SmartGridDRpaper

580 IEEE TRANSACTIONS ON SMART GRID, VOL. 2, NO. 4, DECEMBER 2011

Agent-Based Electricity Market Simulation WithDemand Response From Commercial Buildings

Zhi Zhou, Member, IEEE, Fei Zhao, Student Member, IEEE, and Jianhui Wang, Member, IEEE

Abstract—With the development of power system deregulationand smart metering technologies, price-based demand response(DR) becomes an alternative solution to improving power systemreliability and efficiency by adjusting the load profile. In this paper,we simulate an electricity market with DR from different types ofcommercial buildings by using agent-based modeling and simula-tion (ABMS) techniques. We focus on the consumption behaviorof commercial buildings with different levels of DR penetration indifferent market structures. The results indicate that there is a no-ticeable impact from commercial buildings with price-responsivedemand on the electricity market, and this impact differs with dif-ferent scales of DR participation under different levels of marketcompetitions.

Index Terms—Agent-based modeling and simulation, buildingstock modeling, demand response, electricity market, smart grid.

I. INTRODUCTION

E LECTRICITY markets in a number of countries and sev-eral regions of the United States, including in Australia

[1], England [2], Spain [3], New England [4], New York [5],and the Pennsylvania–New Jersey–Maryland Interconnection[6], have been restructured away from operating as centralizedmarkets to operating as competitive markets. This evolutionhas dramatically changed how power systems operate. In tradi-tional power systems, supply from committed generation unitsis scheduled to follow any change in load demand. In a peakload period, the load can be very high, and more generatorshave to be committed. This usage pattern means that operatorsmust increase their investment in greater generation capacity,which may only be committed for a few hours in a year. De-mand response (DR) is an alternative solution to reduce peakloads and adjust the demand in peak times to postpone theinvestment in new generation capacity. Moreover, in regionswith high penetration of renewable energy sources, DR cantrigger the change of demand to follow the change of supply.

In general, DR programs enable customers to manage theirconsumption of electricity in response to supply conditions. Forexample, many programs have electricity customers reduce theirconsumption at critical peak load hours or in response to marketprices. To achieve this goal, both incentive-based and price-based [7] DR programs are developed. Incentive-based DR pro-grams offer customers some monetary bonus to reduce loadupon operators’ request, whereas price-based programs allow

Manuscript received November 11, 2010; revised April 29, 2011; acceptedAugust 28, 2011. Date of publication November 10, 2011; date of current ver-sion November 23, 2011. This work was supported by Argonne National Lab-oratory, a U.S. Department of Energy Office of Science laboratory, which isoperated under Contract DE-AC02-06CH11357. Paper no. TSG-00209-2010.

Z. Zhou and J. Wang are with the Argonne National Laboratory, Argonne, IL60439 USA (e-mail: [email protected]; [email protected]).

F. Zhao is with the Argonne National Laboratory, Argonne, IL 60439 USA,and also with the Georgia Institute of Technology, Atlanta, GA 30332 USA(e-mail: [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/TSG.2011.2168244

customers to voluntarily adjust their demand based on electricityprices, which can be determined through real-time pricing, crit-ical-peak pricing, and time-of-use rates. We develop this studyin the context of price-based DR programs.

As one of major utility consumers, commercial buildings con-sume more than one third of the total end-use electricity in theUnited States [8]. To simulate the interplay between the con-sumers and suppliers in the electricity market, buildings are typ-ically modeled as predefined, aggregated, and fixed-load pro-files or demand curves on the basis of historic regional elec-tricity consumption data in the existing literature [9]–[11]. Tostudy interventions of load characteristics, Callaway [12] uses asimple dynamic load model which has aggregated coefficientsof the building thermal capacity, resistance, and heat gains. Inreality, however, buildings of different types are typically iden-tical (i.e., having identical energy consumption patterns thatare determined by weather conditions, design styles, and op-erational behaviors) and autonomous (i.e., being responsive toelectricity prices in different ways). Some other models defineloads based on building physics. One example is the equiva-lent thermal parameters (ETP) method. It is originally appliedto transient building energy simulation [13], then simplified andimplemented in the GridLab-D software developed by PNNL tosimulate every individual building in the power distribution net-work [14], [15].

As a new approach to model systems comprised of au-tonomous and interacting agents, ABMS provides an ideal wayof researching “systems that are built from the bottom.” Tocapture the diversity and dynamics of electricity consumptionin buildings based on their design and operations, multiplebuilding stock energy models have been developed to supportpolicy making [16]. Zhao et al. [17] developed an ABMS frame-work based on first-order heat balance equations to estimate thehourly load of commercial building stocks at the regional scale.In this approach, the electricity demand of a building stockis determined by running an hourly quasi-steady-state energycalculation for representative designs in the building stock andscaling the energy use intensity of representative buildingsup to the entire building stock by gross floor area. Differentbuilding operation schedules are also considered for differentbuilding type. This framework estimates large-scale energyconsumptions of buildings without expert-driven, massive,transient energy simulations for each building in the stock.Sometimes, massive simulations are not even applicable whenthe information about buildings in the stock is not adequate.The simplicity of this approach also enables modeling variousDR actions of commercial buildings.

To promisingly model the electricity market with DR fromcommercial buildings, we also use this ABMS platform to ana-lyze the interaction among the consumption behaviors of com-mercial buildings and the power grid and the corresponding

1949-3053/$26.00 © 2011 IEEE

ZHOU et al.: AGENT-BASED ELECTRICITY MARKET SIMULATION WITH DEMAND RESPONSE FROM COMMERCIAL BUILDINGS 581

Fig. 1. Topology of commercial building stock modeling.

economic consequences under different electricity market com-petition levels. Autonomous agents have been widely used tomodel different participants in power systems and electricitymarkets [18]–[20]. There are also some ongoing work on re-search [21]–[25] and development [14], [26]–[28]. In this paper,we model the electricity market as a repetitive, multiunit auc-tion in which generation companies can bid strategically. In ad-dition, both perfect and duopoly market competitions are mod-eled to reflect different market structures and investigate howthe building energy use varies in different markets.

This paper addresses two research questions: 1) From theelectricity market perspective, how different scales of DR par-ticipation will affect the market prices; and 2) from the con-sumer perspective, how different market competition levels willaffect the energy and monetary outcomes of commercial build-ings applying DR. In greater detail, this paper contributes in thefollowing ways.

1) This paper integrates a quasi-steady-state, end-use energymodel of commercial buildings with electricity marketsimulation. Given the bottom-up physical model fordifferent building types, this simulation framework iscapable of modeling the load reduction of buildings underdifferent scenarios by manipulating building operationalparameters.

2) This paper investigates the influence of different levelsof participation in DR from commercial buildings onelectricity prices, consumption, and utility costs usingthe ABMS approach. Simulation results indicate that theimpact is noticeable, and this impact varies with differentscales of DR participation.

3) This paper studies electricity consumption of commercialbuildings in markets with different levels of supply-sidecompetition, which illustrate differences in market dy-namics and individual behaviors from both supply anddemand sides.

We organize the paper as following. Section II presentsthe background and methodology of building stock modeling.Section III presents an agent-based model and defines assump-tions and settings of the experiments. Section IV discussesexperimental results. Section V presents conclusions.

II. BUILDING STOCK ENERGY MODEL

In this building stock energy model, we first estimate the en-ergy consumption of a single building using its design and op-eration specifications. A cluster of buildings of the same typelocated in the same region are then defined as an agent whoseload is estimated by scaling the single building’s electricity con-sumption to the entire building cluster by floor area. Finally,we define a network of multiple building agents to simulate thestock-level electricity demand of commercial building stocks.The topology of this approach is illustrated in Fig. 1.

Fig. 2. Thermal R-C model of a building [17].

A. Hourly Building Energy Model

We adapt a quasi-steady-state approach described in ISO13790 [29] as the starting point to estimate hourly buildingelectricity demand. The building thermal model is based on anequivalent resistance-capacitance (R-C) network (Fig. 2).

In this model, the input parameters include building geom-etry (floor area, elevation, and window-wall ratio), materiality(U-value, light transmission, and absorption factors of enclo-sure), HVAC (schedule, efficiencies, and set-point temperature),and lighting and equipment (intensity and schedule). Typicalmeteorological year (TMY) hourly weather data are also used.Then the heating and cooling needs are found by calculating,for each hour, the heating or cooling power that needsto be supplied to or extracted from the indoor air node tomaintain a certain set-point indoor air temperature. Heat transferby ventilation is connected with the supply air temper-ature and the interior temperature . Heat transferby transmission is split into the window part and non-window part ( and ); only the nonwindow part isconnected by a single thermal capacity , representing thebuilding thermal mass. The heat gains from internal and solarsources are split into three parts ( , , and ) and appliedto the nodes of indoor air , internal environment , andthermal mass , respectively. The detailed calculation pro-cedure is described in ISO 13790.

Validation of the simple hourly method at the thermal needslevel was performed against transient building energy simu-lations in [17], [30], and [31]. On the basis of the calculatedthermal loads, we develop modules to calculate the hourlyend-use energy, which includes the energy consumption ofheating, cooling, lighting (interior and exterior), equipment(interior and exterior), refrigeration, fan, and pump componentsystems according to building design and operation specifica-tions. By summing up the usage of these components, the toolcalculates all end-use consumption of electricity and naturalgas.

Different building types are considered to capture energyconsumption patterns of the entire commercial building stock.The 2003 Commercial Building Energy Consumption Survey(CBECS) [32] provides a list of commercial building types and


TABLE IBUILDING TYPES AND THEIR ELECTRICITY CONSUMPTION IN 2003[17]

their surveyed energy consumption data. From this survey, weselected ten building types (listed in Table I) which constitute83.7% of the total electricity consumption of all commercialbuildings estimated by CBECS.

The U.S. Department of Energy (DOE), in conjunctionwith three of its national laboratories, developed commercialreference buildings, formerly known as commercial buildingbenchmark models [33]. These reference buildings providecomplete descriptions for conducting whole building energyanalysis using EnergyPlus [34], a dynamic building energysimulation tool developed by DOE. In this study, we modelthe representative buildings of the above 10 types using theproposed model to compare the seasonal and diurnal electricitydemand profiles with the results from EnergyPlus runs. Thecomparison shows an overall compliance between results fromthese two approaches.

B. Commercial Building Stock Agents

In general, there are two fundamental methods to modelenergy consumption of a certain number of buildings at thecity/regional/national level: the top-down approach and thebottom-up approach [35], [36]. The bottom-up approach isbased on building physical models that take into accountinformation on building design and operations. This “trans-parent-box” approach is more flexible in simulating theinterventions to the building design and operational parametersthan the other approach, the “black-box” statistical models.Typically, a bottom-up model estimates the energy consump-tion levels of the building stock by employing the followingsteps [37] to:

1) categorize the whole building stock according to buildingtype and energy consumption characteristics;

2) design representative building models, one for eachbuilding stock category that is used as an input dataset forsimulation in the next step;

3) perform energy simulations using these representativemodels to predict the energy consumption per unit floorarea in each building stock category as an agent; and

4) estimate total energy consumption by scaling up the pre-dicted energy consumption to the entire building stock.

In this study, a cluster of buildings of the same type (usingthe same representative design) within the same region (usingthe same weather data) are considered as an agent. The hourly

Fig. 3. Required input parameters for each modeled building.

electricity demand of each agent is determined by multiplyingthe total floor area of this building type in this region with theelectricity use intensity (in MW/m ) of its representative design,calculated by the simple hourly method. Multiple commercialbuilding agents can be located in the same region, and differentregions use different hourly weather files.

Each building agent requires a list of input parameters. Fig. 3illustrates some high level input parameters on its left side.These parameters are classified into the following categories:program; materiality; heating, ventilation, and air conditioning(HVAC); and equipment.

Determination of input parameters for representative build-ings is crucial to achieve the expected level of accuracy. Accu-racy of the calculation can be improved by dividing the area ofinterest into multiple smaller regions and specifying local av-erage data for each building agent if detailed data on buildingdesign are available. However, when local building data are notavailable, which is most often the case, regional statistical dataare used instead. The ranges of energy modeling input parame-ters for commercial buildings by building type in different cli-mate zones were studied, and the corresponding simulation re-sults are checked against the 2003 CBECS data in [32]. Weadapted these results and developed a prototype for the test casesin Section III.

Complete sets of input parameters for each representativebuilding are stored in a database. In addition, total floor area,building age vintage (pre- or post-1980) and primary heatingsource (electricity or nonelectricity) are specified for eachbuilding stock. Corresponding input files are then selected fromthe representative building parameter database and relevantweather data from the weather files database. Then, the inputdata files are sent to the simple hourly model. Calculated hourlyelectricity demands of building agents are aggregated to derivethe hourly demand profile of the region.

III. ELECTRICITY MARKET SIMULATION PLATFORM

In this section, we present an ABMS model for an elec-tricity market that includes generation companies (GenCos),load-serving entities (LSEs), commercial building aggregators(CBAs), and an independent system operator (ISO).


A. Objective

In the electricity market, GenCos bid on the basis of their ownhistorical bids, winning quantities, and clearing prices. GenCoscannot know bidding strategies and winning quantities of eachother. Nevertheless, obtaining this information is critical to en-abling GenCos to make decisions on the next bid. Therefore,GenCos have to acquire the ability to learn to estimate the bid-ding strategies of their opponents and thereby make rational de-cisions. Meanwhile, GenCos’ bidding behaviors can also be in-fluenced by the demand of commercial buildings, which is sen-sitive to the market price. Hence, the objective of our study is tounderstand the consumption behaviors of commercial buildingsin a real-time pricing environment under different market struc-tures, with conditions ranging from that of a duopoly to perfectcompetition.

B. Power Market Model

To make the simulation close to the real-world model and re-veal nontrivial results, an electricity market can be set up withcomprehensive characteristics on physical transmission config-uration, electricity supply, and end-use demand.

1) Transmission Network: The ABMS model is based ona given transmission network, where buses are connected bytransmission lines with transmission capacity limits. GenCos,LSEs, and CBAs are located at different buses. The marketprices are calculated based on the supply and demand condi-tions and the transmission network configuration.

2) Market Participants and Auction Procedure: In the simu-lated electricity market, GenCos, LSEs, CBAs, and the ISO cor-respond to suppliers, buyers, and the market operator, respec-tively. The CBAs aggregate the load from all of the buildingsunder their administration and purchase electricity to satisfy thisdemand. The GenCos compete with each other to sell their elec-tricity. The ISO clears the market by minimizing the total pro-duction cost and determining the prices.

In the day-ahead auction market of our simulation model,CBA and LSEs estimate the load in their administrated areasfor each hour of a day and submit that information to the ISOas bids. The GenCos decide their bids by using their knowledgeabout the environment and opponents. Before the beginning ofDay , the ISO closes the market for day and clears the marketby using a standard bid-based DC optimal power flow formu-lation [38]. The ISO determines generation dispatch levels foreach hour of day to minimize generation operational costs sub-ject to bus balance constraints, transmission line capacity con-straints, and generation operating capacity constraints. For eachhour of Day , a locational marginal price (LMP) is determinedat each bus as the shadow price for the balance constraint at thisbus; this is the price paid to GenCos for power injections at thisbus and paid by LSEs for power withdrawals at this bus duringeach hour. On Day , the GenCos schedule their operations andgenerate the accepted amount of electricity that was bid on andsettled the day before (i.e., on Day ). The LSEs and CBAsreceive the amount of electricity they intended to buy and dis-tribute it to their end customers.

C. Agents

Our model implements four types of agents corresponding tothe four participants in the market: GenCo agents, LSE agents,CBA agents, and the ISO agent.

1) GenCo Agents: Each GenCo has only one generator repre-sented as a generator agent. GenCo agents are differentiated bytheir capacities and cost functions. The cost function is modeledas a polynomial cost function, which is defined in the followingequation:

(1)

where is the capacity; is the amount of electricity suppliedby GenCo ; is the cost for to supply units ofelectricity; and , , and are three coefficients of the costfunction.

When a GenCo agent submits its bid to the ISO agent, it isrequired to report its cost function. If the GenCo agent reportsits marginal cost function, it is called the marginal cost bidder.Another situation is that the GenCo agent can report an adjustedcost function (although it is still assumed the “marginal” costfunction from the ISO’s side). In this case, the agent tries tomake more profit by bidding strategically. If the GenCo agenthas market power, its objective could be maximizing its profitfrom this adjustment.

Because the price is decided by the GenCo’s cost function,a price adjustment is implemented by raising or dropping thecoefficients of the cost function, which is reported to the ISO asthe cost function. The adjustment is represented by the markuprate compared with the coefficients of the true cost function,which is defined by the following equation:

(2)

where is the cost function reported to the ISO and isthe markup rate, and .1 For example, if a GenCo bids ata rate 120% higher than its marginal cost, its markup rate is 2.2.If it bids at the true cost, its markup rate is 1.

2) LSE Agents: The agent forecasts the load for the next dayand reports it to the ISO agent as its demand. It does not takestrategic actions.

3) CBA Agents: Each CBA agent has its unique load reduc-tion actions. An electricity price threshold is assumed at whichCBA agents will become triggered to take actions. For example,if the market price is higher than the price threshold, the CBAagents can choose to turn off some pieces of equipment to re-duce the energy consumption. In this study, CBAs can performany combination of the following actions: turn off lighting bya certain percentage, turn off pieces of plugged equipment by acertain percentage, and set cooling (air-conditioning) or heatingset-points higher (lower) by certain degrees. It is assumed in theday-ahead market that, at the beginning of each day, CBAs referto the price from seven days ago to make DR decisions, becausethe load profile is similar to the one that occurred a week ago.

4) ISO Agents: The objective of the ISO is to regulate themarket by minimizing the total production cost in the market.The ISO agent selects the least expensive generators with ahigher priority. The constraints associated with the cost mini-mization problem by the ISO include unit capacity, transmissionline capacity, etc. The ISO agent collects bids from the GenCos,LSEs, and CBA agents before the market is closed. Then, itclears the market by solving an optimal power flow problem.After the market is cleared, the ISO agent informs the GenCos,LSEs, and CBA agents of their generation schedule.

1The markup rate is only applied to the first two terms in Equation (2) becausethe constant term is eliminated in the corresponding supply function.


D. Market Structures

In an electricity market characterized by perfect competition,all GenCo agents are price takers who bid their true productioncosts into the market. If the market has only one GenCo withdominant market power and can manipulate market prices, it isa monopoly market. If there are two GenCos, it is a duopolymarket. Given the system load, the market price is lowest in aperfect competition market and highest in a monopoly market.

E. Learning Model for GenCo Agents

1) Learning Model: We use the Roth-Erev reinforcementlearning algorithm [39], [40] to model the GenCos’ strategicbidding behaviors. The intuition is that a GenCo agent selectsa bidding action from its action alternatives at each round onthe basis of feedback (i.e., the quantity of scheduled output andmarket price) from the historical round. The agent then updatesthe propensity of its bid options thereafter. Table II lists the no-tation used in this section.

The simulation is initialized in the following two steps:1) For each GenCo Agent , for each of its action ,

and ;2) Initialize simulation coefficients , , and .After the initialization, for each day (Day ),

four steps below are applied to each GenCo Agent :1) Randomly select a bidding action for each hour of Day

according to the selection probability

(3)

2) Calculate rewards (profit) ;3) Update the propensities for its actions for each hour of

the next day

if

if

(4)

4) Update the strategy for each hour of the next day

(5)

IV. NUMERICAL ANALYSIS

To simulate the commercial building DR under differentmarket structures, we use a five-zone transmission network, asillustrated in Fig. 4. Each zone has one LSE, except in Zone4. Zone 4 has multiple CBA agents. These zones are intercon-nected through six transmission lines with capacity limits.

On the supply side, each zone has one GenCo agent, exceptZone 4. In Zone 4, there are six GenCo agents, denoted by

. Compared with the generators in Zone 4,other GenCos have cheaper production costs and can supply thedemand in Zone 4 after satisfying demand in their own zones.However, the amount of electricity exported to Zone 4 is lim-ited by the transmission line capacity, which is denoted by .Among the six GenCos in Zone 4, two (i.e., and ) aregiant companies with identical generation capacity, the sum of

TABLE IINOTATION USED IN THIS SECTION

Fig. 4. An experimental five-zone power system� : The � generator in the� node; � : load in the � node; � : Impedance of the � node.

which is larger than the sum of the capacities of the other foursmall generators, whose capacities are also identical. These foursmaller GenCos are price takers who do not bid strategically. Incontrast, and can use different pricing strategies tomake greater profits. If these two GenCos still bid at their mar-ginal cost, the market becomes a perfect competition market.If they bid more strategically, the market is a duopoly marketsince other GenCos are price takers. On the demand side, theloads in Zones 1, 2, 3, and 5 (i.e., , , , and ) arenon-price-responsive. Because we are investigating the impactof DR from commercial buildings, we simplify the demand pro-files from other sectors as shown in Fig. 5. In Zone 4, however,there are multiple types of CBAs with different design and op-eration specifications (listed in Table III). Each of these CBAsis able to perform load reduction strategies that are triggeredby predefined electricity prices and are independent of otherbuilding agents. We assume that building agents make their de-cisions based on the electricity price of the same hour in theprevious week.

Different participation levels among the entire building sectorresult in different energy and monetary consequences. Whenonly one building type is considered, there is less impact onmarket electricity prices, as compared to a situation in whichall buildings respond to electricity prices. Hence, two scenarios


Fig. 5. Schematic hourly load profile for non-DR loads.

TABLE IIIEMPIRICAL COMMERCIAL BUILDING AGENTS SPECIFICATIONS

TABLE IVINITIAL BUS DATA

TABLE VINITIAL DATA OF TRANSMISSION LINES

with different participation levels are being tested in this numer-ical analysis: the small scale (only offices perform DR) and largescale (all building types perform DR) case. Detailed settings re-lated to the power system are listed in Tables IV and V.

Fig. 6. Office stock DR actions and price thresholds. (a) and (b): power inten-sity adjustment for lighting and plug equipment. (c) and (d): set-point tempera-ture adjustment for cooling and heating.

A. Test Case A: Small Scale-Office Only

To limit the variables in the experiment, we assume that onlyoffice buildings in Zone 4 perform load reduction during peakhours in this test case, while the other types of buildings in Zone4 maintain their individual load profiles irrespective of elec-tricity prices. The DR actions include adjusting the power inten-sity of lighting and plug equipment, as well as the set-point tem-peratures of cooling and heating units. According to the elec-tricity price seven days ago, when the electricity price of anhour reaches a certain threshold, the building agent starts to takeone or multiple actions to reduce the demand. In this case, weare simulating the situation in which only one type of building(namely, office buildings), is capable of performing DR. Thresh-olds of the office agents are illustrated in Fig. 6.

The simulation has been performed for a typical meteorolog-ical year to generate the hourly electricity loads of office build-ings and the electricity prices under different levels of marketcompetition. The results indicate that under perfect market com-petition, as shown in Fig. 7, the building load profile is shavedin comparison to the peak hours of a week ago.

In Fig. 8, we compare the electricity prices without and withdemand response in two different market competitions. Sincethe patterns are similar every day, we only demonstrate results oftwo days in Fig. 8. One observation from the result is that elec-tricity prices in perfect competition scenarios are relatively closeto each other, which indicates that there is no significant impacton market prices under the perfect competition scenario if onlya small representation of building types (offices) performs DR.However, if the market is under duopoly competition, electricityprices are much higher than they are under the other two situa-tions because of the imperfect nature of the market competition.

To evaluate the overall consequence of DR as practiced byoffice buildings, the standard deviation (StdDev) and summa-tion (Sum) of the results are calculated to compare different sce-narios, as listed in Table VI. For the electricity price, standarddeviation is reduced by 2.9% in a perfect competition marketwhen office buildings perform DR actions, which indicates alower volatility of the market. However, in scenario (3), the stan-dard deviation of the electricity price increases by 66.7% com-pared to the baseline. Because of the high prices in scenario (3),total electricity consumption of buildings in scenario (3) is 2.0%lower than it is in the baseline, and is reduced by only 1.8% in


Fig. 7. Building stock total load with and without office DR under perfect market competition during August 5–11.

Fig. 8. Electricity prices under different market competition levels with onlyoffice buildings performing DR during August 5–6.

TABLE VICOMPARISON OF ELECTRICITY PRICE, CONSUMPTION, AND COST UNDER

DIFFERENT MARKET STRUCTURES (SMALL-SCALE DR)

scenario (2). As an overall impact of prices and consumption,the office stock electricity cost in scenario (2) is 2.5% lowerthan that of the baseline. However, buildings spend more than18% for the electricity bill under the duopoly market as com-pared with the baseline.

Results of this test case indicate that under perfect marketcompetition, DR from a small-scale representation of build-ings does not significantly impact electricity market prices. In

addition, small scale of DR behavior does not result in rec-ognizable energy and cost conservation for the buildings withprice-responsive demand. However, if the market competitionis imperfect, electricity prices and volatility are significantly in-creased, which result in slightly increased energy conservationand much higher energy costs of all buildings with or withoutDR behaviors.

B. Test Case B: Large-Scale Participation-All Building Types

In this test case, we simulate a more diverse market in whichall commercial buildings follow their own strategies to performload reduction at peak hours. Similar to the previous case, allthese commercial building agents are located in Zone 4, andeach agent has unique DR threshold curves, similar to thoseshown in Fig. 6.

This simulation is performed for the tested region over a typ-ical meteorological summer, and we use one week in August tocompare the different scenarios. Fig. 9 shows the hourly load ofthe entire commercial building stock in Zone 4 under the condi-tion of perfect market competition, both with and without DR.Similar to the office test case in Fig. 7, DR occurs when theelectricity price rises above the predetermined thresholds. Butin contrast to the office case, Fig. 9 indicates a larger portion ofload reduction because more buildings are involved in DR ac-tions.

In addition, the resulting electricity prices under differentmarket competition levels are plotted in Fig. 10. When com-pared to the previous case in Fig. 8, two observations can bemade. First of all, under both market competition scenarios,when all of the types of buildings perform DR, the electricityprices are much lower than they are in the same market com-petition scenarios without demand response. This result occursbecause the equilibrium prices become lower when a largenumber of buildings reduce the demand at peak hours whilethe supply remains the same. Secondly, electricity prices inscenarios with DR reduce more when large-scale participationis deployed, in comparison with the prices reduction of thescenarios without DR. The main reason for this result is that inthis case, the demand side is more sensitive in reacting to theprice, which prevents the electricity price from rising higher.

Statistics of this test case are shown in Table VII. Comparedto results of Test Case A in Table VI, when more buildings de-ploy DR, electricity prices become less volatile (as reflected inthe standard deviation value for price). In terms of electricityconsumption, participation in DR by a larger-scale representa-tion of buildings results in lower total electricity consumption of


Fig. 9. Building stock total load with and without DR under perfect market competition during August 5–11.

Fig. 10. Electricity price under different market competition levels with allbuildings performing DR during August 5–6.

TABLE VIICOMPARISON OF ELECTRICITY PRICE, CONSUMPTION AND COST UNDER

DIFFERENT MARKET STRUCTURES (LARGE SCALE DR)

the building stock. This indicates a lower total cost under bothmarket structures, compared to Test Case A.

V. CONCLUSIONS AND FUTURE WORK

This paper presents an agent-based simulation platform tomodel the diverse and dynamic impacts when DR is practicedby commercial buildings and to explore their impacts on theelectricity prices at different market competition levels. Two

test cases demonstrate the capability of the proposed platformto estimate both energy and monetary consequences of DR forcommercial buildings participating in the electricity market.By comparing two different scales of DR participation at twomarket competition levels, we draw the following conclusions.

(1) DR actions by commercial buildings shave the load pro-file at the peak hours and reduce the volatility of elec-tricity demand. This phenomenon is more significantunder duopoly market competition because of its higherelectricity prices. This finding is also true when there is alarger-scale representation of buildings participating inDR.

(2) DR actions by commercial buildings reduce electricityprices and volatility when there are more buildings de-ploying DR. This conclusion applies to both the perfectand the duopoly competition markets. Electricity pricesunder duopoly market competition are higher and morevolatile than are the prices under perfect market com-petition. However, this difference is reduced when morebuildings deploy DR.

(3) DR actions by commercial buildings reduce buildingelectricity cost. However, under market competition,larger-scale participation in DR results in reduced mon-etary savings for buildings.

In future work, we will validate and calibrate the building rep-resentative models via a statistical approach to better capture thephysical characteristics and operational behaviors of buildingstocks. For the market modules, we will investigate the case inwhich CBAs can bid into the market. We will also evaluate theimpacts of DR to the power system reliability.

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.

REFERENCES

[1] F. A. Wolak, “An empirical analysis of the impact of hedge contractson bidding behavior in a competitive electricity market,” Int. Econ. J.,vol. 14, pp. 1–39, 2000.

[2] F. A. Wolak and P. H. Patrick, The Impact of Market Rules and MarketStructure on the Price Determination Process in the England and WalesElectricity Market. Berkeley, CA: Univ. California Energy Institute,1997.

[3] N. Fabra and J. Toro, “Price wars and collusion in the Spanish elec-tricity market,” Int. J. Ind. Org., vol. 23, pp. 155–181, 2005.


[4] J. B. Bushnell and C. Saravia, “An empirical assessment of the compet-itiveness of the New England electricity market 2002 [Online]. Avail-able: http://www.iso-ne.com/pubs/spcl_rpts/2002/empir_assess_com-petitiveness_bushnell.pdf

[5] C. Saravia, “Speculative trading and market performance: The effect ofarbitrageurs on efficiency and market power in the New York electricitymarket,” CSEM WP 121, 2003 [Online]. Available: http://www.ucei.berkeley.edu/PDF/csemwp121.pdf

[6] E. T. Mansur, “Vertical integration in restructured electricity markets:Measuring market efficiency and firm conduct CSEM WP 117, 2003[Online]. Available: www.ucei.berkeley.edu/PDF/csemwp117.pdf

[7] “Benefits of demand response in electricity markets and recommenda-tions for achieving them: Report to the United States Congress,” U.S.Department of Energy, 2006.

[8] “Annual energy review 2009,” U.S. EIA, 2009.[9] L. Exarchakos, M. Leach, and G. Exarchakos, “Modelling electricity

storage systems management under the influence of demand-side man-agement programs,” Int. J. Energy Res., vol. 33, pp. 62–76, 2009.

[10] K. H. v. Dam, M. Houwing, and I. Bouwmans, “Agent-based controlof distributed electricity generation with micro combined heat andpower-cross-sectoral learning for process and infrastructure engi-neers,” Comput. Chem. Eng., vol. 32, pp. 205–217, 2008.

[11] P. Vytelingum, T. D. Voice, S. D. Ramchurn, A. Rogers, and N. R.Jennings, “Agent-based micro-storage management for the smart grid,”in 9th Int. Conf. Autonomous Agents Multi-Agent Syst., Toronto, ON,Canada, 2010.

[12] D. Callaway, “Aggregated electricity load modeling and controlfor regulation and load following ancillary services,” Nov. 3, 2009[Online]. Available: http://www.pserc.wisc.edu/documents/gen-eral_information/presentations/pserc_seminars/5psercsemin/call-away_load_aggregation_pserc_nov09.pdf

[13] R. Sonderegger, “Dynamic models of house heating based onequivalent thermal parameters,” Ph.D. dissertation, Princeton Univ.,Princeton, NJ, 1978.

[14] D. P. Chassin, K. Schneider, and C. Gerkensmeyer, “Gridlab-D: Anopen-source power systems modeling and simulation environment,” inProc. IEEE PES Transm. Distrib. Conf. Expo., Chicago, IL, 2008.

[15] Z. T. Taylor, K. Gowri, and S. Katipamula, GridLAB-D Technical Sup-port Document: Residential End-Use Module Version 1.0. Richland,WA: Pacific Northwest National Laboratory, 2008.

[16] I. J. Martinez-Moyano, M. R. Mahalik, D. J. Graziano, and G. Conzel-mann, Developing an Agent-Based Model of the U.S. CommercialBuildings Sector: Literature Review. Argonne, IL: Argonne NationalLaboratory, 2010.

[17] F. Zhao, J. Wang, V. Koritarov, and G. Augenbroe, “Agent-based mod-eling of interaction between commercial building stocks and powergrid,” in Proc. IEEE Conf. Innov. Technol. Efficient Reliable Electr.Supply, Waltham, MA, 2010.

[18] M. Rahimiyan and H. R. Mashhadi, “An adaptive Q-learning developedfor agent-based computational modeling of electricity market,” IEEETrans. Syst, Man, Cybern. C, Appl. Rev., vol. 40, pp. 547–556, 2010.

[19] E. Guerci and M. A. Rastegar, “From uniform auction to discrimina-tory auction: Assessment of the restructuring proposal for the Italianelectricity day-ahead market,” EUI RSCAS, 2009.

[20] Z. Zhou, W. K. V. Chan, J. H. Chow, and S. Kotsan, “Duopoly elec-tricity markets with accurate and inaccurate market goals,” in Proc.2009 Winter Simul. Conf. (WSC), Austin, TX, 2009, pp. 1569–1580.

[21] S. McArthur, IEEE Power & Energy Society Multi-Agent SystemsWorking Group Apr. 19th, 2011 [Online]. Available: http://ewh.ieee.org/mu/pes-mas

[22] F. Sensfuß, M. Ragwitz, M. Genoese, and D. Möst, “Agent-based sim-ulation of electricity markets: A literature review,” Fraunhofer InstituteSystems and Innovation Research, Working Paper Sustainability andInnovation, S 5/2007, 2007.

[23] L. Tesfatsion, “ACE research area: Electricity market research,”Apr. 19, 2011 [Online]. Available: http://www2.econ.iastate.edu/tes-fatsi/aelect.htm

[24] A. Weidlich and D. Veit, “A critical survey of agent-based wholesaleelectricity market models,” Energy Econ., vol. 30, pp. 1728–1759,2008.

[25] Z. Zhou, W. K. V. Chan, and J. Chow, “Agent-based simulation ofelectricity markets: A survey of tools,” Artif. Intell. Rev., vol. 28, pp.305–342, 2007.

[26] D. A. Schoenwald, D. C. Barton, and M. A. Ehlen, “An agent-basedsimulation laboratory for economics and infrastructure interdepen-dency,” in Proc. 2004 Amer. Control Conf., Boston, MA.

[27] G. Conzelmann, G. Boyd, V. Koritarov, and T. Veselka, “Multi-agentpower market simulation using EMCAS,” in Proc. IEEE Power Eng.Soc. Gen. Meeting, 2005.

[28] H. Li and L. Tesfatsion, “Development of open source software forpower market research: The AMES test bed,” J. Energy Markets, vol.2, pp. 111–128, 2009.

[29] Energy Performance of Buildings—Calculation of Energy Use forSpace Heating and Cooling, ISO 13790:2008.

[30] J.-R Millet, “The simple hourly method of prEN 13790: A dynamicmethod for the future,” in Chima 2007 WellBeing Indoors, Helsinki,Finland, 2007.

[31] T. R. Nielsen, “Simple tool to evaluate energy demand and indoor en-vironment in the early stages of building design,” Solar Energy, vol.78, pp. 73–83, 2005.

[32] “2003 commercial buildings energy consumption survey (CBECS),”U.S. EIA, 2006 [Online]. Available: http://www.eia.doe.gov/emeu/cbecs/contents.html

[33] “Commercial reference buildings,” U. S. DOE, 2009 [Online].Available: http://www1.eere.energy.gov/buildings/commercial_initia-tive/reference_buildings.html

[34] EnergyPlus (5.0 ed.) U. S. DOE, Jul. 27, 2010 [Online]. Available:http://apps1.eere.energy.gov/buildings/energyplus/

[35] M. Kavgic, A. Mavrogianni, D. Mumovic, A. Summerfield, Z. Ste-vanovic, and M. Djurovic-Petrovic, “A review of bottom-up buildingstock models for energy consumption in the residential sector,”Building Environ., vol. 45, pp. 1683–1697, 2010.

[36] L. G. Swan and V. I. Ugursal, “Modeling of end-use energy consump-tion in the residential sector: A review of modeling techniues,” Renew-able Sustainable Energy Rev., vol. 13, pp. 1819–1835, 2009.

[37] Y. Yamaguchi and Y. Shimoda, “Database and simulation model de-velopment for modelling the energy use of non-residential buildings,”in Proc. 11th Int. IBPSA Conf., Glasgow, U.K., 2009.

[38] R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, “MAT-POWER steady-state operations, planning and analysis tools for powersystems research and education,” IEEE Trans. Power Syst., vol. 26, pp.12–19, 2011.

[39] I. Erev and A. Roth, “Predicting how people play games: Reinforce-ment learning in experimental games with unique, mixed strategy equi-libria,” Amer. Econ. Rev., vol. 88, pp. 848–881, 1998.

[40] J. Sun and L. Tesfatsion, “Dynamic testing of wholesale power marketdesigns: An open-source agent-based framework,” Comput. Econ., vol.30, pp. 291–327, 2007.

Zhi Zhou (S’09) received the Ph.D. degree in decision science and engineeringsystems from Rensselaer Polytechnic Institute, Troy, NY, in 2010.

He is a postdoctoral appointee in the Center for Energy, Environmental, andEconomic Systems Analysis (CEEESA) at the Decision and Information Sci-ences (DIS) Division at Argonne National Laboratory, Argonne, IL. His re-search interests include agent-based modeling and simulation and electricitymarkets.

Fei Zhao (S’10) is currently working toward the Ph.D. degree in the high-perfor-mance building concentration in the College of Architecture, Georgia Instituteof Technology, Atlanta, where he is conducting research on energy simulationand large-scale retrofit analysis of commercial buildings.

He is a Research Aide with CEEESA in DIS at the Argonne National Labo-ratory, Argonne, IL.

Jianhui Wang (M’07) received the Ph.D. degreein electrical engineering from the Illinois Instituteof Technology, Chicago, in 2007. Presently, heis a Computational Engineer with the Decisionand Information Sciences Division at the ArgonneNational Laboratory, Argonne, IL.

Dr. Wang is the chair of the IEEE Power & EnergySociety (PES) power system operation methodssubcommittee and cochair of an IEEE task forceon the integration of wind and solar power intopower system operations. He is an Editor of the

IEEE TRANSACTIONS ON SMART GRID, a Guest Editor of a Special Issue onElectrification of Transportation of the IEEE Power and Energy Magazine,and a Guest Editor of a Special Issue on Smart Grids, Renewable EnergyIntegration, and Climate Change Mitigation—Future Electric Energy Systemsof Applied Energy. He is the technical program chair of the IEEE InnovativeSmart Grid Technologies conference 2012.

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