NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Operating Reserves and Wind 

Power Integration: 

An International Comparison

Preprint 

Michael Milligan, Pearl Donohoo, Debra Lew, Erik Ela, 

and Brendan Kirby (consultant)

National Renewable Energy Laboratory

Hannele Holttinen

VTT Finland

Eamonn Lannoye, Damian Flynn, and Mark O’Malley

University College, Dublin

Nicholas Miller

GE Energy

Peter Børre Eriksen and Allan Gøttig

Denmark

Barry Rawn and Madeleine Gibescu

Netherlands

Emilio Gómez Lázaro

Spain

Andre Robitaille and Innocent Kamwa

Hydro Quebec

To be presented at The 9th Annual International Workshop on Large-Scale 

Integration of Wind Power into Power Systems as well as on Transmission 

Networks for Offshore Wind Power Plants Conference

Québec, Canada; October 18-19, 2010

Conference Paper

NREL/CP-5500-49019

October 2010
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Abstract—The determination of additional operating reserves 

in power systems with high wind penetration is attracting a 

significant amount of attention and research. Wind integration 

analysis over the past several years has shown that the level of 

operating reserve that is induced by wind is not a constant 

function of the installed capacity. Observations and analysis of 

actual wind plant operating data has shown that wind does not 

change its output fast enough to be considered as a contingency 

event. However, the variability that wind adds to the system does 

require the activation or deactivation of additional operating 

reserves. This paper provides a high-level international 

comparison of methods and key results from both operating 

practice and integration analysis, based on the work in 

International Energy Agency IEA WIND Task 25 on Large-scale 

Wind Integration. The paper concludes with an assessment of the 

common themes and important differences, along with recent 

emerging trends.

Index Terms--operating reserves, power system operation, 

power system reliability, power systems, wind power generation

I. INTRODUCTION

here has been a rapid increase in the utilization of wind 

power plants in the past decade. Wind energy’s zero-cost 

fuel and emissions-free output provide great benefits to 

consumers and society. Utility-scale wind is a relatively new 

resource and is increasing at such a rapid rate that utilities and 

system operators are becoming concerned about the 

integration issues and integration costs that it brings. Wind 

power integration studies have been performed by numerous 

entities to help understand and quantify these impacts [1]-[3]. 

The studies typically simulate a future power system with high 

wind penetrations, and evaluate the impacts on the grid and 

the incremental operating costs that result [3]. These studies 

have been maturing continuously as the state of the art 

advances, with each study generally building on previous 

studies.

Grid operators already have techniques for managing the 

variability of demand and generation on the system through 

reserves. Reserves are operated for diverse purposes across

multiple timescales. The impact of wind integration on reserve 

requirements is a current area of interest for integration studies 

and power system operators. This paper focuses on three 

 

major topics related to operating reserves. Section II describes 

the types and uses of operating reserves and proposes a 

consistent nomenclature to tie together the different 

terminology used in different regions. Section III describes the 

types of operating reserves currently used in North American 

and European systems. Section IV surveys the types of 

reserves considered in wind power integration studies. Finally, 

Section V compares and contrasts operating reserves 

considered in the power systems and integration studies 

described in the previous sections.

II. OPERATING RESERVE DEFINITIONS

Variability and uncertainty are not unique to wind generation; 

similar characteristics exist in aggregate electric demand and 

supply resources and have always posed challenges for power 

system operators. Future loads cannot be perfectly predicted, 

loads and generator outputs can vary substantially in different 

time frames, and large power system equipment can fail at any 

given time without notice. Power system operators secure 

different amounts and types of operating reserves to 

compensate for these characteristics in order to serve load 

reliably and keep the system frequency stable. There are many 

different terms, definitions, and rules concerning what 

operating reserves entail. Due to the variation in definition and 

naming convention of reserves across different systems we 

attempt to construct a consistent nomenclature that categorizes 

the different types into a common framework.

The term operating reserves is defined in this paper as the 

real power capability that can be given or taken in the 

operating timeframe to assist in generation and load balance 

and frequency control. Systems also require reactive power 

reserve as well to provide voltage support and require certain 

targets for installed capacity that is often referred to as 

planning reserve; however, reactive power reserve and 

planning reserve are not discussed in this paper. 

The types of operating reserves can be differentiated by the 

type of event they respond to, the timescale of the response 

and the direction (upward or downward) of the response. 

The first characterization of a reserve is the type of event it 

is responding to. Some forms of operating reserve are kept for 

continuous needs (non events). Other operating reserves can 

be used to respond to either contingency events or longer 

timescale events. Contingencies are instantaneous failures 

such as the loss of a generator or failure of a transmission line. 

Longer timescale events can include net load ramps and 

forecast errors that occur over a longer amount of time. 

Operating Reserves and Wind Power 

Integration: An International Comparison

Michael Milligan, Pearl Donohoo, Debra Lew, Erik Ela, and Brendan Kirby (consultant) NREL;

Hannele Holttinen, VTT Finland; Eamonn Lannoye, Damian Flynn, Mark O’Malley, University 

College, Dublin; Nicholas Miller, GE Energy; Peter Børre Eriksen and Allan Gøttig, Energinet, 

Denmark; Barry Rawn†, Jasper Frunt‡, W.L. Kling‡ and Madeleine Gibescu†, TUD† &TUE‡, 

Netherlands; Emilio Gómez Lázaro, University Castilla La Mancha, Spain; Andre Robitaille and 

Innocent Kamwa, Hydro Quebec

T
2

In addition to the type of event, reserves can be categorized 

by the response time required and the physical capabilities 

needed of the responding participant. For instance, some 

reserves are required to be generating at part load to provide 

spinning reserve, others require automatic generation control 

(AGC), and still others require portions of their reserve to be 

directly responsive to frequency deviations. According to the 

North American Reliability Corporation (NERC) the 

difference between spinning and non-spinning reserves is that 

spinning reserves must be synchronized to the system while 

non-spinning reserves are not necessarily synchronized [4]. 

Spinning reserves respond more quickly as they are already 

synchronized to the system. AGC is a capability whereby a 

centralized party (system operator) sends controls directly to 

the resource on the desired output. Frequency responsive 

capabilities include governor systems that automatically adjust 

input when frequency deviations are sensed. 

Reserves may also be categorized by whether more or less 

supply is needed. Upward response is required when there is 

less generation than load and can be attained by additional 

generating power or a reduction in participating loads. 

Downward response is required when there is more generation 

than load and can be attained by a reduction in generating 

power or an increase in participating loads.

Using the characteristics listed above, five separate types of 

reserves can be defined: frequency response reserve, 

regulating reserve, ramping reserve, load following reserve, 

and supplemental reserve. The characteristics of these reserve 

types are summarized in Table I and graphically in Fig. 1. 

During normal system operation, regulating reserve (seconds) 

and load following reserve (minutes) are used. During 

contingencies, frequency response reserves (seconds) and 

supplemental reserves (minutes) are used for longer timescale 

events; note that the timescales may vary by system. 

Supplemental reserves effectively act as a reserve for other 

reserve categories to replenish the faster responding reserves 

when operating reserves are insufficient to protect the system 

from the next event. As should be expected, the slower 

reserves (ramping, load following and supplemental) have a 

mix of spinning and non-spinning reserves while the faster 

reserves (frequency and regulating reserves) require strictly 

spinning reserves. 

Frequency 

Response Reserves

Regulating Reserve Ramping Reserve Load Following 

Reserve

Supplemental 

Reserve

Purpose of 

Reserve:

Provide initial 

frequency response 

to major disturbance.

Maintain area control 

error due to random 

movements in a time frame 

faster than energy markets 

clear.

Respond to failures and 

events that occur over long 

time frames (e.g. wind 

forecast errors, wind 

ramps)

Maintain area 

control error and 

frequency due to nonrandom movements on a 

slower time scale than 

regulating reserves.

Replace faster 

reserve to restore 

pre-event level 

reserve.

Other Names: Governor 

response, primary 

control, FRR

Frequency control Variable generation 

event reserve, forecast

error reserve, balancing 

reserves

Load following, 

dispatch, tertiary 

reserves

Replacement 

reserve, 

supplemental 

reserve, tertiary 

reserve, substitute 

reserve

Type of Event:

Contingency 

Events

Fast (seconds) Slower (minutes)

Non-Event 

(inherent 

randomness)

Fast (seconds) Slower (minutes)

Longer 

Timescale Events

Fast (minutes-hours) Slow (hours)

Timescale of Response:

Spinning 

Reserve:

X X X X X

Non-Spinning 

Reserve:

X X X

Type of Service

AGC: X X

Upward 

Regulation:

X X X X X

Down 

Regulation:

X X X X

TABLE I

SUMMARY OF RESERVE TYPES
3

Fig. 1 Illustration of Reserve Types

III. OPERATING RESERVES IN PRACTICE

Power system operators secure different amounts and types of 

operating reserves in order to serve load reliably and keep the 

system frequency stable. As discussed in Section II, operating 

reserves can be subdivided into five categories, but these 

categories are not consistent across countries. In this section, 

we overview the types of reserves used by system operators in 

mainland Europe and North America with the results 

summarized in Table II at the end of the section.

A. United States

In the United States, the North America Electric Reliability 

Corporation defines operating reserves as “That capability 

above firm system demand required to provide for regulation, 

load forecasting error, equipment forced and scheduled 

outages and local area protection. It consists of spinning and 

non-spinning reserve” [5]. In North America, the spinning 

reserve and supplemental reserves described above are often 

combined and referred to as contingency reserves, again as in 

Section II. This reserve is used only for instances of generator 

or other loss of supply. Regulating reserve is generally 

procured in both the upward and downward directions (in 

cases of over-generation). Fast frequency response (governor 

response) is not yet explicitly addressed by NERC as a distinct 

operating reserve but the Western Electricity Coordinating 

Council (WECC) has started to study the need for a 30-second 

frequency responsive reserve. 

Both NERC and NERC subregions detail how much a 

balancing area will require of each type of operating reserve 

on its system [5]. For instance, the NERC BAL-002 standard 

requires that a balancing authority or reserve sharing group

maintain at least enough contingency reserve to cover the most 

severe single contingency. For the western interconnection, 

this is extended by a proposal by WECC to state that the 

minimum amount of contingency reserve should be the greater 

of the most severe single contingency or the sum of 3% of the 

balancing area load and 3% of the balancing area generation. 

Detailed specifications of contingency reserve requirements, 

including the amount of spinning compared to supplemental 

reserve, are established by each Regional Reliability 

Organization. Regions typically require at least half of the 

contingency reserve to be spinning. An example of how 

reserves are deployed following a contingency is shown in 

Fig. 1. Unlike contingency reserve, regulating reserve usually 

does not have explicit requirements. Instead, balancing areas 

will maintain sufficient regulating reserves so that they meet 

their NERC Controlled Performance Standards (CPS1 and 

CPS2). In some areas that currently have high penetrations of 

wind power like the Electric Reliability Council of Texas 

(ERCOT), the forecasted wind power output is considered 

when making regulating and other types of operating reserve 

requirements [6].

B. Europe

In Europe, broad guidelines are given by the former TSO 

groupings such as Nordel and the Union for Coordination of 

Transmission of Electricity (UCTE), now part of the European 

Network for Transmission System Operators for Electricity 

(ENTSO-E). ENTSO-E defines reserve in three categories; 

primary, secondary and tertiary control [8]. 

Primary control is activated when system frequency deviates 

by 20 mHz from the set point value and must be fully 

operational within 30 seconds. The purpose of primary control 

is to limit the deviation of system frequency following a 

system event. Secondary control consists of units controlled 

by Automatic Generation Control (AGC) and fast starting 

units. These are engaged 30 seconds after a contingency event 

and must be fully operational within 15 minutes. This category 

of control attempts to restore the frequency to its nominal 

value and reduce the area control error. Primary control 

reserves are required from ENTSO-E members based on their 

share of network use for energy production [9]:

.

max

. cont

ENTSO E

country

ss prod

country

country prod

pri P

f E

E

P ∆

∆ =

∑ −

(1)

where the quantity ∆fmax,ss is a tolerable steady state 

maximum deviation of 0.18 Hz, and the quantity Pcont. is a 

worst case continental contingency of 3000 MW determined 

from a system-wide analysis. 

Secondary control reserves are required from members of 

ENTSO-E in proportion to the maximum of yearly load in 

their region [9]: 

Operating 

Reserve Regulating 

Reserve

(Fast)

Load 

Following 

Reserve

(Slow)

Frequency 

Responsive 

Reserve

(Fast)

Supplemental 

Reserve 

(Slow)

Ramping 

Reserve

(Fast)

Non-Event

Contingency

Slow Event

Shading Indicates Percentage 

of Reserves which are Spinning

-10 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Minutes

Contingency

Occurs

Spinning & Non-Spinning Reserve

Frequency

Response

Reserves "Should" 

be Restored

Reserves 

"Must" be 

Restored

Market Response

Supplemental Operating Reserve

Fig. 2 Reserve deployment as defined by NERC. [7]
4

Psec.

country = a⋅ Lmax + b2 − b (2)

where a and b have been empirical determined as 10 MW and 

150 MW respectively. Tertiary control has a slower response 

and is engaged to restore primary and secondary control units 

back to the reserve state. The procurement of reserves and 

additional types of required reserves are set by the individual 

countries and are presented on a country-by-country basis.

C. Spain

The Spanish system uses four types of reserves: primary, 

secondary, tertiary and deviation. Primary control reserve is 

mandatory in the Spanish system, being a non-paid service 

operated by all the generation units in the regular regime. 

Generators with primary regulation operate with a reserve 

margin of 1.5% [10]. 

Secondary regulation is a market-driven service, which is 

provided by licensed units on automatic generation control 

(AGC) [10]. The Spanish TSO, Red Eléctrica de España 

(REE), procures as much as ±1,500 MW of the secondary 

regulation reserve to balance its system in real-time. 

Secondary reserve in Spain includes many fast-responsive 

hydro power generators, of which a total installed generation 

capacity of 16,657 MW exists on the system. Fig. 4 plots 

deployments of secondary reserves in Spain from 2000 to 

2009 versus the installed wind power capacity. The plot 

shows that secondary regulation is not significantly affected 

by increasing wind generation. 

The tertiary reserve is a 15-minute dispatchable responsive 

reserve that is used for manual generation adjustments to 

address variations in generation and load. This service is 

manual and is dispatched 15 minutes before the beginning of 

an operating hour, or within the hour if required. When 

dispatched, the energy must be sustainable for two hours if 

required. Tertiary regulation is a complimentary optional 

service but with a mandatory bid, managed and remunerated 

by market mechanisms [10]. Fig. 4 plots deployments of 

tertiary reserves versus the installed wind power capacity and 

shows that tertiary regulation is affected by increasing wind 

generation. Therefore, in the Spanish system, tertiary reserve 

requirements are expected to be higher with increasing 

penetrations of wind power generation in the power system.

In addition to primary, secondary and tertiary regulation, 

an additional reserve of active power called deviation reserves 

can be used. Deviation reserve helps to balance large

differences (> 300 MWh) between scheduled generation and 

forecasted demand. The foreseen deviations include 

unavailability or justified changes communicated from 

generation. This reserve is provided by generation and 

pumped storage power units. Deviation management also 

plays a role as a link between the tertiary regulation and the 

intraday markets, providing the TSO with a flexible 

mechanism to solve the imbalances between generation and 

demand without compromising or risking the availability of 

secondary and tertiary reserves. As shown in Fig. 5 wind 

power forecasting errors can increase the cost associated to the 

operation of deviation management and the tertiary reserve.

D. The Netherlands

The Netherlands are represented within ENTSO-E by the 

Dutch TSO TenneT. TenneT is required to maintain minimum 

values of primary and secondary reserve. The minimum 

capability is contracted, but reserves from units available to 

respond within 15 minutes are also deployed and financially 

compensated from a larger pool of parties via a market 

mechanism [11]. Entities that submit production or trade 

schedules are referred to as responsible parties, and can 

comprise both load and generation. These entities are charged 

an imbalance price for deviations from their schedule, but can 

be paid that price if it happens that their deviation contributes 

to reducing system imbalance. They dispatch their generation 

independently of and in addition to what TenneT may require. 

As described above, primary control reserves are required 

from ENTSO-E members based on their share of network use 

for energy production; the share due from the Netherlands, 

based on 2008 load data, amounts to 670 MW/Hz [12]. Based 

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1,5 4,0 6,5 9,0 11,5 14,0 16,5 19,0

GWh

Installed wind power capacity (GW)

Secondary regulation 

(increase)

Secondary regulation 

(decrease)

Tertiary regulation 

(increase)

Tertiary regulation 

(decrease)

Fig. 4. Evolution of secondary and tertiary regulation with the installed wind 

power capacity in Spain, 2000-2009.

Fig. 5. Evolution of deviation management with the installed wind power 

capacity in Spain, 2000-2009.

0

5000

10000

15000

20000

25000

30000

35000

1,5 4,0 6,5 9,0 11,5 14,0 16,5 19,0

GWh

Installed wind power capacity (GW)

Deviation management 

(increase)

Deviation management 

(decrease)

Fig. 3. ENTSO-E and UTCE control mechanisms. [8]
5

on 2008 load data, the Netherlands is also responsible for 300-

MW secondary reserves [12]. 

E. Denmark

The Danish Power System is part of both the Organization for 

the Nordic Transmission System Operators (Nordel) and the 

Union for the Co-ordination of Transmission and Electricity 

(UCTE). Primary reserves are required by both Nordel and 

UCTE to stabilize the system frequency following an 

instantaneous imbalance. Primary reserves activate in seconds 

to minutes and are used for 15 minutes at most. Secondary 

reserves are automatically dispatched to restore frequency 

according to UCTE requirements. These reserves typically 

stay active for 15 minutes. Tertiary reserves are required both 

by UCTE and Nordel and are used to manually restore 

primary and secondary reserves. Tertiary reserves consist of 

start-ups and shutdowns of generators as well as redistribution 

between generators and change of exchange levels. [13]

F. Ireland

The Irish system is a relatively small and isolated power 

system, and has a more granular approach to its definition of 

reserve [14]. There are five main types of reserves including: 

regulating, operating, replacement, substitute, and contingency 

reserves. Regulating reserve is a subset of operating reserves

and acts within 30 seconds of a frequency deviation to restore 

the frequency to within 0.1 Hz of the set point value and 

controls inter-system transfers on the North-South 

interconnector which joins the two systems on the island. 

Operating reserve, as defined in the Irish system, is divided 

into three parts: primary, secondary, and tertiary operating 

reserve. Primary reserve acts for the first 15 seconds to avoid 

transient nadirs below 49 Hz. Secondary reserve then acts to 

avoid continuous system operation below 49.5 Hz, and it is 

fully available from 15 seconds after an event for a further 75 

seconds. Tertiary operating reserve is used to replace the 

primary and secondary reserve. It is split into Tertiary1, which 

restores primary and secondary operating reserve for the first 5 

minutes and Tertiary2 reserve which is available after 5 

minutes for an additional 15 minutes. 

Replacement reserve acts as a longer term resource to 

restore secondary and tertiary operating reserve from 

operation. This is fully available within 20 minutes for a four 

hour period. Substitute reserve is utilized to restore 

replacement reserve after 4 hours for a duration of 24 hours 

and is available for the replacement of emissions-restricted 

plants. Contingency reserve is available to restore all reserves 

24 hours after the event. 

G. Quebec

Hydro Quebec requires six broad categories of reserves: 

stability reserves, 10-minute operations reserve, 30-minute 

operations reserve, energy balancing reserves, frequency 

regulation reserves and load following reserves. Stability or 

spinning reserve, typically 1000 MW, represents 60% of the 

largest single loss of generation. The 10-minute reserves also 

typically operate at 1000 MW and consist of non-firm sales, 

interruptible load and a large portion of stability reserves. 30-

minute reserves, typically about 500 MW, represent 50% of 

the second most severe single loss of generation. Energy 

balancing reserves vary from 1500 MW in the day-ahead time 

frame (1200 MW in the summer) to 500 MW in real-time twohours ahead. They consist of available generating capacity 

and interruptible load that could be deployed when needed to 

offset discrepancies in supply caused by errors on current 

forecasts. Frequency regulation reserves operate using AGC 

and a 500 MW (minimum) modulation range. Finally load 

following reserves do not have a strictly defined standard due 

Reserve Names in 

Operations

Frequency Response 

Reserves

Regulating 

Reserve

Ramping Reserve Load Following 

Reserve

Supplemental 

Reserve

ENTSO-E (Europe) X

(Primary Reserves)

X

(Secondary 

Reserves)

X

(Tertiary Reserves

X

(Tertiary Reserves)

Spain X

(Primary Reserve)

X

(Secondary 

Reserve)

X

(Deviation Reserve)

X

(Tertiary Reserve)

Netherlands X

(Primary Reserves)

X

(Secondary 

Reserves)

X

(Tertiary Reserves

X

(Tertiary Reserves)

Denmark X

(Primary Reserves, 

Instantaneous Reserves)

X

(Manual 

Regulation)

X

(Tertiary Reserves

Ireland X

(Operating Reserves, 

Primary, Secondary and 

Tertiary Reserves)

X

(Regulating 

Reserves)

X

(Replacement/

Substitute)

X

(Contingency/

Substitute)

Quebec X

(Frequency Regulation 

Reserves)

X

(15 Minute 

Operations 

Reserve)

X

(30 Minute Operations 

Reserves, Load 

Following Reserves)

X

(Stability Reserves, 

Spinning Reserves)

TABLE II

SUMMARY OF RESERVE TYPES USED IN OPERATIONS TODAY
6

to the large hydro-generation base (43,000 MW in 2009) 

which allows for load-following without any practical 

constraint.

IV. OPERATING RESERVES IN WIND INTEGRATION STUDIES

Over the past several years, various organizations have 

participated and/or initiated wind power integration studies. 

The studies evaluate a power system in the future with high 

wind power penetrations and simulate the impacts that occur. 

A general process for wind power integration studies can be 

found in [15] and recommendations so far in [3].

A major component of each study is to evaluate the 

incremental need for additional operating reserves for the 

future system that result from high wind penetration. The 

study teams usually consider traditional definitions and 

requirement determinations and propose needed changes to 

maintain reliability while accommodating the variability and 

uncertainty present in the wind power. The uncertainty and 

variability are compared before and after the addition of wind 

because in most cases there are no set rules for the load 

following reserves. This value is generally calculated via 

statistical methods analyzing wind power time series that are 

modeled for the study. The methodologies used to compute 

these values have evolved as each study learns from past 

studies. The most recent studies evaluating very high 

penetrations are using sophisticated methodologies that are 

diverging further from the traditional methods used today in 

actual operations. The types of reserves considered by the 

different studies discussed below are summarized in Table V. 

Note that studies completed in the United States also 

considered contingency reserves; however, the introduction of 

wind generators did not change the required quantity of 

contingency reserves. 

A. Minnesota and New York Integration Studies

In the United States, some of the first major wind power 

integration studies were performed in the states of New York 

and Minnesota [1] [2]. In New York, the study evaluated 

3,300 MW of wind power on the 33,000-MW peak load 

NYISO system. The study concluded that no incremental 

contingency reserves would be needed since the largest single 

severe contingency would not change. The study concluded 

that an additional 36 MW of regulating reserve was required 

on top of the current 175 - 250 MW procured today. This is a 

result of analyzing the standard deviation of 6-second changes 

in load net of wind compared with that of load alone. The 

standard deviation with wind increased from 71 MW to 83 

MW, presenting a 12 MW increase. As was the current 

guideline in New York, the total standard deviation is 

multiplied by three to ensure that the total regulation 

requirement is sufficient to cover 99.7% of all instances, thus 

giving the 36 MW increase.

In Minnesota, the study evaluated 15, 20, and 25% wind 

energy as a percentage of total annual demand (3441 MW, 

4582 MW, and 5688 MW on a system with a peak demand of 

roughly 20,000 MW). Similar to New York, it was concluded 

that there would be no impact on the contingency reserve 

requirement with the added wind penetrations. The regulating 

reserve requirement similarly evaluated the added variability 

of wind but calculated it to be a 2-MW standard deviation for 

every 100-MW wind plant installed. This calculation was

based on operational data from existing wind plants. The ratio 

was used to calculate the regulating reserve requirement as 

seen in equation (1).

Reg Req = k�σload

2 + N(σW100

2 ) (3)

where k is a factor relating regulation capacity requirement to 

the standard deviation of the regulation variations (assumed to 

be 5 in this study reflecting current practices); σload is the 

standard deviation of regulation variations from load; σW100 is 

the standard deviation of regulation variations from a 100-

MW wind plant; and N is the wind generation capacity in the 

scenario divided by 100. The results showed increases of 12, 

16, and 20 MW for the 16, 20, and 25% cases, respectively.

The Minnesota study quantified two other defined 

categories that the New York study did not. In the study these 

are defined as load following and operating reserve margin 

(Load following reserve and ramping reserve in Table I. Load 

following was calculated as twice the standard deviation of the 

five minute changes in the net load, and increases ranged from 

10 to 24 MW for the three cases. The operating reserve margin 

was allocated specifically for hourly forecast errors in the net 

load. This analysis assumed a dynamic requirement, one that 

was not constant for all hours but in fact was a function of the 

amount of expected wind capacity during the operating period. 

The data analysis showed that the variability of wind is 

highest when the wind capacity is in the middle range (i.e., 40-

60% of installed capacity) because the wind turbines are on 

the steepest parts of their wind speed to wind power 

conversion curves. Therefore, more reserve was needed for the 

middle range compared to times of very low wind generation 

or times of very high wind generation.

These studies paved the way for the development of 

dynamic operating reserves for wind energy. More recent 

studies have evolved and developed methods from those 

preceding studies with increasing sizes, penetrations, and 

scopes. The traditional definitions and methods used in current 

operations were simply not feasible with penetrations of 20% 

wind energy and more. 

B. Eastern Wind Integration and Transmission Study

The Eastern Wind Integration and Transmission Study 

(EWITS) evaluated the operational impacts of various wind 

penetrations, locations, and transmission build-out options for 

most of the United States eastern interconnection. The study 

included three scenarios of 20% wind energy with each 

representing different primary locations of the wind, and one 

30% wind energy scenario [16]. The majority of this region is 

currently operated by Independent System Operators (ISO) 

and Regional Transmission Organizations (RTO) who 

administer the wholesale electricity markets. These markets 

have evolved since their inception in the late 1990s. The 

further evolution of the rules and procedures that the markets 

will follow is a key assumption on how operating reserve 

requirements are determined in the study. The boundary 

between operating reserves and what is extracted from subhourly energy markets also has an impact on the method used.
7

The first procedure of the study was to determine the 

contingency reserves required. As many previous US studies 

have done, these assumed the current rule and determined that 

the largest contingency was not affected by the large amounts 

of wind generation. One and a half times the single largest 

hazard in each operating region determined the amount of 

contingency reserves for that region.

Many prior studies in the United States concluded a slight, 

but not insignificant, increase in the amount of required 

regulating reserve due to the increased variability of wind 

added to that of the load. In EWITS, a similar methodology to 

the prior studies was performed. The minute-to-minute 

variability separated from a 20-minute rolling average of a 

100-MW wind plant was used for the analysis and the 

standard deviation was determined to be 1 MW. It was 

assumed that there is no correlation between wind plants for 

power output deltas in this time frame, and therefore the total 

standard deviation for a balancing area was calculated by 

geometrically adding the 1-MW standard deviation for all 

100-MW wind plants on the system. For load-only, the 

regulating reserve requirement was assumed to be 1% of the 

total load, and assumed to be equal to three times the standard 

deviation of the load variability. Since load and all wind 

variability on this timeframe were also considered to be 

independent of one another, the standard deviations of all 

wind and all load were then geometrically added together by 

calculating the square root of the sum of their squares. The 

total standard deviation was increased by less than 1 MW 

when the wind was added to the load, and therefore the 

variability of wind was not considered as part of the regulating 

reserve for the study.1

In contrast to from most studies, however, it was 

determined that the uncertainty in the wind forecasts used for 

economic dispatch would impact the regulating reserve much 

more than what was shown for the variability. Economic 

dispatch programs that run every five minutes would use 

information from at least ten minutes before the operating 

interval. Since it is too late to adjust the economic dispatch for 

any deviations, these deviations would all be met by units 

providing regulating reserve. Assuming a 10-minute-ahead 

persistence forecast, the additional regulating reserve was 

determined by looking at the standard deviation of ten-minute 

changes in wind output (load forecast for 10-minute ahead was 

assumed to be quite good and load forecast error was ignored). 

Fig. 6 shows the standard deviation of the 10-minute-ahead 

wind forecast errors as a function of the average hourly 

production of the total wind. The highest variability is near 

50% production where the anticipated 10-minute change can 

be up or down and also relates to wind turbines being at the 

steepest part of the power conversion curve. The function was 

used for the standard deviation of the hourly wind-related 

standard deviation of the regulating reserve requirement and 

was geometrically added to the load regulating reserve 

requirement discussed above. The equation (3) is shown 

below, where σst is the standard deviation of wind forecast 

errors described in Fig. 6.

 

1 Calculations based on a balancing area with 100-GW load and 60-GW 

wind, which was about the average for the largest ISO balancing areas that 

were a part of the study.

Regulation Requirement

= 3 ∗ ��

1%Hourly Load

3 �

2

+ σST(HourlyWind)2 (4)

Fig. 6. 10-minute ahead wind generation forecast errors as a function of 

production.

A similar approach was used for the hour-ahead wind 

forecast error. However, in this case it was assumed that the 

errors did not occur often could be compensated for with offline non-spinning reserve. Therefore, one standard deviation 

of the hour-ahead forecast error was required to be spinning, 

and two standard deviations could be non-spinning. Also, 

because the reserves were used in the production cost 

simulations for the study, if the reserves had to be used for the 

hour-ahead forecast error of the hour in question, those 

reserves did not have to be kept in real-time. In other words, if 

reserves were needed because less wind was available than 

forecast, the model would release that amount of reserves in 

real-time since the reserves were used for the forecast error 

and not needed further. The total amounts of all reserves used 

in the study are shown in Fig. 7. The reserve requirement was 

an hourly value that was a function of both wind and load 

levels.

Fig. 7. Summary of reserve methodologies for EWITS.

C. Western Wind and Solar Integration Study

The Western Wind and Solar Integration Study (WWSIS) 

investigated the operational impact of up to 35% energy 

penetration of wind, PV, and concentrating solar power on the 

power system operated by the WestConnect group of utilities 
8

in the southwest and mountain states for the year 2017 [17]. In 

this study, the entire Western Interconnection is modeled with 

up to 23% wind and solar penetration in the rest of the 

Western Electricity Coordinating Council (WECC). Three 

geographic scenarios are modeled in which 1) each state meets 

its own wind and solar target, 2) wind and solar sites are 

concentrated in the best quality resource areas, and 3) some 

resources come from within each state and some resources are 

concentrated in high resource areas. Additionally, three 

penetration levels were used in WestConnect: 10% wind/1% 

solar energy penetration, 20% wind/3% solar and 30% 

wind/5% solar, with correspondingly increasing levels of wind 

and solar in the rest of WECC. The high renewables case 

consists of 30% wind and 5% solar energy in WestConnect 

and 20% wind/3% solar in the rest of WECC, for an overall 

average penetration of 27% wind/solar energy in the western 

U.S.

The study employed hourly production simulation 

analysis, statistical analysis down to the 10-minute level, and 

quasi-steady-state power simulations on a 1-minute level to 

examine difficult events. WECC’s Regional Reliability 

Standard dictates that contingency reserves should be 6% of 

load or the largest single contingency, whichever is greater. At 

least half of these contingency reserves must be spinning 

reserves. Since the worst contingency in this region tends to be 

smaller than 6% of load, WWSIS production simulations held 

spinning reserves equivalent to 3% of load. 

Hourly production simulation analysis showed that in the 

high renewables case, there were contingency reserve 

shortfalls for 89 hours of the year. This was driven by the 

extremes of the day-ahead wind/solar forecast errors. There 

were no contingency reserve shortfalls in the perfect forecast 

case. Additional sensitivities showed that increasing spinning 

reserves by 5, 10, 15, 20, or 25% of the day-ahead wind 

forecast would increase costs by up to $2.75 per MWh of total

wind generation. However, even increasing spinning reserves 

by 25% of the day-ahead wind forecast did not completely 

eliminate the contingency reserve shortfalls. Additionally, the 

incremental cost per MWh of increasing spinning reserve 

from 20 to 25% of the forecast was over $100,000/MWh. The 

study concluded that it was more cost-effective to have 

demand response address the 90 hours of contingency reserve 

shortfalls rather than increase spinning reserves for 8760 hours 

of the year. 

In addition to examining contingency reserves, WWSIS 

evaluated the impact of increased variability and the need for 

additional regulating and load following reserves. Unlike 

contingency reserves, WECC does not have a specific 

formulaic rule for regulating and load following reserves, but 

requires sufficient reserves be held to meet NERC’s Control 

Performance Criteria. WECC guidelines ask that expected 

load variability be met 95% of the time. 

Statistical analysis on 10-minute variability was 

conducted to determine that the average requirement to cover 

10-minute variability reserves doubles from ~425MW to ~850 

MW at the WestConnect level in the high renewables case. 

However, the production simulation analyses showed that high 

penetrations of wind and solar displaced other resources, in 

some cases decommitting units, and in some cases backing 

down units. Production simulation results showed that there 

were more up-reserves available in the high renewables case 

than in the no renewables case (see Fig. 8). So while the 

variability reserve requirement approximately doubles, 

economic operation of the power system naturally provides 

this increased reserve requirement so that there is no need to 

commit additional reserves. 

To help utilities develop simple rules for determining 

variability reserve requirements, extensive analysis was 

undertaken on a WestConnect and individual state basis. In the 

high renewables case, the variability reserve requirement for 

the WestConnect footprint could be distilled down to 1.1% of 

load plus 5% of wind online (not capacity) up to 47% of 

nameplate wind. Similar rules for developed for each state 

within WestConnect.

Regulating reserves are a subset of the fast variability 

requirement but are held separately from the 10-minute 

variability reserves. While WWSIS did not evaluate which 

units were on AGC, the minute-to-minute analysis showed 

that sufficient regulating reserve capability was available. 

Fig. 8 There are more up-reserves available in the high renewables case than 

in the no wind/solar case because the additional renewable energy generation 

causes many conventional units to be backed down. [17]

D. All Island Grid Study (Ireland)

The All Island Grid Study in Ireland was published in 2007 

and examined, among other things, the Irish system’s ability to 

integrate various penetrations of wind generation [18]. Six 

plant portfolios were examined to meet the load forecasted for 

2020. Portfolio 1 contains 2 GW of wind; 2, 3 and 4 contain 4 

GW of wind; portfolio 5 contained 6 GW; and portfolio 6 

contained 8 GW of wind generation. This is in the context of 

a projected peak load of 9,618 MW and a load factor of 

63.9%. The study involved hourly scheduling of the system 

with the WILMAR system planning tool [19]. 

The study incorporated a refined implementation for 

reserve provision with only two categories specified in the 

model: spinning and replacement reserve. The definition of a 

unit capable of meeting the replacement reserve standard was 

an off-line unit with a start up time of less than 60 minutes and 

online units whose capacity was not allocated to the spinning 

reserve requirement. This is a highly simplified model given 

the existing structure of reserve provision in the Irish system. 

The requirements for spinning and replacement reserve were 
9

based on a mixture of existing and proven requirements and 

newer techniques for the provision of reserve for wind 

generators. 

Spinning reserve demand is calculated as being the size of 

the largest online unit plus an additional demand for wind 

generation. This is calculated based on the work in [20], which 

links the amount of reserve carried on the system in any hour 

with the reliability of the system over a year. Reserve is 

allocated in such a way as to keep the average risk of having a 

load shedding incident in each hour the same for all hours of a 

year and includes the effect of generator outages and load and 

wind forecast errors.

Ireland is an island system with one 400-MW 

interconnector in operation and a 500-MW interconnector 

under construction. System modeling for the year 2020 

assumed that 100 MW of spinning reserve can be obtained 

through interconnection. Another 50 MW of reserve is 

assumed to be provided from interruptible contract loads. Of 

the remainder, a constraint of a maximum of 50% of reserve 

demand can be provided by pumped storage. Wind generators 

are allowed to provide spinning reserve through curtailment. 

The demand for spinning reserve is illustrated in Fig. 9 on a 

weekly averaged basis. Spinning reserve is required more 

frequently as the amount of wind increases in the portfolio, 

significantly so in portfolio 6. The scheduled outage of the 

largest unit on the system (480-MW CCGT unit) is seen to 

reduce the spinning reserve demand significantly during 

weeks 31 to 34. While the variable generation requires extra 

spinning reserve, the largest contributing factor remains the 

loss of the largest conventional unit. 

Fig. 9. Weekly demand for spinning reserve for each generation portfolio.

Replacement reserve is calculated as a function of the 

possible forced outages of units and an additional margin 

which is a function of the 90th percentile of the net load (loadwind) forecast for each particular scenario. The 90th percentile 

was chosen as it most closely matches experience with proven 

reserve standards. WILMAR implements rolling unit 

commitment and has stochastic optimization functionality. 

This requires the forecast data to be an input to the scenario 

tree tool and thus, replacement reserve is activated accordingly 

by the scheduling tool. Demand for replacement reserve is a 

function of the installed wind power and the forecast error 

over longer timelines. This is shown in Fig. 10 and Fig. 11. 

Fig. 10 shows how the replacement reserve requirement is a 

function of how far ahead the optimization is evaluating. In 

other words, generally, errors will be larger further out and 

therefore more replacement reserve are required than more 

immediate horizons. In Fig. 11, where portfolio 5 contains 6

GW of wind generation, the demand for replacement reserve 

is seen to exceed 3 GW in one instance. This is due to a 1 GW 

load rise at the same time as a 1 GW decrease in wind, 

combined with a forecast error. 

Fig. 10. Average demand for replacement reserve by time horizon.

Fig. 11. Hourly Demand for replacement reserve.

E. Spain

The European Council set a target of 20% share of 

renewable energies in EU energy consumption by 2020. In 

terms of electricity in Spain, 40% should be generated by 

renewable power stations The Spanish target by 2020 is 40 

GW in onshore wind power, together with 5 GW in offshore 

wind farms [21]. The Spanish and Portuguese (REN) TSOs 

are studying the operation of their future power systems facing 

important implications of this penetration to address changes 

resulting from the fluctuations and uncertainty of wind power 

generation and the assessment of the adequacy of the available 

operational reserve. REE and REN have concluded that 

deterministic methods and classical probability methodologies 

are insufficient for demand coverage; instead, a time-stepping 

Monte-Carlo simulation as a method is being studied for 

capturing demand coverage and operational reserve strategies 

in Spain and Portugal [22].

Different scenarios were studied, each with different 

expected electrical system configurations, hourly electrical 

demand and electrical generation in power stations. Wind 

power generation is estimated based on hourly predictions, 

whereas in fast-responsive hydro power generators, historical 

monthly series are used. Scheduled maintenance periods for 

power stations are also considered, together with failure 

periods and repair times according to specific statistical 

distributions and parameters.
10

In power systems with large amounts of wind power, power 

fluctuations impact the security of supply or the system 

operation. Unit commitment has to be redefined to face rapid 

changes in wind power generation, especially when opposing 

the movement of load.

Therefore, reserve regulation (to rise or to fall), and its 

value are topics to be studied in power systems with high wind 

power's penetration. Operational reserve, defined as the one 

mobilized in less than 1 hour, should be sufficient to absorb 

changes in generation/demand that could occur in less than 1 

hour (power station failures, changes in demand or unexpected 

variation wind power generation). Different power plants to be 

added (400 MW in 2015) to the future power system were 

characterized from technical and economic points of view: 

pumped storage power stations, CCGT and OCGT units. 

F. The Netherlands

To explore the effects of aggregation and geographic 

smoothing, several scenarios of up to 12 GW wind were 

statistically evaluated (see Table III); however, the need for 

reserves changes over time, and the response time of reserves 

for one aggregation of load and supply may be different from 

that of another. Flexible and targeted reserve requirements 

increase the efficacy and flexibility of power systems. The 

research program ``Regelduurzaam'' examines technical and 

market concepts for reserves in the context of large amounts of 

decentralized and renewable generation. It is a joint effort of 

several Dutch universities, TenneT, the Energy Research 

Centre of The Netherlands, and the spot market operator APX. 

As part of this program, methods have been explored to 

quantify the reserve needs in the frequency domain based on a 

digitally sampled signal of historical imbalance. For the more 

immediate future a comprehensive range of issues related to 

various wind power integration scenarios in the Netherlands 

were assessed in a study funded by the We@Sea program. The 

study did not assess reserve estimations directly. However, 

detailed simulations were used to assess the adequacy of a 

particular choice of reserves for short-term power systems 

operation. 

TABLE III

STATISTICS OF THE DEVIATION |P(N)-P(N-1)| FOR 15-NINUTE VALUES FOR 

DIFFERENT AGGREGATIONS OF TURBINES

Source Rated 

Capacity

Area 

(km2)

Full

(100%)

3σ

(99.7%)

2σ

(95%)

Per Unit Deviations

Meas., 1 site 25 MW 2 0.7 0.37 0.15

Met. Data 

[24], 1 site

550 MW 39 0.63 0.27 0.09

Met. Data 

[24], 6 sites

1600 MW 9000 0.29 0.15 0.07

Met. Data 

[24], 36 sites

12000 

MW

35900 0.32 0.13 0.05

 1) Reserve Estimation in Frequency Domain 

Study of the frequency components of a signal allows a 

natural de-trending of its content. The range of variations that 

occur faster than a certain frequency ωn and including 

variations up to another frequency ω m can be approximated 

by assuming the components Yk of a signal's discrete Fourier 

transform (DFT) are uncorrelated, and adding them in 

quadrature to obtain an equivalent amplitude 

Pω nω m = YK (ωk )

2

k= n

m

∑ (5)

where n =ωn

(N −1)Ts

2π

, m is defined similarly to n, Ts is 

the sample time, and N is the number of samples. The peak 

to peak value associated with the amplitude (3) can be used to 

analyze reserve requirements [23]. 

To demonstrate the method, power data from a single farm 

are examined here, using a frequency range based on how 

imbalance is handled in the Netherlands. The Netherlands 

energy and imbalance markets are based on 15-minute 

program time units (PTUs). Primary reserves react within 30 

seconds to all disturbances and are replaced by other reserves 

after 15 minute [23]. Secondary reserves act within an hourly 

window to eliminate larger imbalances between PTUs and to 

help achieve new set points when new units are committed. 

The amplitude (3) was computed for the DFT of a wind farm 

power waveform sampled for 30 days at one-minute intervals. 

Ideally the method should be based on data with a resolution 

of 1 second, in order to better assess primary reserves. In this 

case, primary and secondary reserve values were computed 

using the frequency bands listed in Table IV, which correspond

to periods of one hour to 15 minutes, and periods shorter than 

15 minutes. In this paper the peak to peak value is interpreted 

as the total amount of reserves in a class, representing the sum 

of up and down reserves. The third column of Table IV gives 

the outcome of this calculation, and in Fig. 12 and Fig. 13, the 

resulting bands of regulation requirement are plotted on 

detrended versions of the farm output to show how they relate 

to actual power variations. From Fig. 12 two types of events 

not represented by the method are apparent: passing fronts 

(hours 1-3) and long wind ramps (hours 14-15). This is 

acceptable for an estimation of primary and secondary 

reserves, as the former type of event cannot occur at multiple 

farms simultaneously, and the latter type of event could be 

forecast and dealt with via unit commitment. Fig. 13 shows 

that variations around a 15-minute moving average are well 

described by the primary reserves band given by the method. 

Fig. 12. Output of 25 MW wind farm, with secondary reserves band specified 

by frequency domain method superimposed on an hourly moving average.
11

Inspection of the centre of the band confirms that the 

secondary reserves band reasonably accounts for variations in 

the frequency range expected from Table IV. The method 

could also be applied to data aggregated at the country level, 

but is only meaningful for data with at least 1-minute 

(preferably 1-second) resolution. 

 2) Assessing Adequacy through Scenario Simulation

In the Dutch wind integration study [25], the unitcommitment and economic dispatch (UC-ED) optimization 

software PowrSym was used to determine a realistic mix of 

on-line generators for every 15-minute interval in a year. Both 

dispatch to AGC units via the imbalance bidding ladder and 

self-regulation by market parties were modeled using 

Simulink, for worst cases identified from the UC-ED. The 

scenarios studied ranged from 2 to 12 GW, which 

corresponded to a percentage of 5 to 33% of annual energy. 

For the purposes of the study, a secondary reserve of 1600 

MW (equal to twice the size of the largest generator, and 0.13 

p.u. in the 12 GW case) was selected for enforcement by the 

optimization algorithm. System frequency was found to be 

properly maintained (with respect to area control area) even in 

worst case configurations of high wind and low load, and 

conventional generator outages. However, in the worst case 

nearly all of these reserves were deployed. 

TABLE IV

RESERVE CALCULATIONS FOR 25 MW WIND FARM

Reserve 

Class

Frequency Range (Hz) Up + Down: 2⋅ Pω n ,ω m (p.u.)

Primary ∞-1/3600 0.06

Secondary 1/3600-1/900 0.01

G. Denmark

The need for reserves has recently been studied and 

reported in [13]. To ensure security of supply the Danish TSO 

activates so called manual regulation. Up-regulation is 

activated if supply is insufficient to fully meet demand and 

down-regulation if the contrary applies. Activation takes place 

through the Nordic regulating power market. However, 

sufficient capacity may not be directly available in the market 

and often has to be contracted prior to activation, e.g., a day 

ahead of operation. In Denmark manual regulation therefore 

divides into market-based regulating power and contracted 

regulating power, also referred to as manual power reserves. 

Market-based regulation is activated during operation and 

provided through the regulated power market, paying the 

regulated market price. On the contrary, when viewed as 

optioning, contracted regulation is paid an option price on the 

daily auction to ensure available capacity for the following 

operation day. Upon activation, producers are obligated to 

offer the regulating power to the market in the same fashion as 

market-based regulation. Assuming no bilateral contracting, 

the aim is to estimate the amount of regulation power to be 

contracted by the Danish TSO on daily auctions a day ahead 

of operation. 

The modeling framework is based on a partial equilibrium 

model of the electricity system taking into account equilibria 

in a day-ahead and an intra-day market. The day-ahead market 

facilitates delivery of power for the following operation day 

on the basis of forecasts. In contrast, the intra-day market is 

used for handling imbalances due to forecast errors. Hence, 

scheduling decisions divide into stages according to the 

information flow such that day-ahead decisions are first-stage 

and intra-day decisions relate to the remaining stages, the 

result being a mixed-integer multi-stage stochastic 

programming model for unit commitment and dispatch under 

uncertainty, see [26]- [28]. 

Being the main sources of uncertainty, only wind power 

forecast errors and forced outages of the generating units are 

considered. All processes are estimated in accordance with the 

records and the forecast accuracy of the Danish TSO. 

The procedure for estimating the amount of manual 

reserves is based on the difference between social welfare 

optimization of the system and optimal scheduling of the 

producers, taking into account an imperfect market. As an 

attempt to reflect the imperfection of the regulating power 

market it is assumed that producers schedule unit commitment 

and dispatch, considering the day-ahead market only, and 

ignoring the regulating power market. The procedure for 

estimating the amount of manual reserves necessary to 

contract by the TSO is the following:

• Run the model without the regulating power market 

and store the capacity of online units scheduled for the 

following day. Producers start up capacity for spot market 

dispatch, ignoring the regulating power market. The 

resulting capacity may be insufficient for power 

balancing.

• Run the model with the regulating power market and 

store capacity of online units scheduled for the following 

day. From a social welfare perspective, producers should 

start up capacity for spot market and regulating power 

market dispatch such as to ensure sufficient capacity for 

power balancing.

• The difference in online capacity has to be contracted 

ahead of operation for producers to start up sufficient 

capacity. Moreover, producers offer idle online capacity 

as contracted regulating power. 

Fig. 13. De-trended output of 25-MW wind farm, using hourly moving 

average. Dashed curves mark primary reserves band specified by frequency 

domain method superimposed on 15-minute moving average, while horizontal 

lines mark secondary reserve band.
12

Hence, estimate for the amount of manual reserves is the 

sum of insufficient and idle online capacity. 

The estimation procedure applies to day-ahead planning 

of manual reserves.

The hourly amounts of manual reserves simulated in 

Eastern and Western Denmark, respectively, are displayed in 

Fig. 14 and Fig. 15. In contrast to long-term contracts, daily 

auctions allow for the amounts to vary over time which is 

highly valuable in both regions.

The hourly amounts of manual reserves necessary in 

Eastern and Western Denmark, respectively, are displayed in

Fig. 14 and Fig. 15. In contrast to long-term contracts, daily 

auctions allow for the amounts to vary over time which is 

highly valuable in both regions.

As system imbalances are covered by regulating power

(load following reserve in Table I), the amount of manual 

reserves or contracted regulation tends to vary with 

imbalances. From Fig. 14 and Fig. 15, it is evident that 

contracted regulation exceeds average imbalances in order to 

cover extreme ones. Still, extreme imbalances are not fully 

covered by regulating power as even from a social welfare 

perspective this is not economically optimal. Mostly, the 

amounts of manual reserves are substantially larger in Western 

than in Eastern Denmark, reflecting a larger variance of 

imbalances caused by a larger installed wind. 

Fig. 14. Hourly amounts of manual reserve capacity and system imbalances 

simulated for Eastern Denmark, January 2006. The lower red line is the 

average imbalance in simulations. The higher red lime is the extreme, 

maximum imbalance from the simulations. The model does not always choose 

to cover the maximum possible imbalance when it is costly as it is highly 

improbable.

Fig. 15. Hourly amounts of manual reserve capacity and system imbalances 

simulated for Western Denmark, January 2006.

In a perfect market, system imbalances should be covered 

by regulating power offered directly to the regulating power 

market. However, since producers optimize bids to the dayahead market, ignoring the regulating power market, 

regulating power is frequently insufficient. Moreover, option 

payments incite producers not to offer directly to the market. 

Since electricity reserves constitute serious costs of the 

system, estimation of the need for these reserves continue to 

be highly relevant, e.g., in further exploring the structure and 

the imperfections of the market. 

H. Quebec

Hydro Quebec has completed separate studies to determine the 

integration impacts first on regulating reserves and load 

following using statistical analysis and simulation and second 

on ramping or balancing reserves using a risk based 

methodology.

 1) Regulating Reserves and Load Following Reserves

To prepare for large scale wind integration into its system 

operations processes, the ISO undertook a preliminary 

analysis of the impact of wind variability and uncertainty on 

operational time frame reserves requirements within the one 

hour horizon. It was rapidly clear that the first three reserves 

categories are not sensitive to wind energy integration 

essentially because wind plants are limited in size (less than 

200 MW) and geographically spread over relatively large 

areas (1000-km stretch). In this context, the most relevant 

quantities requiring further investigation were the AGC and 

load following reserve capacities. After reviewing the pros and 

cons of the statistical versus simulation approach for assessing 

these quantities, Hydro-Quebec’s ISO opted for the simulation 

approach, due to the following reasons:

1. Unlike many other North-American jurisdictions, 

TransÉnergie’s ACE is dependent only on the frequency 

deviation. The statistical methods do not involve the frequency 

deviation and are therefore less precise in the case of isolated 

systems which are never subjected to generation-demand 

imbalance, but only frequency deviations.

2. Simulation provides a means to obtain important 

results for measuring many other impacts of wind power such 

as the frequency of stop-starts, the efficiency degradation of 

AGC units and, in particular, the AGC’s regulating range.

The simulation approach is currently under development at

IREQ [29] with the goal of applying load flow calculations 

and optimal generation dispatch to reproduce the system 

behavior with a 1-minute time step. Meanwhile, for 

benchmarking purposes, it was decided to apply the statistical 

analysis approach used by BPA for its Rate Case 2010 [30] to 

Quebec Interconnection. The hourly demand data are 2016 

forecasts provided by the load serving entity while the hourly 

wind generation data comes from 11-year historical 

reconstitutions of the first two tenders, 3000-MW wind plants 

production. The minute-by-minute data demand, wind 

generation and forecasts were derived according to [31]. 

Following BPA and CAISO studies, real-time wind forecasts 

are based on a simple 2-hour persistence model. The HydroQuébec results (for 11-year min/min calculation) are 

superposed in Fig. 16 on BPA results taken from [30], in the 

case of a 3155-MW wind capacity. 
13

0

2

4

6

8

10

0 2 4 6 8 10 12 14 16 18 20 22

% Rated Wind Generation

Perfect Load-Following UP 5

10

15

20

25

30

35

0 2 4 6 8 10 12 14 16 18 20 22

% Rated Wind Generation

Forecasted Load-Following UP

BPA(3155MW)

HQ(2995MW)

0

1

2

0 2 4 6 8 10 12 14 16 18 20 22

% Rated Wind Generation

Hour of the day

AGC UP

BPA(3155MW)

HQ(2995MW)

5

10

15

20

25

30

0 2 4 6 8 10 12 14 16 18 20 22

% Rated Wind Generation

Hour of the day

Imbalance UP

Fig. 16. Comparison of Hydro-Quebec and BPA supplemental reserves at 

3000MW wind generation (up-regulation).

According to the BPA method, the supplementary AGC and 

LF reserves to accommodate 3000 MW of installed wind 

capacity will amount to 2 and 22% respectively on a yearly 

average basis. However, there is a considerable disparity 

between the winter months and other months: the additional 

LF requirements increase from 17% of the installed wind 

capacity in winter to 30% during the summer. On the other

hand, the nxsigma criterion [32] resulted in much lower 

incremental reserves requirements with only 0.4% and 7% 

increase of the AGC (n=4) and LF (n=2) respectively.

Currently, there exists an agreement covering all the 

ancillary services provided by Hydro-Québec Production to 

ensure the reliability and security of the provincial power 

supply. This agreement includes a frequency regulation 

service which is specified in terms of the availability of a 

regulating range of the AGC system (up and down) which can 

vary between 500 and 1500 MW. This practice has always 

allowed the ISO to maintain a very high level of conformity 

with the NERC frequency control standards (CPS1 and 

CPS2). It is generally accepted that the evaluation of the 

impacts using a statistical approach is not as accurate as the 

method based on simulation, which is founded upon far more 

realistic system operation hypotheses. IREQ has been 

working on such a simulator since 2006 but the initial results 

on the 3000-MW integration will be published separately 

[29]. The system operations simulator is the proper tool for 

refining these paper’s estimations and, in particular, relating 

them to the terms of the agreement currently covering the 

frequency regulation service in the Quebec interconnection. 

 2) Ramping or Balancing Reserves

In Hydro Québec Balancing Reserves ( BRs) assure the 

short-term reliability to its power system over a time horizon 

of one hour to 1 to 48 hours ahead. Recently, several studies in 

the literature have re-evaluated the actual reserve levels 

required when incorporating wind generation into their 

systems and have proposed increasing their levels, some of 

which are [2], [16], [32], [17].

One methodology to compute balancing reserves, 

integrating several sources of uncertainties, is power system 

reliability theory [33]. It is based on the criterion of loss of 

load probability (LOLP) which is the probability that the 

available generation, including reserves, will not completely 

meet the demand, or equivalently risk. Reserves are computed 

such as to meet a specific LOLP target.

The methodology adopted here borrows from the 

traditional reliability theory and adapts it to the time-horizon 

of 1 to 48 hours ahead. In its final formulation, the balancing 

reserve requirement is a function of a distribution of a net 

forecast error composed of load, wind generation and 

generation unavailability forecast errors rather than the 

forecasts themselves [34],[35].

Distributions of all forecast errors, over the lead times from 

1 to 48 hours, were developed a posteriori by comparing past 

forecasts to the corresponding measurements. Having at hand 

the distributions of forecast errors facilitates both the 

aggregation of the individual forecast errors into a net forecast 

error and the graphical representation of results. The 

anticipated risk was then computed at each forecast lead time. 

It is the value of a function of the net forecast error 

distribution corresponding to a predetermined level of 

balancing reserves. Alternatively, given a target level of risk, 

the associated balancing reserve requirements can be 

quantified. Repeating this computation for each lead time 

over a given time horizon, it reveals the temporal evolution of 

risk or of balancing reserve requirements.

This methodology was used to evaluate additional 

balancing reserves required to integrate 3000 MW of wind 

power capacity into the Hydro-Québec (HQ) system. This 

was done by comparing the balancing reserves required to 

maintain the same level of risk before and after the integration 

of wind generation over numerous system conditions.

Fig. 17 illustrates the risk, R0 , of 17%, corresponding to 

some nominal balancing reserves level, 500 nom BR MW =

(obtained by reading on curve, d u R + ), the additional risk 

incurred, ∆R , and the additional reserves ∆BRs required 

following the integration of two different wind generation 

capacities at a given instant, with the wind forecast error 

uncertainties being modelled as zero-mean Gaussian 

processes. Adding a certain amount of wind generation into 

the system, and keeping the same amount of balancing 

reserves, increases the system risk by an amount of ∆R . See 

full and dotted curves, duw R + − and d uW R + − , corresponding to 

small and large wind generation respectively with wind 

forecast error uncertainties modelled as zero-mean low and 

large variance Gaussian processes. In order to maintain the 

same risk before and after the additions of wind generation, it 

is necessary to provide the system with additional balancing 

reserves of the amount of ∆BRs .
14

Fig. 17. Qualitative illustration of the risk and additional balancing reserves 

for two different wind generation penetration levels.

We note that at each instant the original risk without wind 

generation, R0 , presented to the system depends on the 

statistical characteristics of the uncertainties on the load 

forecast and the forecast of unavailable power and the 

nominal balancing reserves level, nom BR .

Further, looking at the time evolution of the variables, since 

the forecast uncertainties may vary over time, the hour during 

the day and the season, it follows that the risk R0 incurred 

with constant nominal balancing reserves varies over time. 

Alternatively, the balancing reserves BRs required to 

maintain a given risk level also varies over time. The 

additional risk, ∆R , sustained by the system when 

integrating wind generation, and therefore the additional

Fig. 18. Risk with and without wind generation, and added balancing reserves 

to maintain the same risk as before the incorporation of wind generation.

balancing reserves, ∆BRs, depend on the original risk, R0 , 

corresponding to the given level of reserves, nom BR , and on the 

statistical characteristics of the added wind generation

forecast error. The two quantities ∆R and ∆BRs also vary 

over time.

Fig. 18 shows the risk encountered with and without wind 

generation, and the required ∆BRs beyond the predetermined 

balancing reserves to maintain risk over a horizon covering 

the next day. The bump in the curves around the 16:00 hour

reflects the particular signature of load forecast errors. 

Using Hydro-Québec data, risk levels encountered in 

balancing reserves reach up to 5% over the day-ahead 

horizon. This may seem unusually high, but contrary to the 

regulating reserves, acting in the intra-hour time horizon, 

utilities have the leisure to accept larger risk levels here 

300 400 500 600 700 800 900 1000

0

5

10

15

20

25

30

B.R. [MW]

Risk %

Risk : Rd+u, Rd+u-w

Rd+u

Rd+u-w

∆BR

∆R

BRnom

Rd+u-W

Rd+u

Rd+u-w

∆BR

Risk [%]

with wind

without wind

-4 6 16 26 36 ∆ BRs [MW]

Time of day ahead [h]

Risk and ∆BR vs lead time, January

Types of Reserve Considered

(corresponding reserve name used in study)

Study Frequency 

Response Reserves

Regulating Reserve Ramping Reserve Load Following Reserve

Minnesota X

(Regulating Reserve)

X

(Operating Reserve)

X

(Load Following)

New York X

(Regulating Reserve)

EWITS X

(Regulation, variability and 

short-term wind forecast error)

X 

(Regulation, next-hour wind forecast error 

& Additional Reserve)

WWSIS X

(Regulating Reserve)

X

(Load Following Reserve)

Ireland X

(Spinning Reserve)

X

(Replacement Reserve)

Spain X

(Secondary Reserve)

X

(Deviation Reserve)

X

(Tertiary Reserve)

Quebec X

(AGC)

X

(Balancing Reserve)

X

(Balancing Reserves Load Following)

Netherlands X

(Primary Reserve)

X

(Secondary Reserve)

Denmark X

(Tertiary Reserve)

TABLE V

SUMMARY OF RESERVE TYPES USED IN INTEGRATION STUDIES
15

because looking forward they can still call on uncommitted 

yet available resources to remedy undesirable occurrences. 

Since the remedies are implemented at extra cost, the choice 

of risk level is essentially an economic consideration 

associated with the deployment of resources committed at the 

last minute.

In summary with this study, we quantified balancing 

reserve requirements, with and without wind generation, 

based on a risk criterion. Using this procedure we have 

determined the added reserve requirements to maintain a 

specified level of risk before and after the integration of 3000 

MW of wind power capacity.

The conclusion to be drawn is that with current HQ 

balancing reserves being relatively high and risk levels 

relatively low, little additional balancing reserves are required 

to integrate 3000 MW of wind power capacity. The 5% 

maximum risk level revealed in our simulations was not 

predetermined, but rather was revealed by the present study. 

It seems to be acceptable, since current practice in operations 

planning seems satisfactory.

V. COMPARISON OF METHODS AND CONCLUSION

This paper began by establishing a common naming 

convention to describe the different types of reserves used in 

system operations and wind integration studies. These 

reserve types, frequency response reserves, regulating 

reserves, ramping reserves, load following reserves and 

supplemental reserves, allow comparison across systems.

As shown in Section III, the types of reserves (frequency, 

regulating, load following and supplemental) used are 

common across the power systems in the European and North 

American systems considered. The choice to use load 

following reserves or supplemental reserves (or to use both) is 

the main discrepancy between systems. The types of reserves 

considered across integration studies are also consistent. Of 

the studies discussed, all consider regulating and ramping 

reserve and some combination of load following reserve and 

supplemental reserve. Only the Netherlands considered 

frequency response reserves in its study. It should be noted, 

however, that though four integration studies considered 

ramping reserves, only one system operator, Spain, currently 

uses ramping reserves in operations today.

Although the integration studies discussed in this paper 

considered similar types of reserves, the methods for 

determining the quantity of required reserves varied widely.

Each of these studies used different data in this calculation 

based on different assumptions of their system operations. In 

the Minnesota, New York, EWITS, and WWSIS reports, 

regulation reserves were determined statistically using the 

standard deviation of wind variability. In the Dutch system, a 

methodology of transforming the data into the frequency 

domain and considering peak-to-peak analysis was 

demonstrated for one wind farm data. The ramping reserves 

in the Minnesota and Quebec integration studies were 

analyzed dynamically. In the Minnesota study, the 

requirements changed as a function of wind output; in the 

Quebec study the balancing reserves changed dynamically as 

a function of the statistical characteristics of net load forecast 

uncertainties and risk level. On the other hand, ramping 

reserves in the All Ireland Study and Danish study were 

determined using stochastic optimization. Most studies a 

priori concluded that no change in supplemental reserves was 

necessary as the largest contingency on the system remained 

the single largest generator. 

Study Regulating Reserve

Methodology

Ramping or Load 

Following Reserve

Methodology

Minnesota Statistics, standard 

deviation of wind 

variability

Function of wind output 

(dynamic)

New York Statistics, standard 

deviation of wind 

variability

n/a

EWITS Statistics, standard 

deviation of wind 

variability

Statistics, standard 

deviation of wind forecast 

error

WWSIS Statistics, standard 

deviation of wind 

variability

Ireland Stochastic Optimization

Spain Time-step Monte-Carlo 

simulation

Quebec Statistical analysis Statistical analysis, Riskbased (dynamic)

Netherlands Frequency domain peakto-peak analysis, Scenario 

analysis

n/a

Denmark Market-based risk model n/a

VI. ACKNOWLEDGMENT

This paper is a result of the international collaboration IEA 

Wind Task 25 Design and Operation of Power Systems with 

Large Amounts of Wind Power.

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The Alliance for Sustainable Energy, LLC (Alliance), is the 

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This paper provides a high-level international comparison of methods and key results from both operating practice 

and integration analysis, based on an informal International Energy Agency Task 25: Large-scale Wind Integration.

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IEA Task 25; grid integration; operating reserves; power system operation; power system reliability; power systems; 

wind power generation

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