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Abstract -- The smart building and plug-in hybrid electric

vehicle (PHEV) are two promising emerging technologies. The

integration of these two technologies shows great promise in

reinforcing the reliability and flexibility in building energy

management. The control challenge of the smart building is to

maximize the customer comfort with minimum power

consumption. Multi-agent technology with particle swarm

optimization (PSO) has been proposed to address the control

challenge. Moreover, a proper aggregation of a number of

PHEVs turns out to be able to provide both capacity and energy

to enable the building system to be more economic and morereliable by impacting the building energy flow. Case studies and

simulation results are presented and discussed in this paper.

Index Terms-- Smart buildings, renewable energy, plug-in

hybrid electric vehicle, interruptible load, heuristic optimization.

I I NTRODUCTION

ith the developing of the intelligent technologies, it is

safe to say that smart building is becoming more

attractive as well as more viable in the building industry.

Generally speaking, smart buildings are expected to address

both intelligence and sustainability issues by utilizing

computer and intelligent technologies to achieve the optimalcombinations of overall comfort and energy consumption, as

well as using renewable energy to reduce the impact on

natural environment [1], [2]. In order to accomplish this, a

reliable, responsive, and flexible control system with the

primary goal of improving indoor environment needs to be

developed. The primary goal of the control system is to

maximize the overall comfort level and to minimize the total

energy consumption in the meantime. Usually, three basic

factors, thermal comfort, visual comfort and indoor air quality,

determine quality of living in buildings. Temperature,

illumination level and CO2 concentration are three main

indexes for the thermal comfort, visual comfort and air

quality, respectively. The auxiliary heating/cooling system, the

electrical lighting system and the ventilation system are

employed as actuators to control the physical environment of

buildings [3]. Moreover, unlike the traditional buildings, the

Z. Wang, L. Wang, and R. Yang are with the Department of Electrical

Engineering and Computer Science, University of Toledo, Toledo, OH 43606

USA (e-mail: [email protected]).

A.I. Dounis is with the Department of Automation, TechnologicalEducational Institute of Piraeus, 250 P. Ralli & Thivon Str., Egaleo 122 44

Greece (e-mail: [email protected]).

customer preference should be seriously taken into account

when designing the control system for the smart building.

Much effort has been made in this research area to date. For

instance, our earlier works discussed some scenarios of energy

and comfort management in building environments [4]-[6].

The IEEE defines those vehicles, that have at least four

kWh of storage, can be recharged from an external energy

resource and have the ability to drive 10 miles or more without

consuming any gasoline, as the plug-in hybrid electric vehicles

(PHEVs) [7], [8]. The primary advantages of the PHEVs

include cutting down the consumption of fossil fuels and

reducing the emissions of the greenhouse gas [8], [9]. It is

reported that to produce the same kWh energy, the cost of the

electricity is only one seventh compared to the gasoline [10].

These claims lead the PHEVs to the world market, and make

them become a promising transportation in the near future.

Besides the economic and environmental benefits, the

impact of PHEVs on distribution network should be taken into

account. The concept of using the PHEVs as a distributed

energy, which is also known as the vehicle-to-grid (V2G)

concept, is to aggregate a number of PHEVs for connecting to

the energy provider. The PHEVs have in common the

batteries, which can store and release energy. The individualPHEV has a very slight impact and it is even can be

represented as “noise” to the system, but the aggregation of a

large number of PHEVs will affect the system behaviors.

These impacts are far too essential to be ignored [8].

The smart building system with renewable energy

resources proposed in this paper can be seen as a micro-grid

system, the impacts of the aggregation of PHEVs should be

measured and quantified to ensure the reliability of the overall

system. The primary charging patterns include the

uncoordinated charging and coordinated “smart” charging [9].

To maintain the system’s integrity and to minimize the power

loss and voltage drop, the coordinated “smart” charging, that

charges the PHEVs by coordinating within a multi-agentsystem, is chosen in this study.

A hierarchical multi-agent structure is utilized as a

fundamental technology in this building energy control

system, which means the whole system is constituted by

multiple agents. Agent is the key elements of this technology

and it can be a physical entity or a piece of software. Based on

the different functionalities, these agents are distributed

among several layers. Although the agents have distinct

functional properties, they share some common

characteristics. All the agents are somewhat autonomous,

Zhu Wang, Lingfeng Wang, Anastasios I. Dounis, and Rui Yang

Integration of Plug-in Hybrid Electric Vehicles

into Building Energy Management System

W

978-1-4577-1002-5/11/$26.00 ©2011 IEEE

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which means they can work properly without human

intervention; they can communicate with each other through a

special language called “agent communication language”

(ACL); they are capable of perceiving and reacting to the

changes in the environment and of determining the proper

behaviors to achieve the final goal. Taking advantages of the

agents, the hierarchical multi-agent system turns out to be

effective and efficient in dealing with some extremely

complicated situations and has been successfully used invarious fields [10]-[14].

The remainder of the paper is organized as follows.

Section II describes the overall system architecture, including

the smart building system and PHEV system. All the agents

and particle swarm optimizers are also discussed in detail in

this section. In section III case studies and simulation results

are presented. Finally, conclusion and future work are given in

Section IV.

II PROPOSED SYSTEM FRAMEWORK DESCRIPTION

As the Fig. 1 shows, the overall system is composed of the

utility power grid, the building system, the renewable energy

resource and the PHEV system. In this paper, the renewable

energy resource includes solar panels and wind turbines,

which are green energy with zero CO2 emission. The PHEV

system can be considered as a new form of distributed storage.

It is combined with the distributed renewable energy resource

and the controllable load (the smart building) to form a micro-

grid system. Two operation modes have been proposed, which

are in terms of the grid-connected mode and the islanded

mode. The condition of the utility power grid decides which

operation mode to choose. Normally the grid-connected mode

can be chosen to ensure the enough energy to satisfy the

customer demands; but the islanded mode must be used when

the unaccepted disturbance occurs in the utility power grid orthe power rate is much higher than the customers’ expectation.

In order to describe the significant impacts of the PHEV

system to this micro-grid system, the islanded mode is chosen

to simulate in this work.

Fig.1. The proposed framework of the overall system

A. Smart Building System

A hierarchical multi-agent control system with heuristic

optimization is designed. All the agents are classified to four

different layers by their different functions. The first layer is

switch agent, the second layer is called the central

coordinator-agent, the third level has three agents that are in

terms of the local controller-agents and the last level is named

the load agent. The agents for the second and the third layers

are the core elements of the control system. The localcontroller-agents control all the comfort-related devices, while

the central coordinate-agent achieves the control goal through

coordinating the multiple agents and taking the customer

preference into account. Because of the three different comfort

impact factors, which are environmental temperature,

illumination level, and indoor air quality (CO2 concentration),

the agents lay on the third layers are named the temperature

controller-agent, the illumination controller-agent, and the air-

quality controller-agent, respectively. Fig.2 is shown the

architecture of the multi-agent control system of the smart

building.

Fig.2. The structure of the multi-agent control system of the smart building

1. Switch agents

The switch agent bridges the smart building and the utility

power grid. It determines which operation mode to apply to

the micro-grid system. When the utility power grid under

normal situation, the “close” signal is sent and the overall

system operates in the grid-connected mode. Considering the

requirement of minimum CO2 emission, the renewable energyresource has been applied as the primary energy provider

while the utility power grid is seemed as the backup. Loads

only utilize the power from the utility grid when the renewable

energy is insufficiency. Moreover, in order to maximize the

customer benefits, the switch agent determines and monitors

the energy flow. That means the switch agent has the

responsibility of buying the energy from the utility grid when

the renewable energy is in shortage and selling the redundant

energy back to the utility grid when the renewable energy

production is higher than the demands. Our previous work

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[15] has presented the successful simulation results applying

the switch agent in the grid-connected mode. When the

abnormal situation occurs, such as the electricity disturbance

and the unexpected high power rate, the “open” signal is sent

to disconnect the smart building from the utility power grid.

The overall system runs in the islanded mode and the

renewable energy resource is the only energy supply. In this

paper, PHEV sub-system can be used as a distributed energy

supply when the batteries are discharging to enhance thereliability of the overall system.

2. Central Coordinator- agent

The central coordinator-agent is used to maintain the high-

level customer comfort; meanwhile, to efficiently manage the

energy dispatch. With the help of the particle swarm

optimization, the central coordinator-agent autonomously

responses multiple varying information, such as the

environmental information, the user preference and lower-

level agent demands, to achieve the control goal that is to

minimize the energy consumption without compromising the

customer comfort level.

The mathematic model of the central coordinator-agent isshown as follows. The comfort value is represented by an

indoor comfort model based on information fusion. Our

previous study [16] has discussed this mode in detail. The

ordered weighted averaging (OWA) aggregation operator is

applied to determine a representative comfort value [16]-[18].

i

i

i N bOWAC OWAComfort ∑=

===

3

1

)()( ω μ (1)

)0),,1,max(min(34

4

12

1

N N

N

N N

N N

aa

N a

aa

a N

−

−

−

−= μ (2)

05.1/21 N N aa = (3)

95.0/34 N N aa = (4)k

ink

N P P =∑ (5)

k k in P P max≤ , A LT N ,,= (6)

where:

Comfort is the representation of the comfort value. It falls

in the range of ]1,0[ .

N is the aggregated object which represents the individual

comfort value of the three comfort factors using a trapezoidal

membership function.

],[32 N N

aa is the comfort zone which can be set by the

customers.

iω is the OWA weight of the ith factor. It represents the

importance of each comfort factor. ]1,0[∈iω and 1

3

1

=∑=i

iω .

ib is the ith largest of the collection of the three aggregated

objects A LT μ ,, .

A LT N ,,= are the real measured values of the comfort

factors, which are temperature, the illumination and the air

quality, respectively. They can be obtained from the multiple

local controller-agents.k

P is the power dispatch to the local controller-agents.

in P is the total power injected to the building system.

max P is the maximum available power from all the power

supplies.

k is the time instant.

In order to obtain the OWA weightω , a basic unit-intervalmonotonic (BUM) function )(r Q is introduced as follows

[16]-[18]:λ r r Q =)( , 100 ≤≤ λ (7)

)3/)1(()3/( −−= jQ jQ jω , 3,2,1= j (8)

The implementation of the mathematic mode for the central

coordinator-agent can be described as follows.

1) Determine the temperatureT , the illumination L and the

air quality A as aggregated objects.

2) Determine membership functions of the respective

comfort factors based on the customer preferences.

3) According to the characteristic of the OWA operator,

reorder of vector elements in a descending order.

4) Tune λ of the BUM function to get the OWA weight ω

5) Calculate comfort value and dispatch the energy to the

local controller-agents according toω .

3. Particle Swarm Optimizer (PSO)

In 1995, the particle swarm optimization was first

introduced by Kennedy and Eberhart, and it was inspired by

the animal social behaviors such as bird flocking and fish

schooling [19]. PSO is a self-adaptive and population-based

heuristic method which has been widely utilized to solve the

large and complex optimization problems. In PSO, all the

possible solutions are seen as particles, and the best solution

can be found in the multidimensional space associated with

updating locations l and velocities v . The update rules are

described as follows and here pbest and gbest represent the

local and global best solutions, respectively [20]-[24].

][][ 22111 k k k k k k l gbest r l pbest r vv −+−+=

+ α α φ (9)11 ++

+=k k k

vl l (10)

maxminmaxmax /)( k k n×−−= φ φ φ φ (11)

where φ is the inertia weight factor, maxφ and minφ are the

maximum and minimum values of the inertia weight; 1α and2α are two positive acceleration constants; 1r and 2r are two

uniform random numbers in [ ]1,0 ; k is the iteration index

, nk is the current number of iterations, and maxk is the

maximum number of iterations.

In this study, two particle swarm optimizers embedded in

the central coordinator-agent. The first PSO is named as WT-

PSO which represents the PSO for the OWA weight, and it is

utilized to adjust the λ of the BUM function to get the optimal

OWA weightω . The objective function is shown in (1) and

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the optimization goal is to maximize this objective function.

The second PSO, which is termed SP-PSO, is applied to tune

the set points according to the outdoor information and the

customer’s preferred comfort zones. The objective function is

defined as follows.

A LT N N N N set set ,,,])/)((1[2

=−−∑ (12)

where set N is the set point of each comfort factor. It can be set

by the customers directly or be optimized by the PSOaccording to the comfort zone. The optimization goal is the

same as the first PSO, which is to maximize the objective

function.

To illustrate the procedures of the PSO, the following steps

are given:

1) Set the iteration number.

2) Randomly initialize particles.

3) Evaluated the fitness value to each particle according to

the objective function.

4) Calculate pbest and gbest for each particle

5) Adjust the velocity and position of each particle based on

the updating rules.

6) Repeat 3) to 5) until stopping criterion is satisfied.

7) Repeat 2) to 6) until the interaction number is achieved.

4. Local Controller-agents

Three local controller-agents are distributed in different

local subsystems to control thermal comfort, visual comfort

and air quality, respectively. Through controlling the actuators

of the subsystem, the fuzzy controllers are applied to calculate

the power demand which is used to maintain the high comfort

level. The structure of local controller-agents is shown in Fig.

3. The error is defined as the difference between the outdoor

sensor data and the set point, and it is utilized as the inputs to

the fuzzy controller. Our previous work [5] has discussed the

membership functions and rules for three local controllers in

details. Comparing to the required power and adjusted power

derived by the central coordinator-controller, the real power to

be consumed can be obtained. The usage of the real consumed

power is to drive all the actuators to control the building

system. Those actuators in smart building are auxiliary

heating/cooling system, electrical lighting system and

ventilating system for controlling the thermal comfort, visual

comfort and air quality, respectively. PID control is used to

control the indoor environmental parameters which determine

the customer’s comfort level. If the power dispatched bycentral controller-agent is sufficient, the comfort value can be

maintained at the maximum value 1; otherwise, the indoor

comfort value will decrease.

Fig.3. The structure of the local controller-agent

5. Load Agent

The load agent controls all the devices that have no direct

connection to the comfort impact factors. Those loads are

named as interruptible loads, which can be shed when the

energy supply is deficient and the load agent delivers a

“shedding” signal. In our previous work [25], a GUI isdesigned to provide configuration flexibility to the customers.

So customers can choose the interruptible loads and define

their priorities according to different functions of the buildings

as well as their own preferences. The load agent decides the

order and the amount of shed loads based on the customer

preference. After the load agent is applied, more energy is

dispatched to the comfort-related devices to maintain the high-

level customer comfort.

B. Plug-in Hybrid Electric Vehicles (PHEVs) System

From the system level, a single PHEV has negligible

impact on the overall system. However, an aggregation of

PHEVs produces significant impacts so that it cannot beignored. The PHEV system not only provides a distributed

energy resource when it is discharging, but also adds a load

when it is charging. So an agent termed PHEV-agent is

designed to manage the aggregation of the PHEVs. Fig. 4

depicts the framework of the PHEV system.

Fig.4. The structure of the PHEV system

For the charging scenario, the coordinated “smart” charging

has been determined to apply. The PHEV-agent cooperates

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denotes the interruptible loads used in this study. These loads

can be shed when the load agent is activated.

TABLE I

I NTERRUPTIBLE LOAD PROFILES

Load Power Number Operation

Periods

Priority

Television 150W 600 8:00-22:00 4

Computer 300W 1800 0:00-3:00,8:00-24:00

1

Decoration

Bulb

300W 800 0:00-24:00 2

Coffeemaker 600W 300 10:00-

17:00

3

Time(hours)

P o w e r d e m a n d s ( M

W )

0 4 8 12 16 20 240

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Fig.7. The total power demands without SP-PSO

In order to illustrate the impact of the varying number of

PHEVs, two tests have been presented. Those two tests are in

the same environmental condition, using the same batteries

and following the same simulation procedure. The only

difference is the number pattern, which means they have

different number N in the 24-hour period. The parameters are

set as: C =30kWh, minS =17%, maxS =90%, t Δ =3min; the

comfort zones are used in the WT-PSO, and are defined as

]2.73,70[],[ maxmin =T T (k), ]820,780[],[ maxmin = L L (lux),

]820,780[],[ maxmin = A A (ppm). Fig.8 illustrates the number

patterns of the two tests, and Fig. 9 shows the SOC value of

the PHEV system.

0 4 8 12 16 20 240

2000

4000

6000

8000

10000

12000

Time (hours)

N u m b e r o f P H E V s

Number pattern for test1

Number pattern for test2

Fig.8. The number of PHEVs

0 4 8 12 16 20 240

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time(hours)

S O C

V a l u e

Fig.9. The SOC value of the PHEV system

The PHEV is integrated to the renewable energy source,

and the goal of procedure is to track the power demands. Fig.

10 shows the total energy production of the renewable source

integrated with PHEV system. By also observing the Fig 9, it

can be seen that the varying number impacts the energy

production only in the period of maxmin S S S << . Moreover, it

can be observed that the test 2 tracks the customer demands

better than the test 1.

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0 4 8 12 16 20 240.5

1

1.5

2

2.5

3

3.5

4

4.5

Time (hours)

P

o w e r ( M W )

Power demands

Power Output for tes t1

Power Output for tes t2

Fig.10. The energy output of the renewable source integrated PHEV system

The impact of the PHEV system’s number pattern can be

seen from the system level as well. Fig. 11 and Fig. 12

illustrate the change of comfort value for those two different

tests. From Fig. 10-Fig. 12, it can be concluded that a large

number of PHEVs can be beneficial to maintain the customer

comfort and enhancing the system reliability.

0 4 8 12 16 20 240

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time(hours)

C o m f o r t v a l u e

Fig.11. The comfort value of test 1

0 4 8 12 16 20 240

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time(hours)


Fig.12. The comfort value of test 2

Because of the power limitation of the islanded mode, the

comfort value is different to maintain at the maximum value

only depending on the PHEV system. SP-PSO and load agent

are designed to help the system to enhance the comfort level

with limited useable energy. The comfort zones of SP-PSO

remain the same as we defined before in this section. After

optimizing the set points, the total power requirement from the

loads is decreased; meanwhile, the comfort value is increased.

Fig. 13 and Fig. 14 illustrate the positive impact on thecustomer comfort level after the SP-PSO is applied. The

power demand is less than before, and the discomfort time

period (the comfort value is less than 1) is reduced. This

means the comfort level is improved with less power

consumption compared to the previous test.

0 4 8 12 16 20 241

1.5

2

2.5

3

3.5

4

4.5

Time (hours )

P o w e r d e m a n d

s ( M W )

Power demands without SP-PSO

Power demands with SP-PSO

Fig.13. The power demands with and without SP-PSO

0 4 8 12 16 20 24

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time(hours)


Fig.14. The comfort value after the SP-PSO is applied

While the load agent is activated to shed interruptible loads,

in order to ensure more energy can be dispatched to the

comfort-related devices. The order of load shedding is

determined by the load priority set by the customers. Finally,

as shown in Fig. 15, after the load agent is used, the comfort

value successfully maintains at the maximum value 1 even in

the islanded mode where the energy production is really

limited in some time periods.

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0 4 8 12 16 20 240.9

0.92

0.94

0.96

0.98

1

Time (hours)

C o m

f o r t v a l u e

Fig.15. Comfort value with the load agent and SP-PSO

IV CONCLUSIONS

In this paper, a multi-agent building control system is

proposed to manage the integrated smart building and PHEVsystem, which provides a high degree of flexibility to the users

for the input and the satisfaction of their preferences. From all

the simulation results above, the control system has shown to

be able to achieve high comfort level during periods of limited

energy production in the islanded mode, with the help of the

PHEV system, optimization and multiple agents. In the future,

economical analysis of the integrated smart building and

PHEV system considering the electricity market information is

needed. A future study is the incorporation of wireless sensor

network technology in a micro-grid constituted of smart

buildings and plug-in hybrid electric vehicles. The noteworthy

capabilities of wireless technology can easily meet the

requirements of a micro-grid system for control and

communication challenge. Also, an intelligent energy building

management system using f uzzy logic will constitute the

supervisor of the micro-grid system.

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