CN109866628B

CN109866628B - Ordered charging control method for electric vehicle with active power distribution network

Info

Publication number: CN109866628B
Application number: CN201910049088.9A
Authority: CN
Inventors: 谢伟; 苏向敬; 孟祥浩; 符杨; 谢邦鹏; 米阳; 刘舒
Original assignee: Shanghai University of Electric Power; State Grid Shanghai Electric Power Co Ltd
Current assignee: Shanghai University of Electric Power; State Grid Shanghai Electric Power Co Ltd
Priority date: 2019-01-18
Filing date: 2019-01-18
Publication date: 2022-07-15
Anticipated expiration: 2039-01-18
Also published as: CN109866628A

Abstract

The invention relates to an orderly charging control method for an electric vehicle with an active power distribution network. Compared with the prior art, the method has the advantages of reducing voltage fluctuation, network unbalance and the like.

Description

Ordered charging control method for electric vehicle with active power distribution network

Technical Field

The invention relates to the field of orderly charging of electric automobiles, in particular to an orderly charging control method for an electric automobile of an active power distribution network based on the reactive power capability of an inverter.

Background

The global warming trend is exacerbated by excessive emissions of greenhouse gases. The electric automobile as a new generation of transportation has incomparable advantages compared with the traditional automobile in the aspects of saving energy, reducing emission and reducing the dependence of human on traditional fossil energy. The increasing popularity and large-scale access of electric vehicles (PEV) to the power grid will have a considerable impact on the operation and planning of the power system. The method mainly comprises the following steps: 1) an increase in load; 2) increasing the difficulty of operating and optimizing the power grid; 3) the quality of the power is affected; 4) and new requirements are put forward for the planning of the power distribution network. Meanwhile, the massive access of the roof photovoltaic brings new challenges to the safety and stability of the power grid. Combine together electric automobile and roof photovoltaic, regard electric automobile as energy memory, carry out reactive compensation again when insufficient voltage simultaneously, reducible or eliminate above-mentioned harmful effects through coordinated operation.

The interaction between the electric automobile and the power grid (G2V/V2G) means that the electric automobile is used as a distributed energy storage unit and participates in the regulation and control of the power grid in a charging and discharging mode. The core idea is to use a large amount of energy storage sources of the electric automobile as the buffer of a power grid and renewable energy. When the load of the power grid is too high, the electric automobile energy storage source feeds power to the power grid; and when the load of the power grid is low, the power grid is used for storing the surplus generated energy of the power grid, so that waste is avoided. By the mode, the electric automobile user can buy electricity from the power grid when the electricity price is low, and sell electricity to the power grid when the electricity price of the power grid is high, so that certain income is obtained. The electric automobile ordered charging strategy is characterized in that on the premise of meeting the charging requirement of the electric automobile, the electric automobile is guided and controlled by using practical and effective economic or technical measures, the load curve of the power grid is subjected to peak clipping and valley filling, the variance of the load curve is smaller, the construction of installed power generation capacity is reduced, and the coordinated interactive development of the electric automobile and the power grid is ensured.

The traditional ordered charging control strategy does not contain reactive injection of the electric automobile to a power grid, so that the voltage quality is reduced.

Disclosure of Invention

The invention aims to overcome the defects in the prior art and provide the ordered charging control method for the electric automobile with the active power distribution network.

The purpose of the invention can be realized by the following technical scheme:

the method is used for preventing overload, undervoltage and overvoltage of a line or a transformer by making an intelligent charging decision of an electric vehicle battery, reducing power generation cost and controlling voltage unbalance, and improves voltage quality by providing reactive injection according to day-ahead reactive power price while orderly charging at a selected single-phase node.

Preferably, orderly active charging and reactive discharging of the electric vehicle is performed at selected nodes according to active and reactive power rates to reduce voltage fluctuation and network imbalance.

Preferably, under the intelligent charging decision control, positive and negative of the active power P (t, K) of the node K respectively represent output power and input power, and the active power and reactive power output of the electric vehicle inverter are calculated by the following formula (1-4):

P(t，k)＝P_PV(t，k)-P_PEV(t，k)-P_L(t，k) (1)

P_PEV(t，k)＝S_PEV(t，k)·cosθ (2)

Q_PEV(t，k)＝S_PEV(t，k)·sinθ (3)

Q(t，k)＝Q_PEV(t，k)-Q_L(t，k) (4)

in the formula, P (t, k) and Q (t, k) respectively represent the active power and the reactive power of the node k at the moment t, and the positive and negative respectively represent output power and input power; p_PV(t，k)，P_PEV(t，k)，P_L(t, k) respectively represent the active power of the photovoltaic, the electric automobile and the load at a time t node k; q_PEV(t, k) represents the reactive power of the electric vehicle at time t node k, S_PEV(t，k)，θ，Q_L(t, k) are the apparent power, the power factor angle and the reactive load of the electric automobile at a time t node k respectively;

preferably, the intelligent charging decision model is as follows:

the objective function is to minimize the system loss F in 24 hours_Cost-Loss(t) cost of charging F_Cost-gen(t) andcoefficient of voltage unbalance F_Cost-VUF(t) and maximizing the benefits of reactive power delivery to the grid;

in the formula:

where Δ t is the time interval set to 15 minutes, k is the number of nodes and N_nodeThe total number of nodes; k_E(t) is the cost per kWh of electrical energy loss; k is_p(t) and K_S(t) cost coefficients based on cost per kWh of real-time electricity prices and photovoltaic output, respectively; v^-(t，k)、V⁺(t, K) are negative and positive sequence voltages, respectively, K_VUF(t) is a penalty factor for VUF; f_Cost-Q(t) earnings for reactive power output of electric vehicles, K_Q(t) is the unit price of electricity with reactive power output, assuming that the unit price of electricity with reactive power is 10%, and the minus sign represents reactive power output; f_Cost-Q(t)、V^-(t，k)、V⁺(t，k)。

Preferably, both target equations (5) and (9) need to satisfy the following network and element constraints:

power limitation for preventing overload of charging and discharging of electric automobile

In the formula, P_l(Δ t, k) and P_PEV(Δ t, k) is the active load and the electric vehicle charging power at node k in each time interval, respectively, where P is_l(Δ t, k) is derived from the load curve, P_max(Δ t, k) is the maximum output of the active power at node k; the following formula indicates whether the electric vehicle is available, and the schedule is available only when the electric vehicle is connected to the grid at the corresponding time;

upper and lower limits of node voltage variation

ΔV(t，k)＝|V(t，k)-1|≤6％，for k＝1，...，N_node (12)

Δ V (t, k) is the amount of change in voltage, and V (t, k) is the per unit value of the actual voltage at node k at time t.

③ upper limit of node voltage unbalance coefficient

Upper and lower limits of SOC level of electric automobile

SOC_min＜SOC_i＜SOC_max (14)

SOC_minAnd SOC_maxRespectively the minimum state of charge, SOC of the electric vehicle_iIs the current state of charge.

Preferably, the ordered charging control process is as follows:

the method comprises the steps of firstly obtaining the current state and the demand of the electric automobile, then obtaining the current photovoltaic power generation amount and the current electricity price, and combining the data of the new coming and leaving electric automobiles to obtain the number of charging time sections and the number of staying time sections of the electric automobiles.

Compared with the prior art, the invention has the following advantages:

(1) and according to the active and reactive power prices, orderly active charging and reactive discharging of the electric automobile are performed at the selected nodes to reduce voltage fluctuation and network unbalance.

(2) By exciting consumers to enter a reactive trading market, the number and the effect of the electric vehicles participating in the orderly active charging and reactive discharging scheme are ensured.

Drawings

Fig. 1 is a block diagram of a residential rooftop photovoltaic and electric vehicle connected to an electric grid.

Fig. 2 is a flow chart of an online optimized ordered charging strategy based on active control.

Fig. 3 is a flow chart of an online optimized ordered charging strategy based on active and reactive power control.

Fig. 4(a) is a three-phase four-wire distribution network used for simulation in this patent, and fig. 4(b) is a scale diagram of a rooftop equipped photovoltaic and electric vehicle.

Fig. 5 is a graph of power demand and VUF over 24h under normal conditions, where (a) is a graph of power demand and (b) is a graph of VUF.

Fig. 6 shows changes in power demand, VUF, and AB two-phase voltages in 24 hours during random charging of the electric vehicle, where (a) is a power demand graph, (B) is a VUF graph, (c) is an a-phase voltage graph, and (d) is a B-phase voltage graph.

Fig. 7 shows the power demand, VUF and AB two-phase voltage changes in 24 hours under the active control-based online optimization ordered charging strategy, where (a) is a power demand graph, (B) is a VUF graph, (c) is an a-phase voltage graph, and (d) is a B-phase voltage graph.

Fig. 8 shows changes of power demand, VUF and AB two-phase voltages in 24 hours under the online optimized ordered charging strategy based on active and reactive power control, where (a) is a power demand graph, (B) is a VUF graph, (c) is an a-phase voltage graph, and (d) is a B-phase voltage graph.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.

The invention provides an orderly charging and discharging control mode of an electric automobile by combining a three-phase four-wire unbalanced low-voltage network and a roof photovoltaic aiming at the current situation of disordered charging of the electric automobile. By making intelligent electric vehicle battery charging decisions, line/transformer overload (due to extreme PEV charging), undervoltage (at peak load time) and overvoltage (due to photovoltaic generation) are prevented, while the degree of voltage imbalance caused by random charging by the user is also reduced. Meanwhile, the specified electric automobile is enabled to transmit reactive power to the power grid for voltage regulation by combining the current electricity price, so that users are benefited and promoted to enter the active and reactive energy markets to realize better regulation.

The present invention is further described in terms of control strategy introduction, problem modeling, specific control methods, validation, and the like.

1. Introduction to control strategy

This patent is based on roof photovoltaic and electric automobile are interactive in the residential quarter, and every family's photovoltaic and electric automobile all insert through the tie point and join in marriage the net. As shown in FIG. 1, the structure can realize that the PEV charges and meanwhile, the reactive power is transmitted to the power grid.

The traditional orderly charging control strategy does not contain reactive power injection of the electric automobile to the power grid. The innovation point is to make an intelligent charging decision of the battery of the electric automobile so as to prevent the generation of overload, undervoltage and overvoltage of the circuit/transformer, and simultaneously reduce the power generation cost and control the voltage unbalance degree. The proposed method improves voltage quality by providing reactive injection according to the day-ahead reactive power price while orderly charging at selected single-phase nodes. Compared with the existing PEV charging mode, the method is more effective and has the following advantages:

according to the active and reactive power prices, orderly active charging and reactive discharging of the electric automobile are carried out at selected nodes to reduce voltage fluctuation and network unbalance.

Secondly, the number and the effect of the electric vehicles participating in the orderly active charging and reactive discharging scheme are ensured by exciting consumers to enter a reactive trading market.

Under the control strategy, the positive and negative of the active P (t, K) of the node K respectively represent output power and input power, and the active and reactive outputs of the inverter of the electric automobile can be calculated by formulas (1-4):

P(t，k)＝P_PV(t，k)-P_PEV(t，k)-P_L(t，k) (1)

P_PEV(t，k)＝S_PEV(t，k)·cosθ (2)

Q_PEV(t，k)＝S_PEV(t，k)·sinθ (3)

Q(t，k)＝Q_PEV(t，k)-Q_L(t，k) (4)

in the formula P_PV(t，k)，P_PEV(t，k)，P_LAnd (t, k) respectively represent the active power of the photovoltaic, the electric automobile and the load at a time t node k. S. the_PEV(t，k)，θ，Q_LAnd (t, k) are the apparent power, the power factor angle and the reactive load of the electric automobile at a time t node k respectively.

2. Problem modeling

Based on the control strategy, the following can be modeled, and the objective function is to minimize the system network loss F within 24 hours_Cost-Loss(t) Charge cost F_Cost-gen(t) and a voltage unbalance factor F_Cost-VUF(t) and maximizing the benefits of reactive power delivery to the grid.

In the formula:

where Δ t is the time interval set to 15 minutes, k is the number of nodes and N_nodeThe total node number is; k is_E(t) is the cost per kWh of electrical energy loss; k is_p(t) and K_S(t) cost coefficients based on real-time electricity prices (of table 1) cost per kWh and photovoltaic output, respectively; k_VUF(t) is a penalty factor for VUF; k_QAnd (t) is the unit price of electricity with reactive power output, and the negative sign represents reactive power output, assuming that the unit price of electricity with reactive power is 10% of the price of electricity with reactive power.

Both target equations (5) and (9) need to satisfy the following network and element constraints:

In the formula, P_l(Δ t, k) and P_PEV(Δ t, k) is the active load and the electric vehicle charging power at node k in each time interval, respectively, where P is_l(Δ t, k) can be derived from the load curve. The following equation indicates whether the electric vehicle is available, and the schedule is available only when the electric vehicle is connected to the grid at the corresponding time.

Upper and lower limits of node voltage variation

ΔV(t，k)＝|V(t，k)-1|≤6％，for k＝1，...，N_node (12)

③ upper limit of node voltage unbalance coefficient

Electric automobile SOC level upper and lower limits

SOC_min＜SOC_i＜SOC_max (14)

3. Concrete control method

The method selects a genetic algorithm as an optimization algorithm, and adopts a forward-backward substitution method for the load flow calculation of the distribution network. As shown in fig. 2, the day is divided into 96 time periods at 15 minute intervals, t represents which time period is currently the current time period, and k represents which electric vehicle is the current electric vehicle. Taking the PEV1 electric vehicle as an example, assuming that the current time is t1, the red time period indicates that the electric vehicle needs to be charged at this time according to optimization. The blue time period represents the charging from the first time period t1 to the time period t8, the total required charging time period is 8, and the green time period t1-t9 represents the number of time periods during which the electric automobile stays. The flexible adjustment of the charging period of the electric vehicle in the stay period is a means for achieving the expected control effect.

FIG. 3 is a specific flowchart of the proposed online sequential charging and discharging technique for electric vehicles: firstly, the current state and the demand of the electric automobile are obtained, then the current photovoltaic power generation amount and the current electricity price are obtained, and the number of charging time sections and the number of staying time sections of the electric automobile can be obtained by combining the new data of the coming and leaving electric automobiles. If an electric vehicle is charged in an emergency (the charging time period is equal to the staying time period), a new round of ordered charging and discharging is carried out according to the established model, and the latest charging and discharging sequence shown in fig. 2 is updated.

4. Validity verification

This patent adopts certain australia 415/215V three-phase four-wire unbalanced distribution network to verify. As shown, the network is powered by a 220kVA 22kV/415V distribution transformer, and comprises 74 nodes and 56 residential users, wherein 34 users have rooftop photovoltaic. The distribution is 11 households of A phase, 11 households of B phase and 12 households of C phase. The time-of-use electricity price is shown in the table I. Meanwhile, the active charging power of the PEV is set to be 5.6kW, the reactive discharging power is set to be 0.56 kWr, the battery capacity is 24kWh, and the upper and lower limits of the SOC of each electric automobile are both 20-90%, wherein the table 1 is a power price change table.

TABLE 1

Time	Price (cent/kWh)
		7AM-11AM	53.37
11AM-5PM	26.64
		9PM-7AM	13.86

In the patent, four cases are selected to verify the effectiveness of the method, as shown in table 2 below, and table 2 shows four control cases.

TABLE 2

	Control mode	Simulation results
			Case 1	Normal network	FIG. 5
Case 2	Disordered charging of electric automobile	FIG. 6
			Case 3	Online optimization ordered charging strategy based on active control	FIG. 7
Case 4	Online optimization ordered charging strategy based on active and reactive power control	FIG. 8

Case 1: normal network

As shown in fig. 5, in the absence of any electric vehicle, a diagram represents the electric energy demand of three phases ABC of the power distribution network, and positive and negative represent input and output of electric energy, respectively. And the graph b shows the voltage unbalance coefficient of each node in 24 hours. The peak load and VUF maximum occur between 5:00PM and 7:00PM, at-40 kW and 0.8% respectively, with the specific data shown in Table 3.

Case 2: disordered charging of electric automobile

In this case, it is assumed that the user performs random charging according to his or her own habit, and thus the charging of the electric vehicle causes an increase in peak-to-valley difference, an overload of the transformer, and an increase in VUF. As can also be seen from fig. 6, problems such as transformer overload, high VUF, large fluctuation of the AB two-phase voltage, and the like exist. According to the data in the third table, the electric vehicle does not need to be charged, which causes the problems.

Case 3: online optimization ordered charging strategy based on active control

For the comparison of case 3 with active and reactive simultaneous control, the case of active control only is chosen here. In this case, the solution of the model (5) is optimized following the constraints (10) - (14). The method enables the electric automobile to be charged orderly at each time interval, so that VUF can be reduced on the whole and charging cost can be reduced. It can be seen from the table that the voltage of the whole system is significantly increased compared to case 2. As shown in fig. 7, VUF drops significantly during morning and evening peak periods, but some node voltages of phase B drop to 0.89p.u. during peak load periods. In addition, the input power of the phases B and C slightly exceeds-40 kW, so the method is low in user friendliness to the end of the line.

Case 4: online orderly charging strategy based on active and reactive combined control

In this case, the electric vehicle inverter allows for the simultaneous input of active power and output of reactive power. That is, each electric vehicle can simultaneously perform active charging and reactive discharging in order to minimize charging cost and reduce VUF based on the change in price. As can be seen from the table, the total charge cost at this time is $1179.6 per day, where $1180 is the charge cost and $ 1.53 is the reactive compensation revenue. This method provides a better solution than case 3, with a cost reduction of $ 90.2. Furthermore, it can be seen from FIG. 8(a) that the power requirements are limited to within-40 kW, FIG. 8(b) that VUF is also reduced compared to before, and FIG. 8(c-d) that the voltages at all nodes are maintained within the system constraints.

TABLE 3

4. Conclusion

This patent has researched and developed an online orderly charge-discharge technology of electric automobile, lets electric automobile carry out reactive compensation to the electric wire netting through the dc-to-ac converter when charging in order to this reduces total charging cost and the net loss of system, and promotes renewable energy's permeability. The Australian real distribution network simulation verification shows that the method provided by the patent is feasible and effective.

While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. The method is characterized in that an intelligent charging decision of an electric vehicle battery is made, so that the generation of overload, undervoltage and overvoltage of a line or a transformer is prevented, the power generation cost is reduced, the voltage unbalance is controlled, and reactive injection is provided according to the day-ahead reactive power price to improve the voltage quality while the selected single-phase node is charged in order;

under the intelligent charging decision control, the positive and negative of the active P (t, K) of the node K respectively represent output power and input power, and the active output and the reactive output of the electric automobile inverter are calculated by a formula (1-4):

P(t，k)＝P_PV(t，k)-P_PEV(t，k)-P_L(t，k) (1)

P_PEV(t，k)＝S_PEV(t，k)·cosθ (2)

Q_PEV(t，k)＝S_PEV(t，k)·sinθ (3)

Q(t，k)＝Q_PEV(t，k)-Q_L(t，k) (4)

in the formula, P (t, k) and Q (t, k) respectively represent the active power and the reactive power of the node k at the moment t, and the positive and negative respectively represent output power and input power; p_PV(t，k)，P_PEV(t，k)，P_L(t, k) respectively represent the active power of the photovoltaic, the electric automobile and the load at a time t node k; q_PEV(t, k) represents the reactive power of the electric vehicle at time t node k, S_PEV(t，k)，θ，Q_L(t, k) are respectively the apparent power, the power factor angle and the reactive load of the electric automobile at a time t node k; p (t, k), P_PEV(t，k)、Q_PEV(t，k)、Q(t，k)。

2. The method for controlling orderly charging of the electric vehicles with the active power distribution network according to claim 1, wherein orderly active charging and reactive discharging of the electric vehicles are performed at selected nodes according to the active and reactive power prices to reduce voltage fluctuation and network imbalance.

3. The ordered charging control method for the electric vehicles with the active power distribution network according to claim 1, wherein the intelligent charging decision modeling is as follows:

the objective function is to minimize the system loss F in 24 hours_Cost-Loss(t) Charge cost F_Cost-gen(t) and a voltage unbalance coefficient F_Cost-VUF(t) and maximizing the benefits of reactive power delivery to the grid;

in the formula:

where Δ t is the time interval set to 15 minutes, k is the number of nodes and N_nodeThe total node number is; k_E(t) cost per kWh of power loss; k_p(t) and K_S(t) cost coefficients based on cost per kWh of real-time electricity prices and photovoltaic output, respectively; v^-(t，k)、V⁺(t, K) are negative and positive sequence voltages, respectively, K_VUF(t) is a penalty factor for VUF; f_Cost-Q(t) earnings for reactive power output of electric vehicles, K_Q(t) is the unit electricity price of the output reactive power, assuming that the unit electricity price is 10% of the active power price, and the minus sign represents the output reactive power; f_Cost-Q(t)、V^-(t，k)、V⁺(t，k)。

4. The ordered charging control method for the electric vehicles with the active power distribution network according to claim 3, wherein the target equations (5) and (9) both need to satisfy the following network and element constraints:

In the formula, P_l(Δ t, k) and P_PEV(Δ t, k) is the active load and the electric vehicle charging power at node k in each time interval, respectively, where P is_l(Δ t, k) is derived from the load curve, P_max(Δ t, k) is the maximum output of the active at node k; the following formula indicates whether the electric vehicle is available, and the schedule is available only when the electric vehicle is connected to the grid at the corresponding time;

upper and lower limits of node voltage variation

ΔV(t，k)＝|V(t，k)-1|≤6％，for k＝1，...，N_node (12)

Δ V (t, k) is the amount of change in voltage, V (t, k) is the per unit value of the actual voltage at node k at time t;

③ upper limit of node voltage unbalance coefficient

Electric automobile SOC level upper and lower limits

SOC_min＜SOC_i＜SOC_max (14)

5. The ordered charging control method for the electric vehicle with the active power distribution network according to claim 3, characterized in that the ordered charging control process specifically comprises the following steps: