Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources

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IEEE TRANSACTIONS ON SMART GRID 1

Stochastic Energy Scheduling in Microgrids WithIntermittent Renewable Energy Resources

Wencong Su, Member, IEEE, Jianhui Wang, Senior Member, IEEE, and Jaehyung Roh, Member, IEEE

Abstract—Renewable energy resources such as wind and solarare an important component of a microgrid. However, the inherentintermittency and variability of such resources complicates micro-grid operations. Meanwhile, more controllable loads (e.g., plug-inelectric vehicles), distributed generators (e.g., micro gas turbinesand diesel generators), and distributed energy storage devices (e.g.,battery banks) are being integrated into the microgrid operation.To address the operational challenges associated with these tech-nologies and energy resources, this paper formulates a stochasticproblem for microgrid energy scheduling. The proposed problemformulation minimizes the expected operational cost of the micro-grid and power losses while accommodating the intermittent na-ture of renewable energy resources. Case studies are performed ona modified IEEE 37-bus test feeder. The simulation results demon-strate the effectiveness and accuracy of the proposed stochastic mi-crogrid energy scheduling model.

Index Terms—Microgrid, plug-in electric vehicle (PEV), renew-able energy, smart grid, stochastic programming.

NOMENCLATURE

Indices:

Distributed generator.

Battery bank.

Wind scenarios.

Solar scenarios.

Hour.

Wind generator.

Solar generator.

PEV.

Bus.

Variables andFunctions:

Probability of scenario .

Cost function ($/h) of generating kWhfrom the th unit.

Manuscript received June 08, 2013; revised nulldate; accepted August 18,2013. Paper no. TSG-00438-2013.W. Su is with the Department of Electrical and Computer Engineering, Uni-

versity of Michigan-Dearborn, MI 48128 USA (e-mail: [email protected]).J. Wang is with Argonne National Laboratory, Argonne, IL 60439 USA

(e-mail: [email protected]).J. Roh is with Department of Electrical Engineering, Konkuk University,

Seoul, Korea (e-mail: [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TSG.2013.2280645

Cost function ($/h) of buying/sellingkWh from/to the utility grid.Power output (kW) of the th unit underscenario at the th hour.Power from/to the utility grid (kW) at theth hour.Start-up/shut-down cost ($) of the thunit at the th hour.Overall electrical conversion efficiency(%) of battery charger.Rated energy capacity of the th batterybank (kWh).Total energy delivered or absorbed by theth battery bank (kWh) under scenario .Actual battery life cycle of the th batterybank under scenario .Actual battery life (kWh) of the thbattery bank under scenario .Capital cost ($) of the th battery bank.

Battery degradation cost ($/kWh) of theth battery bank under scenario .Production cost function coefficients ofthe th unit.Degradation cost function coefficients ofthe th battery bank.Grid electricity price ($/kWh) at the thhour.Power flow equations under scenario atthe th hour.Power losses (kW) under scenario at theth hour.Base load (kW) at the th hour.

Aggregate PEV charging load (kW) at theth hour.Charging load (kW) of the th PEV at theth hour.State-of-Charge (%) of the th batterybank under scenario at the th hour.Voltage (per unit) of the th bus underscenario at the th hour.

Constants:

Minimum power generation (kW)requirement if the th unit is on.Maximum power generation (kW)requirement if the th unit is on.Minimum State-of-Charge requirement(%) for the th battery bank.Maximum State-of-Charge requirement(%) the th battery bank.

1949-3053 © 2013 IEEE


2 IEEE TRANSACTIONS ON SMART GRID

Final State-of-Charge requirement (%).

Minimum voltage (per unit) requirementat the th bus.Maximum voltage (per unit) requirementat the th bus.

I. INTRODUCTION

T HE increasing penetration of intermittent and variable re-newable energy resources (e.g., wind and solar) has sig-

nificantly complicated energy system management for micro-grids [1]. Unlike conventional generation sources, wind andsolar power output is highly uncertain and unpredictable. Evena small error in wind and solar power forecasting may resultin great uncertainties for real-time operations of a microgridgiven its limited scale and size. Moreover, in microgrids, thecustomers play a more important role by managing the control-lable loads compared with conventional power distribution sys-tems, in which consumers have little flexibility to fully partici-pate in electricitymarkets. The term “controllable load” refers toa type of non-critical load that can adjust its own electric energyusage on the basis of a real-time set point. One special class ofcontrollable loads is a Plug-in Electric Vehicle (PEV) fleet. Un-like other controllable loads, these vehicles can be connected topower grids any place and any time, allowing more spatial andtemporal diversity but also more uncertainty. A sophisticatedmicrogrid energy scheduling model that takes these unique fac-tors into consideration is urgently needed.Most published works focus on deterministic microgrid op-

erations [2]–[6]. However, the stochastic energy managementmethods that have been widely researched in transmission-levelenergy management [7]–[11] demonstrate promising results incapturing the uncertainty associated with renewable energy re-sources and considering worst-case scenarios. The focus of thispaper is to apply stochastic methods to microgrid energy sched-uling problems. To the best of the authors’ knowledge, micro-grid energy scheduling using stochastic methods has not beenwell documented in the literature. Details about the classicaltwo-stage or multi-stage stochastic programming can be foundin [12]. In [13], the authors applied online stochastic optimiza-tion to residential appliance energy management with privacyprotection. In [14], a stochastic method for the hourly sched-uling of optimal reserves is presented to consider the hourlyforecast errors of wind energy and load. In [15], a stochastic en-ergy scheduling model is developed for a local-area smart-gridsystem with a single energy source and multiple energy con-sumers. In [16], a stochastic model predictive control methodis proposed for microgrid operations. However, the paper doesnot consider the network constraints, which may not be real-istic. In [17], a chance-constrained programming framework isproposed to model stochasticity rather than the scenario-basedapproach proposed in this paper. However, the power flow con-straints are not considered in [17].The major contributions of this paper include the following:1) Propose a two-stage stochastic microgrid operation modelfor the optimal decisions on the day-ahead energy transac-tions in the first stage while mimicking the real-time windand solar power variability in the second stage.

2) Develop a decomposition scheme to solve the proposedmodel. As the proposed model is a mixed integer non-

linear programming problem, which is difficult to solve,the problem is decomposed into onemaster problem for en-ergy scheduling and one subproblem for power flow com-putation. The two problems are solved iteratively as dis-cussed in detail in Section III.

3) Determine optimal energy scheduling for distributedgenerators (DGs) and distributed energy storage devices(DESDs) considering battery degradation costs.

4) Investigate the impact of PEVs on microgrid energy sched-uling under various charging schemes.

Section II proposes the formulation of the two-stage sto-chastic microgrid energy scheduling model. Section III intro-duces the test platform and presents the details of the proposeddecomposition scheme to minimize the expected operationalcost while reducing power losses in the interconnected mi-crogrid mode. Section IV provides a detailed case study andcompares the simulation results with those obtained using atraditional deterministic approach under various operationalconditions. Section V concludes the paper.

II. PROBLEM FORMULATION

A two-stage stochastic microgrid energy scheduling modelis proposed in this paper to make an optimal decision on theday-ahead energy transactions in the first stage while mim-icking the real-time operations with the wind/solar powervariability in the second stage. The idea of establishing such amodeling framework is motivated by the increasing challengeof addressing the variability and uncertainty from renewableenergy resources on a microgrid. Since the generation resourcesare usually limited on a microgrid, renewable generation unitssuch as wind and solar may account for a large portion of thetotal generation portfolio. In the meantime, as a microgrid isself-confined and self-balanced, one of the primary goals ofoperating a microgrid is to minimize the operating costs withhigh reliability, by reducing the uncertainty and variability ofon-site renewable generation and the risk of energy transactionswith the utility grid. By the proposed two-stage framework,we can determine the day-ahead energy scheduling in the firststage with the objective to identify the optimal amount ofelectricity to be purchased from/sold to the utility grid andthe commitment of distributed generation units over the next24 hours. These first-stage decisions do not vary across thescenarios in the second stage.

A. Objective Function

The objective function minimizes the expected operationalcost in the interconnected microgrid mode over the next 24hours. In this study, the authors use scenarios tomodel the uncer-tain power output of wind and solar resources. In other words,the power output of wind and solar generating units in one sce-nario is different from another. The production cost of the re-newable energy resource (i.e., wind and solar energy) is as-sumed to be negligible


SU et al.: STOCHASTIC ENERGY SCHEDULING IN MICROGRIDS 3

(1)

Frequent charging and discharging may significantly affectthe operational life of DESDs (e.g., battery banks). Therefore,it is important to consider and formulate the additional batterydegradation cost for microgrid energy scheduling. The batterydegradation cost can be expressed as a function of the actualbattery cycle life [18]. In this paper, all the DESDs are assumedto be identical with the same charging/discharging efficiency

. The depth of discharging of the th battery bankis defined as

(2)

The battery cycle life can be formulated as a function ofdepending on the type of battery.

(3)

For example, the relationship between Lead-Acid batterycycle life and can be expressed as a linear function [19]

(4)

Then the actual battery life (kWh) is expressed as

(5)

The th battery degradation cost ($/kWh) under each indi-vidual scenario is determined by the battery capital cost and theactual battery life.

(6)

Therefore, the battery degradation cost ($) for the th batterybank under each individual scenario is formulated as

(7)

For distributed generators, the cost function can be formu-lated as

(8)

Because small DG units have negligible start-up/down times,the start-up cost can be simplified as a constant for each unit.For the main utility grid, the energy transaction payment can

be formulated as

(9)

B. Constraints

The system constraints considered in this paper include thefollowing.

1) Power balance

(10)

For any scenario at any time step, the left-hand term is thepower output fromwind, solar, DG, utility grid, andDESD,respectively. The right-hand term includes the power loss,base load, and PEV charging load, respectively.

2) Technical limits of DG

(11)

For any scenario at any time step, the power output of DGlies in a certain operating range.

3) Technical limits of DESD

(12)

(13)

(14)

For any scenario at any time step, the power output ofDESD lies in a certain operating range. To avoid any over-charging or overdischarging, once reaches the upper/lower bounds, the DESD (e.g., battery bank) switches to astand-by mode.

4) Technical limits of the PEV charging load

(15)

(16)

Similarly, the PEV battery chargers have limitation on theamount of charging power at any given time step.Power flow constraints are taken into consideration as well,as shown in (17), (18). In this paper, AC power flow iscalculated by the embedded solver in EPRI’s Open-sourceDistribution System Simulator (OpenDSS) V7.6.1 [20],[21]. Therefore, (17) is a general mathematical formulationto implicitly represent a variety of power flow constraints.

5) Power flow constraints

(17)

6) Limits of bus voltage

(18)

III. SOLUTION ALGORITHM

Because the formulated problem is a mixed-integer nonlinearprogramming problem and AC power flow is performed in eachscenario, we decompose the original problem into a masterenergy scheduling problem and a power flow subproblem.The master problem is to solve the energy scheduling problem(1)–(16), without considering the power flow constraints (17),



Fig. 1. Flowchart of the proposed decomposition scheme.

Fig. 2. Framework of co-simulation platform.

(18). Then the dispatch solutions from the master problemare tested in the power flow subproblems to see whether thepower flow constraints (17), (18) are satisfied in each scenario.The two problems are solved iteratively until the difference ofthe overall expected loss calculated across all the scenarios issmall enough between two adjacent iterations. The output ofthe model is to decide how much energy can be purchased orsold in the day-ahead market and the commitment of on-sitedistributed generators at the day-ahead stage. Fig. 1 shows aflowchart of the proposed decomposition solution algorithm.Fig. 2 illustrates the proposed software-based simulationtestbed architecture.After initialization ( , are the iteration numbers for

the two iterative loops, respectively), the principal procedure isdescribed as follows.1) The formulation of the master problem including the ob-jective function and the system constraints (10)–(16)is implemented in Matlab and solved by the IBM ILOGCPLEXOptimizer 12.2 [22]. The optimal control variables

found by CPLEX are expressed as a vector of, , and .

Fig. 3. Hourly load profile and grid electricity prices.

2) Matlab passes the optimal control variables toOpenDSS which is dedicated to the advanced analysis ofdistribution systems. OpenDSS runs the sequential powerflow of the proposed distribution system over successivetime intervals and check the power flow constraints shownin (17), (18).

3) We calculate the power losses of the feasible solu-tions obtained from the Step (2). The most updated powerloss is fed back to the master problem and used toupdate the total power loss in (10) as long as the change ofpower loss is noticeable (i.e., ). Here,is a small pre-defined value. If the results fromStep (1) do not lead to the convergence of power flow, weadd a small random vector to the last feasible solution

to recalculate the loss and rerun the masterproblem with updated power balance constraints (10) inthe master problem. This kind of local search method canhelp the algorithm converge to at least a local optimal so-lution. The master problem and subproblem are solved it-eratively. The power flow solutions vary in each scenario.Each solution corresponds to a probability that quantifiesthe likelihood of the scenario. Accordingly, is theexpectation of power losses over all the scenarios.

4) After a few iterations, the system converges to a newsteady-state condition in which the best available optimalenergy scheduling solutions are found. The simulationresults in Section IV will also show the convergence of theproposed decomposition scheme, which has been appliedto many other power system applications [23].

IV. CASE STUDIES

In this section, we examine three cases to test the proposed al-gorithm and discuss the simulation results. All simulations wererun on an Intel(R) Core i5 CPU [email protected] GHz computerwith a 6.00 GB memory.

A. Case Configuration1) Base Load and Electricity Price Data: For the sake of

simplicity, the base load profiles of all the uncontrollable micro-grid loads are assumed to be the same, as shown in Fig. 3. Fig. 3also shows the hourly electricity prices of the utility power grid.The data is derived from [24].2) PEV Charging Load: This case study mainly focuses on

level 2 charging which is typically described as the “primary”and “standard” method [25] for both private and public chargingfacilities during early adoption of PEVs. The PEV charging load



Fig. 4. Scenarios of wind power output over 24 hours.

can be derived from the PEV data estimation. In order to sim-ulate PEV data that incorporate some uncertainty, a number offactors including plug-in time, expected plug-out time, initialbattery state-of-charge , and battery capacity need to beconsidered. Ideally, the proposed energy scheduling model re-quires the real-time estimation and prediction of PEV chargingload. The prediction problem is to forecast the PEV chargingload over the whole prediction horizon utilizing the historicaldynamic pattern. A detailed description of the PEV load data es-timation can be found in [26], [27]. One of our future researchtopics [28] is to investigate a robust real-time estimation andprediction method of PEV load under real world traffic condi-tions and traveler choice behaviors.3) Wind and Solar Scenarios: In this paper, we use the time-

stamped wind power generation output data from several hypo-thetical sites in Illinois based on the National Renewable EnergyLaboratory’s (NREL’s) Eastern Wind Integration and Transmis-sion Study [29]. Those wind data were generated by a weathersimulation model and a composite power curve of wind farms.Multiple day-ahead wind power forecasts were aggregated intoone time series. The forecasting error varies with the geograph-ical location, weather conditions, and time horizon. In order tocapture the uncertainty of the wind power predictions, MonteCarlo simulations [30] were performed to generate a pool ofwind power scenarios each day. Some scenario reduction tech-niques can be used to further reduce the computational cost [31].Fig. 4 shows a set of 10 probabilistic wind power scenarios overa period of 24 hours.The solar data were extracted from the System Advisor

Model (SAM) developed by NREL [32]. Due to the lack of theexisting scenario-based solar forecasting model, we generate aset of solar scenarios by assuming the forecasting errors followa normal distribution. Fig. 5 shows a set of 10 probabilisticsolar power scenarios over a period of 24 hours.Each wind and solar power scenario corresponds to a proba-

bility that quantifies the likelihood of this scenario. In this casestudy, the wind scenarios are assumed to be independent withsolar scenarios. Therefore, there are 100 independent scenariosin total. The probability of each independent scenario is 0.01.A modified IEEE 37-bus distribution test feeder [33] shown

in Fig. 6 was used to verify the proposed stochastic microgridenergy management model. The microgrid DGs, wind gener-ators, solar generators, DESDs, and PEVs are connected withnodes 701, 722, 730, 720, and 737, respectively. It is worth men-tioning that the proposed stochastic microgrid energy sched-

Fig. 5. Scenarios of solar power output over 24 hours.

Fig. 6. Microgrid test system based on a modified IEEE 37-node test feeder.

uling model can be easily extended using real-world utility’sdistribution system data.

B. Simulation and Analysis

In the case studies, the point forecasts of wind and solar poweroutput are also adopted as a deterministic alternative for com-parison purposes. Actually the deterministic approach can beconsidered as a special case of the stochastic one with only onescenario which is corresponding to the point forecast. The totalpower loss of the microgrid system over the 24-hour period isshown in Fig. 7, which illustrates how the entire problem is it-eratively solved to converge.Initially, the total power losses over the 24-hour period are

assumed to be zero for both deterministic and stochastic en-ergy scheduling models. Then the total power losses change to2,374.2 kW and 2,417.3 kW, respectively. The most updated

, is fed back to the power balance constraint (10) as long asthe change of losses between two iterations, , is notice-able. The master problem is performed again to obtain the newenergy scheduling solution. Gradually, the total power lossesconverge (i.e., 2,372.3 kW and 2,278 kW respectively for deter-ministic and stochastic approaches) as becomes smallerand smaller. After four iterations, the system converges to anew steady-state condition in which the optimal solutions are



Fig. 7. Total power losses at each iteration over 24 hours.

Fig. 8. Hourly battery power output under deterministic microgrid energyscheduling.

found. The optimal microgrid operational costs are $23,500 and$22,276, using deterministic and stochastic approaches, respec-tively.To compare the energy scheduling results from the determin-

istic (point forecasts) and stochastic (scenarios) approaches, weuse the same set of actual wind and solar power output profilesto simulate the real-time operation of the test system. In otherwords, we first run the deterministic and stochastic methods toobtain the day-ahead energy scheduling solutions, respectively.We then fix the amount of energy transactions , replace thepoint forecasts with the real wind and solar power output and runthe deterministic approach again. Fig. 8 shows the hourly bat-tery power output under deterministic energy scheduling. Thesevalues are subject to the battery charger limit (i.e., [ 250 kw,250 kw]) and the battery requirement (i.e., [10%, 90%])

at any time step. The rated battery capacity is 4,000 kWh. Thepositive/negative power output corresponds to the discharging/charging processes, respectively. Because the grid electricityprice is cheaper in the early morning, the battery bank starts tostore as much energy as possible. A large number of PEVs startto connect to the microgrid at approximately 7:00 a.m when thegrid electricity price increases as well. Accordingly, the batterybank starts to return the stored energy to the grid at peak demandtime.It is worth mentioning that, this case study does not con-

sider any additional reward for storing extra energy in batterystorage at the end of a day. Ideally, the most cost-effective final

should be close to initial . In other words, the mostcost-effective at the end of a day should be very closeto zero. Fig. 9 compares the trends of under determin-istic and stochastic microgrid energy scheduling. In the deter-ministic microgrid energy scheduling, the final reaches

Fig. 9. Trends of SOC under deterministic and stochastic microgrid energyscheduling.

a relatively large non-zero value of 15.08%. That’s because theenergy scheduling solution from deterministic microgrid energyscheduling is very sensitive to the point forecasts of wind/solarpower output over the look-ahead horizon. Since electrical gen-eration and load must remain in balance in real time, the onsiteDESDs have to operate properly in order to compensate for thewind/solar point forecasting errors. The large non-zeroat the end of a day indicates that the deterministic microgrid en-ergy scheduling may have large extra operational cost caused bythe intermittency and the non-dispatchable nature of wind/solarenergy production. In comparison, the end-of-the-dayusing stocashtic scheduling is approximately 6.14%, which ismuch lower than 15.08% obtained from the deterministic oneand closer to the ideal final of zero. Therefore, there isstrong evidence that the stochastic microgrid model can achievebetter energy scheduling for DESDs.Also note that is mainly determined by wind power

as the battery bank is closer to the wind units than the solarunits as shown in Fig. 6. The size of is also affectedby the renewable energy forecasting errors. In this paper, thewind generation capacity is larger than the solar generation ca-pacity. Because the wind forecasting error is typically within10%–20%while the solar forecasting error is assumed to followa normal distribution with a 5% standard deviation, the windpower has a larger impact on than solar. However, this ob-servation may not be generalized to other cases with a differentpower generation portfolio and forecasting accuracies althoughthe simulation method still applies. We have tried to run severalcase studies under different operating conditions. The simula-tion results exhibit the similar tendency of the final underdifferent scheduling approaches (deterministic vs. stochastic),demonstrating the effectiveness of the proposed stochastic en-ergy scheduling. More specifically, under the same operatingconditions and forecast data, the stochastic approach alwaysachieves smaller non-zero values of at the end of a day.Due to the limited space, we only presented one representativesimulation result here.To further compare the performances of microgrid energy

scheduling, we perform both stochastic and deterministic ap-proaches for a week. Figs. 10 and 11 show the daily operationalcost and power losses over the week under both the stochasticand deterministic approaches. The stochastic approach outper-forms the deterministic one in terms of the operational cost andthe total power losses.



Fig. 10. Daily operational costs over one week under stochastic and determin-istic approaches.

Fig. 11. Daily power losses over one week under stochastic and deterministicapproaches.

Fig. 12. Hourly base load, PEV charging load, and power losses under deter-ministic microgrid energy scheduling.

Fig. 12 shows the hourly base load, PEV charging load, andpower losses under deterministic microgrid energy scheduling.In the early morning, the power loss is quite small because ofthe low load level. From 7:00 a.m. to 5:00 p.m., the larger powerloss occurs as the PEV charging load is heavily introduced intothe microgrid. After 5:00 p.m., the power loss is mainly causedby the base load.To investigate the impact of PEV charging, we evaluate two

types of charging schemes. In both charging schemes, all PEVbatteries are guaranteed to be fully recharged at plug-out.• Uncontrolled charging: The charging process starts imme-diately when a vehicle arrives at the public charging fa-cility (e.g., parking deck).

• Constrained charging: Assuming the total charging time isknown, the required charging load is equally distributedover the entire period of parking.

TABLE ISIMULATION RESULTS UNDER VARIOUS PEV CHARGING SCHEMES

Table I compares the objective function values under the pro-posed two charging schemes, as well as the deterministic/sto-chastic microgrid energy scheduling approaches. As most ve-hicles are expected to park for longer than the actual chargingtime, the constrained charging scheme can reduce the opera-tional cost of the microgrid by taking full advantage of timingflexibility as shown in Table I.

V. CONCLUSION

This paper first describes the development of a two-stagestochastic microgrid energy management model in the inter-connected mode. A typical microgrid runs in two operationalmodes: an interconnected mode linked to the main grid throughthe distribution substation transformer vs. an islanded (au-tonomous) mode when it is isolated from the main grid during ablackout or brownout. The system operators may have differentconsiderations in interconnected and islanded modes. For ex-ample, in the islanded mode, the microgrid remains operationaland functional as an autonomous entity. Therefore, the overallsystem reliability and security should be the top priority. Anumber of different objective functions could be formulated toachieve that. While we focus on the interconnected mode inthis paper, stochastic energy scheduling for islanded microgridoperations is one of our future research topics.The proposed stochastic model accommodates the inherent

intermittency and variability of renewable energy resources(i.e., wind and solar). In addition, the proposed problem formu-lation minimizes the expected operational cost of the microgridwhile reducing power losses by optimally dispatching thePEV charging load and scheduling DGs and DESDs. Thesimulation results demonstrate the effectiveness and accuracyof the proposed stochastic microgrid energy systems undervarious operating conditions and real-world scenarios. More-over, the proposed framework can be easily extended to othermicrogrid operation applications to accelerate the developmentof full-scale commercial microgrids in the near future. Theproposed model can also be tailored to take into considerationother uncertainties such as load and customer behavior.

ACKNOWLEDGMENT

The submitted manuscript has been created by UChicagoArgonne, LLC, Operator of Argonne National Laboratory(“Argonne”). Argonne, a U.S. Department of Energy Of-fice of Science laboratory, is operated under Contract No.DE-AC02-06CH11357. The U.S. Government retains for it-self, and others acting on its behalf, a paid-up nonexclusive,irrevocable worldwide license to reproduce, prepare derivativeworks, distribute copies to the public, and perform publicly anddisplay publicly, by or on behalf of the Government.



Jaehyung Roh’s work is sponsored byKETEP(2001T100100424).

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[26] W. Su and M.-Y. Chow, “Performance evaluation of an EDA-basedlarge-scale plug-in hybrid electric vehicle charging algorithm,” IEEETrans. Smart Grid, vol. 3, no. 1, pp. 308–315, Mar. 2012.

[27] W. Su and M.-Y. Chow, “Computational intelligence-based energymanagement for a large-scale PHEV/PEV enabled municipal parkingdeck,” Appl. Energy, vol. 96, pp. 171–182, August 2012.

[28] W. Su, J. Wang, K. Zhang, and M.-Y. Chow, “Framework for investi-gating the impact of PHEV charging on power distribution and trans-portation networks,” in Proc. 38th Ann. Conf. IEEE Ind. Electron. Soc.,Montreal, QC, Canada, Oct. 25–28, 2012.

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Wencong Su (S’06–M’13) received the B.S. degree (with distinction) fromClarkson University, Potsdam, NY, USA, in May 2008, the M.S. degree fromVirginia Tech, Blacksburg, VA, USA, in December 2009, and the Ph.D. degreefrom North Carolina State University, Raleigh, NC , USA, in May 2013, respec-tively.He is currently an Assistant Professor in the Department of Electrical and

Computer Engineering at University of Michigan-Dearborn, MI, USA. Heworked a Research Aide at Argonne National Laboratory from January toAugust 2012. He also worked as a R&D engineer intern at ABB U.S. CorporateResearch Center in Raleigh, NC, from May to August 2009. His researchinterests include Smart Grid, grid integration of plug-in electric vehiclesand renewable energy, microgrids, distribution system analysis, intelligentenergy management, computational intelligence, power system optimization,modeling and simulation.

Jianhui Wang (M’07–SM’12) received the Ph.D. degree in electrical engi-neering from Illinois Institute of Technology, Chicago, IL, USA, in 2007.Presently, he is a Computational Engineer with the Decision and Information

Sciences Division at Argonne National Laboratory, Argonne, IL, USA. He isalso an affiliate professor at Auburn University.Dr. Wang is the chair of the IEEE Power & Energy Society (PES) power

system operation methods subcommittee. He is an Editor of the IEEETRANSACTIONS ON POWER SYSTEMS, the IEEE TRANSACTIONS ON SMARTGRID, an Associate Editor of Journal of Energy Engineering, an Editor of theIEEE PES Letters, and an Associate Editor of Applied Energy.

Jaehyung Roh (M’05) received the B.S. degree in nuclear engineering fromSeoul National University Seoul, Korea, in 1993 and the M.S. degree in elec-trical engineering from Hongik University, Korea, in 2002. He received Ph.D.degree in electrical engineering from Illinois Institute of Technology, Chicago,IL, USA.During 1992–2001, he was with Korea Electric Power Corporation, and for

2001–2010, he was with Korea Power Exchange. Since 2010, he has been withElectrical Engineering Department, Konkuk University, Seoul, Korea, as an As-sistant Professor. His research interests include power systems restructuring,smart grid and resource planning.

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