Optimal EV Charging Control Strategy Based on DC Microgrid

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Peer-review under responsibility of the organizing committee of CPESE 2016 doi: 10.1016/j.egypro.2016.10.172

Energy Procedia 100 ( 2016 ) 243 – 247

ScienceDirect

3rd International Conference on Power and Energy Systems Engineering, CPESE 2016, 8-12

September 2016, Kitakyushu, Japan

Optimal EV charging control strategy based on DC microgrid

WU Shengjun

a

, XU Qingshan

a

, LI Qun

b

, YUAN Xiaodong

b

, CHEN Bing

b*

a

School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu Province, China; b

Jiangsu Electric Power Research Institute, Nanjing 211103, Jiangsu Province, China

Abstract

Electric vehicles (EVs) charging facilities and renewable energy systems can be directly associated within microgrid to achieve coordination and complementary, and reduce the negative impact on the grid. This paper firstly introduces the system structure of DC microgrid and its unit functional models. Secondly, the appropriate microgrid energy management control strategy based on day-ahead prediction is developed, according to the different power demand scenarios. Finally, the actual DC microgrid case verifies the optimal control strategy’s synergies with renewable energy and electric vehicle charging equipment. It provides a reference to solve large-scale electric vehicle charging power demand and renewable energy consumptive problems in the distribution grid.

Peer-review under responsibility of the organizing committee of CPESE 2016.

Keyword:electric vehicle; photovoltaic generation; energy storage; DC microgrid; optimization control

1. Introduction

Development of renewable energy and electric vehicle have been established as an effective way to ensure energy security and realize emission reduction by many countries. However, photovoltaic and wind power and other renewable energy sources with the output volatility and intermittent characteristics, making large-scale of renewable energy connected to the grid still have many barriers to overcome. On the other hand, it is difficult to keep the power grid stable operation and optimize the scheduling for large-scale electric vehicle charging uncertainty and large load characteristics [1]. If the EV charging power is generated by coal-fired power plants, the emission advantage for EV is not obvious [2]. To achieve the true meaning of low carbon and emission reduction targets: one is improving the grid absorptive capacity for renewable energy by the study of grid-connected renewable energy technologies; the second is the establishment of electric vehicle charging infrastructures directly associated with the renewable energy generation through microgrid [3-4].

This paper studies an EV charging optimal control problem in DC microgrid. Firstly, a DC micro-grid model with electric vehicle, photovoltaic and energy storage is established. Then optimal control strategy is proposed for EV charging in different scenarios. Finally, DC microgrid in Jiangsu EPRI as an example, to verify the effective of the EV charging optimization control strategy.

*Corresponding author. Tel.: +86-13813859781. E-mail address: [email protected].

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2. DC microgrid model

2.1 The structure of DC microgrid

A DC microgrid is mainly composed of PV system, EV charging device, battery energy storage system, grid-connected system, and microgrid energy management system, as shown in Fig.1.

2.2 Function of system components 2.2.1 PV system

The PV system is composed of PV modules and related DC/DC converter. The DC/DC converter is running in the MPPT strategy [5]. The actual output power of a PV system is mainly affected by both solar irradiation and environmental temperature. DC DC 0.4/10kV DC BUS DC DC DC DC 0LFURJULGHQHUJ\ PDQDJHPHQWV\VWHP (QHUJ\VWRUDJH (9 39 DC AC *ULGFRQQHFWHG

Fig.1 Structure of the DC microgrid

2.2.2 EV charging device

The EV charging device is composed of DC charger and EV battery, battery charging power and current are controllable. Recently the battery also is charged with a constant power. In the constant power charging mode, the charging time is determined by the charging capacity and the charging power.

2.2.3 Energy storage system

The energy storage system is composed of lithium-ion battery and DC/DC converter. The energy storage system completes load shifting, PV dissolved and power fluctuations smoothing function.

2.2.4 Grid-connected system

The grid-connected system is mainly composed of bidirectional AC/DC inverter and distribution transformer. During the operation, the grid-connected system transfers energy between distribution grid and DC microgrid, and keeps the voltage of DC bus stable.

2.2.5 Microgrid energy management system

The microgrid energy management system monitors and controls the operation of each modules in the DC microgrid. Based on the monitoring data, the energy management system controls the charging power of EV and the charging power supplied by the PV system, energy storage system or distribution grid. The microgrid energy management system helps to achieve PV power dissolved, optimization of EV charging, and microgrid balance. 3. Optimal control strategy of EV charging

3.1. PV generation forecast

PV generation forecast can be divided into ultra-short-term, short-term and long-term forecast by the duration. In this paper, the PV generation forecast is used to develop schedule plans. The PV generation has strong correlation between the future and the history output when its weather conditions are similar. This paper establishes the forecast model using historical PV generation data combined with weather data.

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3.2. Analysis of EV charging demands

The EV charging demands are closely related to daily mileage, kilometers power consumption and battery capacity. According to the US Department of Transportation survey on the national household vehicles, daily mileage of the vehicle approximately log-normal distribution, its probability density function as follows:

2 D 2 D ln -1 ( ) e x p 2 2 D D d f d d P V V S ª º « » « » ¬ ¼ (1) Where,ȝD represents trip distance expectations, ıD represents the standard deviation.

EV power consumption is generally 15 ~ 30 kWh per hundred kilometers, subject to the specific types of EV, battery age, temperature and road traffic and other factors.

3.3. Economic analysis of (9charging

EV charging economy relates to electricity prices and equipment operating efficiency. If the EV charging energy from the grid, then charge fees associated with the peak-valley electricity price. If the PV generation energy directly uses to EV charging, its energy efficiency is high. Therefore, the EV charging energy directly from the PV generation is the best economy for the EV charging.

3.4. Energy storage system control strategy

During the EV charging, there are three main tasks for the energy storage system: 1) mitigating the fluctuation caused by PV generation; 2) improving proportion of PV energy dissolved in the microgrid; 3) increasing the economy of EV charging. To achieve these three tasks, the energy storage system should store energy in the valley and discharge in the peak to compensate for the lack of power.

3.5. The EV charging control strategy

If the EV charging without optimization control, part of the PV generation energy cannot be used by EV charging. While the EV charging gets power from distribution network during peak period, it increases peak load and the cost of charging is high. So it is necessary to optimize the control of EV charging for achieving economical optimization and reducing peak loads through microgrid energy management. EV charging optimized control strategy implements through the microgrid energy management system, the control strategy optimization process shown in Fig.2. 4. Case study

4.1. Background

This paper takes a DC microgrid in Jiangsu electric power research institute to study the optimal EV charging control strategy. The DC microgrid configures with 100kW grid-connected AC/DC converter, 30kW EV charging device, 30kW PV generation system, 30kW/30kWh lithium battery storage system, and 20kW controllable load. The following case is the application of the optimal EV charging control strategy in the commuter bus.

PV generation and EV changing demand day-ahead forecast

Calculate the lack of charging electricity

Microgrid energy optimization control

EV charging power tracks PV generation

Energy storage system compensates the lack of

charging energy

Energy storage system and the grid compensate the lack

of charging energy Scene 1

Scene 2

Scene 3

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4.2. The analysis of the EV charging

Since the DC charging pile rated power is 30kW, less than 0.3C EV charging power, so the general EV charging mode is 30kW charging until full. The optimal EV charging strategy takes advantage of DC microgrid, charging process optimization as shown in Fig.2. General and optimized charging modes contrast are shown in Fig.3.

0 4 8 12 16 20 23 -30 -20 -10 0 10 20 30 40 Time/h Po w e r/kW PV general charging optimized charging general grid power optimized grid power

Fig.3 general and optimized EV charging results in contrast

The general and optimized EV charging results contrast in a typical sunny day shown in Fig.3. In the sunny day, the PV generation power is much more than the amount of EV charging need, the charging energy can be fully provided by the PV generation. The general EV charging with 30kW from 8:30 until the battery is full. For the PV generation power is less than 30kW, it acquires power from the distribution network to supply the EV charging. After the EV battery is full, the PV generation power through the inverter feeds into the distribution network, and energy storage system does not operate in the general charging mode. In the optimized EV charging mode, the charging power tracks PV generation output from 8:30, its charging time is slightly longer than the general mode. The energy storage system absorbs the excess PV generation power according to setting grid-connected power, so the maximum exchange power of DC microgrid and distribution network is limited to 10kW, and the EV charging process does not take power from the distribution network as shown in Fig.3.

4.3. Evaluation analysis of the optimized EV charging

The evaluation indexes of optimized charging mode are much better than general mode, as follows:

1) The optimized charging mode improves PV self-consumptive rate about 30% than the general mode, which achieves a better self-consumptive of PV generation power.

2) The optimized charging mode does not get energy from distribution network in the peak hours, however, the general mode without optimized charging solution increases the grid peak load.

3) The electricity price of optimized charging mode is much lower, for it takes full advantage of PV generation power and low price of the valley electricity.

5. Conclusion

For EV charging increases grid peak load and renewable energy grid integration problems, this paper presents an optimized EV charging control strategy based on DC microgrid. The charging strategy through forecasting the demand of EV charging energy and PV generation power to schedule optimization of energy management processes in three different scenes. The case study results show that the strategy can improve self-consumptive rate of PV generation, decrease peak electricity consumption during the EV charging and reduce the cost of EV charging. The optimization strategy is useful to solve large-scale distributed power integration and EV charging load impact on the grid problems.

References

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