Development of Physical-Based Demand Response-Enabled Residential Load Models

8/9/2019 Development of Physical-Based Demand Response-Enabled Residential Load Models

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This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

IEEE TRANSACTIONS ON POWER SYSTEMS 1

Development of Physical-Based DemandResponse-Enabled Residential Load Models

Shengnan Shao, Student Member, IEEE, Manisa Pipattanasomporn, Member, IEEE, andSaifur Rahman, Fellow, IEEE

AbstractIn order to support the growing interest in demand

response (DR) modeling and analysis, there is a need for phys-ical-based residential load models. The objective of this paper is topresent the development of such load models at the appliance level.These include conventional controllable loads, i.e., space cooling/space heating, water heater, clothes dryer and electric vehicle. Val-

idation of the appliance-level load models is carried out by com-paring the models output with the real electricity consumptiondata for the associated appliances. The appliance-level load modelsare aggregated to generate load profiles for a distribution circuit,which are validated against the load profiles of an actual distri-bution circuit. The DR-sensitive load models can be used to study

changes in electricity consumption both at the household and thedistribution circuit levels, given a set of customer behaviors and/orsignals from a utility.

Index TermsAppliance-level load model, demand response(DR) and distribution circuit, electric vehicle, physical-based.

I. INTRODUCTION

T HERE has been a lot of interest in load modeling inthe past several decades. Scale of load models can vary,ranging from the appliance level to the power grid level. In1995, IEEE provided a load model bibliography for powerfl

ow and dynamic performance simulation. This bibliographycovered almost all relevant load modeling work in late 20thcentury[1]. The classic load models defined load as a functionof voltage and frequency, which are referred to as static loadmodels. The dynamic load models were developed for tran-sient studies. To better guide the load modeling work, authorsin[2] provides the basic definitions and concepts. Obviously,different simulations need different load model representations.

In order to study demand management, published workhas focused on physical-based load models, especially onheating, ventilation, and air conditioning (HVAC) loads andwater heating loads [3][7]. Physical-based load models forresidential HVAC were developed in[8],[9]and tested againstutility data. The models captured thermodynamic principlesof building structures and the diversification was created byrandom distribution functions when building the distributioncircuit load profile. These load models have been mostly usedfor direct load control (DLC) studies [10][12]. Other than

Manuscript received June 06, 2011; revised November 21, 2011 andFebruary 15, 2012; accepted March 29, 2012. This work was supported inpart by the United States Army Corps of Engineers Humphreys under grantW912HQ-09-C-0058. Paper no. TPWRS-00536-2011.

The authors are with the Advanced Research Institute, Virginia Tech,Arlington, VA 22203 USA (e-mail: [email protected]; [email protected];[email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TPWRS.2012.2208232

a physical-based methodology, load models were developedbased on consumers behavior [13]. Load models built fromstatistical survey data and historical measurements were pre-sented in[14][16]with proper random functions designed foraggregation diversity.

With the development of the smart grid, there is a need forload models that can facilitate the study of changes in electricityconsumption in response to customer behavior and/or signalsfrom a utility. Such load models are useful to evaluate the im-pact of demand response (DR) at a distribution circuit level. Forthe load models to represent DR activities, the following char-acteristics are required, which have not all been all addressed inthe published literature:

Comprehensivethe models should cover all major typesof controllable loads so that DR can be simulated consid-ering consumer choices instead of simple load curtailment.

Physically basedthe models should be built according tothe physical and operational characteristics of the appli-ances to reflect the real-world situation.

Interactivethe models should allow interfaces for ex-ternal signals to simulate the DR control actions.

Reasonably aggregatedthe algorithm should providereasonable load diversification and aggregation to repre-sent the distribution circuit load profile.

This paper proposes a set of DR-enabled load models at theappliance level, including space cooling/heating, water heating,clothes drying and electric vehicle loads, for a residential dis-tribution circuit (Comprehensive). The models are designedbased on their physical characteristics (physical-based) andcan be controlled externally (interactive) to reflect any desiredDR strategies. At the same time, these models are aggregatedusing a stochastic method to create load profiles of a distribu-tion circuit (reasonably aggregated). The resulting load pro-files are validated against the utility measurement data in bothsummer and winter to show representativeness of the proposedload model. In addition, the developed load models are easy touse as the models parameters are easily adjustable to fit dif-

ferent network sizes present in different locations.

II. LOADCATEGORIZATION

Hourly load curves of an average household by load typeare available from the RELOAD database [17], which is usedby the Electricity Module of the National Energy ModelingSystem (NEMS) [18]. The load data are available for twelvemonths (January to December), and three day types (typicalweekday, typical weekend and typical peak day). Based on theRELOAD database, residential loads are classified by the fol-lowing load types: space cooling/heating, water heating, clothdrying, cooking, refrigeration, freezer, lighting and others.

For the purpose of this study, these load types are classified

into two categories: controllable and critical. Controllable loads

0885-8950/$31.00 2012 IEEE
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2 IEEE TRANSACTIONS ON POWER SYSTEMS

Fig. 1. Block diagram of the space cooling/space heating load model.

are defined as the loads that can be controlled without noticeableimpacts on consumers life styles. The critical load categorycontains loads that are either very important or noticeable whenbeing controlled.

For the residential load, space cooling/heating, water heatingand clothes drying loads are controllable; and all other loadsare considered critical and cannot be controlled. As a new typeof residential load, EV is not mentioned in RELOAD database.Considering that the electric vehicle (EV) can usually be con-trolled without noticeable effect on consumers, this paper putsit into the Controllable category.

Sections IIIandIVbelow present the detailed load modelsfor each controllable load type (space cooling/heating, waterheating, clothes drying and EV), their validation against themeasurement data from a real house, and the methodology toperform load aggregation by appliance type. The bottom-up ag-gregation approach for each load type enables demand responsestudies in a large-scale distribution circuit. All load models aresimulated in 1-min intervals. Data from the RELOAD databaseare used to construct the critical load profile, which is presentedinSection VII.

III. DR-ENABLEDSPACECOOLING/HEATINGLOADMODEL

This section presents the development of a DR-enabled spacecooling/heating load model, together with its validation and ag-gregation to create the load profile at a distribution circuit level.

A. Model Development for the Space Cooling/Heating Load

Fig. 1illustrates the block diagram of the DR-enabled space

cooling/space heating load model.Inputs to the model are the DR control signal , timeseries outdoor temperature data , thermostat set point

, allowable temperature deviation or dead band andtime series room temperature data . The model outputsare the time series electric power consumption inkilowatts of the space cooling/space heating unit, and the roomtemperature at the next time step. The room temperatureoutput is used as an input to the load model at the next timestep. The model needs additional house parameters, includinghouse structures, number of people dwelling and the electricalcharacteristics of the space cooling/space heating unit.

1) Calculation of the Electric Power Demand : Acentral space cooling/space heating unit with a thermostat worksin an on-off mode and will simply run at its rated power whenturned on. In general, the thermostat control is set such that the

room temperature willfluctuate around the thermostat setpoint within the dead band of . For each time step ,the demand for electricity of the space cooling/space heatingunit is calculated as

(1)

where isthe rated power of the space cooling/space heatingsystem (kW), and is the status of the space cooling/spaceheating unit in time slot , , .

For space cooling, the unit is ON when the room temperatureincreases above the set point, plus thethreshold. The unit is OFFwhen the room temperaturedecreases below a certain value. Thestatus of the unit remains the same if the room temperature iswithin the acceptable band. This relationship is presented in(2):

(2)For space heating, the operation is similar to above:

(3)where is the DR control signal for the space cooling/spaceheating unit in time slot , .

The electric power demand also depends on the DR controlsignal . This signal is implicitly derived from therevised thermostat set point preset by a homeowner during a DRevent. This scenario is possible with the use of a home energymanagement (HEM) controller. For example, if during a normaloperating condition the homeowner sets the thermostat set point

at , but pre-determine that during a DR event his/her newset point should be set at , this implies the signal of

. is positive for space cooling and negative for spaceheating. Note that with a HEM controller, the value canbe configured to allow demand response implementation thattakes into account the priority of all end-use loads in a house,and customer comfort levels. This topic is not in the scope ofthis paper. More information about a possible DR strategy isreported in[19].

2) Determination of Room Temperature : For each timestep , the room temperature is calculated as

(4)

where

room temperature in time slot ;

length of time slot (hour);

heat gain rate of the house during time slot ,positive value results in an increase in roomtemperature; and negative value results in adecrease in room temperature (Btu/h);cooling/heating capacity, positive for heating andnegative for cooling (Btu/h);energy needed to change the temperature of theair in the room by .

3) Calculation of Other Parameters ( and ): For eachtime slot , the heat gain rate of the house is calculated as
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SHAO et al.: DEVELOPMENT OF PHYSICAL-BASED DEMAND RESPONSE-ENABLED RESIDENTIAL LOAD MODELS 3

(5)

where , , and represent the area of thewall, ceiling, and window of the house, in . , ,and represent the heat resistance of the wall, ceiling,and window, in [20].

number of air changes in each time slot (1/h);

house volume, in ;

outdoor temperature in time slot ;

solar heat gain coeffi

cient of windows[21];solar radiation heat power );

heat gain from people (Btu/h).

To change the house temperature by , the energy requiredis calculated as

(6)

where is the specific heat capacity of air for a typical roomcondition ( or ), and isthe volume of the house .

B. Model Validation for the Space Cooling/Heating Load

To validate this model, the space cooling/space heating modelis run with inputs (outdoor temperature, thermostat set point,dead band, house structure parameters, and HVAC unit size)from a real house in Virginia. Using the same inputs, the modeloutputs (power consumption and indoor temperature) are com-pared with the actual measurements. The comparisons in 1-minintervals are illustrated inFig. 2for two 24-h periods: (a) onein August for cooling demand; and (b) the other in January forheating demand (kW).

Fig. 2 indicates similarities between the actual power con-sumption and the model output. The energy difference is 1.4%for summer and 1.2% for winter. Any discrepancies may resultfrom the assumptions associated with the house structure pa-rameters.

C. Aggregation of Space Cooling/Heating Loads

The input parameters for space cooling/space heating modelare divided into three categories: temperatures, building struc-tures and the space cooling/space heating unit characteristics.These are parameters needed to be randomized for differenthomes in the same distribution circuit.

The temperature category includes outdoor temperatures andindoor temperature set points. The outdoor temperatures are ac-quired from the National Climatic Data Center (NCDC) [22],which should be the same for all houses in the same neighbor-hood. For the indoor temperature set points, a uniform randomfunction is used to determine the variation in temperature setpoints among different houses in the same distribution circuit.

Fig. 2. Space cooling/space heating model validationComparison of loadprofile (kW). (a) Space cooling comparison: The total energy difference = 1.4%,the total daily energy = 14.5 kWh (model); 14.3 kWh (measurement). (b) Spaceheating comparison: The total energy difference = 1.2%, the total daily energy= 17.2 kWh (model); 17.4 kWh (measurement).

The lower and upper limits for the temperature set points aredetermined based on data from ASHRAE[23].

The building structure category includes the floor plan of thebuildings, areas of walls, ceilings and windows as well as theR-values for each of them. Similar to the indoor temperature, auniform random function is used to determine the variation in

R-values and areas of wall, window, ceiling and floor amongdifferent houses in the same distribution circuit. The lower andupper limits for these values are acquired from a survey of theservice area. Such data for residential houses are obtained fromthe American Housing Survey[24].

The space cooling/heating characteristic category includesthe cooling/heating capacities and power consumptions, whichis usually known as the unit sizing. Usually, the sizing is basedon the building floor plans, activities, occupants and environ-ment. The unit sizing is calculated according to ASHRAE [25].

After getting all three sets of random functions for these inputparameters, the demand aggregation for space cooling/heating isquantified by running the space cooling/heating model times

with different parameters. is the number of residential housesin the distribution circuit.

IV. MODELING, VALIDATION, AND AGGREGATIONOFWATERHEATINGLOAD

This section presents the development of a DR-enabled waterheating load model, together with its validation and aggregationto obtain load profiles of the water heating load at the distribu-tion level.

A. Model Development for the Water Heating Load

Fig. 3shows the block diagram of the developed water heatermodel.

For each time step i, the demand for electricity of the waterheating unit is calculated as
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Fig. 3. Block diagram of the water heater load model.

(7)

where

rated power of the water heater (kW);

efficiency factor;

status of the water heater in time slot , ,;

DR control signal for water heater, ,.

The water heater status is determined according to

the following rules: when the water temperature in the hot watertank goes above the set point, it does not operate. When thewater temperature drops below a lower bound, the heating coilsstart working again at its rated power until the outlet hot watertemperature reaches the upper bound:

(8)

where

hot water temperature set point ;

lower tolerance ;

mixed water temperature in the tank .

The water temperature in the tank is calculated as

(9)

where

temperature of inlet water ;room temperature ;

Fig. 4. Water heater model validationload profile comparison. (a) Waterheater power consumption data from TED[26]. (b) Modeled water heater loadprofile.

hot waterflow rate in time slot (gpm);

surface area of the tank ;

volume of the tank (gallons);

heat resistance of the tank ;

duration of each time slot (minutes).

Note that the unit of the third term in(9) is Btu/lb, which isequivalent to as 1 Btu is the amount of energy required toheat 1 lb of water by , i.e., .

The electric power demand also depends on the DR controlsignal received from an external source, such as anin-home controller, or a utility. The DR control signal of 0 willshut off the unit, and the DR control signal of 1 will turn the uniton.

B. Model Validation for the Water Heating Load

To validate the developed water heater model, the followingdata from a real house were used as inputs to the water heatermodel: inlet water temperature, ambient temperature, waterheater temperature set point, tank sizes and rated power of thewater heating unit. The water heater load profile was measuredin 1-min intervals for a 24-h period using the Energy DetectiveDevice (TED), and compared with the model output. Fig. 4illustrates this comparison.

The comparison is to show the power consumption character-istics of a water heater, which are similar to the model output.This implies that the developed model can be reasonably usedfor simulations and analysis.

C. Aggregation of Water Heating Loads

The input parameters for water heater model are divided intothree categories: temperature profile, water heater characteris-tics and hot water usage.

The temperature profile includes tank ambient temperatures,inlet water temperatures and hot water temperature set points.Tank ambient temperature is assumed to be the same as theroomtemperature, which can be acquired from the space cooling/space heating model. Inlet water temperature is assumed to bethe same as the ground temperature, which can be acquired fromSoil Climate Analysis Network (SCAN)[27]. For the hot water
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Fig. 5. Clothes dryer model validationload profile comparison.

temperature set points, a uniform random function is used to de-termine the variation in temperature set points among differenthouses in the same distribution circuit.

The lower and upper limits for the hot water temperature setpoints are determined based on data from [28], which speci-fies typical residential hot water temperature set points between

and .The water heater characteristics include the R-values, tank

sizes and rated power. Similar to the hot water temperature, auniform random function is used to determine the variation inR-values, water heater tank sizes and rated power among dif-ferent houses in the same distribution circuit. The typical rangesfor these values are acquired from[28]and[29].

For hot water usage, the hourly fraction data from CaliforniasHourly Water Heating Calculations [30] are taken as a reference.At the same time, hot water usage is categorized into differenttypes in percentage of the daily household hot water usage[31].Therefore for each type of hot water usage, the water consump-tion duration in a minute is the hot water demand in gallon di-

vided by thefl

ow rate in gallon per minute (gp). Monte Carlomethod is used to decide when the hot water is consumed basedon the hot water hourly usage fraction shown inFig. 5for dif-ferent residential houses.

V. MODELING, VALIDATION, AND AGGREGATIONOFCLOTHESDRYERLOAD

This section presents the development of a DR-enabledclothes dryer load model, together with its validation andaggregation at the distribution level.

A. Model Development for the Clothes Drying Loads

The power consumption of a typical clothes dryer is from themotor and the heating coils. The power demand of the motorpart is usually in the range of several hundred watts, but that ofthe heating coils can be several kilowatts.

For each time slot , the demand for electricity of the clothesdrying unit is calculated as

(10)

where

rated power of clothes-dryer heating coil (kW);

drying level ;

total number of drying levels;power consumption of the dryers motor (kW);

status of the clothes-dryers heating coils in timeslot , , ;DR control signal for clothes dryer in time slot, , .

The electric power demand also depends on the DR controlsignal received from an external source, such as anin-home controller, or a utility. For the clothes dryer load, when

a DR controlsignal is received, only theheating coil will be con-trolled (ON/OFF) but the motor part will not be controlled. Thisimplies that the clothes dryer will be spinning during the con-trol period, thus consuming only a fraction of the overall load(several kW).

B. Model Validation for the Clothes Drying Loads

For validation, the rated power of a real clothes dryer and itsusage profile were used as inputs to the clothes dryer model.The clothes dryer load profile was measured in 1-min intervalsduring its operation using TED, and compared with the modeloutput.Fig. 5illustrates this comparison.

The close match between the actual and modeled power con-sumption characteristics of a clothes dryer implies the useful-ness of the clothes dryer model for simulation and analysis.

C. Aggregation of Clothes Drying Loads

The clothes dryer load profile is developed based on a prob-ability distribution function that is similar to the dryer powerconsumption profile obtained from the measurement of a homein Florida [32]. The dryer running time generally starts fromlate morning to midnight, with the mean in the evening. Afterfinding out the dryers running time, the demand aggregation ofclothes dryers is acquired by running the developed model toobtain power consumption of all dryers in the distribution cir-

cuit.

VI. MODELING ANDAGGREGATION OFEV LOAD

This section presents the development of a DR-enabled EVcharging load model, together with its aggregation.

A. Model Development For the Electric Vehicles (EV)

To model EV charging profiles, three parameters are essen-tial: the rated charging power, the plug-in time and the batterystate-of-charge (SOC). Equation(11) shows the calculation ofthe EV charging profile:

(11)

where

EV charge power in time slot (kW);

EV rated power (kW);

EV connectivity status in time slot , 0 if EV isnot physically connected to the outlet and 1 if EVis connected;uncontrolled EV charging status in time slot ,which depends on the battery SOC as shown in(12): 0 if EV is not being charged and 1 if EVis being charged;

DR control signal for EV in time slot , ,.
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TABLE IEVS IN THEU.S. MARKET[33][37]

(12)

The battery SOC at time slot is a function of the SOC at theprevious time slot, the energy used for driving and the batteryrated capacity, which is determined by

(13)

(14)

where

battery SOC when EV is plugged in;

length of time slot (minute);

energy used for driving (kWh);

battery rated capacity (kWh).The EV power demand also depends on the DR control signal

received from an external source, such as an in-homecontroller, or a utility. The DR control signal of 0 will stop thecharging of EV while the DR control signal of 1 will allow theEV to start charging. The developed EV model will extend theEV charging complete time if the EV charging is interrupted bythe DR signal. That is, the EV will continue to be charged untilit is fully charged.

B. Aggregation of EV Load

To determine the EV fleet charge profile in a distribution cir-

cuit, three parameters (vehicle rated charging power, plug-intime and the battery SOC) have to be reasonably diversified.For EV rated charging power,Table Ishows the basic battery

charge data of three popular EVs in the US market. The chargepower presented in Table I is used to determine the charge powerof EV fleet with a reasonable mix of different EV makes andmodels.

The EV plug-in time is derived from the National House-hold Travel Survey[38]. The EV plug-in time is modeled usinga normal distribution with a mean and a variance derived ac-cording to the data presented in[39].

EV driving patterns are used to determine the EV energystorage status, i.e., the battery SOC. The daily driving distancefor each EV in a distribution circuit is determined based on thedata presented in[38]using Monte Carlo simulation. The bat-tery SOC of each EV when plugged in is determined by(13).

Fig. 6. EVfleet charging profile.

To illustrate the aggregation of EV load at a distribution cir-cuit level, Fig. 6 shows an example charging profile of a 100-EVfleet with the mix of 70% Chevy Volt, 20% Nissan LEAF and10% Tesla Roadster. The EV fleet mix (70/20/10) is chosenas a representative mix to show a sample aggregation of dif-ferent EV types. As EV market share changes over time and newEV models enter the market, EV fleet mix can change, but themethodology will stay the same. The recommended chargingpower rates inTable Iare chosen for the simulation. The EVsare assumed to come back home and plugged in at different timeaccording to a normal distribution with mean at 18:00 and 1-hvariance.

Note that since the daily driving distance of each EV is de-termined by Monte Carlo simulation, it is unlikely that all EVswill come home with empty battery. This is the reason for thenarrow EV loading time shown inFig. 6.

VII. EXAMPLE LOAD PROFILES FOR A RESIDENTIALDISTRIBUTIONCIRCUIT ANDVALIDATION

To demonstrate the aggregation of the developed load modelat the distribution circuit level, a distribution circuit in the Vir-ginia Tech Electric Service (VTES) service area is taken as acase study. This circuit has 34 laterals with 117 transformersserving 780 residential/small commercial customers.

A. Controllable Loads

The aggregated load profile of controllable loads in a dis-tribution circuit is created using the load models presented inSections IIIandV. The parameters inTable IIare used for loaddiversification. The percentage of the households owning eachelectric appliance, known as ownership rate, is taken into ac-count according to the method presented in [40]. The owner-ship rates can be found from the survey data in [24]. EV is notincluded in the analysis due to the lack of EV penetration andmeasurement data.

B. Critical Loads

The aggregated load profile of critical loads is created basedon the historical data of the industry-accepted database, i.e.,RELOAD database. According to the categories inSection II,five load types cooking, lighting, refrig-erator, freezer, and othersare considered critical loads.Assuming in the distribution circuit, there are residentialhouses. The critical load profile in a house in each time slot(in 1-min interval) is derived based on(15). Note that the own-ership rate has already been taken into account in the RELOADdatabase:
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TABLE IIPARAMETERS ANDFUNCTIONS FORLOADMODEL DIVERSIFICATION

(15)

wherenumber of residential houses;

power consumption of critical load in timeslot ;

hourly fraction of annual residential loadfor load type in the hour of time slot ;average annual household electricityconsumption (kWh) for load type [44].

C. Distribution Circuit Load Profiles and Validation

The aggregation of the controllable loads, together with thecritical loads, of a distribution circuit with 780 customers is pre-sented inFig. 7as solid bold lines for both (a) summer and (b)winter seasons. The actual hourly load profiles of the same cir-cuit for several days are also presented for comparison purposes.As shown in Fig. 7, the aggregation of the proposed load modelsreflects the real load profile of a residential distribution circuitreasonably well.

VIII. CONCLUSIONS

This paper presents a methodology to develop the bottom-upDR-enabled load models of space cooling/heating, waterheating, clothes drying and electric vehicle loads. The modelsare developed at the appliance level taking into account thephysical and operational characteristics of different load types.A methodology is also discussed showing how to aggregateeach load type to obtain the distribution circuit load profileusing a stochastic method. Each load model is validated against

Fig. 7. Validation of the distribution circuit load profiles. (a) Summer load pro-file comparison. (b) Winter load profile comparison.

the real electricity consumption data of the associated loadtype. The comparison of the aggregated load profile at thedistribution circuit level also shows similarity with the actualdistribution circuit load profiles. This similarity proves that theproposed load modeling methodology can be used to createrealistic distribution circuit load profile for DR studies.

With the DR-enabled load models developed and presentedin this paper, researchers in academia and/or electric utilitiescan use the developed models to perform detailed DR studies.For example, studies of the impact of different demand responsestrategies and different customer behaviors on distribution cir-cuit load shape changes can be conducted; and observations canbe made at both the appliance level and the distribution circuit

level.

ACKNOWLEDGMENT

The authors would like to thank UVA for providing the datafor HVAC model validation.

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Shengnan Shao (S08) received the B.S. and M.S. degrees in electrical engi-neering from Tsinghua University (THU), Beijing, China, in 2005 and 2007,respectively. She is pursuing the Ph.D. degree in the Department of Electricaland Computer Engineering at Virginia Tech, Arlington.

She is now a research assistant at the Advanced Research Institute of Vir-ginia Tech. She is a member of the team working on Intelligent DistributedAutonomous Power Systems (IDAPS) project at the Virginia TechAdvancedResearch Institute. Herfields of interest include smart grid, demand response,

electric vehicle, power distribution, and renewable energy systems.

Manisa Pipattanasomporn(S01M06) received the B.S. degree from theElectrical Engineering Department, Chulalongkorn University, Thailand, in1999, the M.S. degree in energy economics and planning from the AsianInstitute of Technology (AIT), Thailand, in 2001, and the Ph.D. degree inelectrical engineering from Virginia Tech, Arlington, in 2004.

She joined Virginia Techs Department of Electrical and Computer Engi-neering as an Assistant Professor in 2006. She is working on multiple researchgrants from the U.S. National Science Foundation, the U.S. Department of De-fense, and the U.S. Department of Energy, on research topics related to smartgrid, microgrid, energy efficiency, load control, renewable energy, and electricvehicles. Her research interests include renewable energy systems, energy effi-ciency, distributed energy resources, and the smart grid.

Saifur Rahman (S75M78SM83F98) is the director of the Advanced Re-search Institute at Virginia Tech, Arlington, where he is the Joseph Loring Pro-fessor of electrical and computer engineering. He also directs the Center forEnergy and the Global Environment at the university. He has published over300 papers on conventional and renewable energy systems, load forecasting,uncertainty evaluation, and infrastructure planning.

Professor Rahman is currently the chair of the U.S. National ScienceFoundation Advisory Committee for International Science and Engineering. In2011, he is serving as the vice president for New Initiatives and Outreach ofthe IEEE Power & Energy Society and a member of its Governing Board. He isa member-at-large of the IEEE-USA Energy Policy Committee. He served asa program director in engineering at NSF between 1996 and 1999. In 2006 heserved as the vice president of the IEEE Publications Board, and a member of

the IEEE Board of Governors. He is a distinguished lecturer of the IEEE PES.

Documents

Development of Physical-Based Demand Response-Enabled Residential Load Models