Chun-guo FEI, Hong-shuang HUO

(College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China)

Abstract: The random and disorderly charging of a large number of electric vehicles will cause certain negative effects on the operation safety of the power distribution network and increase the peak-to-valley ratio of power grid. According to the concept of vehicle-to-grid (V2G), the present paper proposes the micro-grid dynamic price adjustment strategy to guide the orderly charging of electric vehicles. The results of experimental simulation show that the regulation and control of dynamic price adjustment strategy can guide vehicles to participate in orderly charging regulation and charging controlling. At the same time, the strategy can satisfy various needs of charging customers and increase the responsiveness to emergency charging event.

Key words: Electric vehicle, Orderly charging, Micro-grid, Dynamic price

Promotion and use of electric vehicles can effectively reduce the use of traditional fossil fuels and reduce carbon emission. With the application cost of batteries being lowered, vehicle manufacturing technology enhanced and charging equipment platform improved, electric vehicles are at a stage of rapid development and the possession amount of electric vehicles will increase sharply. Due to the different habits of customers in charging vehicles, the random and disorderly charging conduction of a large number of vehicles will cause certain negative effect on the operation safety of power distribution network. It is very necessary to find out an effective control strategy. The strategy should be able to guide the charging consumption of vehicle owners. The peak-to-valley ratio and operational stability of distribution grid finally can be improved.

The Vehicle-to-grid (V2G) is supported by the intelligent power grid. The V2G can real-time control the energy flowing in two-way between the vehicles and the power grid. It means that the electric vehicles not only can get energy from power grid when they are power consumptions, but also can give the energy stored in electric vehicles back to power grid. The distribution grid can interact with charging vehicles through charging and discharging equipment, and obtain all kinds of information such as the interacting information energy, customer appeal information, operational information of distribution network and vehicle battery information. V2G makes it possible to realize the various charging needs of customers [1-3].

With reference to the development trend of relevant intelligent terminal technology, this paper proposes the dynamic price adjustment strategy based on the micro-grid in order to improve the peak-to-valley ratio and operational stability of power grid. At the same time, various charging demands of customers should be satisfied and customers are guided to adjust their vehicle charging consumption [4-8].

1 Micro-grid orderly charging control based on dynamic price adjustmentstrategy

Based on the concept of V2G [9], this paper proposes a micro-grid dynamic price adjustment strategy for peak shaving and valley filling. Through the analysis and guidance of the command and control center, the strategy is executed to satisfy the different charging demands from customers. The structure of the strategy is shown in Fig.1.

Fig.1 The structure of the micro-grid dynamic price adjustment strategy

1.1 Dynamic price adjustment strategy

If the charging power of unit vehicle at Nodekis denoted asPk,sgl, the total power of charging load at Nodekis as follows:

Pk,all=mPk,sgl

(1)

Where,mis the total number of electric vehicles accessed at Nodek.

The command and control center adjusts the charging system status once every period of Δt[10]. During the non-adjustment period of the system status, it is assumed the charging powerPk,sglfor electric vehicles remains constant. The number of time slicesJk,isatisfies the need of the electric vehicle charging, when it accesses at Nodekof the charging system.Jk,icould be evaluated as follows.

(2)

Where, Δtis usually set as 10～30 min; 「a? indicates the minimum integer which is not less thana. The number of time slices available for No.ivehicle accessed at Nodekis expressed as

(3)

Where, ?a」 indicates the maximum integer which is not greater thana.

When an electric vehicle accesses the charging node, the command and control center updates the information to next time control node according to the running status of the charging pile. According to the existing status information and charging demand of the vehicle, theTk,iandJk,iwill be calculated for new accessing vehicle. Within the vehicle parking periodTk,i, the control margin available for charging load is calculated by using Eq..

Mk,i,t=αPk,trans(t=1,2,…,Tk,i)

(4)

The range ofαvalue is [0, 1].

The smallerMk,i,tvalue indicates the available charging power value for electric vehicles is smaller during the relevant period[11]. For differentMk,i,tvalues, different charging guided prices are set up to guide the vehicles to orderly charge.

Before determining the preferential price for new access vehicles, the command and control center pre-assesses whether the charging pile can meet the charging demand of the customer during the parking period.Mk,i,t≤Pk,iindicates that the charging demand can be satisfied during the controling period to charge the electric vehicle. At the same time, theHk,iis counted for vehicle charging under such condition.

WhenHk,i

(5)

When,Hk,i≤Jk,i, it indicates that the system can, under the grid load constraint, implement the effective energy supply during the electric vehicles in charging period. Moreover, the charging period of vehicles can be regulated. In order to achieve better effect of peak shaving and valley filling, in the micro-grid controllable system, the starting point of valley price can be analyzed and estimated according to Eq..

(6)

In order to ensure the charging operation satisfying the customer demands, the starting point of charging will be revised according to Eq. after the optimization,

(7〕

Moreover, according to the results calculated by Eq., the charging cost will be informed to customers, which is produced under orderly charging control and regulation.

(8)

When the customer immediately chooses starting to charge, ifMk,i,t≤Pk,i, the vehicle begins to be recharged from the next control period. Customer should pay for charging by the unit pricep. When the customer accepts the orderly dynamic regulation and control, the vehicle will be charged inMk,i,t≤Pk,iperiod under the requirements set in advance. The charging fees are calculated according to Eq..

1.2 Micro-grid orderly charging of dynamic price adjustment strategy

The flow chart of the micro-grid orderly charging control based on dynamic price adjustment strategy is shown in Fig.2, which is implemented by the command and control center. Firstly, the command and control center is initialized to set micro-grid load power and price information. Then the center reads the state information of the accessed vehicle and customer’s charging demand[12-13]. According to the collecting information, the dynamic time-of-use price is formulated by comprehensively analyzing and the corresponding information will be present to the customers. After the vehicle owner chooses the relevant charging mode, the relevant operation is implemented.

2 Simulation and analysis

2.1 Simulation research

(1) Normal load

In the micro-grid control system, the fluctuation curve of conventional daily load power over time is shown in Fig.3. It includes residential, commercial and industrial loads. The figure shows that the industrial load is relatively stable with less fluctuation, the commercial load is mostly centralized in the daytime and residential power peak appears at 19∶00.

(2) Access of new energy

The proportional curve of the energy output power for PV and wind new energy is shown in Fig.4, mainly with PV generation for daytime and wind power generation for nighttime.

(3) Access status of charging vehicles

When the number of charging vehicles is relatively big, their charging behaviors obey approximately the normal distribution of the law of large numbers. According to the statistics and analysis of charging time and charging location, electric vehicles are usually recharged in two cases. One is in the work area during the day; the other is in the residential area in the night. The normal distribution areN(10,0.62) andN(19,1.52) the relevant statistical ratio is 0.3 and 0.7 for the two cases, respectively. The maximum acceptable charging power for each vehicle is not more than 20kW. The total number of electric vehicles participating in the regulation and control is 1 500. TheSOCk,i,endis 0.8 during the day and theSOCk,i,endis 1 in the night. The probability that customers obey the orderly charging regulation and control is 0.65 during the day time and 0.9 in the night time. Because the price p has no influence on the qualitative analysis of the orderly charge control effect, the base price p is set at RMB 1/(kW·h) for simplifying the analytical process.

Fig.2 Flow chart of micro-grid orderly charging control strategy with dynamic price

Fig.3 Fluctuation curve of conventional daily load power

Fig.4 Proportional curve of wind power and PV generation output power

2.2 Simulation results and analysis

Fig.5 shows three curves. One is the conventional load power curve, one is vehicle’s disorderly charging load power overlapping curve, and the other is vehicle’s orderly charging load power overlapping curve after orderly control.

Fig.5 Three load power curve

(1) Comparison between disorderly charging and orderly charging

From Fig.4 to Fig.6, it may be observed that under the disorderly mode, there are two charging peaks: 09:30 and 17:30. This phenomenon leads to increasing the peak-to-valley ratio and the fluctuation disturbance. It also reduces system operation stability. When electric vehicles are orderly charged under the guidance of the dynamic pricing adjustment strategy, electric vehicles completed adding the electric energy during off-peak period of conventional power consumption in distribution grid. The distribution to grid system is reduced. The peak-shaving and valley-filling is realized.

Fig.6 The statistical relation between average charging cost of vehicles and response ratio of vehicle owner to orderly dynamic charging

(2) The effect on the regulation and control from customer’s response ratio of orderly charging

The dynamic price adjustment strategy for orderly charging depends on the response ratio of vehicle owners to the orderly charging. If the customers’ response ratio is higher, the system has the more periods to regulate. Finally, the effect on peak-shaving and valley-filling is better. Fig.6 shows the statistical relationship between average charging cost of vehicles and vehicle owners’ response ratio to orderly dynamic charging. From this figure, it is known that the higher the customers’ response ratio to orderly controlling, the lower the average charging cost of vehicles is. The reduced operating cost of power grid is fed back into the charging cost of customers.

(3) The effect on the regulation and control from the peak to valley price ratio

Fig.7 shows the statistical relationship between average charging cost of vehicles and the peak to valley price ratio. From this figure, it is known that the higher peak to valley price ratio, the lower the vehicles’ average charging cost is. This result reflects that the rational peak to valley price plays an active guiding role promoting the response of electric vehicle customers to the orderly charging control.

As the simulation results shown, the micro-grid dynamic price adjustment strategy has a better control capability for peak-to-valley ratio. This strategy reserves a certain margin for emergency power consumption event, while guiding the orderly charging of vehicles.

Fig.7 The statistical relationship between average charging cost of vehicles and the peak to valley price ratio

3 Conclusion

The paper studies the orderly charging based on micro-grid and proposes the dynamic orderly charging control strategy for electric vehicles. The strategy establishes the dynamic price regulation and control mechanism that guides customers to orderly charge their electric vehicles. The orderly charging regulation and control procedure of the strategy is given in this paper. Finally, the simulation research is made for the strategy. The simulation experimental results indicated that the strategy could guide the charging behavior of customers to avoid resulting in the load peak, under dynamic price regulation and control mechanism. Additionally, with certain charging margin reserved for emergency charging, the responsive capacity of the distribution network is increased for emergency. Therefore, it has certain practical application value and reference meaning.