A Charging Management of Electric Vehicles Based on Campus Survey. Data ... However, massive integration of electric vehicles (EVs) may cause a trouble ..... This study is supported by the Yildiz Technical University Research Projects Fund ...
A Charging Management of Electric Vehicles Based on Campus Survey Data Bunyamin Yagcitekin1, a, Mehmet Uzunoglu2, b, Arif Karakas3, c 1,2,3
Department of Electrical Engineering, Yildiz Technical University Istanbul,Turkey [abunyamin, buzunoglu, cakarakas] @yildiz.edu.tr
Keywords: electric vehicle; smart grid; smart charging; charging management; demand management
Abstract. Road transport electrification has a great potential to reduce greenhouse gas emissions and oil consumptions. However, massive integration of electric vehicles (EVs) may cause a trouble on power grid. This point of view looks at the future state of EV development, charging scheduling and drivers’ behaviors are becoming increasingly important to charging management strategy. An optimal charging management leads to minimum effects on power grid, reducing peak power via vehicle to grid (V2G) mode and lower charging costs. In order to make an optimum charging management strategy, it requires some information of driver behavior, such as daily driving, parking times and usage frequency of the vehicle. At this perspective, a campus based driver behavior is gathered with face to face survey in Yildiz Technical University, Turkey. Survey data is utilized to determine daily optimum charging profile and increase the functionality of EVs. In this study, the comparison of possible effects of different charging scenarios on power grid are presented and analyzed. A case study is performed in the university campus and the simulation is realized in MATLAB/Simulink environment with actual data. Introduction The depletion of oil reserves as well as environmental concerns growing in entire world has lead to a growing importance of employing alternative energy based technologies. Among these technologies, vehicular systems have a specific importance due to the fact that a great portion of greenhouse gas (GHG) emissions which are significantly harmful to environment are released by the utilization of internal combustion engine (ICE) based vehicular systems. 26% of primarily energy is used in the transportation sector in all around the world and 23% of energy-related GHG emissions are produced by transportation sector. Moreover, the increasing of transportation based demand and the consumption of oil is nearly peaking every day [1, 2]. Some alternative technologies for vehicular systems have been proposed in order to overcome these environmental issues as well as to reduce the dependency on fossil fuel [3-6]. In particular, the development of the electric vehicles (EVs) has come into prominence and some kinds of EV model have been developed by important vehicular system manufacturers [7]. EVs have an important potential to be used widespread in the future [8]. Some technologies of EVs such as battery based electric vehicle, plug-in hybrid electric vehicle, etc. need electricity for charging the battery. Besides, more electricity generating capacity will be needed to supply a possible huge recharging requirement when the great numbers of EVs connected to the grid [9]. Also, many studies have been proposed on the using of EVs on energy storage units (which works vehicle to grid (V2G)) and their advantages [10, 11]. EV optimal charging management strategies have been proposed in ref. [12, 13]. Hence, the charging & discharging of EVs is a challenging issue that needs a detailed analysis in terms of future grid capacity, smart charging management strategy and V2G concept. This study aims to show the comparison of possible effects of different charging scenarios on power grid. University campus based driver behavior analysis of EVs shows that the charging time is important for power grid safety, power quality, peak shaving and economic profit of EVs. Driver behavior and demand side power condition is also evaluated for both charging and discharging process to provide the necessary electrical energy requirement for peak shaving of power grid. Thus, this study may be useful for upcoming researches specifically about utilization of the EVs in such campus life style. This paper is organized as follows: Section II presents a methodology and describes the details of
the study. Section III presents the obtained results with necessary evaluations and finally concluding discussions are given in Section IV. Methodology The charging&discharging operations are performed in Davutpasa Campus of Yildiz Technical University, Istanbul/TURKEY. Driving profile of academic and electricity demand of the buildings are used for developed model. Electric Electronic Faculty Building Model In this study, Yildiz Technical University Electrical and Electronics Faculty (EEF) Building which is shown in Fig. 1a is used to perform the simulations. Electricity power consumption profile for one month is shown in Fig. 1b and Faculty power demand is similar whole weekdays.
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a) Settlement plan of the EEF Building and general overwiev of the proposed system, b) One month energy demand of EE Building
Driving profiles A survey is performed among the academic staff in order to get driving behavior in campus. Through this survey, driver behaviors such as daily driving, working days in a week and average parking hours are obtained. According to the results of survey, a probability distribution of daily driving distance is shown in Fig. 2a. A typical maximum distance is 84 km in this survey results. The charging&discharging of EVs is determined by initial battery SoC. In this regard, daily driving information is used for calculating initial SoC. In addition, it assumed that each trip (from home) starts with 100% battery SoC and decreasing the SoC level according to daily driving distance. Probability density of battery SoC is shown in Fig. 2b. In this study, it is assumed that battery capacity is 24 kWh and average energy consumption is assumed as a 0.15 kWh/km [14]. It is also considered that the drivers may regularly trip the same route (from home to work). Consequently, SoC is calculated by a following equation; %SoC (i) %SoC (i ) 0
Ebat DEDV (i) EC E
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where %SOC (i) is the battery SoC (kWh) after the trip for each day, %SOC (i) 0 is the initial battery SoC, E bat is the total capacity of battery (kWh), D EDV (i ) is the each EVs distance from home to work (km), EC is average energy consumption per-km (kWh/km) and i indicates the each drivers.
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b) a)Daily driving distance (km), b) Battery SoC (%)
Charging station model EEF building has three parking area with 120 parking capacity. There are 24 charging units which are uniformly allocated between parking areas in the simulation which is shown in Table I. Besides, international standard IEC 62196-2 has defined several charging modes and Mode 2 is selected as a single-phase charger in this study [15]. Every charging station is distributed to different phases to allow for balanced loading. Charging stations and EE building are modeled via MATLAB/Simulink environment. A charging station model is designed for bi-directional energy transfer with a control unit. This controller collects several data such as phase voltages and power capacity of substation, power demand of EE building for protecting the power grid any harmful conditions during the charging process by controller. For this purpose, controller is not allowed to exceed the substation limit. Beside EVs are charged in the safety limitations, which is not over specified limit. Specified limit is determined by the average value of the day time which is between 09.00 and 19.00 hours. In this period, EVs can be used as an energy source and peak demand reduction is obtained. Furthermore, with the use of electric vehicles as energy sources, possible economic revenues can be utilized. Load model of EVs In the model, it is assumed that each group of EVs is connected to different phases which are shown in Table I. In this study all EVs are modeled with a capacity of 24 kWh and each driver uses 80% of their battery. Table 1 Initial condition of EVs Total Battery Total V2G Number of Average Initial Capacity capacity EVs SOC (%) (kWh) (kWh) * Phase a 11 264 89 129 Phase b 10 240 87 113 Phase c 11 264 87 124 *limited battery usage to 40% of the rated capacity
Total required energy for charging (kWh) 29 31 34
EVs’ charging rate is assumed as 3.3 kWh with 90% efficiency and modeled with constant current source. Driving profiles, usage frequency and parking hours are defined in the model for each vehicle. Charging Strategy EEF building energy demand is gotten every 15 minutes for one month which is shown in Fig.1b. This profile shows that daytime energy consumption is significantly higher than the time-off period. In addition, academician driving profile consists of some important features. For instance, longterm parking period in the campus area, regular driving habits and generally short distance travel from home to work is used. Based on the above information, three different scenarios can be considered. In the first scenario (home charging), every driver charges EV at home during off-peak time and sells the surplus energy from batteries to the grid at daytime, when building demand is over the specified loading limit. This surplus energy is used to decrease daytime peak loading of EEF building and to assess the economic benefit of EVs by using the V2G mode. In Figure 6, the driver starts the day with 100% SoC in the first scenario. After that, EV is not charged or discharged when battery is full, which is defined as idle mode. Besides, charge depleting
mode defines driving time in a day. After 09.00 (when driver get in campus), if it is needed, EV can be worked as a V2G mode until 18.00. When driver is finished his work according to driver’s parking hours, EV is working in a charge depleting mode. When driver get home in peak time, electricity price is high and driver wait until off-peak time (22.00) for charging. One of the driving profiles is shown in Fig. 3a for one day in Scenario1. In contrast to the first scenario, all EVs are charged their batteries during day time in Scenario 2 (campus charging). They worked their EVs only grid to vehicle (G2V) mode. In the last scenario, mixed charging mode is considered. Some EVs can be worked as a V2G mode and some of them G2V mode. Simulation results and discussion EVs are distributed on three phases of power grid and survey data is processed. The simulation is controlled continuously for comparison of the EEF faculty power demand and specified demand. In the first scenario, each EV daily trip began at 100% SOC (from home to work) and each EVs initial SOC is different regarding the daily distance. 5
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In simulation, a home-work round trip is considered and other trips are not taken into account. If the power consumption is higher than specified limit of power demand, controller is allowed to discharge the EVs (V2G mode) until dropping to 40% SoC of battery level. Specified minimum battery level is determined for meeting the power needs of farthest distance. All EVs are charged at home during off-peak time with cheap electricity price. The simulation results, which are shown in Fig. 3b based Scenario 1 shows good agreement with peak shaving which is nearly decreased 15%. Additionally, V2G mode can provide economic revenue in the first scenario but it is not considered in this study. In the second scenario, we assume all EVs are charged (G2V) in campus when they
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are plugged. EVs charging impact on power grid is shown by Scenario 2. In second scenario, peak loading is increased nearly 40% which is shown in Fig. 3c. In the last scenario, we assume some drivers, which daily driving distance is short, are not willingness to use G2V mode their EVs in campus and they are used their vehicles V2G mode. Drivers’, who’s daily driving distance is long, want to use V2G mode their EVs in the campus. This scenario is convenient for meeting the required energy among EVs and results are shown in Fig.3d. Conclusion and future work In this study, Faculty of Electrical and Electronics Engineering was modeled based on measured daily power demand profile. Due to the results of a survey, driving profiles of 32 EVs were defined. A controller for bi-directional charging station was developed. Proposed model has three different charging scenarios for determining the convenient concept of campus based driving behavior. Academicians generally parked their cars more than 6 hours and they have regularly driving habits (from home to work or work to home). According to results of study, V2G concept is useful for such driving habits and it has some advantages both of power grid (improving power quality) and EV drivers can be get extra economic income with selling energy to the grid. In the future, this study may be developed with dynamic pricing methods which come up as an addition to smart grid substructure, thus economic analysis may be calculated more accurately. Though, it has not seen any harmful impact to grid in current model, it may cause some problems when use of EVs becomes widespread that will be analyzed in the future. Acknowledgement This study is supported by the Yildiz Technical University Research Projects Fund under Grant 2012-04-02-KAP05. References [1] D.Mc Collum, C Yang, “Achieving deep reductions in US transport greenhouse gas emissions: Scenario analysis and policy implications”, Energy Policy 37 (2009), p. 5580–5596. [2] X. Ou, X. Zhang, S. Chang, “Alternative fuel buses currently in use in China: Life-cycle fossil energy use, GHG emissions and policy recommendations”, Energy Policy 38 (2010), p. 406–418. [3] K.C. Bayindir, M. A. Gozukucuk, A. Tekel, “A comprehensive overview of hybrid electric vehicle: Power train configurations, power train control techniques and electronic control units”, Energy Conversion and Management 52 (2011), p. 1305–1313. [4] C. Guille, G. Gross, “A conceptual framework for the vehicle-to-grid (V2G) implementation”, Energy Policy 37 (2009), p. 4379–4390. [5] G.J. Offer, D. Howey, M. Contestabile, R. Clague, N.P. Brandon, “Comparative analysis of battery electric, hydrogen fuel cell and hybrid vehicles in a future sustainable road transport system”, Energy Policy 38 (2010), p. 24–29. [6] C.E. Thomas, “Fuel cell and battery electric vehicles compared”, International Journal of Hydrogen Energy (2009), p. 6005 – 6020. [7] W. Kempton, T. Kubo, “Electric-drive vehicles for peak power in Japan”, Energy Policy 28 (2000), p. 9-18. [8] D. Yamashita, T. Niimura, H. Takamori, H. Takamori, “A Dynamic Model of Plug-in Electric Vehicle Markets and Charging Infrastructure for the Evaluation of Effects of Policy Initiatives”, Power Systems Conference and Exposition (PSCE),IEEE/PES (March 2011), p. 1-8. [9] M. Duke, D. Andrews, T. Anderson, “The feasibility of long range battery electric cars in New Zealand”, Energy Policy 37 (2009), p. 3455–3462. [10] Y. Ma, T. Houghton, A. Cruden, D. Infield, “Modeling the Benefits of Vehicle-to-Grid Technology to a Power System”, IEEE Transactions on Power Systems 27(2) (2012), p. 1012-1020. [11] J. Dong, Z. Lin, “Within-day recharge of plug-in hybrid electric vehicles: Energy impact of public charging infrastructure”, Transportation Research Part D 17 ( 2012), p. 405–412. [12] P. Zhang, K. Qian, C. Zhou, B. G. Stewart, D. M. Hepburn, “A Methodology for Optimization of Power Systems Demand Due to Electric Vehicle Charging Load” IEEE Transactions on Power Systems 27 (3) (2012), p.16281636. [13] P. Richardson, D. Flynn, A. Keane, “Local Versus Centralized Charging Strategies for Electric Vehicles in Low Voltage Distribution Systems”, IEEE Transactions on Smart Grid 3(2) (2012), p.1020-1028. [14] R.T. Doucette, M.D. McCulloch, “Modeling the CO2 emissions from battery electric vehicles given the power generation mixes of different countries”, Energy Policy 39 (2) (2011), p. 803–811 [15] International Electrotechnical Commission (IEC) standarts of electrical connectors and charging modes for electric vehicles, IEC 62196 http://en.wikipedia.org/wiki/IEC_62196 (Accessed, august 2013)
