Abstract — Plug-in Hybrid Electrical Vehicle (PHEV) is

attracting an increasing attention due to its benefits to

reduce fuel consumption and greenhouse gas emissions,

improve energy efficiency. This paper investigates the

relationship between reducing PHEV charging costs and

power quality improvement. A multi-objective Particle

Swarm Intelligence algorithm for optimizing the charging

process is proposed, which is able to provide a balanced

optimal charging solution for PHEV at minimal charging

cost with acceptable power quality.

I. INTRODUCTION

Recently, the research of Plug-in Hybrid Electric Vehicles (PHEVs) has gained a momentum due to their benefits to the environment. Key aspects studied include PHEV driving patterns, energy efficiency, and charging characteristics. However, the potential impact of PHEV charging on distribution grid networks has been less attended, which is considered to be critical for the future to address the climate change [1]. Optimization technologies are popularly employed to either optimize PHEV charging costs or improve power quality [2], [3], [4], [5], [6]. One open question is how to find an optimal charging solution balancing the minimizing charging costs and power quality improvement caused by large-scale PHEV penetration. There are two reasons for doing this: a) the cost of using PHEVs has to be cut to a level acceptable to customers; b) The impact on power quality such as power loss and voltage deviation has to be regulated to the minimal level for the safety of the distribution grid. This paper investigates the relationship between reducing PHEV charging costs and power quality improvement. Charging PHEVs may bring power quality problems for distribution grids including voltage deviation, power loss and frequency deviation [6]. Optimization technologies were popularly applied to improve the voltage deviation and power loss problems [2], [6]. Garnesh etc. [3] applied the binary Particle Swarm Optimization (PSO) to minimize charging costs, which is dependent to charging schedule according to the dynamic electricity price market. Since developed by Kennedy and Eberhart [7] in 1995, PSO has been a most popular evolutionary optimization technology due to their advantages of fast running, easy to program and high accuracy and efficiency. To solve the real-world engineering problems which usually have multiple targets to achieve, multi-objective PSO technologies have been applied [8], [9].

The purpose of this paper is to simultaneously reduce PHEV charging costs and power losses. Due to the conflicting relationship between power loss and charging price discovered

in this paper, a balanced charging solution is proposed. The multi-objective PSO is applied to simultaneously reduce charging costs and power losses.

II. THE METHODOLOGY

The methodology for the investigation is divided into two parts: i) investigation of the relationship between charging costs and power quality and ii) optimal balance of charging costs and power quality. To test the impact of PHEV charging on residential grids, an IEEE 34-Node Test Feeder radial network [Fig.1] was modeled in DigSilent Power Factory [10] which is able to simulate power systems with power flow analysis including voltage profiles, load profiles.

Fig .1 IEEE 34 - Node Test Feeder

The background household load profiles are modeled with a daily regional load demand profile as shown in Fig. 2 investigated in the Victoria State, Australia through AEMO (Australia Electricity Market Operator) [11]

Fig .2 Victoria regional daily load demands profile

24/05/2011, Victoria, Australia

Evaluating Impact of Plug-In Hybrid Electric Vehicle Charging on Power Quality

Zhaofeng Yang, Xinghuo Yu, Grahame Holmes School of Electrical and Computer Engineering, RMIT University, Australia

E-mail: x.yu@rmit.edu.au, yzf830305@gmail.com

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From the data investigated, the load demands are dependent on time series Eq. (1), which is described below

MWt

ttttt

tttt

ttt

ttLBackground

)14.5325

77.5915.6593.3942.1597.3

705.010*85.810*9.710*

11.510*34.210*44.710*

56.110*94.110*09.1(

23456

78293104

115127138

14101512

+−

+−+−+

−+−

+−+

−+−=

−−−

−−−

−−

(1) In this study, the vehicle batteries are assumed to be fully empty and the charged batteries are assumed to be 100% full. According to the PHEV standards [12], a normal Li-Ion PHEV battery will be with roughly 15.1kWh energy capacity which requires 3.75 hours charging at 4kW charging power. For convenience, in this research the PHEV battery capacity is assumed to be 16kWh.Through the simulations, it is discovered that the power loss levels caused by PHEV charging are dependent on total load levels as Eqs. (2) and (3):

PHEVBackground LLLoad += (2)

24 **10*7.1 LoadkPloss

−

= (3)

Where is a constant coefficient. From the simulation results

with DigSilent Power Factory, it was found that the lost power

is related to power load with 410*7.1 −

multiply by a constant.

In this study, the power loss measured is scaled to power load

with 410*7.1 −

so, 2=k in this study.

The daily electricity price profile is investigated meanwhile with load demand profile through AEMO system (Fig.4).

)8.5250*77.3*001.0*

10*1891.1*10*2054.5(Pr23

7412

+−+

−=−−

LoadLoadLoad

Loadice

$/MW (4)

Fig .3 Victoria regional electricity price profile

24/05/2011, Victoria, Australia

Based on the investigations above, a simple relationship between the power loss and electricity price can be described as

8.5250)(*7.289)(*10*47.3

)(*10*37.5)(*10*81.1Pr

2

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+−+

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k

P

k

P

k

Pice

lossloss

lossloss

(5) It is observed that there is some sort of conflict between power losses and electricity prices while charging PHEV as Fig.4 shows.

Fig. 4 Electricity prices vs power load demands, 24/05/2011,

Victoria, Australia

For the multi-objective PSO task, the research target to get an balanced charging solution which has capability to minimize charge costs with acceptable power losses through a multi-objective PSO algorithm:

Objective: Min ))(2),(1( tftfF (6)

∫=

n

mlossPWtf *)(1 (7)

∫=

n

miceWtf Pr*)(2 (8)

Where m is the charging start time, n is the charging stop

time, and W is the charging power. There are several

weighted aggregation techniques introduced in [12]. One simple one is

)(2*2)(1*1))(2),(1( tftftftfF αα += (9)

121 =+ αα (10)

In this study, the conventional weighted aggregation is applied to in the bi-objective PSO algorithm with MATLAB 2010R to achieve a balanced solution between charging costs minimization and power loss reduction.

III. SIMULATION RESULTS

We conducted several simulations to show the effectiveness of our approach. For studying power losses, PHEVs were plug-in as loads on the testing grid during six time periods: 0-4h; 4-8h; 8-12h; 12-16h; 16-20h; 20-24h. The power losses are measured with variety of PHEV penetration levels: 20% penetration; 40% penetration; 60% penetration; 80% penetration and 100% penetration. As shown in Table 1, significant power losses were observed during simulation with DigSilent Power Factory. Meanwhile, the results indicate that the PHEVs charged in peak hours would cause more power losses with more load demands on tested nodes.

Table.1 Uncontrolled Power Loss Ratio Charging

Period/PHEV Penetration

20% 40% 60% 80% 100%

0-4h 3.3% 5.8% 8.1% 10.5% 12.9%

4-8h 3.3% 5.8% 8.1% 10.5% 12.9%

8-12h 3.8% 6.5% 9.0% 11.4% 13.8%

12-16h 3.8% 6.4% 8.9% 11.3% 13.7%

16-20h 4.0% 6.7% 9.2% 11.6% 14.0%

20-24h 3.7% 6.2% 8.8% 11.2% 13.6%

To avoid the severe power quality problem on distribution grids, it is critical to find an optimized charging schedule and consequently reduce power loss levels to an acceptable range.

Table.2 Minimized Charging Power Loss Ratio Charging

Period/PHEV Penetration

20% 40% 60% 80% 100%

2.11-6.11h 3.1% 5.6% 7.9% 10.3% 12.7%

It is shown from Table 2 that the rescheduled charging reduced power loss by roughly 1% from peak hour charging which will be massive in a complex distribution system with large-scale PHEV penetration. As shown in Fig. 3 and Table 3, the owners of PHEVs who charge their vehicles at peak hours would need to pay for more charging costs. The real-time electricity prices are fluctuating with each half hour and dependent upon load demands at each time slot.

Table.3 Uncontrolled Individual Charging Costs

Charging Schedule Charging Cost

0-4h $4.09

4-8h $4.53

8-12h $4.88

12-16h $4.95

16-20h $5.17

20-24h $4.92

Table.4 Minimized Charging Costs

Charging Period Charging Cost

0.07h-4.07h $4.088

From Table 2 and Table 4, it is easily seen that the tasks of minimizing charging costs and reducing power loss level are conflicting tasks where the charging with lowest power loss with non-minimal charging costs. A multi-objective PSO is expected to be capable to solve the multi-objective optimization problems with conflicting constraints and conditions. The experimental results shown in Table 5 indicated that a balanced bi-objective optimization solution can be discovered. Multi-objective optimization rescheduled the fitness charging at 0.12h-4.12h. This charging schedule indicates relative low charging cost at $4.09 with acceptable power loss levels with variety of PHEV penetration levels compared to random charging especially peak hour charging.

Table.5 Multi-Objective Optimization Solution

Charging Period/PHEV Penetration

20% 40% 60% 80% 100%

0.12-4.12h 3.3% 5.8% 8.1% 10.5% 12.9%

Cost $4.089

IV. DISCUSSION AND CONCLUSION This research has developed a practical PHEV charging solution to reduce charging costs and improve power quality. Data modeling has been done to describe the relationship between charging costs and power quality. Multi-objective PSO has been applied to derive an optimal charging schedule for PHEVs.

V. REFERENCES

[1] R. C. Green II, L. Wang, M. Alam, “The impact of plug-in hybrid electric vehicles on distribution networks: A review and outlook”, Renewable & Sustainable Energy Reviews, vol. 15, pp. 544-553, 2011 [2] H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama and Y. Nakanishi, “A particle Swarm Optimization for reactive power and voltage control considering voltage security assessment,” IEEE Transactions on Power Systems, vol. 15, no. 4, pp. 1232-1239, November 2000. [3] G. K. Venayagamoorthy, P. Mitra, K. Corzine and C. Huston, “Real-time modeling of distributed plug-in vehicles for V2G transactions,” IEEE Energy Conversion Congress and Exposition, pp. 3937-3941, 2009.

[4] P. Kulshrestha, L. Wang, M.-Y. Chow, S. Lukic, “Intelligent energy management system simulator for PHEVs at municipal parking deck in a smart grid Environment”, IEEE Power & Energy Society General Meeting, pp. 1-6, 2009.

[5] S. L. Judd, T. J. Overbye, “An evaluation of PHEV contributions to power system disturbances and economics”, 40th North American Power Symposium, pp. 1-8, 2008. [6] K. Clement-Nyns, E. Haesen, J. Driesen, “The impact of charging plug-in hybrid electric vehicles on a residential distribution grid”, IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 371-380, 2010. [7] J. Kennedy, R. Eberhart, “Particle swarm optimization” , IEEE International Conference on Neural Networks Proceedings, vols 1-6, pp. 1942-1948, 1995

[8] C. A. C. Coello, M. S. Lechuga, “MOPSO: A proposal for

multiple objective particle swarm optimization”, CEC'02: Proceedings of the 2002 Congress on Evolutionary Computation, vols 1 and 2, pp. 1051-1056, 2002. [9] S. Kitamura, K. Mori, S. Shindo, Y. Izui, Y. Ozaki, “Multi-objective energy management system using modified MOPSO”, International Conference on Systems, Man and Cybernetics Proceedings, vol 1-4, Pages 3497-3503,2005. [10]http://www.digsilent.de/Software/DIgSILENT_PowerFactoy [11] http://www.aemo.com.au/data/price_demand.html [12] M. S. Duvall, “Battery evaluation for plug-in hybrid electric vehicles”, 2005 IEEE Vehicle Power and Propulsion Conference (VPPC), pp. 338-343, 2005 [13] K.E. Parsopoulos, M.N. Vrahatis, “Particle Swarm Optimization Method in Multiobjective Problems”, ACM Symposium on Applied Computing, pp. 603-607, Madrid, Spain, 2002.