economic inefficiency 

occurs when marginal benefit diverges from marginal cost, as shown in Figure 2. This 

distortion in cost and price signals does not encourage behavior that helps the power 

system, and discourages helpful behavior 

17
Allocated Regulation Variability

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(MW StDev)

Wind

Geothermal

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AGC Generator

Load

Figure 11. Tariffs should recognize and reward favorable behavior 

Load Following vs. Energy Markets 

When FERC introduced electric industry restructuring in 1996 with Order 888, it did not 

establish a load following service. Instead, load following has been provided by energy 

markets. Some load following needs (the morning load pickup and evening load 

reduction) can be forecast and at least partially addressed by day-ahead markets. Markets 

clearing at 5-minute intervals can certainly respond quickly enough to meet the remaining 

load following requirements. But do fast energy markets capture load following costs? 

This is an interesting and underappreciated question requiring further research that we 

will explore here. 

Note first that the minute-to-minute regulation balancing ancillary service is a capacity 

service. It is generation (or responsive load) capacity held in reserve for use by the 

system operator to respond to variations in aggregate system load and uncontrolled 

generation. It is not fundamentally an energy resource. Any net energy that comes out of 

or goes into a regulation resource is incidental and is paid for separately. Presumably load 

following would also be a capacity service with (slower) responsive reserves held back to 

enable the system operator to balance aggregate generation with aggregate load. Any net 

energy into or out of the load-following resource would, presumably, be incidental and 

settled separately. Alternatively, fast energy markets are fundamentally energy markets 

that require an incidental response (ramp) so that the unit is correctly positioned to 

provide energy for the transaction. This distinction between the basic commodity and the 

incidental response is at the center of the problem. Is load following basically a capacity 

service like regulation or is the required capacity incidental to the energy being provided 

like hourly energy? 

Figure 12 presents a typical daily load curve with four classes of generators serving the 

load. Nearly 20,000 MW can be served from base load generators that can run 

continuously. The lowest-cost generators will be selected as base load units in both the 

vertically integrated economic dispatch environment and in the market environment. The 

18
base load units do not need to have any maneuvering capability to successfully meet their 

energy obligations. Nuclear plants, for example, can meet this need. 

0

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Intermediate 1

Intermediate 2

Peaking

Figure 12. Participation in energy markets requires maneuverability for all but base load units 

Something interesting happens when the next generators are selected. Again this applies 

equally to both the vertically integrated and the market environments. Additional power 

is not needed all day long. To be selected to provide the next block of energy, the 

intermediate units must be able to turn on for the hours when they are needed and off for 

the hours when they are not needed. It is probably a help to Intermediate Unit 1 that the 

requirement ramps up and down because the unit may not be able to turn on and off 

instantaneously. Once on, however, Intermediate Unit 1 has a flat output until it ramps 

off. 

The requirements for Intermediate Unit 2 and the Peaking Unit are more interesting. They 

must have output flexibility simply to be in the energy market (or available for economic 

dispatch). The amount of output that will be required in any given hour depends on the 

overall system load, which varies from hour to hour, day to day, season to season, and 

year to year. Regardless of the load following requirements, the last generators in the 

loading order, the most expensive units, must be flexible in the amount of power they can 

generate or they will be unable to successfully sell their energy output. 

Let us belabor this point a bit. These last generators must be flexible concerning their 

output levels and run times not because of load following requirements but simply to be 

able to sell energy into a variable market. Flexibility would be needed even if the load 

was known in advance (it is still different from day to day). Flexibility would be needed 

even if the load made perfect step changes hourly (both run time and level would still 

vary). Inflexible units such as nuclear plants simply could not serve this part of the load 

19
or sell energy into this market. But a high capital cost, low operating cost resource like a 

nuclear plant probably could not economically survive in this market, which has fewer 

operating hours than the base energy market. 

The basic question of whether we need load following or fast energy markets can be 

looked at slightly differently now. Do the generators that are built to meet the 

intermediate and peaking energy markets (higher operating cost, lower capital cost) 

inherently have enough response capability to meet the system’s load following needs? If 

so, there is little point in creating or paying for a load following service. If the generators 

do not inherently have sufficient maneuvering capability, then a load following service is 

required or the energy markets will be distorted. 

A similar situation would exist for night-time contingency reserves. Contingency reserve 

prices are typically near zero at night because ample generation capacity is available 

waiting for the next day’s peak energy need. If contingency reserves were only needed at 

night, markets would not be created for them; contingency reserves would be a free 

byproduct of the energy markets. It is the on-peak requirement for contingency reserves, 

in which the generators must forgo potential sales in the energy markets, that make 

contingency reserve markets necessary. 

We can hypothesize a system in which ramping limits influence energy prices and a load 

following service seems necessary. Figure 13 shows a system with ample $10/MWh base 

load capacity. Unfortunately the base load units can only ramp at 1 MW/minute. When 

the load ramps from 2550 MW to 2850 MW in 30 minutes at 8:00 the base load units 

simply cannot keep up. Peaking units costing $90/MWh (the only other generators in this 

example system) are required to serve load for 5 hours until the base load units can catch 

up. In a simple market with no load following service, the energy price would jump from 

$10/MWh to $90/MWh for those 5 hours. An alternative would be to let the energy 

market clear at $10/MWh, purchase ramping capability (load following) from the fast 

responding generator, and also compensate the load following unit for the incidental 

energy it had to supply while following the load. 

In Figure 12 and Figure 13, we see two different views of load following based on the 

inherent capabilities of the generators that are trying to serve the energy market. If there 

are ample, reasonably flexible generators on the margin, then a specific load following 

service is probably not justified. If a lack of ramping capability restricts which units can 

respond, then energy markets will be distorted and a load following service would be 

beneficial. 

20
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MW

Base Load - $10/MWh

Peaking - $90/MWh

Total Load

Figure 13. In this simple example, load following is required from an expensive peaking generator, 

but energy is only an incidental product 

It is difficult to obtain comprehensive public data concerning the ramping capabilities of 

generators within a balancing authority. To perform an initial scoping analysis, we 

obtained 1 year of hourly data for total system load and individual thermal generator 

output for three balancing authorities (Kirby and Milligan, 2005). From this we 

calculated the total system ramping requirements for each hour. We also calculated the 

total ramping capability available in the direction of the load ramp from the currently online thermal generators. Thermal generation data are available because of emissions 

reporting requirements; data from hydro and nuclear units are not available. This data 

omission understates the system’s available ramping capability. Figure 14 shows that 

significant excess ramping capability is typically available in all three balancing 

authorities in spite of the fact that hydro resources with significant ramping capabilities 

are excluded from the analysis. The same analysis showed that the load following 

requirements imposed by example wind plants was significantly smaller than that 

imposed by aggregate load. 

In some cases there is significant existing ramping capability that is not currently 

available to the Balancing Authority system operators. This can happen if there is no 

market mechanism that can be readily utilized, whether the issue is to obtain ramping 

capability or energy in a very short time frame. If such capability is needed but not 

available, this can result in a deadweight loss to the system similar to what is shown in 

panel (b) of Figure 2. 

These results tend to support FERC’s ancillary service definitions, which exclude load 

following and rely on fast energy markets. If, however, fast energy markets are found to 

exhibit times when prices are set by unit ramp rate constraints (rather than by simple 

availability to provide energy), then a load following service should be examined. 

21
-400

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WAPA Load Ramping Need

2002 Data

Ramp Up

CA ISO PJM WAPA Ramp Down

 42,400 55,600 3,087 MW Peak Load

 Generation

 17,500 35,000 2,911 Fossil (measured)

 13,100 2,500 700 Hydro

 4,600 13,500 0 Nuclear

 3,700 600 11 Other

Figure 14. Preliminary analysis conducted with public data indicates that excess ramping capability 

typically exists in these three balancing authority areas 

A first solution should always be to ensure that all physically available generating 

resources also have the opportunity to respond to real-time energy requirements. This 

will increase economic efficiency for loads and generators and mitigate the need for a 

separate load following service. If, however, fast energy markets do not provide for 

adequate ramping capability, and if this shortfall were to be rectified by a market for load 

following, then this would likely be a capacity service. In that regard, the characteristics 

of a capacity-based load following market would be similar to the way that regulation can 

be characterized. 

Conclusions 

In this paper, we have proposed several principles that we believe are fair for all buyers 

and sellers of electricity. The basic goal is that the tariff should be based on cost causality 

so that those who impose costs on the system bear those costs. Conversely, if an 

individual load or resource provides something of value, they should be paid. We develop 

several corollaries to these principles, along with a small number of tests that can help 

determine whether a proposed tariff is consistent with cost-causality principles. If the 

goal is to provide an economically efficient level of ancillary services (not only for wind, 

but for the entire system), then tariffs must provide the correct signals for buyers and 

sellers. 

In the regulation time frame, the requirements of individuals are not additive. This 

implies that a tariff that is intended to capture regulation impacts must carefully assess 

the physical characteristics of regulation and that capturing variability does not recognize 

22
helpful variability vs. unhelpful variability. When the cost of unhelpful variability is 

assessed, it is important to distinguish it from the cost imposed by uncertainty. Tariffs

that focus solely on uncertainty will not pick up the impact of variability and will 

therefore not provide correct market signals. This also leads to a deadweight loss. 

ased on a significant body of data from real wind power plants, we also show that 

will 

e also discuss load following and whether this service can be adequately provided by 

References 

; Kirby, B. (1999). Short-term Power Fluctuation of Wind Turbines: 

irby, B.; Milligan, M. (2005). Method and Case Study for Estimating the Ramping 

r 

arsons, B.; Milligan, M.; Smith, J.; DeMeo, E.; Oakleaf, B.; Wolf, K.; Schuerger, M.; 

B

scaling small wind plants or individual turbines to represent large wind plant output 

overstate the variability of wind. If analyses based on this type of scaling are performed 

as a foundation for tariff development, then it is likely that the cost impact (and therefore

tariff collection) will be too high. This also leads to a deadweight economic loss. 

W

fast energy markets. More work needs to be done to separate the characteristics of this 

service and to determine whether and how markets should account for ramping and 

energy in this relatively short time frame. 

Ernst, B.; Wan, Y.

Looking at Data from the German 250 Mw Measurement Program from the Ancillary 

Services Viewpoint, American Wind Energy Association Windpower ‘99 Conference, 

Washington, DC, June. 

K

Capability of a Control Area or Balancing Authority and Implications for Moderate o

High Wind Penetration: Preprint. 19 pp.; NREL Report No. CP-500-38153. 

www.nrel.gov/docs/fy05osti/38153.pdf 

P

Zavadil, R.; Ahlstrom, M.; Yen Nakafuji, D. Grid Impacts of Wind Variability: Recent 

Assessments from a Variety of Utilities in the United States. Presented at the Nordic Wind 

Power Conference, May 22-23, 2006. Espoo, Finland. 

23
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Cost-Causation-Based Tariffs for Wind Ancillary Service Impacts: 

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14. ABSTRACT (Maximum 200 Words)

Conference paper discussing the integration cost of wind. Although specific tariffs for wind generation for ancillary 

services are uncommon, we anticipate that balancing authorities (control areas) and other entities will move toward 

such tariffs. Tariffs for regulation and imbalance services should be cost-based, recognize the relevant time scales 

that correspond with utility operational cycles, and properly allocate those costs to those entities that cause the 

balancing authority to incur the costs. In this paper, we present methods for separating wind’s impact into regulation 

and load following (imbalance) time scales. We show that approximating these impacts with simpler methods can 

significantly distort cost causation and even cause confusion between the relevant time scales. We present results 

from NREL’s wind data collection program to illustrate the dangers of linearly scaling wind resource data from small 

wind plants to approximate the wind resource data from large wind plants. Finally, we provide a framework for 

developing regulation and imbalance tariffs, we outline methods to begin examining contingency reserve 

requirements for wind plants, we provide guidance on the important characteristics to consider, and we provide 

hypothetical cases that the tariff can be tested against to determine whether the results are desired. 

15. SUBJECT TERMS

wind; wind power; wind energy; tariffs; wind plant integration 

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