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

A Flexible Power System 

Operations Simulation Model for 

Assessing Wind Integration 

Preprint

E. Ela and M. Milligan

National Renewable Energy Laboratory 

M. O’Malley

University College Dublin

To be presented at the Power & Energy Society General Meeting

Detroit, Michigan

July 24-29, 2011

Conference Paper

NREL/CP-5500-50641

March 2011
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1 

Abstract--With the advent of wind power generation on 

worldwide power systems, many operators and researchers are 

analyzing the impacts that higher future amounts may have on 

system operations. Many of the tools are analyzing longer term 

impacts on the steady-state operations of power systems and are 

primarily using cost as a metric. They are also using tools that 

are often inflexible to accommodating different market designs or 

operational structures. In this paper a model was developed to 

mimic operator behavior using a combination of securityconstrained unit commitment, security-constrained economic 

dispatch, and automatic generation control programs. New

metrics are used to compare reliability in terms of energy 

imbalance for different systems or different market and 

operational structures at very high time resolution. Finally an 

example application of the tool and results for a test system are 

shown.

Index Terms— automatic generation control, economic 

dispatch, power system operations, unit commitment, wind 

integration 

I. INTRODUCTION

IND power generation has seen a tremendous growth in 

past years as its environmental benefits and zero 

variable costs have been viewed as an acceptable alternative to 

other more conventional sources of power generation. Wind 

power is considered variable generation (VG) however, and 

can be both variable and uncertain. Unlike non-VG generation 

sources, it has a maximum generation limit that changes 

through time (variability) and this limit is also not known with 

perfect accuracy at times in the future (uncertainty). These 

impacts can create challenges for system operators when 

ensuring enough units will be online to meet reliability 

requirements, and to schedule the system to maintain a stable 

system frequency and minimize the imbalance between 

generation and load. Many entities therefore have been 

studying these impacts using production cost simulation 

models and statistical analyses [1]. The state of the art in the 

modeling techniques to analyze wind integration methods has 

been described in [2], [3].

Many production costing programs have been developed 

and used for a variety of applications, see for example [4]-[6]. 

Although the production cost models used in these analyses 

are very sophisticated and are efficient tools for use in many 

applications, there are a few drawbacks to using them for 

 Erik Ela and Michael Milligan are with the National Renewable Energy 

Laboratory.

Mark O’Malley is with the University College Dublin.

steady-state power system integration impacts. Many are fixed 

to certain operating and market structures (e.g., scheduling 

intervals, optimization horizons, reserve requirement rules, 

etc.). This makes it difficult to analyze the impacts that 

different operating and market structures have on the 

integration of wind. Recently, many research models have 

been built to give more flexibility toward solving different 

types of issues involved with wind integration. For example, 

models have been built that incorporate the stochastic input of 

wind power and other stochastic variables to better estimate its 

integration impacts [7]-[10]. In [11], a model was built that 

ensured possible scenarios of wind production could be met 

with a given unit commitment set, but based on minimizing 

the cost of one scenario. In [12], a model was developed 

similar to commercial unit commitment programs, but with 

sophisticated modeling to represent real-time pricing showing 

the influence of demand response on wind integration impacts. 

However, these are all built at the hourly “unit commitment” 

time resolution and therefore impacts that occur inside the 

hour may be hidden. Since it is difficult to see any reliability 

impact result at average hourly resolutions, the only 

meaningful metric is total costs. These models also do not 

capture the detailed costs from units dispatch inside the hour 

to follow the intra-hour variability through automatic 

generation control (AGC) or from being deployed as reserve. 

Many of the models used for wind integration studies are 

very capable of showing the costs of requiring and holding 

operating reserves due to sophisticated solvers that cooptimize energy and operating reserves. However, they do not 

typically show the utilization of these operating reserve or 

mimic operator actions when deploying operating reserves. 

Therefore, the required operating reserves are usually 

determined off-line using statistical analysis as can be seen in 

[13] and these reserve values are rarely validated using 

simulation.

We introduce the Flexible Energy Scheduling Tool for 

Integration of Variable generation (FESTIV), which is a high 

resolution steady-state power systems simulation model that 

has parameters that can be flexibly configured by the user. The 

model uses various combinations of security-constrained unit 

commitment (SCUC), security-constrained economic dispatch 

(SCED), and AGC to schedule the system at different 

operating time frames. At the finest scheduling interval, the 

frequency at which AGC is run (which in North America is 

normally 4-6 seconds), the imbalance between generation and 

load is calculated, as are the production costs. The costs and 

imbalances at this resolution give realistic metrics that can 

A Flexible Power System Operations Simulation 

Model for Assessing Wind Integration

Erik Ela, Member, IEEE, Michael Milligan, Senior Member, IEEE, Mark O’Malley, Fellow, IEEE

W
2 

show how well the system is balanced, how well it avoids 

extreme imbalances and how much it costs to run that system. 

These metrics can be used to compare different operating 

strategies (e.g., dispatch intervals) as well as different inputs 

(e.g., wind penetrations).

This paper introduces the tool and describes some of the 

applications it has for evaluating the impacts of wind power 

on power system operations. In section II, we describe the 

overall structure of FESTIV and the individual models that 

make up FESTIV, including its SCUC, SCED, and AGC. In 

section III, we then show an application of the tool where we 

evaluate the performance of different economic dispatch 

scheduling intervals, forecast errors, and mode of AGC

operation on a test system with a high penetration of wind 

power. Section IV concludes the paper. 

II. DESCRIPTION OF MODEL

FESTIV includes three models that are each run at time 

intervals configured by the user: SCUC, SCED, and AGC. In 

general, the model was tailored towards evaluating the shortterm operational impacts that occur, while the hourly 

resolution production cost models that are currently being 

used for evaluating the impacts of wind integration are a better 

choice for evaluating the long-term impacts (i.e., annual or 

longer). The model attempts to replicate actual system 

operations at a high resolution and allows for flexibility to 

accommodate the many different market and operational 

structures that are in existence throughout the world. Fig. 1 

shows the high-level process and data flow of FESTIV. Full 

lines represent the flow of process and dashed lines represent 

the flow of data.

A. SCUC

Two SCUC models are included: Day-Ahead SCUC 

(DASCUC) and Real-time SCUC (RTSCUC) with the key 

difference being what generating units the program can turn 

on or off. The DASCUC is run for the entire day and gives the 

initial commitment status for all units. This would be similar 

to the day-ahead unit commitment currently performed in 

many regions. After this, the daily operation begins and the 

RTSCUC is repeated throughout the day to update unit 

commitment based on new forecasts and system conditions. 

This SCUC is very similar to DASCUC, but can only start and 

stop units differently than the status given from the initial 

SCUC, if the units have a start time less than tRTCSTART, which 

is configurable by the user.

The DASCUC and RTSCUC model designs are very 

similar. They minimize costs including variable, no load, start 

up, and reserve costs and include load shedding and 

insufficient reserve penalty costs. They ensure typical 

generator constraints including min./max. capacity, ramp rate, 

minimum on/off time, maximum starts per day, etc. DASCUC 

also allows for variable startup costs that depend on the offline 

time of the unit starting up. Start-up trajectories are also 

modeled in both programs similarly to [14] where the start-up 

ramp rate is always Pmin/SUtime. SCUC also models the 

transmission network including contingencies via a dc load 

flow method using power transfer distribution factor (PTDF) 

and line outage distribution factor (LODF) matrices. The 

algorithms also allow for HVDC lines and phase shifting 

transformers to be part of the network.

Fig. 1. Process flow diagram for FESTIV.

B. SCED

RTSCED is very similar to RTSCUC except that it cannot 

change commitment status that was provided by RTSCUC. 

SCED minimizes variable costs including reserve costs and 

load shedding and insufficient reserve penalty costs. It also 

contains all of the generator constraints except for 

commitment-based constraints, and network constraints 

including contingencies using PTDF and LODF. It includes 

modeling of HVDC lines and phase-shifting transformers as 

well.

C. AGC 

Lastly, the AGC model is a rule-based algorithm. Unlike 

SCUC and SCED, it is not optimizing the scheduling of units. 

Instead it uses all units that are providing regulating reserve as 

scheduled by SCED to assist in correcting the Area Control 

Error (ACE). ACE is calculated by subtracting the total load 

from the total generation (which is similar to actual ACE 

assuming nominal frequency). In most cases, AGC will 

schedule the ACE correction needed, proportional to the units 

regulating schedules and ramp rates provided by the last 

SCED. AGC is run at the finest of all time resolutions, tAGC, 

which is also configurable by the user. Units not providing 

ACE regulation are given a schedule by AGC that interpolates 

one SCED schedule to the next.

The AGC built in FESTIV currently has four different 

modes it can be run at. The first mode, which we designate as 

“blind mode,” basically does not provide any ACE regulation. 

All units, whether they were given a regulating schedule by 

SCED or not, are interpolated from one SCED schedule to the 

next regardless of the ACE (i.e., AGC is “blind” to the ACE). 
3 

The second mode, designated as “fast mode,” will use its units 

providing ACE regulation to follow each instantaneous ACE. 

This would create incredible wear and tear, and most units 

would be incapable of following. The third mode is designated 

as “smooth mode,” where regulating units from SCED follow 

a proportional integral ACE signal, also called smoothed ACE 

(SACE) [15]. This would follow most closely what is done in 

actual operation [16] [5]. This is shown in (1) and both the 

proportional and integral terms are configurable by the user. 

The last mode is designated as “lazy mode.” It is based on [17] 

and is essentially a combination of “blind mode” and “smooth 

mode.” The units given AGC regulation schedules by SCED 

do not correct ACE unless it appears that the CPS2 interval 

will violate the CPS2 compliance. The anticipated violation is 

based on the ACE that has occurred since the beginning of the 

CPS2 interval and the assumption that the ACE will stay 

constant at the current instantaneous ACE for the remainder of 

the CPS2 interval. Therefore, when this violation is 

anticipated, the units providing AGC regulation will follow a 

SACE signal such that the integral and proportional terms are 

calculated based on (2), (3), and (4).

Each of the four AGC modes acts as a different benchmark 

for analysis. Note that if, for example, AGC is run at 6-second

intervals, the AGC will be run 14,400 times for a single-day 

simulation. The rule-based algorithm without optimization 

allows for this operation to occur and is most closely related to 

AGC programs used in ISO/RTO operations. 

D. Model Interface and Communications

Fig. 2 shows how the different programs are run in the 

FESTIV model based on the configurable time parameters. 

The blocks represent the running of the models with each 

point following this block representing a future time interval 

that is part of the scheduling horizon of that model run. For 

DASCUC, the interval resolution is IDA, which in today’s 

systems would normally be one hour. DASCUC is performed 

just once per day and would have an optimization horizon of 

one day. RTSCUC is repeated every tRTC and run at an interval 

resolution of IRTC, which normally would be the same as tRTC, 

but can be different. RTSCUC would have a horizon of HRTC. 

RTSCED is run every tRTD minutes with interval resolution of 

IRTD and optimization horizon of HRTD. An additional 

parameter IRTD-ADV is also used to represent the fact that the 

time resolution for dispatch is very important in the immediate 

future, and less so further out ahead in the optimization. 

Therefore, IRTD-ADV is always larger than IRTD and represents 

all but the first interval length. The AGC model is repeated 

every tAGC, and for only one time point, which is tAGC ahead.

Fig. 2. Timeline for DASCUC, RTSCUC, RTSCED, and AGC in FESTIV 

model.

The three models have significant communication between 

each other (see Fig. 1). The unit status and startup for all units 

with start times greater than tRTCSTART are an output of the - 

DASCUC and cannot be changed by any of the other models

(unless contingencies are simulated). It should be noted that 

by making tRTCSTART very high, this would effectively 

eliminate this constraint. RTSCUC takes the fixed status from 

the DASCUC and as output sets the unit status and startup for

all units. RTSCED would then take the unit status of all units 

as set by RTSCUC and as output set the economic dispatch 

signals and reserve schedules for all units. Lastly, AGC would 

then take the unit status and unit startup from SCUC and 

dispatch and reserve schedules from SCED to compute the 

actual generation of all units and the ACE. An additional 

model called a reserve pick up (RPU), which is similar to the 

RTSCUC, is also part of FESTIV. The RPU reflects an 

operator action if a contingency occurs or the ACE is over 

some threshold, and an immediate correction is more desirable 

than waiting for the next tRTD or tRTC.
4 

The communication of data does not only flow from the 

lower resolution models to the higher resolution models (e.g., 

SCUC to SCED) as shown in Fig. 1, but also from lower 

resolution to higher resolution models. The unit status of prior 

points in time are needed for each real-time SCUC to ensure 

that any decisions it makes in future SCUC solutions do not 

violate any minimum on time or other commitment 

constraints. The actual generation of units should also be 

known for the RTSCUC, RTSCED, and AGC processes so 

that the program does not give infeasible scheduling solutions. 

This is important when modeling the actual detailed operation 

of the system and the interaction between regulating units and 

economic dispatch schedules. For example, SCED needs to 

know both the actual generation of the units it is scheduling as 

well as the dispatch schedule that the prior SCED gave these 

units. This is due to the time delays involved with both the 

running of the model as well as the fact that the dispatch is for 

some time in the future. With these two pieces of information 

known, SCED can now give a dispatch schedule that is both 

feasible based on where the unit is actually operating at the 

time the program starts and is feasible based on the predicted 

direction the unit will be moving toward while solving the 

program using the individual unit’s ramp rate. This practice is 

based on actual operations at the New York Independent 

System Operator (NYISO) [18]. For example, Fig. 3 shows 

the operating range that the current SCED can schedule a unit 

based on its actual output at the start of the program 

initialization and the prior SCED schedule considering its 

ramp rate. This assumes a 5-minute process time and a 5-

minute dispatch interval. The shaded region is the range where 

the current SCED is allowed to give it a schedule. With this 

implementation, units that were directed by the AGC to 

correct ACE differently than RTSCED directed it are not 

given infeasible schedules by the next RTSCED.

Fig. 3. Use of ramp constraints based on actual output and last schedule. 

It should be noted that different forecasts of wind and load 

are needed for every RTSCUC and RTSCED run. So for 

instance, if tRTC is 15 minutes and tRTD is 5 minutes, this would 

mean one day would require 384 real-time forecast sets (96 

RTSCUC + 288 RTSCED) for every wind plant and load as 

well as one more for the DASCUC program. Since each 

SCUC and SCED may be optimizing over periods of time and 

not single instances, there is actually a forecast for each time 

point of each of the aforementioned sets. For instance, if HRTC

is 3 hours (meaning 12 points at 15-minute intervals), there 

would be 12 forecasts for every wind plant and load for every 

single RTSCUC. This is important for analyzing the impacts 

of wind integration and makes the simulation as close to 

reality as possible. 

Other options are included in the model including behavior 

rates and contingency simulations. Behavior rates model how 

well the conventional generating units follow their dispatch 

schedules based on random numbers. A behavior rate of 1 

would follow AGC perfectly, whereas a behavior rate of 0 

would give completely random output. Contingencies can be 

simulated using random numbers for either generator or 

network outages. The model disregards frequency response, 

voltage magnitudes, and reactive power flows. It also 

currently models all conventional units alike without detailed 

multi-mode constraints for combined cycle gas turbines or 

hydrological constraints for hydropower units. The flow of 

processing between the models and the flow of data is mainly 

implemented with Matlab. AGC is built in Matlab and SCUC 

and SCED models are built in GAMS using CPLEX MILP 

and LP solvers, respectively [19]. Matlab calls GAMS for 

each optimization and retrieves its output data based on the 

process implemented in [20].

III. APPLICATION OF THE TOOL TO WIND INTEGRATION 

IMPACTS

The major impacts that wind and other VG can cause on 

power system imbalance are caused by its variability and 

uncertainty. Variability and uncertainty are certainly 

interrelated. However, we attempt to distinguish how each 

may affect the operations of the power system. For example, 

both may have to be managed in different ways. The way that 

they are managed may be through the SCUC, SCED, and 

AGC programs and operating reserves that may be held in one 

model to be used in another. Although the main factor to the 

degree to which variability and uncertainty impact the power 

balance are due to the variable and uncertain variables 

themselves (e.g., wind output), the way in which the system 

prepares for that variability and uncertainty can also have a 

significant impact on these impacts as well as the costs to 

manage the impacts. The following case studies therefore all 

use the very same stochastic variable time series, the actual 

load and wind power, with simple adjustments in how the 

model prepares for and manages the system through the 

SCUC, SCED, and AGC processes.

In order to test the impacts, we first define our metrics. As 

mentioned earlier, both imbalance and costs are calculated at 

every tAGC. The absolute value of the imbalance (ACE) is also 

taken at every tAGC and summed up for the entire study period. 

We refer to this measure as AACEE, for Absolute ACE in 

Energy, which has the units of MWh. The performance of 

different systems can also be measured with CPS2 violations, 

which is based on the North American Electric Reliability 

Corporation (NERC) standard [21]. The user can configure the 

L10 value (ACE limit) as well as the CPS2 interval (nominally 

10 minutes according to NERC) to what is deemed as a 

violation for the particular system. This can show how often 

the system being evaluated has extreme instances of 

imbalance, whereas the AACEE shows overall imbalance 

performance. A standard deviation of the ACE can also be 

calculated. Lastly, the detailed costs of the resources meeting 

the demand at every tAGC can be calculated and summed up to 

compare costs for the operating period being evaluated. We 
5 

will use the AACEE, CPS2 violations, σACE, and costs as the 

metrics in the following case studies. 

We test the variability and uncertainty impacts on the PJM 

5-bus system with wind power added at bus E as shown in Fig. 

4. Generator data is shown in Table I. Wind, load, and net load 

for the day are shown in Fig. 5. The wind and load data are 

actual data and are at 6 second time resolution, which is tAGC. 

Fig. 4. PJM 5-bus system with variable wind added to bus E. 

Fig. 5. Wind, load, and net load used in test system.

A. Variability Impacts and Scheduling Resolution 

All of the scenarios mentioned in these first two analyses 

will use the ‘blind mode’ of AGC, meaning that there will be 

no units regulating the ACE but only moving from one 

dispatch schedule to the next. For the DASCUC, all use hourly 

resolution (i.e., IDA = 1 hr) and the DASCUC forecast is 

perfect for both wind and load. There are no simulated 

contingencies of transmission or conventional generator

outages, all behavior rates are set to 1, and RPU is not used. 

tRTCSTART is set at 0.5 hours, meaning that only “Sundance” 

can be started by the RTSCUC. Also, even though the units 

are operating on ‘blind mode,’ there is regulating reserve that 

is scheduled by RTSCED along with spinning and nonspinning contingency reserve. The regulating reserve would 

simply leave upward and downward room to regulate but 

never do so. The L10 value for determining CPS2 violations is 

25 MW in a 10-minute interval, which is similar to L10 values 

of North American systems of similar size. 

If perfect foresight is known of all possible uncertain 

variables, the only possibility of imbalance is variability 

occurring within a dispatch interval or because of a physical 

constraint (e.g., units not having enough ramping capability 

even if it is known the ramp that is needed). A perfect forecast 

in this case refers to one that is exactly the average of the 

predicted variable for the length of the particular interval. To 

understand the impacts strictly based on the variability 

occurring within the dispatch interval we vary the RTSCUC 

and RTSCED timing parameters as shown in Table II. Note 

that in this table, H refers to the number of interval points in 

the optimization rather than the optimization horizon time.

TABLE I 

TEST SYSTEM UNIT DATA

Minimum 

Capacity 

Maximimum 

Capacity 

Incremental 

Cost

No Load 

Cost

Startup 

Cost

Regulation 

Cost

Min

Run/Down 

Time

Ramp Rate Startup Time

Alta 40 MW 110 MW $14/MWh $100/h $450 $5/MWh 4 h 2 MW/min 3 h

Brighton 200 MW 600 MW $10/MWh $100/h $1200 $8/MWh 8 h 0.5 

MW/min

6 h

Park City 40 MW 100 MW $15/MWh $100/h $900 $10/MWh 4 h 2 MW/min 3 h

Solitude 100 MW 520 MW $281/MWh $100/h $300 $4/MWh 6 h 5 MW/min 3 h

Sundance 50 MW 200 MW $40 /MWh $50/h $150 $1/MWh 1 h 5 MW/min 0.5 h

 1 Solitude also uses a piecewise linear cost curve ranging from $28 to $40/MWh at different parts of its capacity.
6 

TABLE II

REAL-TIME INTERVAL TEST CASE DESCRIPTION

Interval description tRTD, IRTD IRTD-ADV HRTD tRTC, IRTC HRTC

5 5 15 5 15 12

10 10 30 3 30 6

15 15 30 3 30 6 

30 30 30 3 60 3 

60 60 60 2 60 3

Each case progressively has longer time between updates 

and longer interval resolution. Each of these cases was run on 

FESTIV for a full day. Results are shown in Table III. This 

can be thought of as the impacts of the net load variability.

TABLE III

RESULTS FOR PERFECT FORECAST AT DIFFERENT RESOLUTIONS

Interval AACEE

CPS2 

Violations σACE Cost

5 40.9 MWh 0 2.39 MW $355,705.62

10 58.14 MWh 0 3.34 MW $356,522.71

15 77.59 MWh 0 4.45 MW $357,102.62

30 120.29 MWh 0 6.96 MW $356,768.03

60 175.51 MWh 3 9.86 MW $356,233.64

The first notable result is that the 60-minute SCED is the 

only case where any intervals violate the CPS2. This case had 

3 10-minute intervals with ACE over 25 MW (or below -25 

MW). In general, the ACE metrics increase with increasing 

dispatch resolution. The costs do not have a trend of any sort. 

Since there is no cost to having ACE or CPS2 violations built 

in the model, and units are not regulating the ACE between 

intervals, the costs are simply based on what the dispatch is 

telling the units to do to meet the average of the dispatch 

interval net load. Fig. 6 shows the AACEE and σACE as a 

function of tRTD. With a linear trend line, the plot shows that 

the variability of the net load increases the AACEE about 2.4 

MWh for every minute longer the SCED dispatch interval is. 

Similarly, the standard deviation of ACE increases about 0.13 

MW for every minute longer the SCED dispatch interval is. 

These rates decrease as the dispatch interval increases for both 

standard deviation and AACEE.

0

2

4

6

8

10

12

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70

MW

MWh

SCED interval resolution - tRTD (minutes)

AACEE

standard 

deviation 

(ACE)

Fig. 6. AACEE and σACE as a function of dispatch interval resolution.

B. Uncertainty Impacts and Scheduling Resolution

In order to test both the variability impacts of the wind 

generation as well as the uncertainty impacts we will compare 

the prior perfect forecast case with two imperfect forecast 

cases. One will have a perfect load forecast with a persistence 

wind forecast, and the other a persistence load and persistence 

wind forecast. In our definition, the persistence forecast 

assumes the future will be the same as the last actual reading 

that occurred. Note that due to the maturity of load forecasting 

a persistence load forecast is not likely to occur in today’s 

system operations, but we use for comparison purposes. 

Persistence wind forecasts on the other hand are the most 

common method of forecasting in the very short-term.

Many of the integration studies in [1] determine that the 

forecast errors have larger impacts on costs and operations 

than does the variability. While most of these studies focus on 

the day-ahead forecasts, we will focus on the short-term 

forecast errors. While day-ahead forecasts have large impacts 

on costs, they usually will have fewer impacts on ACE and 

reliability as long as there are sufficient quick-start resources 

that can be started up in time. Short-term forecast errors, 

however, can impact ACE and costs. To show these impacts, 

we will run each of the prior cases of Table II with persistence 

wind forecasts, and then with persistence wind and load 

forecasts. Note that the longer the time interval for both the 

RT SCUC and RT SCED, the larger the forecast errors. This is 

because the end of the interval is further ahead from when the 

persistence forecast was created. For example, the end of a 5-

minute SCED is basing its forecast on the actual from 10 

minutes ago, while the end of a 60-minute SCED is basing its 

forecast on the actual 65 minutes ago.2

 2 All SCED runs are assumed to take 5 minutes regardless of the interval 

parameters and all SCUC runs are assumed to take 15 minutes regardless of 

the interval parameters. This is where the additional 5 minutes is from.

Fig. 7 shows the 

AACEE and Fig. 8 the CPS2 violations for all cases. Again, 

all of these cases are using the exact same actual wind (since 

there was no curtailment) and load data at the tAGC interval.
7 

0

100

200

300

400

500

600

700

0 20 40 60 80

MWh

Scheduling Interval Frequency

Balance error (AACEE) as function of interval frequency

Perfect

Persistence Wind

Persistence Wind and Load

Fig. 7. AACE for different forecast errors.

0

10

20

30

40

50

60

70

5 10 15 30 60

Violations

Scheduling Interval Frequency

CPS2 Violations

Perfect

Persistence Wind

Persistence Wind 

and Load

Fig. 8. CPS2 violations for different forecast errors.

It can first be observed that introducing the forecast error 

has a significant effect on the imbalances. Since increasing the 

dispatch interval increases both the variability impacts by 

having resources set at a longer time at one optimal level, and 

the uncertainty impacts by increasing the error the further out 

in time, both CPS2 violations and AACEE have a much higher 

rate of increase per dispatch resolution minute when forecast 

error is introduced. According to NERC BAL001, a balancing 

area’s CPS2 score must be above 90% to be acceptable. This 

means that of the 144 10-min intervals in a day, 14 or fewer 

CPS2 violations are acceptable. Therefore, with no AGC and 

no operator action whatsoever, both 30-minute forecast error 

cases, both 60-minute forecast error cases and the 15-minute

persistence wind and load forecasts case would have violated 

CPS2.

C. Uncertainty and Variability Impacts and AGC Operation 

Mode

To correct the system ACE that is occurring due to 

variability and uncertainty on the system, power system 

operators rely on units with AGC to automatically adjust 

output in order to reduce the ACE and bring it close to zero. 

All four AGC modes were tested with the 5-minute dispatch 

and 60-minute dispatch cases for both perfect forecasts and 

persistence forecasts. It is very important to note that all cases 

required the same amount of regulating reserve capacity of 

between 10 and 25 MW up and down depending on the time 

of day. Therefore, when all regulating range was used up in 

the particular time interval, there was no more the AGC could 

do. Fig. 9-11 show CPS2 violations, AACEE, and total costs 

for each AGC mode. 

0

10

20

30

40

50

60

70

5 Minute Perfect 5 Minute 

Persistence

60 Minute Perfect 60 Minute 

Persistence

Violations

CPS2 Violations for each AGC Mode

Mode1

Mode2

Mode3

Mode4

Current allowed CPS violations per day

Fig. 9. CPS2 violations for different forecast errors.

0

100

200

300

400

500

600

700

5 Minute Perfect 5 Minute 

Persistence

60 Minute Perfect 60 Minute 

Persistence

AACEE (MWH)

AACEE for each AGC Mode

Mode1

Mode2

Mode3

Mode4

Fig. 10. CPS2 violations for different forecast errors.

$346,000.00

$348,000.00

$350,000.00

$352,000.00

$354,000.00

$356,000.00

$358,000.00

$360,000.00

5 Minute Perfect 5 Minute 

Persistence

60 Minute 

Perfect

60 Minute 

Persistence

Total Costs

Costs for each AGC Mode

Mode1

Mode2

Mode3

Mode4

Fig. 11. CPS2 violations for different forecast errors.
8 

Generally, the more heavily AGC is used will lead to lower 

imbalance and higher costs. This generally follows the order 

from light AGC to heavy AGC of mode 1, mode 4, mode 3, 

and mode 2. A few outliers are noticed, however. For the 5-

minute perfect case, the “smooth mode” actually increases the 

AACEE. This is because the integral term of the SACE 

algorithm is three minutes and therefore the regulating units 

are correcting the mistakes of the last three minutes, whereas 

the SCED, with its perfect foresight, is already correcting what 

it knows will occur in the future. Also, the 60-minute perfect 

case has a reduction in costs when using active AGC modes (2 

and 3) compared to “blind mode.”3

AGC mode 4 can be seen to have significant differences in 

AACEE only when there are significant CPS2 violations. 

Lastly, it is interesting to note the similarity in reliability 

between the 5-minute persistence case and the 60-minute

perfect case. It seems that in this system for this day, the 

combined uncertainty and variability impacts when using a 5-

minute dispatch resolution are the same as the variability 

impacts alone when using a 60-minute dispatch resolution. 

Through the analysis, it 

was seen that due to the ramping constraints (see Fig. 3) in this 

scenario, using AGC used 25 MWh less of Solitude generation 

(at $28-30/MWh) and 34 MWh more of Brighton (at 

$10/MWh) among some other differences, and overall used 13 

MWh less of total generation throughout the day. AGC also 

does not model the transmission network as does SCED, 

which could lead to the reason why Brighton was used more. 

IV. CONCLUSIONS

This paper introduces a flexible model FESTIV to analyze 

the detailed impacts of integrating large penetrations of wind 

power onto the power system. The model interfaces between 

SCUC, SCED, and AGC programs and imitates actual system 

operations at a high time resolution. The model also creates 

some very flexible options so that users can not only compare 

different inputs, but different operational and market 

structures. With this flexibility, system operators can not only 

observe the impacts of high penetrations of wind power, but 

also what operating and market structures work best for 

integrating wind power in terms of both reliability and costs. 

A case study was performed using FESTIV to compare the 

impacts of wind power on a small test system. The impacts 

that wind and other sources may have on the imbalance of a 

power system are variability, uncertainty, and the physical 

inability of resources to meet both impacts. By adjusting the 

dispatch and commitment intervals, amount of forecast error, 

and mode of AGC, the way these impacts change can be 

observed. Further analysis can give insight into how other 

combinations of operating strategies using SCUC, SCED, and 

AGC programs can improve the operations of systems with 

high wind penetrations.

 3 Note that there is no cost for “cycling” or ramping accounted for in this 

analysis and all costs are from energy, no load, start up, and ancillary services.

V. REFERENCES

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of wind power integration studies: past, present, and future,” 

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[2] J. C. Smith, M. Milligan, E. Demeo, and B. Parsons, “Utility Wind 

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[3] M. Milligan, E. Ela, D. Lew, D. Corbus, Y. Wan, “Advancing Wind 

Integration Study Methodologies: Implications of Higher Levels of 

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[4] X. Feng, L. Teng, Z. Wang, J. Yang, W. Wong, H. Chao, R. Mukerji, “A 

new breed of software tool for integrated electrical power system and 

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[5] A.J. Wood and B.F. Wollenberg, Power Generation Operation and 

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and Value of Wind Generation,” IEEE Trans. Power Syst. Vol. 25, no. 2, 

pp 241-748. May 2010.

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O’Malley, “Evolution of Operating Reserve Determination in Wind 

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Minneapolis, MN, July 2010.

[14] J.M. Arroyo, A.J. Conejo, “Modeling of start-up and shut-down power 

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1562-1568. August 2004.

[15] P. Kundur, Power System Stability and Control. New York: McGrawHill, 1994.

[16] N. Jaleeli, L.S. VanSlyck, D.N. Ewart, L.H. Fink, A.G. Hoffmann, 

“Understanding automatic generation control,” IEEE Trans. Power Syst.

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[17] Y. Makarov, S. Lu, J. Ma, T.B. Nguyen, “Value of Regulation 

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[18] Market Rules Assessment Real-Time Generation Scheduling and 

Performance – Update. Presented at NYISO Market Issues Working 

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24/MIWG_Real_Time_Rules_Assessment_52407.pdf

[19] GAMS: The Solver Manuals. Washington, DC: GAMS, 2005.

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