The Local Ordered Charging Strategy of Electric Vehicles Based on Energy Routers under Three-Phase Balance of Residential Fields

Abstract

With the popularization of electric private cars and the increase of charging facilities in residential areas, disorderly charging will affect the power supply efficiency of their distribution transformers and the quality of electricity used by users in residential areas. In severe cases, it may even cause vibration of the power grid, causing serious three-phase imbalance problems such as single-phase burnout of transformers or insulation breakdown of household appliances. This paper analyzes the influencing factors of the unbalanced operation of each phase of the distribution transformer and the electrical load characteristics of typical residential areas. Based on the photovoltaic output of the station area, the charging and discharging capacity of the energy storage system, and the orderly charging plan of residential electric vehicles, a local orderly charging strategy for electric vehicles based on energy routers under the three-phase balance of the residential area is proposed. This strategy can realize the three-phase balance control of the distribution transformer. The effectiveness of the method is verified by a typical scenario example. The control method is changed to minimize the three-phase imbalance in residential areas and improve the low utilization rate of the distribution network and the comprehensive utilization efficiency of adjustable resources in residential areas.

1. Introduction

In recent years, as the depletion of petroleum resources, environmental pollution, global warming, and other issues have become increasingly prominent, energy conservation and emission reduction will be the direction of future industrial development, and photovoltaic and other renewable energy power generation and electric vehicles have been vigorously promoted [1,2]. In 2021, domestic private car-mounted charging piles will increase by 426,000 units, public AC charging piles will increase by 252,000 units, public DC charging piles will increase by 111,000 units, and charging stations will increase by 21,100 units. When electric vehicles are connected to the distribution network as a new type of load, due to the large uncertainty in their temporal and spatial distribution, the phenomenon of simultaneous charging may occur in a certain period of time in a local area [3]. As the scale of the development of electric vehicles becomes larger and larger, the superposition of the load generated during the disorderly charging of electric vehicles with the regular load of the system is prone to generate new load peaks, and the duration is longer. Especially under high penetration rate, it will inevitably aggravate the three-phase imbalance problem, which will have a greater impact on the safe and reliable operation of the distribution network [4,5].

Three-phase balance is the basis for ensuring power quality, power supply safety, and energy-saving. Under normal operating conditions, the unbalance of the power supply link or the power link of the power system will cause the three-phase imbalance of the power system. Among them, the disordered output of photovoltaics and the asymmetry of the load are the main reasons for the three-phase imbalance in residential areas [6,7,8]. With the improvement of residents’ living standards, the types and quantities of residents’ electricity load have increased sharply. Household appliances are mainly single-phase loads. Although low-voltage power distribution networks mostly use three-phase four-wire power supply, due to problems in actual operation and management, it often causes too much load on a certain phase or a certain two-phase. Coupled with the continuous increase in the penetration rate of residential users of electric vehicles and the high overlap between the charging period and the peak power consumption [9], the problem of load three-phase imbalance has become more serious.

Taking the load change in residential area as an example, if the charging period of electric vehicles is not guided, the distribution transformer will inevitably be overloaded under the scenario of large-scale electric vehicle access, which will affect the power supply efficiency of the distribution transformer and the quality of electricity consumption by users in residential areas. Moreover, the aggravated three-phase unbalance phenomenon will cause the vibration of the power system structure, which will lead to a series of three-phase unbalance problems such as single-phase transformer burnout or insulation breakdown of household appliances [10,11,12]. These problems will affect the distribution network [13].

At present, a large number of scholars have carried out research on the impact of electric vehicle access on the distribution network. The authors of [14] conducted an orderly charging study on the power supply capacity limitation of distribution transformers encountered in the construction of electric vehicle charging facilities in residential communities. The grid selection method was used to solve the power distribution capacity limitation and maximize the charging revenue. Reference [15] proposed a dynamic orderly charging strategy that takes into account the prediction and compensation of charging requests and verified the effectiveness of the strategy through analysis of different examples. Reference [16] proposed a comprehensive energy management control and scheduling strategy that combines direct and indirect load control without changing the primary topology of the existing power distribution system, which maximizes user benefits and solves photovoltaic reverse transmission problem. Reference [17] analyzed the serious problems caused by disordered charging and studied the impact of electric vehicles on the three-phase imbalance of the distribution network under different penetration rates. Reference [18] proposed a novel unbalanced active distribution network electric vehicle orderly charging control strategy based on inverter’s reactive power injection and three-phase selection, which realized the optimization of the grid’s operation within 24 h. Reference [19] considered the three-phase imbalance of grid voltage and the cost of electric vehicle charging and battery aging, and proposed a two-stage optimization method based on active power and reactive power control to meet the needs of electric vehicle users and reduce the impact of the access of electric vehicles on the three-phase imbalance of the grid voltage. Reference [20] proposed a management method for photovoltaic electric vehicle charging stations, and divides this management method into three stages, taking into account the installation requirements of local stakeholders, the needs of users, and the configuration of users. Finally, the performance evaluation of photovoltaic charging stations was realized, which provides a reference for improving the scale and operation mode of electric vehicle charging stations. Reference [21] proposed a plug-in electric vehicle charging control algorithm—adjustable real-time valley filling algorithm to improve the charging of plug-in electric vehicles, and reduce the uncontrolled plug-in electric vehicle charging adverse effects on the three-phase imbalance of the power grid. Reference [22] evaluated the impact of electric vehicle access in a DC microgrid based on nonlinear control theory. The results show that, in terms of improving the three-phase imbalance of the power grid, the load of electric vehicles has higher stability and feasibility of realization. This shows that the study of three-phase unbalance control, taking into account electric vehicles, is effective and feasible

In summary, if the orderly charging of electric vehicles is guided reasonably, electric vehicles will become an effective means to solve the three-phase imbalance problem of the power system [23]. At the same time, it solves the problem of strong time-variability of distributed power sources and improves the ability to absorb clean energy [24,25]. Reference [26], aiming at the problem in which the current photovoltaic consumption control technology is not applicable to the three-phase four-wire low-voltage distribution network, proposed a low-voltage distribution network photovoltaic-energy storage coordinated control based on the three-phase four-wire optimal power flow. However, it does not consider the impact of disorderly charging of electric vehicles on the grid and the positive role of orderly charging of electric vehicles in three-phase balance control. In view of the impact of the charging load generated by a large number of electric vehicles on the power system of residential areas, it is necessary to effectively guide or control the charging behavior of electric vehicles [27,28,29]. That is, on the premise of meeting the charging demand of electric vehicles, scientifically guide electric vehicles to charge in an orderly manner through effective charging strategies, meet the requirements of safe operation of distribution transformers, and reduce the degree of three-phase imbalance in residential areas.

Therefore, in view of the possible impact of the disorderly charging of electric vehicles on the power quality of users in residential areas, this paper regards the user’s regular load, electric vehicles, and residential photovoltaic and energy storage units as a microgrid system. By analyzing the influencing factors of the unbalanced operation of the distribution transformers and the characteristics of typical residential areas, a local orderly charging strategy for electric vehicles based on energy routers under the three-phase balance of residential areas is proposed. Finally, the effectiveness of the method is verified through typical scenarios. The results verify the effectiveness of the proposed strategy in reducing the three-phase imbalance in residential areas, improving the low utilization rate of the distribution network in residential areas, and improving the comprehensive utilization efficiency of adjustable resources in residential areas.

2. Characteristic of Power Load in Typical Residential Area

The power consumption system of residential areas, including charging facilities, consists of a microgrid system, which is mainly composed of users’ conventional load, electric vehicles, photovoltaic units, and energy storage units, as shown in Figure 1.

In order to ensure the electric power supply of electric vehicles and the safety of power grid operation in the intelligent community, the energy router is integrated in the charging pile, which acts as the execution device of the charging plan. According to the charging plan, control instructions, such as start charging, stop charging, or charge power regulation, are sent to the charging pile at the specified time point. Energy routers have charging metering function and can realize flexible access and interaction of electric vehicle charging piles, energy storage, distributed photovoltaic and other energy-using devices on the customer’s side. In the orderly charging application, the energy router receives and stores the charging plan issued by the energy dispatching center and realizes the real-time control of electric vehicle charging by executing the charging plan and issuing instructions to the charging pile at the specified time point, to realize the orderly charging of electric vehicle. The functional structure of the energy router is shown in Figure 2.

2.1. Analysis of Power Consumption Characteristics of Typical Equipment of Residential Users

2.1.1. Characteristics of Household Load Power Consumption

Household load is mainly composed of lighting, air conditioning, refrigerator, TV, etc., and electricity consumption characteristics are affected by family factors, environmental factors, and living habits [30]. Among them, family factors mainly include family economic situation, member composition, member occupation, etc. Environmental factors are mainly determined by weather conditions; living habits mainly refer to users’ daily life and rest, and different living habits will form different electricity load.

Affected by the above factors, the power consumption curve of residential user load presents the afternoon peak and evening peak, and the maximum load is concentrated between 20:00 and 22:00 [31], which is superimposed with the charging peak time of the electric vehicle in the case of disordered charging. Taking a certain station area as an example, the load curve of distribution transformer on the maximum load day of the year is shown in Figure 3. The transformer capacity is 500 kVA, and the maximum load time of the whole year is the time when household electric vehicles enter the parking space to charge. If the charging behavior is not guided, the distribution transformer will inevitably be overloaded after a large number of electric vehicles are connected at peak hours. During the low hours of the morning, the load is below 200 kVA, which can accommodate multiple electric vehicles charging at the same time.

2.1.2. Residents Electric Vehicle Travel and Charging Rules

Electric vehicle users in residential areas usually charge their electric vehicle after the last trip every day and before leaving on the second day, with a long interval and high controllability [32]. According to the influence of residents’ occupation and living habits, electric vehicles in residential areas travel and return in large numbers in a short period of time, presenting a certain stability, and vehicles have no influence on each other’s travel and return. Therefore, in this paper, the distribution function of return time, travel time, and daily driving distance of electric vehicles in residential areas was obtained by referring to the results of the American Family Travel Survey in 2017 [33].

The probability density function of electric vehicle return time is

$$f_s(t)=\{\begin{array}{l}\frac{1}{{\sigma }_s\sqrt{2\pi }}\exp (-\frac{{(t-{\mu }_s)}^2}{2{\sigma }_s^2})\\ \hspace{1em}\hspace{1em}\hspace{1em}({\mu }_s-12)<t\le 24\\ \frac{1}{{\sigma }_s\sqrt{2\pi }}\exp (-\frac{{(t+24-{\mu }_s)}^2}{2{\sigma }_s^2})\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} 0<t\le ({\mu }_s-12)\end{array}$$

(1)

where ${\sigma }_s$ is the standard deviation, 3.4; ${\mu }_s$ is the expected value, 17.5.

The probability density function of electric vehicle travel time is

$$f_e(t)=\{\begin{array}{l}\frac{1}{{\sigma }_e\sqrt{2\pi }}\exp (-\frac{{(t-{\mu }_e)}^2}{2{\sigma }_e^2})\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}0<t\le ({\mu }_e+12)\\ \frac{1}{{\sigma }_e\sqrt{2\pi }}\exp (-\frac{{(t-24-{\mu }_e)}^2}{2{\sigma }_e^2})\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}({\mu }_e+12)<t\le 24\end{array}$$

(2)

where ${\sigma }_e$ is the standard deviation, take 3.2; ${\mu }_e$ is the expected value, 8.

The daily mileage probability density function of electric vehicles is

$$f_D(L)=\frac{1}{L{\sigma }_D\sqrt{2\pi }}\exp (-\frac{{(\ln L-{\mu }_D)}^2}{2{\sigma }_D^2})$$

(3)

If the expected SOC of the owner after each charge is 1, charging once a day, then the minimum charging time of electric vehicle each time $t_{\min }$ is

$$t_{\min }=\frac{LW}{P_{c\_\max }{\eta }_c}$$

(4)

where W is the power consumption per kilometer of electric vehicle; $P_{c\_\max }$ is the maximum charging power of electric vehicles; ${\eta }_c$ is charging efficiency for electric vehicles.

3. Local Orderly Charging Strategy for Electric Vehicles Based on Energy Router under Three-Phase Balance in Residential Areas

3.1. The Optimization Goal

Based on the abovementioned influencing factors of the three-phase imbalance in the station area and the analysis of the power load characteristics of the typical residential area, a local orderly charging strategy for electric vehicles based on energy routers under the three-phase balance of the residential area is proposed. The local orderly charging strategy for electric vehicles that meets the three-phase balance in residential areas is required to maximize the charging demand of electric vehicles and the local maximum consumption of photovoltaic and meet the load demand of the platform area first. At the same time, the energy storage system reduces the peak load to prevent the distribution transformer in the platform area from exceeding the limit and guides the charging period of electric vehicles reasonably by issuing the charging plan to minimize the three-phase power imbalance in the platform area. At the same time, the priority rules of charging vehicles are followed, that is, the vehicles connected to charging piles first have priority to charge, and the vehicles connected to charging piles later have priority to be regulated. In order to meet the requirements of three-phase balance in residential areas, the sum of Euclidean distance between the power of each phase and the average three-phase load is selected to represent the three-phase unbalance degree of the station area, and the goal is to minimize the degree. It is expressed as follows:

$$\min \phi ={{\displaystyle \sum _{t=0}^{t_{\max }}{\displaystyle \sum _{i=A,B,C}(P_i+{\displaystyle \sum P_{EV.N}}-\frac{1}{3}{P}_t)}}}^2$$

(5)

where $P_i=P_{iL}+P_{iC}+P_{iE}-P_{iG}$. Among them, $P_{iL}$ is the residential electricity load, $P_{iC}$ is the energy storage charging and discharging power, charging is positive, and discharging is negative, $P_{iE}$ is the electric vehicle charging power, and $P_{iG}$ is the solar output power; ${\displaystyle \sum P_{EV.N}}$ is the electric vehicle charging load required to meet the three-phase balance.

In addition, the above objective function needs to satisfy the following constraints.

• Charging time constraint

The time when the user starts charging should be between the return time of the last trip and the trip time, and the shortest charging time should be greater than the difference between the start time of charging and the trip time.

$$t_{in}\le t_{st}<t_{out}$$

(6)

$$t_{\min }<t_{out}-t_{st}$$

(7)

where $t_{st}$ is the time when the electric vehicle starts to charge; $t_{in}$ is the time when the user of the electric vehicle returns from the last trip; $t_{out}$ is the time when the user travels the next day; $t_{\min }$ is the shortest charging time for the electric vehicle.

2.

Transformer capacity constraint

$$P_T\le P_{\mathrm{L}}$$

(8)

where $P_T$ is the transformer capacity, $P_{\mathrm{L}}$ is the transformer over-limit capacity.

3.

Photovoltaic output power constraints

$$P_{iPV\_\min }\le P_{iPV}(t)\le P_{iPV\_\max }$$

(9)

where $P_{iPV\_\min }$, $P_{iPV\_\max }$ are the maximum and minimum output power of photovoltaic output in the system, and the unit is kW.

4.

Energy storage operation constraint

$$\{\begin{array}{l}{P}_{\mathrm{c}\min }\le P_c(t)\le P_{\mathrm{c}\max }\\ SO{C}_{c\min }\le SO{C}_c(t)\le SO{C}_{c\max }\end{array}$$

(10)

where $P_{\mathrm{c}\min }$ and $P_{\mathrm{c}\max }$ are the minimum and maximum charge and discharge power, and the unit is kW. $SO{C}_{c\min }$ and $SO{C}_{c\max }$ are the minimum and maximum battery state of charge.

5.

Constraints on charging capacity of electric vehicles

$$SO{C}_{\min }\le SOC(t)\le 1$$

(11)

where $SO{C}_{\min }$ is the minimum state of charge of the electric vehicle, taking 0.15.

3.2. Solution Strategy

The specific steps of local orderly charging strategy for electric vehicles based on energy router under three-phase balance in residential areas are as follows:

Step 1: Read the model information of the station area, including the rated capacity of the distribution area distribution transformer $P_{\mathrm{R}}$, real-time three-phase total power $P_t$, each single-phase power $P_i$$(i=A,B,C)$, calculate the power of each phase. For example, phase A: $P_A=P_{AL}+P_{AC}+P_{AE}-P_{AG}$ ($P_{AL}$ is the residential electricity load, $P_{AC}$ is the charging and discharging power of energy storage, charging is positive, and discharging is negative, $P_{AE}$ is the electric vehicle charging power, $P_{AG}$ is the solar output power).

Step 2: Determine whether $\mathrm{\Delta }{P}_1=1/3P_t-P_i$ is 0. If it is greater than 0, obtain the information of electric vehicles that have been connected to the charging pile but not charged, including the charging pile number, location, electric vehicle charging power, and time of use. If the charging power required for all electric vehicles that are connected but not charged is ${\displaystyle \sum P_{EV.i}}$, go to step three; If it is less than 0, go to step 7.

Step 3: Determine whether $\mathrm{\Delta }{P}_2=\mathrm{\Delta }{P}_1-{\displaystyle \sum P_{EV.i}}$ is 0; if it is greater than 0, calculate the charging power according to the idle pile, if it is less than 0, charge sequentially according to the time of use of the car.

Step 4: After calculating the charging power based on the idle piles, release the charging plan to the user and inform the user of the idle pile number, location, and available charging power. According to the charging plan, the energy router sends control instructions for starting and stopping charging to the charging pile at the specified time point.

Step 5: Determine whether $\mathrm{\Delta }{P}_3=\mathrm{\Delta }{P}_2-{\displaystyle \sum P_{EV.N}}$ is 0; if it is greater than 0, go to step 6, if it is less than 0, charge sequentially according to the time of use of the car, ${\displaystyle \sum P_{EV.N}}$ is the charging power required in the charging plan.

Step 6: Determine whether the energy storage SOC is less than 50% [34]; if it is less, the excess power will charge the energy storage, go to step 10, if it is greater, then give up charging the energy storage, and go to step 10.

Step 7: Determine whether $1/3P_{\mathrm{L}}-P_i$ is 0; $P_{\mathrm{L}}=P_{\mathrm{R}}\times 70\%$ means over-limit power. If it is greater than 0, go to step 8; if it is less than 0, go to step 9.

Step 8: Determine whether the undischarged energy storage SOC of the phase is greater than 0. If it is greater than 0, obtain the positive charging vehicle information of the phase, including the number, location, and charging power, switch to energy storage charging, and go to step 10; if it is less than 0, go to step nine.

Step 9: Cut off the charging electric cars one by one according to the user’s time of using the car and calculate $P_i^{\prime }$ after the removal and go to Step 2. At this time, $P_i^{\prime }$ is equivalent to $P_i$ brought into the calculation.

Step 10: The end of the strategy

The orderly charging strategy flowchart is shown in Figure 4. In the orderly charging strategy mentioned above, the smart cell scheduling center plans the existing charging requests in the current control period and the expected charging requests in the future control period, obtains the charging plan according to the solution of the optimization goal, and the energy router receives and executes the issued charging plan. The daily load curve of the system is relatively fixed, but the charging request time of electric vehicle varies greatly due to its randomness. Therefore, in the time series, the scheduling center will update the charging plan at the end of each control time and make a new charging plan for electric vehicles newly connected to charging piles. The new plan made at the beginning of the later control period was used to replace the plan made at the end of the previous control period to optimize the implementation effect of the orderly charging strategy through this rolling plan modification.

4. Simulation Analysis

Taking a residential area as an example, the rated capacity of a transformer in a residential area is 1250 kVa, and the maximum active power of the transformer is 875 kW. As shown in Figure 5, in this residential area, 12 single-phase AC charging piles are successively connected to phase A, phase B, and phase C lines with a voltage grade of 0.4 kV, with built-in energy router. At the same time, six photovoltaic generating sets (PGS) with a rated capacity of 20 kW and a set of 60 kW/200 kWh battery sets (BS) are connected to each phase.

Area photovoltaic output-selected typical sunrise power curves were analyzed, and so were the electric vehicles charging with periodic and random load change. In this paper, the type Equations (1)–(3) showing the probability density function are combined with the Monte Carlo method to extract the electric car return time, travel time, and mileage, will stack sampling effects, and obtain different time 50 times simulation average state of electric vehicles. Among them, there are 150 electric vehicles in the community. The capacity of electric vehicles is set as 30 kWh, the power consumption per kilometer is 0.15 kWh, and the charging power is set as 7 kW. When the number of simultaneous charging exceeds the charging pile, the order will be delayed according to the return time.

Figure 6 shows the phase A conventional load, photovoltaic and energy storage output, and the optimized electric vehicle charging load. It can be seen from Figure 6 that during the period from 11:00 to 16:00, the energy storage system can not only absorb the excess electricity generated by the photovoltaic system, it also absorbs the three-phase unbalanced power that cannot be stabilized due to the small number of charging piles. During the peak period of power consumption from 19:00 to 23:00, discharge of energy storage system occurs to prevent power overrun. Figure 7 shows the B-phase conventional load and photovoltaic and the energy storage output and the optimized electric vehicle charging load; Figure 8 shows the C-phase conventional load, photovoltaic and energy storage output, and the optimized electric vehicle charging load. It can be seen from Figure 6, Figure 7 and Figure 8 that the orderly charging load of electric vehicles with the goal of three-phase balance tends to smooth the fluctuations of the load of each phase. This strategy meets the charging demand of electric vehicles and maximizes the consumption of photovoltaics to the greatest extent. After first meeting the load demand of the station area, the energy storage system absorbs the excess power and guides the charging period of electric vehicles to reduce the load on the grid.

Figure 9 shows the normal load of each phase and the total load curve of each phase after optimization. In period 1, the total load curve of each phase after optimization has increased by 90 kW compared with the conventional load curve of each phase, which greatly increases the resource utilization rate during the low period of load power consumption. In time period 2, the power of the optimized total load curve of each phase is reduced by 113 kW compared with the conventional load curve of each phase, and energy storage is used to feed power to the grid during the time when the load uses more electricity to prevent the distribution transformer of the station from exceeding the limit. In time period 3, it can be seen that this strategy can still effectively improve the three-phase imbalance in the station area during peak power consumption. On the whole, the total load of each phase is controlled by the three-phase balance of the residential area to achieve the purpose of reducing the three-phase imbalance, so that the three-phase total load curves basically coincide.

5. Conclusions

This paper analyzes the characteristics of the electricity load in a typical residential area, as well as the influencing factors of the unbalanced operation of each phase of the distribution transformer. Based on the photovoltaic output of the station area, the charging and discharging capacity of the energy storage system, and the orderly charging plan of residential electric vehicles, a local orderly charging strategy for electric vehicles based on energy routers under the three-phase balance of residential areas is proposed. This strategy realizes the three-phase balance control in the station area and uses a typical scenario example to verify the effectiveness of the method. From the results, it can be known that during the low period of load power consumption, the total load power of each phase after optimization is increased by 90 kW compared with the conventional load power of each phase, which greatly increases the resource utilization rate. During periods when the load consumes more electricity, the optimized total load curve of each phase reduces the power of each phase by 113 kW, compared with the conventional load curve of each phase. Energy storage is used to feed power to the grid to prevent the distribution transformers in the station from exceeding the limit. In addition, this strategy can still effectively improve the three-phase imbalance in the station area during peak hours. In summary, this strategy has greatly improved the three-phase imbalance of the load in the station area, and, at the same time, achieved the goal of shifting the peak charging load and preventing the station from exceeding the limit, greatly improving the resource utilization of the distribution network in the residential area.