04106006

IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 22, NO. 1, MARCH 2007 181

Large-Scale Integration of Wind GenerationIncluding Network Temporal Security AnalysisSantiago Grijalva, Member, IEEE, Scott R. Dahman, Member, IEEE, Kollin J. Patten, Member, IEEE,

and Anthony M. Visnesky, Jr.

Abstract—This paper presents a methodology to assess large-scale wind generation projects that considers their effect on net-work security. The proposed method is based on contingency anal-ysis, including temporal study. Inputs to the simulation are gridmodel, forecasted load, conventional generation profiles, and windvariability of proposed projects. A time-step simulation is run forthe time horizon to produce benefit indices for every location (bus)in the system. The congested transmission elements that require ex-pansion are identified and ranked as part of the simulation. Eachwind project in the proposed portfolio can result in benefits orcosts for grid security. Policy makers can then use the method todesign policies that ensure preservation of long-term system se-curity. Developers could use the tool to identify security effectsand assess their wind portfolios. Measuring network security anddetermining benefits of large-scale wind projects is a complex plan-ning task that involves several aspects: temporal wind variability,spatial distribution of flows, multiple load and generation profiles,and numerous possible contingencies. All these wind project de-velopment aspects must be isolated to identify and correctly assignsecurity costs and benefits.

Index Terms—Generation planning, transmission loading relief(TLR), wind integration, wind variability.

I. INTRODUCTION

W ind power is playing an increasingly important role inmodern electric power systems. Wind projects today

are large enough to have a significant effect on transmissionnetwork security, operation, and planning. Worldwide, rapidinstallation growth, increased turbine size, and large-scale windfarm development demand an integration of large-scale windprojects with generation and transmission planning, to ensuregeneration adequacy and secure grid operation. This has createdseveral engineering challenges not encountered in conventionalgeneration planning [1], [2].

Large-scale wind projects usually consist of a rather largenumber of turbines arranged in a wind farm. The power pro-duced by the farm is usually gathered by a lower voltage col-lector system of transmission elements and then interconnectedto an existing high voltage substation. In most instances, thissubstation was not originally designed for this purpose and usu-ally had to be expanded. Often, a completely new high-voltage

Manuscript received July 12, 2006; revised October 16, 2006. This work wassupported in part by the California Energy Commission. Paper no. TEC-00303-2006.

S. Grijalva, S. R. Dahman, and K. J. Patten are with PowerWorld Corpora-tion, Champaign, IL 61820 USA (e-mail: [email protected]; [email protected]; [email protected]).

A. M. Visnesky, Jr. is with Anthony Engineering Associates, Pompano Beach,FL 33069 USA, and also with Trexco LLC, Indianapolis, IN 46204 USA.

Digital Object Identifier 10.1109/TEC.2006.889617

substation and long transmission lines are needed to inject windpower [3].

There is an increasing need for systematic, integrated plan-ning processes that ensure energy adequacy and identify windresources’ broader impact on grid security [4], [5]. Utilitiescould consider system security goals, strategically site windgeneration, and allow the grid to move toward healthier op-erating conditions. An adequate level of security is critical topower systems operation. For example, in systems operatedunder competitive market regimes, poor transmission planningcreates long-term congestion, price volatility, and opportunitiesfor the exercise of market power.

This paper proposes a novel methodology to assess generationimpacts on system reliability and locational value representa-tions for strategic siting, portfolio evaluation, and policy design.

Integration of a large-scale wind project depends on differentelements that may be observed as dimensions of a planningproblem:

1) The location of the wind generation project and its pointor points of connection (spatial dimension).

2) The size of the project and the variability of wind outputacross time (temporal dimension).

3) The different events that result in overloaded elementsrequiring transmission system expansion (contingencydimension).

To capture these multidimensional aspects, a set of securitymetrics is designed and used to assess new project’s effect onsecurity. Starting with a given system security level, the newproject’s generation output and its designed transmission ex-pansion result in a measurable change to system security thatcan be represented as project cost/benefit.

We start by defining and measuring grid security for a singlepoint in time in Section II. Temporal analysis and the determina-tion of security costs are introduced in Section III. In Section IV,we extend temporal analysis to wind generation portfolio eval-uation. A small system is used to demonstrate the details ofthe calculations. A large-scale realistic system example is alsopresented.

II. MEASURING SECURITY

A. Aggregate Contingency Overload

From a steady-state viewpoint, network security considers: noloss of load, bus voltages within power quality bounds, transmis-sion element flows within thermal limits, and system operationaway from the static voltage collapse point [6], [7].

Contingency analysis drives the design of system expan-sion that determines quasi-optimal network configurations and

0885-8969/$25.00 © 2007 IEEE

182 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 22, NO. 1, MARCH 2007

injections of power into the system. The North American Elec-tric Reliability Council has recommended that systems be de-signed and operated to withstand N−1 and N−2 contingen-cies. Nevertheless, most realistic systems are not compliant withN−1 criteria during periods of high demand. Operators use re-medial action schemes to correct postcontingency violations.

Contingency analysis can also rank transmission elements bytheir relative “weakness.” Weakness of a transmission elementis hereby understood as the need to apply system upgrades or todesign system expansion to avoid thermal overloads.

We define the active power aggregate contingency overload,PACO, as the sum of all overload flow present on a transmissionelement during a processed set of contingencies. This quantityis expressed in megawatts (MW) and is usually calculated as themillivoltampere (MVA) rating of the element multiplied by thesum of the percent overload detected under each contingency:

PACO,BRANCH j k= MVARatingBRANCH jk

×∑

Contingencies that

overloaded branch jk

(%Overload − 100).

(1)

Since the PACO can be computed for every branch in the

system, a system aggregate contingency overload metric PSYSACO

can be defined as the sum of the PACO of each branch

PSYSACO =

∑

Branchesjk

PACO,BRANCH jk. (2)

B. Weak Element Ranking and Weighted Transmission LoadingRelief (WTLR) Sensitivities [8]

If we run contingency analysis (i.e., N−1) for any realisticpower system during peak load, we will identify several ele-ments that present thermal violations. These elements can beranked based on their PACO value. Elements that were not over-loaded during any contingency will have zero PACO.

When a wind project is installed at a certain location (bus)in the system, the injection will presumably replace generationat some conventional units. This represents a transfer from thewind location to a multiple-point sink. As the output of thewind unit (the transfer) increases, the flows in the transmissionelements will change. TLR sensitivities can be calculated toassess branch flow change with respect to wind output duringnormal operation

TLRBUS i, BRANCH jk =∆MWFlowBRANCH jk

∆MWInjectionBUS

i. (3)

For contingency conditions, it is necessary to calculate post-contingency TLR sensitivities, defined as the branch postcon-tingency flow change with respect to the injection at a certainbus assuming a transfer sink

TLRBUS i, BRANCH jk CONT c

=∆PostContMWFlowBRANCH jk CONT c

∆MWInjectionBUS i

. (4)

A wind power injection will simultaneously affect severalbranches to varying degrees under different contingency condi-tions. An equivalent TLR (ETLR) sensitivity can be obtainedto capture the overall change of contingency flows due to theinjection

ETLRBUS i

=∑

jk ∈ Overloaded

Elements

∑

Contingencies that

overloaded branch jk

TLRBUS i, BRANCH jk CONT c.

(5)

However, to take into account the severity of the contingencyoverloads, we weight the TLRs by the PACO. This WTLR can becomputed using postcontingency TLR values or approximatedusing base-case TLR values as follows:

WTLRBUS i =NCONT

P SYSACO

×∑

jk∈Branches

CODirBRANCH jk×TLRBUS i,BRANCH jk×PACO,BRANCH jk

(6)where NCONT is the number of contingencies and the TLR

weight is given byPACO,BRANCH jk/PSYSACO. CODir is the over-

load direction defined as 1 if the line is overloaded in the forwarddirection during all the contingencies that overloaded that line,−1 if it is always overloaded in the reverse direction during allcontingencies, and 0 otherwise.

The ETLR sensitivity represents the total expected MW con-tingency overload reduction, in all branches and under all con-tingencies, if 1 MW is injected at that particular bus. Negativevalues of ETLR and WTLR indicate that new generation willtend to reduce overloads and increase overall system security.Positive values mean the opposite.

C. Temporal Analysis of Security

We have seen how instantaneous system security can be mea-

sured through the PSYSACO metric. As system demand changes

following daily, weekly, and seasonal cycles, the flows in trans-mission elements will vary accordingly and overloads will ingeneral be reduced compared to peak demand. Contingencyanalysis can be run for each hour of demand input values, andPACO values can be obtained for each transmission element andfor each hour. If we integrate the PACO values of a transmis-sion element across time (e.g., one year), we will obtain theaggregate contingency overload energy of that element. Ap-proximating this as the sum of PACO values for each timepoint,we have

EACO,BRANCH jk =T∑

t=1

PACO,BRANCH jk, t. (7)

A metric could be obtained to measure overall system securityfor the time horizon considered

ESYSACO

=T∑

t=1

P SYSACO, t

=∑

Branches jk

EACO,BRANCH jk,t. (8)

GRIJALVA et al.: LARGE-SCALE INTEGRATION OF WIND GENERATION 183

Fig. 1. Seven-bus system. Base case and weak lines based on their EACO.

The determination of security metrics such as PACO and EACO isa key aspect within integrated planning and operation. Elementsthat are consistently overloaded under contingencies (congestedelements) will have high values of EACO, affecting overall sys-tem security.

a) Planning can use the metrics to identify and rank weakelements and design transmission expansion alternatives.

b) Operators can get a better feel for real-time operation when

the PSYSACO reaches high levels or experiences unexpected

excursions.c) Regulators can use the E

SYSACO to determine how the over-

all system security evolves year after year, and proposepolicies and regulations to try to maintain adequate levels.

D. Security Evaluation Example

Consider the seven-bus case shown in Fig. 1, which has fourexisting generators at buses 1, 4, 6 and 7. Three different windprojects of 50 MW each have been proposed at buses 2, 3 and6. These new generators are initially disconnected and appeargrayed-out.

We want to analyze the security of this system and assessthe new wind generation’s effect, using a temporal simulation.Only “peak hours” (ninth hour to the twentieth hour) of a typicalday are simulated, since it is known that new projects will notcause overloads during nonpeak hours. Thus, 12 time points aresimulated to capture relevant information regarding temporalsystem variations and to determine the EACO metrics. Generatorsare dispatched based on participation factors [9].

Fig. 1 shows the normal operation solution for an assumed300 MW demand. The line arrows indicate normal operationactive flows and the pie charts show the line percentage load-ing. The visualization represents the weak element ranking, asdetermined by the branch EACO values shown in the last rowof Table I, which shows the results of the temporal simula-tion, driven by changes in system demand shown in the secondcolumn.

TABLE IWEAK ELEMENT PACO FOR THE SEVEN-BUS SYSTEM

The temporal simulation does the following:for each time point doset load demand valuessolve power flowrun contingency analysisdetermine branch PACO

endThe system demand is distributed proportionally to each load.

To meet the load and losses, generators move according to theirparticipation factors. The power flow solution is a full AC algo-rithm and considers all device control, voltage regulation, etc.Contingency analysis takes place using full AC power flow aswell.

For each hour, a PSYSACO value can be determined. This is shown

in the last column of Table I. For each branch, an EACO value iscomputed. This is shown in the last row. The cell in the bottom

right of the table corresponds to the ESYSACO, i.e., the security of

the system. We will call this system solution without new windgeneration or transmission expansion, the base case.

E. Injection Sensitivity Example

Sensitivity information about the effect of new generation inthe system can be obtained by computing ETLR and WTLRvalues for each bus. To do this, a transfer is defined from thewind generator to the distributed sink, which is assumed to beprovided by the existing generators at buses 1, 4, 6, and 7, basedon their participation factors. TLRs are computed for all systembranches using the peak hour (sixteenth hour). Then, ETLR andWTLRs are determined using (5) and (6). The results shown inTable II indicate that buses 1–4 would be beneficial for systemsecurity, since injections at these buses have negative ETLR,which would decrease the overloads. However, new generationat buses 5–7 would increase the flows, worsening contingencyoverloads. The proposed generation at bus 6 would thereforerequire transmission expansion to maintain the same level ofsecurity.

Fig. 2 shows a visualization of the ETLR values in the secondcolumn of Table II. By comparing Fig. 2 with Fig. 1, we note


TABLE IIINJECTION SENSITIVITIES FOR THE SEVEN-BUS SYSTEM, BASE CASE

Fig. 2. ETLR visualization: Lighter regions represent locations where newgeneration would be beneficial for system security.

that positive ELTR values are at the sending end of the weak el-ements, while negative ETLR locations are at the receiving endof the weak elements. This is consistent with transmission sys-tem congestion: in a market environment, prices will be higherat the receiving end, providing a signal for investment at thoselocations.

III. DETERMINING WIND SECURITY EFFECTS

A. Temporal Analysis

Connecting new (wind) generation to the network will change

the value of ESYSACO. If the change is positive, it means that the

new project causes more overloads in the system, i.e., the systemis less secure due to increased branch overload. If the change isnegative, the project increases system security.

The value of ESYSACO can be maintained by implementing trans-

mission expansion to remove overloads caused or worsened bythe new project. The strategy should be to add enough transmis-

sion expansion so that the change in ESYSACO is close to zero or

negative (improvement of security).A method to evaluate the cost of system security consists

in determining the cost of the transmission expansion or thecost of the generation redispatch that are needed to maintainthe original level of security. Since transmission expansion isusually bundled, this will often result in extra benefit for the

Fig. 3. Effect on system security with different wind projects: The larger theareas below the curve, the less secure the system.

project. In a similar manner, some project implementations may

actually reduce the value of ESYSACO, providing extra benefit for

the system.

B. Preserving System Security

We continue our seven-bus case example with temporal anal-ysis. The simulation developed for the base case is repeated, butnow with wind. We increase 50 MW at each of the wind projectlocations, one at a time. Wind generation will be kept fixed atthis level and the varying system demand will be balanced byconventional units.

Fig. 3 shows the values of PSYSACO for the base case and for each

of the three wind projects: G2, G3, and G6. G2 and G3 clearly

reduce the PSYSACO; G6 increases it significantly. Note that this is

consistent with the expected response of the system providedby the TLR sensitivities. While the security of the system in

the base case was equal to ESYSACO = 414.53 MWh, this metric is

improved to 150.54 MWh with G2 and to 120.4 MWh with G3.G6, however, worsens the system security resulting in a value

of ESYSACO = 782.0 MWh.

Let us assume that due to technical, geographic, and otheraspects, G6 is a very attractive wind project, despite the factthat the project worsens system security. Policy makers shoulddesign policies to determine the course of action in this case.Suppose that the planning policy is to at least maintain the cur-

rent level of security, equal to ESYSACO = 414.53 MWh. Transmis-

sion expansion is then proposed to counterbalance the increasein overloads produced by the 50 MW of new wind power pro-vided by G6. The temporal simulation reveals that most of the

ESYSACO increase is due to increased overload in line 6 to 2. Three

expansion alternatives have been proposeda) Build an identical second circuit from 6 to 2.b) Reconductor circuit 1 of line 6 to 2 to increase its capacity

from 100 MVA to 150 MVA.c) Reconductor circuit 1 of line 6 to 2 to increase its capacity

from 100 MVA to 120 MVA.The proposed expansion alternatives are implemented in

the system and simulated using the temporal analysis. Fig. 4


Fig. 4. Effect on system security with different transmission expansion alter-natives for wind project G6.

shows the results of the impact of transmission expansion.Implementation of the second circuit corresponds to the smallercurve in the graph. This represents a substantial improvement of

the security of the system, reducing ESYSACO to 44.76 MWh. How-

ever, the cost of building a second circuit is considerable. If thedeveloper builds this transmission line, it should be recognizedby the International Organization for Standardization, since itwill improve system security beyond the original level withoutthe wind project. Upgrading the circuit to 150 MVA results in

ESYSACO = 271.53 MWh, and reconductoring to 120 MVA results

in ESYSACO = 439.63 MWh, compared to the base-case level of

ESYSACO = 414.53 MWh. Since the system security obtained with

the 120 MVA transmission upgrade alternative is only slightlyover the original level of security, planners may decide this levelof transmission expansion to be adequate. The developer of thewind project G6 must include the cost of reconductoring line 6to 2 to a 120 MVA level in the wind project budget.

Note that if projects G2 or G3 are pursued, then the corre-sponding developers should be recognized for enhancing systemsecurity. The security benefits derived from those wind projectsshould be assessed and considered as benefits of the project.

IV. INTEGRATED ANALYSIS OF WIND PORTFOLIOS

A. Wind Production Intermittency

The difficulty in forecasting wind availability requires thetransmission network to support a wide range of wind output.Simulation scenarios can be based on planned resources andhistorically observed patterns of wind availability [9], [10]. Theobjective is to determine which statistically plausible generationpatterns could cause transmission overloads, and the severityand duration of those overloads.

Wind capacity can be divided into geographic regions basedon spatial correlation analysis. Temporal analysis can be seg-mented by season and load conditions considering representa-tive days and hours. Simulation data can be developed usinghistorically observed wind production capacity factors by re-

Fig. 5. Regional wind production capacities. Values shown are from the 2006power flow case used in the simulation.

gion, season, and time of day. This method captures the hourlyvolatility, the periodic variation due to season and time of day,and the correlation of capacity factors between regions. Theseobserved parameters are applied to the planned installed windcapacity for each studied time period. Variable wind generationis displaced by peaking resources.

Temporal contingency analysis simulations are used to varywind generation for each hourly sample and to record the lineloading as a percentage of rating.

Contingency analysis is performed to develop the EACO

figures. To simplify contingency analysis, only contingenciesat higher voltages (100 kV and above) are considered for thelarge-scale system. Only transmission elements within the con-trol area are monitored for overloads. Representative hours ofthe day and of each season are evaluated to make the simulationstatistically sound. To isolate the effects of time-varying windproduction, load is held constant at the associated seasonal andtime-of-day levels.

B. Example

To demonstrate the methodology, a realistic California sys-tem was utilized to determine wind effect during peak summerconditions. Intermittency impacts were examined for the exist-ing 2000 MW California wind capacity on the 2006 system. Thelocations of the modeled wind capacity are shown in Fig. 5.

The hourly summer peak capacity factors recorded by windregion in the 2004 data were applied to a 2006 peak summercase. Representative loadings on three transmission lines areshown in the time plot in Fig. 6 and the corresponding durationcurves are shown in Fig. 7.

Most transmission facilities remained within their thermallimits for all conditions. Most could also be characterized ac-cording to average loading, loading volatility, shape of the load-ing duration curve, or correlation of loading with regional windproduction.

Some transmission facilities became overloaded during spe-cific wind production patterns, but otherwise exhibited normaloperation. Such lines are good candidates for upgrades or op-erational schemes to relieve congestion. Several lines in south-ern California, including many 66-kV Tehachapi collector lines,tended to overload during periods of high Tehachapi production.


Fig. 6. Transmission line loadings for 2006 hourly intermittency simulation.

Fig. 7. Transmission line loading duration for 2006 hourly intermittencysimulation.

Congestion generally occurred for Tehachapi production over600 MW, observed during 3% of the simulated peak summerhours. The loading patterns are exemplified by the heaviest linein Figs. 6 and 7. It is anticipated that this congestion will bereduced by the planned upgrade of the Tehachapi transmissionto 230 kV.

A group of 115-kV transmission lines in southern Californiawas adversely impacted by low wind production in San Gor-gonio. Lines in this group were typically overloaded duringabout 25% of the peak hours and experienced significant vari-ability depending on wind conditions. The high dependence ofline loading on the wind output suggests that such lines are goodcandidates for upgrade. This loading pattern is illustrated by themedium gray line in Figs. 6 and 7.

Another notable loading pattern was observed on several60-kV Altamont collector lines, as shown by the light grayline in Figs. 6 and 7. These lines exhibited low mean loading,but high volatility. The loading correlated positively with windproduction at Altamont and Solano. Though none of the linesbecame loaded beyond 80% of its rating during the simulation,the high volatility suggests that several may require upgrading

Fig. 8. PSYSACO duration curve.

if more capacity is installed at Altamont or Solano. If the meanproduction level increases sufficiently, the maximum loadingswill exceed ratings.

C. Example: Contingency Operation

Contingency analysis was performed on the 2006 SummerPeak case to determine the impact of the intermittent wind re-

sources on PSYSACO. The total simulated E

SYSACO over 336 summer

peak hours was 5575 GWh, with an average PSYSACO of 16 591 MW.

The PSYSACO duration curve is shown in Fig. 8.

PSYSACOexceeded 18 000 MW for 2.1% of the simulated hours.

No single region’s wind production was strongly correlated with

high system PSYSACO, though the hours that produced P

SYSACO greater

than 18 000 MW occurred during either high production atTehachapi (over 600 MW), or low production at San Gorgonio(below −30 MW). These conditions are depicted in the steeplysloping left-hand portion of the plot in Fig. 8.

The contour plot of Fig. 9 shows the EACO for each line thatexhibited contingency overloads during the temporal simula-tion. Fig. 10 shows a contour of each transmission element’sEACO relative to the EACO that would result if wind production

remained constant at the level exhibited during the medianPSYSACO

hour (Fig. 8). It is a measure of how much the temporal windoutput causes variation in hourly PACO. Contours reveal trans-mission lines that experience greater average overloads across

all hours than they did during the median PSYSACO hour.

As with the precontingent transmission line loadings, thetime-varying PACO results exhibited different characteristicshapes and volatility. Some lines were adversely impacted bywind production levels in certain regions. Fig. 11 representsthe PACO over time of two transmission lines. The PACO of theheavy line was positively correlated with wind production atAltamont and Solano, while that of the light line was negativelycorrelated.

Several lines in Southern California exhibited high PACO

during high Tehachapi production. As discussed earlier, it is


Fig. 9. EACO contour plot: Darker lines are week elements in California.

Fig. 10. EACO relative to the median hour.

anticipated that such congestion will be mitigated by plannedTehachapi transmission upgrades. The independent recognitionof this need by the operators of the California transmissionsystem is consistent with the conclusions produced by the tem-poral security analysis presented herein.

Fig. 11. Hourly PACO.

V. IMPACT ON PROPOSED TRANSMISSION SYSTEM WITH

PROPOSED INSTALLED WIND CAPACITY

In a next phase of the study, intermittency impacts will be ex-amined for the planned 2010 and 2015 installed wind capacities,system loads, and transmission topologies. Known transmissionupgrades will be incorporated into each seasonal study case.

The comparison of hourly PSYSACO in 2006 and future study

years will yield an illustration of how system security changeswith increasing load and wind capacity. It is anticipated that the

2010 and 2015 cases will exhibit increasing ESYSACO and increas-

ing variability of PSYSACO. However, known transmission upgrades

such as the Tehachapi 500-kV expansion may mitigate the trend.By examining the results, we may be able to recommend addi-tional transmission upgrades and strategic expansion of other re-newable resources to maintain given levels of security over time.

VI. CONCLUSION

A model to measure electricity system security and to assesslarge-scale wind integration projects including system securityhas been described.

Effective security metrics can be derived from temporal con-tingency analysis simulations that capture wind variability. Ben-eficial locations for wind generation based on grid security canbe identified using ETLR and WTLR sensitivities.

The proposed methodology allows determination of the effectof new wind project on system security, including proposedtransmission expansion alternatives. The security benefits can beidentified and incorporated into an integrated economic spatialmodel to determine viable renewable and distributed generationprojects that provide economic benefits.

Security metrics developed in this paper can be utilized byplanners and regulators to assess wind projects benefits, andto determine policies that promote long-term power systemsecurity.

ACKNOWLEDGMENT

The authors would like to thank the California Energy Com-mission for providing data for the large-scale example.


REFERENCES

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[2] R. E. Brown, “Modeling the reliability impact of distributed generation,”in Proc. IEEE Power Eng. Soc. Summer Meeting, Jul. 21–25, 2002, vol. 1,pp. 442–446.

[3] R. Piwko, N. Miller, J. Sanchez-Gasca, Y. Xiaoming, D. Renchang, andJ. Lyons, “Integrating large wind farms into weak power grids with longtransmission lines,” in Proc. IEEE PES T&D Conf. Exhib.: Asia Pac.,Aug. 15–18, 2005, pp. 1–7.

[4] J. W. Smith, J. A. Taylor, D. L. Brooks, and R. C. Dugan, “Interconnectionstudies for wind generation,” in Proc. Rural Elect. Power Conf., May 23–25, 2004, pp. C3-1–C3-8.

[5] R. C. Dugan, T. E. McDermott, and G. J. Ball, “Planning for distributedgeneration,” IEEE Ind. Appl. Mag., vol. 7, no. 2, pp. 80–88, Mar.–Apr.2001.

[6] N. W. Miller, “Generation uncertainty in long range transmission plan-ning,” in Proc. IEEE-PES Gen. Meeting, Jul. 21–25, 2002, vol. 3,pp. 1038–1040.

[7] R. Nadira, R. R. Austria, C. A. Dortolina, and F. Lecaros, “Transmis-sion planning in the presence of uncertainties,” in Proc. IEEE PES Gen.Meeting, Jul. 13–17, 2003, vol. 1, pp. 289–294.

[8] S. Grijalva and A. M. Visnesky, “The effect of generation on networksecurity: Spatial representation, metrics, and policy,” IEEE Trans. PowerSyst., vol. 21, no. 3, pp. 1388–1395, Aug. 2006.

[9] L. Furong and B. Kuri, “Generation scheduling in a system with windpower,” in Proc. IEEE PES T&D Conf. Exhib.: Asia Pac., Aug. 15–18,2005, pp. 1–6.

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Santiago Grijalva (S’00–A’02–M’03) received the electrical engineering de-gree from EPN-Ecuador Quito, Ecuador, in 1994, the M.S. certificate in in-formation systems from ESPE-Ecuador Sangolquı́, Ecuador, in 1997, and theM.S. and Ph.D. degrees in electrical engineering from the University of Illinois,Urbana-Champaign in 1999 and 2002, respectively. He completed the Postdoc-toral program in power and energy systems at the University of Illinois in 2004.

From 1995 to 1997, he was with the Ecuadorian National Center for EnergyControl (CENACE) Quito, Ecuador as EMS Engineer and Head of the SoftwareDepartment. Since 2001, he has been a Senior Consultant with PowerWorldCorporation, where he is engaged in the development of advanced optimizationand visualization applications. His current research interests include EMS sys-tems, power system computational algorithms, and electricity markets.

Scott R. Dahman (S’91–M’95) received the B.S. degree in electrical engineer-ing and M.B.A. degree from Washington University, St. Louis, MO in 1993 and1994, respectively. He received the M.S. degree in electrical engineering fromthe University of Illinois, Urbana-Champaign in 2003.

From 1994 through 1999, he was with the Material Procurement Depart-ment, Emerson Electric Company, St. Louis, MO, directing the developmentof business applications software and managing strategic supply agreementsfor engineered products. From 1999 through 2003, he was a Project Managerat Zurheide-Herrmann, Inc., Champaign, IL, where he directed the delivery ofconstruction documents and consulting engineering services for the building,transportation, insurance, and electric power industries. Currently, he is Direc-tor of Business Development at PowerWorld Corporation, where he coordinatesmarketing efforts and power system studies.

Mr. Dahman is a Registered Professional Engineer in Illinois.

Kollin J. Patten (S’97–A’98–M’03) received the B.S. and M.S. degrees in elec-trical engineering from the University of Illinois Urbana-Champaign in 1997and 1998, respectively.

In 1995, he gained industrial experience in telecommunications planningand system measurements at American Electric Power in Columbus, OH. Since1996, he has been with PowerWorld Corporation, Champaign, IL, where heis now the Director of Engineering. His current research interests includepower system stability and security, electricity markets, and real-time systemvisualization.

Anthony M. Visnesky, Jr. received the B.S. degree in electrical engineeringfrom Purdue University, West Lafayette, IN in 1969 and completed the M.S.I.A.program from the Krannert Graduate School of Industrial Administration, WestLafayette in 1971. From 1972 to 1974, he participated in the Ph.D. program atthe Krannert Graduate School of Industrial Administration and Purdue Univer-sity School of Electrical Engineering, West Lafayette.

From 1974 to 1988, he was with Central Illinois Public Service (Ameren-CIPS) in different technical and managerial positions. From 1988 to 1998, hewas with The Illinois Commerce Commission, Springfield, as Manager of theEnergy Programs Division. He has been Co-Chairman of the National Asso-ciation of Regulatory Utility Commissioner (NARUC), Washington, DC, andRegulatory Representative to EPRI’s Advisory Committee for Electric Gener-ation & Transmission Research. Since 1998, he directs Anthony EngineeringAssociates, Pompano Beach, FL, an independent research and consulting firm.He is also the Chief Technology Officer of Trexco LLC, Indianapolis, IN, acompany specializing in power transformer cooling technology. His currentresearch interests include power system analysis and planning, electric powerpolicy, and energy regulation.

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