2.5 3 3.5 4 4.5 5 5.5 60

100

200

300

400

500

600

700

800

900

1000

r1 (c/kWh)

Cha

rgin

g de

man

d (k

W)

Pchg1 (30 chargers)

Pchg2 (30 chargers)

Pchg1 (25 chargers)

Pchg2 (25 chargers)

Ø  Charging stations are interconnected by a road system

Ø Price sensitivity function of PEV drivers

Ø Exponentially distributed single PEV charging demand and PEV inter-arrival time

2.5 3 3.5 4 4.5 5 5.5 60

50

100

150

200

250

300

r2 (c/kWh)

Cha

rgin

g de

man

d (k

W)

Pchg1 (30 chargers)

Pchg2 (30 chargers)

Pchg1 (25 chargers)

Pchg2 (25 chargers)

Plug-in Electric Vehicle Charging Demand Estimation based on Queueing Network Analysis

Dr. Hao Liang (Assistant Professor)

Department of Electrical and Computer Engineering, University of Alberta, Email: hao2@ualberta.ca

Smart Grid Architecture

Objectives of this Research

Bulk Generation Transmission

Renewable

Non-Renewable

Step-UpTransformer

Distribution Customer

Residential

Industrial

CommercialMicrogrid

Wind Solar

Smart Meter

WAN NAN / FAN HAN / BAN / IAN

Energy Flow

Bulk Generation and Power TransmissionSystem Operation Power Distribution System and Microgrid Operation Customer Energy

ManagementInformationSystem

Electric PowerSystem

CommunicationSystem

Information Flow

Battery EV FlywheelStep-DownTransformer

² Definition based on the IEEE 2030 Standard (September 2011)

² A key enabler of the smart grid is the

two-way communications throughout the power system, based on which an advanced information system can make optimal decisions on power system operation and control

Queuing Network Analysis

Ø Address the problem of plug-in electric vehicle (PEV) integration in the future smart grid

Ø Estimate the charging demands of PEVs, while taking into account the information obtained via smart grid communication:

1) Statistics of single PEV charging demand

2) Price sensitivity of PEV drivers

3) Statistics of PEV traffic flow in a road system

Ø Validate the analytical model based on realistic vehicle statistics

Related Work and Open Issues

Ø Related work: 1) Monte Carlo simulation study; 2) Single queue models for independent charging stations

Ø Open Issue I: How to model the correlation of charging demands among nearby charging stations

Ø Open Issue II: How to model drivers’ response to charging prices

System Model

Charging Station 1

Charging Station 2

LDCSubstation

PEV

Internal CombustionEngine Vehicle

1

2

3 4 5 6 7 8

9 10

Charging Station Operator

Communication Link

�(rs) = max

(1�

✓rs � r

min

rmax

� rmin

◆2

, 0

), rs > r

min

Ø Three types of service centers:

Charging service center (CS) Routing service center (RS) Decision service center (DS)

Ø Number of PEVs in each service center

Ø Stationary distribution (for a BCMP network)

Ø The average charging demand of each charging station can be calculated based on π(n), while the traffic balance equations are solved to obtain the parameters

Arrival

Arrival

Service

Service

Departure

Departure

CS1

CS2

DS1

DS2

RS12

RS21

p1,2(r1)

p1,1(r1)

p2,1(r2)

p2,L(r2)

p2,2(r2)

p1,L(r1)Routing

Routing

⇡(n) = �SY

s=1

Gds(n

ds) ·

SY

s=1

Gcs(n

cs) ·

SY

s=1

SY

s0=1s0 6=s

Grs,s0(n

rs,s0)

n = {nds , n

cs, n

rs,s0 |s, s0 2 {1, 2, · · · , S}, s 6= s0}

Case Study and Future Research

Urban Rural

Ø Traffic statistics: 2009 National Household Travel Survey and New York State Transportation Federation Traffic Data Viewer

Ø Electricity price: Hourly Ontario Energy Price (HOEP)

Ø Future research directions include the utilization of the analytical results to facilitate distribution system planning and the incorporation of the analytical model in distribution load flow analysis for optimal distribution system operation in smart grid