Comprehensive Management Strategy for Plug-in Hybrid Electric

Vehicles using National Smart Metering Program in Iran

(Called FAHAM)

Masoud Honarmand, Nader Salek Gilani and Hadi Modaghegh

Iran Energy Efficiency Organization (IEEO), Tehran, Iran

Keywords: Billing Solutions, Charging Management, Plug-in Hybrid Electric Vehicles, Smart Metering.

Abstract: Plug-in hybrid electric vehicles charging management as well as billing solutions may be the most important

challenges in the coming years. In this paper, a comprehensive management strategy (CMS) for plug-in

hybrid electric vehicles (PHEV) using national smart metering program in Iran (called FAHAM) is

proposed. The proposed strategy considers PHEVs charging management and billing solutions as well. An

optimization method is applied in order to shift PHEVs’ charging load and maximize load factor.

1 INTRODUCTION

Although the widespread implementation of plug-in

hybrid electric vehicles (PHEV) may introduce a

solution to the world fossil fuel shortage as well as

the air pollution crisis, the anticipation of connection

of PHEVs into the power network may bring up

many technical drawbacks that need to be addressed

properly. In the near future, a huge number of

PHEVs will add a large-scale energy demand to

power systems. An emerging issue is that a large

number of EVs simultaneously will be connected to

the grid that may put at risk the overall power

system quality and stability (Sortomme & El-

Sharkawi, 2012; Sousa et al., 2012; Pillai & Bak-

Jensen, 2011).

The intelligent scheduling and control of PHEVs

as loads or power sources have great potential for

evolving a sustainable integrated electricity and

transportation infrastructure (Honarmand et al.,

2014; Honarmand et al., 2014; Honarmand et al.,

2015; Brahman et al., 2015). Large numbers of EVs

have the potential to put at risk the stability of the

power network. The charging demand of PHEVs

needs to be managed very carefully in order to avoid

interruption when several thousand of them are

introduced into the system over a short period of

time.

In this paper, a comprehensive management

strategy (CMS) for PHEVs using national smart

metering program in Iran (called FAHAM) is

proposed in order to consider PHEVs charging

management and billing solutions as well. The

proposed strategy is capable of controlling charging

procedure of PHEVs.

The rest of this paper is organized as follows:

National smart metering project in Iran is presented

in Section II. The comprehensive management

strategy for PHEVs is stated in Section III. The

problem formulation for maximizing the load factor

is presented in Section IV. Simulation data and

results are presented and discussed in Section V.

Finally, the conclusion is given in Section VI.

2 NATIONAL SMART

METERING PROGRAM IN

IRAN (CALLED FAHAM)

Deploying an Advanced Metering Infrastructure

(AMI) is an essential early step to grid

modernization. AMI is not a single technology but it

is an integration of many technologies such as smart

meter, communication network and management

system that provides an intelligent connection

between consumers and system operators (Jadid et

al., 2013; Modaghegh & Zakariazadeh, 2013). AMI

gives system operator and consumers information

they need to make smart decisions, and also the

ability to execute those decisions that they do not

currently able to do.

252

Honarmand, M., Gilani, N. and Modaghegh, H.

Comprehensive Management Strategy for Plug-in Hybrid Electric Vehicles using National Smart Metering Program in Iran (Called FAHAM).

In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2016), pages 252-256

ISBN: 978-989-758-184-7

Figure 1: The communication architecture of FAHAM project (Jadid et al., 2013).

In Iran, like many other developed countries, Smart

Grid implementation is regarded as a unique way for

encountering many serious environmental and

economic challenges. FAHAM is the National Smart

Metering Program in Iran. The functional, technical,

security, economic, and general requirements of this

project is published as a document after a long-time

workgroup of various stakeholders including

representative of grid operators, meter manufactures,

communication providers, business layer software

providers, domestic and international consultants.

Iran Energy Efficiency Organization (IEEO) is

responsible for implementation and deployment of

FAHAM. The IEEO follows promoting energy

efficiency and load management, improve system

reliability, and reduce operational costs by

implementing national smart metering project.

The simple structure of communication system in

FAHAM project is shown in Fig. 1 which consists

of:

 Smart meters: These may be single phase or

three phase smart meters. Electricity meter

provides the various information for

customers such as amount of consumption

(kWh, KVarh), consumption parameters

(voltage and current), equipment status and

last information of water and gas meters.

 Communication interface: Power Line

communication (PLC) and General packet

radio service (GPRS) are two communication

interface that connect two different part of

FAHAM subsystem together. Data

concentrators installed in 20kV/400V

transformer to manage all smart meters

“measured data” from such installations. Data

concentrators integrate PLC communications

that exchange information with smart meters

to communicate with central meter data

management systems.

 Central Access Systems (CAS): CAS is

responsible for the management of all

information and data related to smart

metering, as well as the configuration, control

and operation of all system components. The

CAS in order to manage the FAHAM network

shall have 2 following parts: a) AMI Head

End (AHE) that has the responsibility to

manage the configuration, WAN and LAN

network management system, managing the

network equipment, Registration of equipment

and Operation & Maintenance of filed

equipment in the network. b) MDM/R that

manages and archives the acquired data from

the AHE.

 Legacy Systems: These are the existing

commercial or technical systems that manage

the business processes of the utility.

3 COMPREHENSIVE

MANAGEMENT STRATEGY

FOR PHEVS

As the number of PHEVs increases, charging points

in both parking structures and private garages will

become more prevalent. These charging points will

be responsible for meeting the requirements of the

distribution grid and PHEV owners. These charging

points should perform many functions such as

Comprehensive Management Strategy for Plug-in Hybrid Electric Vehicles using National Smart Metering Program in Iran (Called FAHAM)

253

supporting the RFID cards, metering and

communication.

Figure 2: Separate billing using CMS.

The proposed CMS supports all possible

charging forms such as charging in homes, offices,

commercial buildings, charging stations, etc. To

achieve this goal, using RFID chargers has been

taken into account. In this strategy, each PHEV

takes smart energy RFID card. These cards cover

both aspects of energy sources which are used by

PHEVs, gasoline and electricity.

RFID tags inside the smart energy cards and

chargers equipped with RFID readers together with

middleware and a charging controller to authorize,

assign, and enable charging is required.

In order to simplify the charging authorization

process and make it more convenient for users, an

authentication system based on an RFID is

proposed. The layered architecture for managing a

variety of automatic identification hardware,

communicates directly with a network coordinator

and charging control server.

The other important capability of the CMS is to

separate charging bill and the general bill. In this

strategy, the PHEV owner can charge his/her PHEV

by plugging it into every possible charging facility

provided in the electrical grid and still has a

separated bill. As an example assume that PHEV

“A” which belongs to home “B”, is charging in

official building “C”. The RFID charger located at

“C” reads the PHEV’s ID number and sends it along

with the consumed energy and the charger’s ID

number to the data management center at the end of

the charging procedure. By subtracting the

consumed energy from the meter reading of the

main meter located in “C” and adding it to the

charging bill of “A”, the separated bills will be

obtained, and at the specified periods of time two

separated bills will be sent for “B”.

4 PROBLEM FORMULATION

An optimization is applied in order to shift PHEVs’

charging load for achieving the following objective

function which is maximizing load factor.

ti,t

LCh,PHEV

OBJ / MD

æö

ç+

÷÷

èø

åå

(1)

where

is the basic load of the feeder,

i,t

Ch ,PHEV

is the charging load of the i th charger,

and

D is the maximum demand of the feeder.

The maximization of the objective function is

subjected to the following constraints:

 PHEV’s charger constraint:

i,t i

Ch ,PHEV Ch ,max

P P ; t£"

(2)

where

Ch ,max

P is the maximum charging power

of the

i th charger. This constraint determines the

maximum charging rate that the charger can provide.

 SOC limits:

ii,ti

min max

SOC SOC SOC ; t££ "

(3)

where

max

SOC

and

min

SOC

are the maximum

and minimum SOC of the

i th PHEV, respectively.

This constraint allows the SOC to vary between

predefined minimum and maximum SOC.

 Charging rate limits:

i,t i

max

SOC SOC ; tDD£"

(4)

where

max

SOCD

is the maximum allowable rate

for charging of the

i th PHEV. The charging rate of

battery are limited by this constraint.

 Battery charging constraint:

1i,t i,t i,t

Ch,PHEV G2V

tRBC Cap ; thD

´´£ ´ "

(5)

SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems

254

where

G2V

is the PHEV’s battery charging

efficiency,

1i,t

RBC

is the remaining battery

capacity, and

i,t

Cap

is the battery capacity. This

constraint limits the charging power of each PHEV

based on the remaining battery capacity in each

period of time.

The proposed model is solved using mixed

integer linear programming (MILP) solver CPLEX

under GAMS on a Pentium IV, 2.6 GHz processor

with 4 GB of RAM.

5 SIMULATION AND RESULTS

The Arrival and departure times of PHEVs are

assumed as random variables. A commercial feeder

is considered for this study and the PHEVs’

penetration is considered 30 percent. In this paper,

the distribution system operator manages the

charging procedure of each PHEV considering its

arrival time, approximate duration of presence in the

parking lot, and open market pricing signal. After

that the main controller uses the processed data to

optimally schedule the charging procedure of

PHEVs.

There are several types of electric vehicles in the

market with various battery capacities from 8 kWh

to 48 kWh (Pieltain Fernandez et al., 2011). In this

paper, all electric vehicles supposed to be Chevy

Volt which it is an average electric vehicle with 16.5

kWh battery capacity.

The initial SOC of each EV is considered as a

continuous uniform random number between 0.1 and

0.7. Table 1 provides the hourly electricity price of

the open market. Fig. 3 depicts the basic load of a

typical commercial feeder.

In order to analyze the robustness of the CMS,

the problem is addressed in two scenarios:

 Scenario 1: There is no control on the charging

procedure of the PHEVs.

 Scenario 2: The distribution system operator

manages the charging procedure of PHEVs in

order to maximize the load factor and flatten

the load profile of the feeder.

Fig. 4 shows the basic load profile of a typical

commercial feeder together with PHEVs charging

demand in scenario 1. It is obvious that the

uncontrolled charging demand of PHEVs can cause

difficult situations for distribution feeders by

overloading the feeders.

Fig. 5 shows the basic load profile of a typical

commercial feeder together with PHEVs charging

demand in scenario 2. As illustrated, by applying

Table 1: The hourly electricity price in the open market.

Hour Price Hour Price

0.033

0.215

0.027

0.572

0.020

0.286

0.017

0.279

0.017

0.086

0.029

0.059

0.033

0.050

0.054

0.061

0.215

0.181

0.572

0.077

0.572

0.043

0.572

0.037

500

1000

1500

2000

1357911131517192123

Power (kW)

Time(Hour)

BasicLoadProfile

Figure 3: The basic load profile of a typical commercial

feeder.

‐500

500

1000

1500

2000

2500

1 3 5 7 9 11 13 15 17 19 21 23

ower

(kW)

Time(Hour)

BasicLoadProfil e FinalLoadProfile ChargingDemand

Figure 4: The basic load profile of a typical commercial

feeder together with the PHEVs charging demand in

scenario 1.

‐500

500

1000

1500

2000

2500

1 3 5 7 9 11131517192123

Power (kW)

Time(H our)

BasicLoad Pro fil e FinalLoad Profile ChargingDeman d

Figure 5: The basic load profile of a typical commercial

feeder together with the PHEVs charging demand in

scenario 2.

Comprehensive Management Strategy for Plug-in Hybrid Electric Vehicles using National Smart Metering Program in Iran (Called FAHAM)

255

controlled charging through FAHAM infrastructure

and CMS strategy, the load profile of the feeder is

flattened.

The Table 2 shows the load factor of the feeder

in basic load, scenario 1, and scenario 2.

Using controlled charging leads to higher load

factor, and this can be considered as a big

opportunity of FAHAM infrastructure for the system

operator to achieve a system with much more

efficiency.

Table 2: Load Factor.

Basic

Load

Scenario

Load Factor

0.785 0.703 0.859

6 CONCLUSIONS

The integration of PHEVs in the power networks

makes new challenges; accordingly, there is a

growing necessity to address the implications of this

technology on the power network. In this paper, a

comprehensive management strategy based on

national smart metering program in Iran is proposed.

The proposed strategy helps the system operator to

enhance the overall system efficiency. The results

showed that the charging was carried out in the

hours with lower loading, while in the hours with

higher loading, the charging demand was curtailed.

Simulation results evidenced that the use of

FAHAM infrastructure for managing of the charging

of PHEVs has eliminated the risk of an electricity

demand growth during the peak load of the network.

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