We develop an event-driven simulation model (Gan & Lin, 2007) to compute the output measures for the FCS and the MCS networks. Based on the simulation experiments, this section compares the performance of these two networks. In our simulation experiments, the speed of an EV is uniformly distributed between 60 km/hr and 100 km/hr, which are the lower and the upper speed limits of TNE1, respectively. The initial power of an EV is uniformly distributed between 25% and 100% (the value 25% is the minimal required power for an EV to drive to the nearest FCS). A fully-charged EV can last for 160 km (e.g., when an EV travels for a trip longer than 160 km in the highway, it must be recharged at the FCSs), and the time
for an EV charging from 0% to 100% is 30 minutes. Each EV is charged to 100% at an FCS, and the charging time is linearly proportional to the amount of charged
energy. We simulate 1,000,000 EV arrivals. For the FCS workload indication, the weighted factor is set to 1.5.
The input parameters in the simulation experiments are the EV arrival rate (λ), the number of FCSs (F), the number of CPs at each FCS (Pf), the number of MCSs (M), and the number of CPs on each MCS (Pm).
The major output measure in this paper is the average waiting time W for an EV before it is charged at an FCS location.
Without loss of generality, there are two types of EV traffic patterns: In the first pattern, the EVs drive from point A to point C as illustrated in Figure 1. In the second pattern, the EVs drive from point B to point D. The length of each path is 340 km.
Note that the paths (A, C) and (B, D) have an overlapping segment (B, C) that covers four FCSs (i.e., from FCS5 to FCS8). The EVs arrivals are a Poisson process with the rate λ ranging from 1.02 to 1.06, which is about 8.5% to 8.8% of the average vehicle traffic of Sijhih at Taiwan National Expressway 1 (TNE1) in southward direction (i.e., for every minute, there are 12 car arrivals, and among them, 1.02 to 1.06 are EVs) (Ministry of Transportation and Communications, 2011). Each EV selects the farthest FCS for charging. When an EV enters an FCS and then is served by a CP, we collect
its waiting time at the FCS location (i.e., the time that the EV waits for charging).
We assume that there are 12 FCSs, and each FCS has 20 CPs in the FCS network (i.e., the total number of CPs is 240). There are M MCSs and each MCS is equipped with Pm CPs. The speed of an MCS is set to 90 km/hr, which is the upper speed limit of large truck on TNE1. To make a fair comparison, the total number of
CPs in the MCS network is also set to 240 in the following simulations (i.e., FPf+MPm=240, where F is fixed to 12).
Figure 10: The CP distribution among the FCS locations (λ=1.04; FCS network:
F=12, Pf=20, M=Pm=0; MCS network: F=12, Pf=17, MPm=36)
Figure 10 compares the CP distribution between the FCS network and the MCS network. Note that in the FCS network, every FCS has 20 CPs (see the □ line). In the MCS network, we allocate 15% of total CPs to 2, 4, and 6 MCSs respectively (i.e., each MCS has 18, 9, and 6 CPs, respectively) and the arrival rate λ is fixed to 1.04.
Since the overlapping segment (B, C) covers FCS5 to FCS8, it is more likely that the hot spots occur in these four FCSs. Figure 10 shows that FCS5 to FCS7 receive more extra CPs that will mitigate workloads of the hot spots. The MCS network can distribute CPs flexibly since the mobile CPs can be donated to the hot-spot FCSs and effectively reduces the queueing effect in these FCSs.
Figure 11: The W performance (FCS network: F=12, Pf=20, M=Pm=0; MCS network:
F=12, Pf=17, MPm=36)
Figure 11 compares the average waiting time W between the FCS network and the MCS network, where the total number of CPs is fixed to 240. In this simulation experiment, the EV arrival rate λ ranges between 1.02 and 1.06. We allocate 15% of total CPs to 2, 4, and 6 MCSs, respectively. Intuitively, W increases as λ increases. In
the FCS network, if λ is small, W increases slowly as λ increases. When λ>1.05, W increases fast as λ increases since the queueing effects of the hot spots are intensified
with the high EV arrival rates. On the other hand, the MCS network can effectively distribute the CPs to the hot spots (i.e., FCS5 to FCS7) to reduce the queueing effect of these FCSs (see Figure 10). Therefore, the W values in the MCS network increase insignificantly for all λ values, and are much lower than those in the FCS network.
The advantage of the MCS network over the FCS network becomes very significant when traffic load is heavy.
(a) Effect of the percentage of CPs carried by the MCSs (b) Effect of the number of MCSs
Figure 12: The improvement I where 5% to 20% CPs are mobile (λ=1.04;
FPf+MPm=240, e.g., in the 5% case, F=12, Pf=19, and MPm=12).
Figure 12 (a) shows the waiting time improvement I for the various percentages of CPs carried by the MCSs, where the total number of CPs is fixed to 240, and we consider the scenarios where 5%, 10%, 15% and 20% of CPs are equipped on MCSs in the experiments. The EV arrival rate λ is fixed to 1.04. Clearly, when the percentage of mobile CPs is fixed, the improvement I of the waiting time increases with the number of the MCSs M.
Figure 12 (b) shows the waiting time improvement I as the function of MCS number. Compared to the FCS network, the improvement of W performance is significantly improved by the MCS network with 10% mobile CPs allocated to the
MCSs; that is, allocating a small amount of CPs to MCSs can obtain significantly performance improvement. Thus if the budget for building an MCS network is limited, and the MCS network operator would like to reduce the waiting times of EVs, then allocating 10% CPs to MCSs is an appropriate choice. Furthermore, the improvement I increases insignificantly after M>6. Thus for better MCS investment, the operator can limit the number M to less than 6 to save the operating costs and still efficiently reducing the waiting times of EVs.
5. Conclusions
This paper proposed two types of smart grid networks for an EV charging: the Fixed Charging Station (FCS) and the Mobile Charging Station (MCS) networks. The FCS network only utilizes fixed charging stations. The MCS network combines FCSs with mobile charging stations dispatched by a Mobile Charging Information Management System (MC-IMS). The simulation experiments are conducted to investigate the waiting time performance for these smart grid networks. Our experiments indicated that the MCS network has better waiting time performance than the FCS network. The advantage of the MCS network over the FCS network becomes very significant when the EV arrival rate is large.
In this paper, every FCS location has infinite waiting capability. In the future, we will extend our study to consider FCS locations where the parking spaces are limited.
Acknowledgement
This paper was supported in part by the ITRI/NCTU JRC Research Project, the ITRI Advanced Research Program under B301EA3300, B301AR2R10, and B352BW1100, NSC 100-2221-E-009-070, Chunghwa Telecom, IBM, Arcadyan Technology Corporation, Nokia Siemens Networks, Department of Industrial Technology (DoIT) Academic Technology Development Program 100-EC-17-A-03-S1-193, the MoE ATU plan, and the Technology Development Program of the Ministry of Economic Affairs (MoEA TDP), Taiwan.
Reference
Abdi, M.R., & Sharma, S. (2007). Strategic/tactical information management of flight operations in abnormal conditions through Network Control Centre.
International Journal of Information Management, 27(2), 119-138.
Baker, S. (2011). V2G and G2V: It’s about grid scale storage. Retrieved on
2012-05-20, from
<http://bpiconference.com/blog/wp-content/uploads/2011/10/Baker_Scott.pdf>.
Gan, C.-H., & Lin, Y.-B. (2007). Push-to-Talk service for intelligent transportation systems. IEEE Transactions on Intelligent Transport Systems, 8(3), 391-399.
Imai, K., Ashida, T., Zhang, Y., & Minami, S. (2008). EV range extender: Better mileage than plug-in hybrid? Paper presented at the IEEE vehicle power and propulsion conference (pp. 1-3), Hei Longjiang, China.
Kim, P.-S. (2003). Cost modeling of battery electric vehicle and hybrid electric vehicle based on major parts cost. Paper presented at the fifth international conference on power electronics and drive systems (pp.1295-1300).
Lee, M.-Y. (2007). Electric Vehicle is much better in Taiwan. Retrieved on 2012-05-20, from <http://sa.ylib.com/read/readshow.asp?FDocNo=1712>.
Li, Z., Sahinoglu, Z., Tao, Z., & Teo, K. H. (2010). Electric vehicles network with nomadic portable charging stations. Paper presented at the IEEE 72nd vehicular technology conference fall (pp. 1-5), Ottawa, Canada.
Miao, Y.-Y. (2008). Wind power generation yearly supports the energy consumption of 230 thousand households in Taiwan. Retrieved on 2012-05-20, from
<http://e-info.org.tw/node/37206>.
Ministry of Transportation and Communications (2011). Monthly statistics of transportation and communications. Retrieved on 2012-05-20, from
<http://www.motc.gov.tw/uploaddowndoc?file=mebook/10004book.pdf&filedisp lay=10004book.pdf&flag=doc>.
Nissan USA (2012). The new car: features and specifications. Retrieved on
2012-05-20, from
<http://www.nissanusa.com/leaf-electric-car/index#/leaf-electric-car/specs-featur es/index>.
Nor, J. K. (1993). Art of charging electric vehicle batteries. Paper presented at the WESCON/'93. conference (pp. 521-525), California, USA.
NUVVE Corporation (2012). SMART GRID powered by V2G. Retrieved on 2012-05-20, from <http://www.nuvve.com/>.
Ogiela, M. R., & Ogiela, L.(2012). DNA-like linguistic secret sharing for strategic information systems. International Journal of Information Management, 32(2), 175-181.
Sahinoglu, Z., Tao, Z., & Teo, K. H. (2010). Off-Grid portable EV charging network management with dynamic energy pricing (pp. 403-408). Paper presented at the international IEEE conference on intelligent transportation systems, Madeira
Island, Portugal.
Schofield, N., Yap, H. T., & Bingham, C. M. (2005). A H2 PEM fuel cell and high energy dense battery hybrid energy source for an urban electric vehicle. Paper presented at the IEEE international conference on electric machines and drives pp.1793-1800, Texas, USA.
Su, W., Eichi, H. R., Zeng, W., & Chow, M.-Y. (2008). A survey on the electrification of transportation in a smart grid environment. IEEE Transactions on Industrial Informatics, 8(1), 1-10.
Taiwan Area National Freeway Bureau of MOTC (2012). The official site of Taiwan area national freeway bureau of MOTC. Retrieved on 2012-05-20, from
<http://www.freeway.gov.tw/>.
Tesla Motors (2011). Increasing energy density means increasing range. Retrieved on 2012-05-20, from <http://www.teslamotors.com/roadster/technology/battery>.
U.S. Fuel Economy (2011). Electric vehicles. Retrieved on 2012-05-20, from
<http://www.fueleconomy.gov/feg/evtech.shtml>.
Verma, A. K., Singh, B., & Shahani, D.T. (2011). Grid to vehicle and vehicle to grid energy transfer using single-phase bidirectional ACDC converter and bidirectional DC - DC converter. Paper presented at the international conference
on energy, automation, and signal (pp.1-5), Bhubaneswar, India.
Winkler, T., Komarnicki, P., Mueller, G., Heideck, G., Heuer, M., & Styczynski, Z. A.
(2009). Electric vehicle charging stations in Magdeburg (pp. 60-65). Paper presented at the IEEE vehicle power and propulsion conference.