Dynamic Simulation of EV Fast Charging with Integration of Renewables

Yao Chen ABB Corporate Research China

Power Systems Department Beijing, China

cathy-yao.chen@cn.abb.com

Hector Zelaya De La Parra ABB Corporate Research Sweden

Power Technologies Vasteras, Sweden

hector.zelaya@se.abb.com

Fabian Hess

Product Group EV Charging Infrastructure

ABB Switzerland Turgi, Switzerland

fabian.hess@ch.abb.com

Abstract: This paper presents the results of a dynamic simulation of various scenarios of Electric Vehicle (EV) fast charging in combination with renewable generation. Based on a hypothetical distribution network, the possible benefits from coordinated controls of fast charging and renewable generation are studied under grid connection and islanding condition. For grid connection condition, strong and weak grid scenarios are both simulated; for islanding condition, energy storage requirements are considered. The simulation results show that with proper control strategies, fast charging infrastructure can help to compensate active and reactive power in different conditions, so as to improve the operation performance of power systems with renewable energy integration.

Keywords: EV charging infrastructure; DC fast charging; renewable generation; islanding operation; supervisory controls; grid impact analysis

I. INTRODUCTION World governments put high priority on electric vehicle

industry development in recent years as EV is regarded to be one of the most promising ways to achieve the target of cutting the vehicle emissions. Take China for example, which has the second largest vehicle market after the U.S., the government forecasts 500,000 EVs on road by end of 2015, and is targeting 5 million EVs by 2020 [1]. In order to provide charging service for massive EVs, the existing power grids need to be upgraded by building up charging infrastructure. As announced by State Grid Company of China, they plan to set up 1000 charging stations and 240,000 charging poles by end of 2015, and the overall investment is estimated to be over 20 billion RMB [2].

The large scale charging infrastructure, left without proper regulation, might lead to new challenges for power system operation, for example change of power flow patterns, increase of peak load, power quality problems [3-4]. The technologies to realize a smooth Vehicle to Grid (V2G) integration become the foci of proactive studies [5-8] and are being introduced into some of the utilities pilot projects for demonstration. However, these studies tend to treat EV charging stations separately and few of them look into the dynamics of the system when applying a supervisory control strategy. Papers

[9-10] start to look at the possible dynamic control for fast charging stations, but still lacking supervisory control strategies and comprehensive demonstration.

In this paper, an EV charging infrastructure is not considered as an isolated part of the power system, but in combination of renewable generation which is also under rapid development nowadays, in order to understand the interactions in between and the coordination requirements to maintain the normal operation of power grids. Firstly, a dynamic system model of a hypothetical distribution network has been developed using Matlab/Simulink environment. This also includes fast charging stations comprised of several poles and renewable generation to investigate the interaction of fast charging stations when connected to the vicinity of wind or solar power stations. Secondly, several conditions of interest are analyzed, either due to low wind conditions combined with weak or strong grid or under islanding condition when the charging stations can contribute with active and reactive power injection into the system. The paper investigates those different scenarios with particular attention to active and reactive power requirements and bus voltages and frequency behavior. Thirdly, the control strategies of fast charging in coordination with renewable generation are developed and implemented in the simulation model. The performance improvements are demonstrated by the simulation results.

II. MODELLING

A. Power System (Grid) Model The power system model is based on Matlab/Simulink as

has already been reported in [9]. The model objective is to investigate EV fast charging interaction with the grid. The single line diagram is shown in Fig. 1, where Node 799 represents the Point of Common Coupling (PCC) of the distribution network, the network parameters and the loading condition of each nodes, either symmetrical or asymmetrical, are set according to IEEE 37 Node Test Feeder [11]. As indicated in Fig. 1, N=3 fast charging stations with a total capacity of 500 kW each are connected to Node 712, Node 720 and Node 709 respectively. Each charging station may consist

of M=5~10 DC charging points with rated capacity 50kW [12] to 100kW and above [13]. A distributed renewable generation is connected to Node 708 with fluctuating output. Besides, there is a synchronous generator connected to Node 703 which is stand-by in grid-connected operation and will be switched in when islanding occurs.

Alternatively, a setup of N>25 highly distributed smaller charging systems with an aggregated capacity of 500kW can be considered, the rated capacity of each charging point could be in the range of 10~50kW. However this scenario is not modeled in this paper because it is equivalent to the N=3 scenario from a coordinated control strategy point of view.

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Figure 1. Power system topology diagram

B. Fast Charging Station Model The fast charging station model consists of several blocks:

battery and management system, line-side active rectifier capable of four-quadrant operation, bi-directional DC/DC converter allowing battery charging and discharging, and corresponding converter control systems.

Two different models can be developed: (i) detailed model with the two power electronics converters which allows detailed analysis of the harmonics and power factor correction available in the system; (ii) phasor model that simplifies the complexity of the modulation strategy for system level investigations. The latter one is adopted in the results presented in this paper as shown in Fig. 2.

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Figure 2. Fast charging station phasor-model

C. Modelling of Renewable Sources The renewables sources of generation have been modeled

as simple power sources in the system as the purpose of the investigation is to study system effects during step changes. The output of renewable generation will vary according to predefined tables to simulate its fluctuation characteristics. Based on which, the distributive fast charging stations will be controlled so as to optimize the operation of the network.

III. GRID-CONNECTED SYSTEM The following two scenarios are simulated for grid-

connected operation by changing the short-circuit level and the X/R ratio (reactance at the nominal frequency 50Hz over resistance) of the external grid.

- Strong Grid Connection: system short-circuit level = 100MVA, X/R ratio = 7;

- Weak Grid Connection: system short-circuit level = 50MVA, X/R ratio = 3.

During grid-connected operation, the frequency is determined by the large external grid and is therefore considered to be constant within the studied distribution network. However, the voltage profile might be different from node to node depending on the power flow. The change of loading level and the fluctuation of renewable generation can both lead to voltage variation which may exceed the acceptable range in certain cases. Therefore the main issue to address is how to utilize the four-quadrant operation capability of fast charger for reactive power regulation and voltage support.

A. Control Strategy Given a step change in the renewable power available, the

bus voltages in the system are affected with a corresponding variation from nominal operation. The control strategy consists of applying the required reactive power in order to maintain zero voltage variations. The supervisory control at high level verifies first the availability of power on each charging station and it then decides the reactive contribution per station. Since there are many system voltages available in the system, the controller uses the bus further away from the PCC point of the network as representative of the bus voltages in the system. In the simulation, the voltage signal of Node 709 is selected as the control objective. The reactive power injection is then calculated to compensate for this worst case node in the

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system. A block diagram of the control function is shown in Fig. 3.

Figure 3. Control strategy flow chart for grid-connected scenario

B. Results The results of the grid-connected system are shown in

Fig.4 and Fig. 5, corresponding to strong grid connection and weak grid connection respectively. In the simulation, the loading level of the network is about 2.5MVA. The total charging power of the three fast charging stations is about 550kW. Renewable generation varies during a time interval of 1.5s between a level of 1.5MW and 0.3MW to simulate the fluctuation of wind speed or PV output due to clouds.

The results show that the DC fast charging stations can take care of the reactive power needs for the simulated scenarios. Under both scenarios, it can be noticed that with the coordinated reactive power control of fast charging stations, the voltage drop at each observed nodes can be reduced by 50% when step changes of renewable generation occur. This is a simple and elegant solution that emphasizes the advantages of a fully controlled four-quadrant AC/DC converter on the chargers front end, eliminating the need for external equipment such as SVC.

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Figure 4. Voltage profile comparison under strong grid connection condition

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Figure 5. Voltage profile comparison under weak grid connection condition

IV. ISLANDING OPERATION During islanding operation, the synchronous generator as

mentioned above will be switched in to control the frequency and voltage of the islanded network. However, the dynamic changes from renewable generation will produce transients that might have an effect on the voltage stability and the frequency of the system. In this section, the possible performance improvement by coordinate active and reactive power control of EV charging stations will be studied. The same power system diagram of Fig. 1 applies here.

A. Control Strategy The control strategy during islanding is different to the

previous case. The concern here is to maintain the balance between the active and reactive power. The control strategy also include detection of the islanding condition, this can be achieved by one of several available methods, e.g. natural frequency detection, THD (total harmonic distortion) measurement, automatic disconnection signal, etc.

As long as islanding operation is detected, besides reactive power compensation, the supervisory controller will also enable coordinated active power control of the distributed fast charging stations. It is assumed that the fast charging stations has a certain battery storage capability available. The supervisory controller will verify the available energy storage capacity of each station, measure the output variation of renewable generation, and decide whether to charge or discharge the energy storage system and the corresponding charge/discharge power according to the built-in logic. The block diagram of the control function is shown in Fig. 6.

Figure 6. Control strategy flow chart for islanding scenario

B. Simulation Results The results of the islanding system simulation are shown in

Fig. 7 and Fig. 8, which are the speed and the terminal voltage of the synchronous generator under the fluctuation of renewable generation with or without the coordinated active and reactive power control of EV charging stations. In the simulation, the capacity of the synchronous generator is 4MVA. The total energy storage capacity of the three fast charging stations is 1MVA. Renewable generation varies

during a time interval of 0.8s between a level of 0.8MW and 2.5MW to simulate the possible wind power output during gust.

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Figure 8. Generator terminal voltage during islanding operation under

renewable generation fluctuation

The results show that the fast charging stations can help to reduce the impact of fluctuating renewable generation on system frequency and voltage substantially during islanding operation. The amplitude of frequency variation of the generator is reduced by 50%. The voltage stabilizing effect is even more obvious, not only is the voltage variation reduced during renewable generation fluctuation period, but also the oscillation damping after the recovery of renewable generation is much faster with the developed control strategy.

V. CONCLUSIONS This paper presents the simulation results of a hypothetical

smart grid system composed by several sources of renewable generation that interact together with fast charging stations for both conditions: grid-connected and islanding. It has been shown that the four-quadrant operation of the DC fast charging station has the advantage of being able to compensate for active and reactive power in different conditions of the power system. Two supervisory control strategies have been studied to manage the flow of active and reactive power in the system. The performance improvements under both grid-connected and islanding operation have been demonstrated by the simulation results.

REFERENCES [1] Chinas EV Industry Leaps Forward. http://www.chinadaily.com.cn/busi

ness/2010-09/03/content_11237059.htm

[2] SGCC plans to invest 20 billion for electric vehile charging infrastructure during the Twelveth Five-Year-Plan. http://www.escn.com.cn/2011/1230/91538_2.html

[3] L. Z. Xu, G. Y. Yang, Z. Xu, et al. Impacts of Electric Vehicle Charging on Distribution Networks in Denmark, Automation of Electric Power Systems, Vol. 35, No. 14, pp. 18-23.

[4] H. L. Li, X. M. Bai. Impacts of Electric Vehicles Charging on Distribution Grid, Automation of Electric Power Systems, Vol. 35, No. 17, pp. 38-43.

[5] H. Y. Han. The Study on the Control Strategy of V2G Participating Peak Regulation and Frequency Regulation of the Grid, Master Thesis, Beijing Jiaotong University, June 2011.

[6] R. S. Li, X. L. Wang, F. Q. Zhou, et al. The system of electric vehicle intelligence charge station with smart power flow control, Power System Protection and Control, Vol. 38, No. 21, pp. 87-90.

[7] K. Yunus, H. Zelaya De La Parra, M. Reza, Distribution Grid Impact of Plug-In Electric Vehicles Charging at Fast Charging Stations Using Stochastic Charging Model, European Power Elctronics Conference, Birmingham, England, 2011.

[8] E. Pouresmaeil, D. Montesinos-Miracle,o. Gomis-Bellmunt, Control scheme of a three level H-bridge converter for interfacing between renewable energy resources and the AC grid, European Power Elctronics Conference, Birmingham, England, 2011.

[9] Y. Chen, A. Oudalov, J. S. Wang, "Integration of Electric Vehicle Charging System into Distribution Network," the 8th International Conference Power Electronics, Jeju, Korea, 2011.

[10] Y. Chen, B. Su, J. S. Wang. "Integration of Electric Vehicles into Grid with Bi-directional Power Conversion Technology," IET Annual Power Symposium, HongKong, China, 2011.

[11] IEEE PES Distribution System Analysis Subcommittee. IEEE 37 Node Test Feeder. http://ewh.ieee.org/soc/pes/dsacom/testfeeders/index.html.

[12] Technical Specifications of Quick Charger for the Electric Vehicle. CHAdeMO Association, April 2010.

[13] Connection set for conductive charging of electric vehicles - Part3: DC charging coupler. Draft national standard in China, December 2011.

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