Distribution network optimization method, device and system considering reactive power supporting capability of flexible charging facility

Abstract

The invention particularly relates to a method, a device and a system for establishing and optimizing a distribution network optimization model considering the reactive power support capability of a flexible charging facility. The method comprises the following steps: S1, providing a distribution network optimization strategy considering the reactive power support capability of the charging facility; s2, constructing a charging facility collaborative optimization model; and S3, the optimal power flow model is converted into a second order cone optimization (SOCP) model, and the whole model is solved by adopting a commercial solver Gurobi. In the step S2, the constraint conditions for constructing the charging facility collaborative optimization model are as follows: S2.1, power flow safety constraint; s2.2, active power constraint of flexible charging facility cooperation; s2.3, carrying out reactive power output constraint on the charging station; s2.4, carrying out OLTC constraint on the on-load voltage regulating transformer; and S2.5, carrying out parallel capacitor bank CB constraint.

Description

Technical Field

[0001] The present invention specifically relates to a method, device and system for establishing and optimizing a distribution network optimization model taking into account the reactive support capability of flexible charging facilities. Background Art

[0002] The reliable operation of the power system requires a certain amount of reactive power. Reactive power is mainly used for the exchange of electric and magnetic fields. It is a necessary prerequisite for the reliable transmission and conversion of electric energy. Insufficient reactive power will affect the normal operation of electrical equipment, and the reduced power factor will cause the equipment performance to deteriorate. At the same time, it will also lead to increased losses during the transmission of the power system and increased operating costs. The generator in the system can generate reactive power, but the reactive power provided by the generator at the power supply end alone cannot be transmitted to the entire system to meet the demand. Long-distance transmission is extremely uneconomical and the transmission cost is high. And when only relying on the generator to provide reactive power, a large amount of reactive power will flow in the system, causing a large fluctuation in voltage, reducing voltage quality, increasing line losses, and reducing the overall economic benefits. Therefore, it is necessary to set up more reactive compensation devices in the system and perform distributed reactive compensation in multiple links.

[0003] Moreover, with the rapid development of the electric vehicle industry and the further improvement of charging facilities, a large number of disorderly charged electric vehicle loads are incorporated into the distribution network, which causes load fluctuations at the distribution network nodes, exacerbates the peak-to-valley difference of the distribution network load, increases the active network loss of the distribution network, and may even further cause the node voltage to exceed the limit. The reactive regulation of the reactive compensation device in the system can maintain the stability of the distribution network node voltage, so the demand for reactive device capacity in the distribution network system increases with the expansion of the scale of electric vehicle charging. However, most of the existing reactive devices are fixed by planning, and capacity expansion or addition of equipment will lead to higher investment costs. With the continuous development of power electronics technology, bidirectional four-quadrant intelligent charging facilities with V2G response capabilities have received widespread attention. This facility can use the remaining capacity of the converter to provide reactive compensation services to the distribution network when EV is charging. After regulation, it can support the reactive optimization operation of the distribution network as a reactive device, and the flow of reactive power is only generated between the charging facility and the power grid, which will not cause loss to the electric vehicle battery.

[0004] Some current studies have combined the orderly charging and discharging of electric vehicles to study the reactive power compensation capability of charging facilities and distribution network optimization, and proposed strategies such as coordinated control of active and reactive power and electric vehicle charging and discharging control. However, these methods are aimed at fixed charging piles that have already entered the charging state, so it is necessary to design a new method to better meet actual needs. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a distribution network optimization method, device and system taking into account the reactive power support capacity of flexible charging facilities, in view of the deficiencies of factors considered in the existing charging facility orderly charging power coordination strategy and the limited reactive power support capacity, taking into account the user experience, improving the active power distribution of the distribution network, supporting the reactive power optimization of the distribution network, and improving the operation stability and safety and economy of the distribution network.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a distribution network optimization model taking into account the reactive power support capacity of flexible charging facilities and its solution, comprising the following steps:

[0007] S1. Propose a distribution network optimization strategy that takes into account the reactive power support capacity of charging facilities;

[0008] S2. Construct a collaborative optimization model for charging facilities;

[0009] S3. The optimal power flow model is converted into a second order cone program (SOCP) model, and the overall model is solved using the commercial solver Gurobi.

[0010] Specifically, in step S1, the distribution network optimization strategy taking into account the reactive power support capability of charging facilities is:

[0011] The goal is to minimize the total active network loss and node offset of the distribution network in 24 hours the next day. Based on the prediction and processing of data such as power load in future time periods in the distribution network and charging and discharging requirements of each charging station, the tap position of the on-load tap-changing transformer, the number of switching groups of parallel capacitor groups and the reactive output of distributed generators are used as control variables. The predetermined time period is used as the optimization time granularity to carry out segmented optimization control for each time period of the next day. The key decision is made on the number of dispatchable flexible charging facilities for each charging station in each time period. At the same time, the charging facilities of the coordinated charging type are considered, the reactive response capability of the intelligent charging facilities is considered, and the charging stations are regarded as reactive optimization devices to provide a certain reactive capacity.

[0012] The distributed generator set described in step S1 is a distributed gas turbine set or a distributed diesel engine set, preferably a distributed gas turbine set.

[0013] The predetermined time period in step S1 is 1 hour.

[0014] Specifically, in step S1, the goal is to minimize the sum of active network loss and node offset of the next day's distribution network for 24 hours, as shown below:

[0015]

[0016] In the above formula, the first term on the right is the active power loss of the distribution network, where is the branch l in period t ij The current flowing through; L is the branch set; r ij For branch l ij resistance. is the node voltage offset, where U j is the voltage amplitude of the distribution network node j; U max , U min They are the upper and lower limits of voltage amplitude respectively; is the expected voltage value of distribution network node j, which is set to 1.0pu.

[0017] Specifically, in step S2, the charging facility collaborative optimization model is constructed, and the constraints are as follows:

[0018] S2.1 Power flow safety constraints;

[0019] S2.2 Active power constraints for flexible charging facility coordination;

[0020] S2.3 Reactive power output constraints of charging stations;

[0021] S2.4 On-load tap-changing transformer OLTC constraints;

[0022] S2.5 Parallel capacitor bank CB constraints.

[0023] Specifically, the S2.1 power flow safety constraint is:

[0024] The optimal power flow model of the distribution network and the second-order cone linearization conversion method are adopted, with the output of distributed gas turbines and the injection of external grid electricity from the balancing node, as shown below.

[0025]

[0026]

[0027]

[0028]

[0029]

[0030] The branches l from node i to node j are ij The active power balance equation, the composition equation of the active load connected to the distribution network, the amplitude constraint of the branch complex power S, the branch l from node i to node j ij Reactive power balance equation and reactive output constraints of distributed gas turbine units. is the branch l in period t ij The meritorious trend; is the active power purchased from the external network at node j during period t; is the active power output of the distributed gas turbine at node j during period t; is the branch l in period t ij The current flowing through is the active load at node j during period t; π(j) is the set of end nodes of all branches with the head end being j; L is the set of branches; is the basic load of access node j during period t; is the charging load of EV connected to node j during period t; is the branch l in period t ij Reactive power flow; r ij 、x ij For branch l ij Resistance and reactance; is the reactive power output of the distributed gas turbine at node j during period t; is the reactive power output of the parallel switched generator at node j during period t; is the reactive power output by charging station j at time t; is the reactive load at node j in period t, and the reactive load is the basic load reactive power. , where They are the upper and lower limits of the reactive output of the distributed gas turbine unit at node j respectively.

[0031] Specifically, S2.2 Active power constraint for flexible charging facility coordination, the specific formula is as follows.

[0032]

[0033] The above formulas indicate that the scheduling of flexible charging facilities is subject to the constraints of the orderly charging and discharging plan of EVs and the number of charging facilities at each charging station. is the orderly charging and discharging EV load connected to node j during period t; the system EV charging and discharging state is considered with charging facilities as the unit, where p j,num N is the number of charging facilities at each node; j,c,t is the number of EVs charged at node j during period t; P c is the average charging power of EV; N j,d,t is the number of EV discharges at node j in period t; P d is the average discharge power of EV; the number of EVs that can participate in the distribution network charging and discharging scheduling is known, η c , η d are the charging and discharging efficiency of charging facilities; Mp j,c,num 、Mp j,d,num are the number of flexible charging facilities required for each node j of the decision variables; Mp max There is a limit on the number of flexible charging facilities.

[0034] Specifically, S2.3 Reactive power output constraint of the charging station, the specific formula is as follows.

[0035]

[0036] In the above formula, is the reactive power output by charging station node j at time t, is the upper and lower limits of reactive power output of the charging station corresponding to node j at time t, according to the formula The reactive power support capacity of the charging facility depends on the maximum apparent capacity S of the rectifier inverter. max The active power during charging and discharging.

[0037] Specifically, the S2.4 on-load tap changer OLTC constraint is:

[0038] OLTC is an important voltage regulating device in the power system. It changes the voltage distribution of the power grid by adjusting the gear of the tap to change its ratio information. OLTC is limited in terms of root node voltage constraints, gear voltage change values, and the upper limit of the number of operations within a dispatch cycle. It does not generate reactive power itself; however, the position of OLTC taps is limited, and it is also a discrete voltage regulating device, often used in conjunction with CB. The specific formula is as follows.

[0039]

[0040]

[0041]

[0042] In the above formula, is the gear position of OLTC during period t; It is the maximum gear position of OLTC; It is the limit of the number of actions of OLTC in the scheduling cycle; is an auxiliary 0-1 variable that characterizes the OLTC action state. If the sum of the two is 0, it means that the gear position of OLTC in period t is consistent with that in period t-1, and OLTC is not in action. If the sum of the two is 1, it means that the OLTC gear position is adjusted in period t; k2 is the OLTC ratio; k0 is the reference ratio; k1 is the unit step size; V is the φ-phase voltage of node j associated with OLTC during period t; base It is the primary voltage reference value of OLTC.

[0043] Specifically, S2.5 parallel capacitor bank CB constraint, that is, CB is composed of several single capacitors connected in series and in parallel.

[0044] It is used to adjust the voltage when the reactive power of the power grid is insufficient. When connected to the power grid, it can provide the reactive power consumed by the inductive load and reduce the flow of reactive power in the power grid. CB is flexible and convenient to operate and maintain, with low investment cost, so it is widely used in reactive power compensation of power systems. At the same time, the use of CB has certain limitations, such as the discreteness of reactive power regulation, easy over-compensation or under-compensation, and cannot be frequently switched on and off.

[0045]

[0046]

[0047]

[0048] In the above formula, are the action gear and maximum gear of CB at node j in period t respectively; It is a 0-1 auxiliary variable of the mth CB gear at node j in period t, indicating the switch state of the CB. It is an auxiliary 0-1 variable that characterizes the action status of CB. If the sum of the two is 0, it means that the gear position of CB in period t is consistent with that in period t-1, and CB has not acted. If the sum of the two is 1, it means that the gear position of CB has been adjusted in period t. is the action number limit of CB at node j within the scheduling period.

[0049] The present invention also provides a distribution network optimization method system taking into account the reactive support capacity of flexible charging facilities, which includes a computer device; the computer device is configured or programmed to execute the steps of the method of the present invention.

[0050] The present invention also provides a computer-readable storage medium, which includes a program running on a processor; the program is configured or programmed to execute the steps of the method of the present invention.

[0051] Compared with the prior art, the present invention has the following advantages:

[0052] 1. The scheduling goal of the present invention is to minimize the sum of the active network loss of the distribution network and the node voltage deviation, taking into account multiple indicators of safety and economy, and improving the power supply reliability of the distribution network.

[0053] 2. The present invention proposes to take into account the reactive support capability of flexible charging facilities. Compared with the general reactive support method for electric vehicles that does not consider charging piles and assumes that electric vehicles are charged in an orderly manner, it emphasizes the use of the reactive regulation capability and flexible dispatchable characteristics of charging facilities, which can improve the active load distribution of the distribution network and the charging experience of electric vehicle users.

[0054] The present invention adjusts active and reactive power at the same time. In practical applications, the intelligent flexible charging pile can not only flexibly adjust the reactive power, but also has the characteristics of lightweight and mobility. It can be moved in space to reduce the queue during charging peak, making charging more orderly. The flexible charging pile targeted by the present invention is a bidirectional four-quadrant intelligent flexible charging pile with V2G response capability. It only adjusts its reactive power during the charging process and improves the active load distribution through scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The control framework diagram of the distribution network optimization strategy proposed in the present invention is shown.

[0056] Figure 2 The topological structure diagram of the 33-node example used in the present invention is shown.

[0057] Figure 3 The graph shows the change of active network loss of the distribution network in 24 hours in each scenario of the optimization result of the example of the present invention.

[0058] Figure 4 The voltage diagram of a node in the distribution network of each reactive power optimization scenario of the optimization result of the example of the present invention is shown. DETAILED DESCRIPTION

[0059] Refer to the attached Figure 1-4 The technical problem to be solved by the present invention is to provide a method, device and system for establishing and optimizing a distribution network optimization model taking into account the reactive support capacity of flexible charging facilities in view of the deficiencies in the prior art.

[0060] The technical solution adopted by the present invention is a distribution network optimization strategy taking into account the reactive power support capacity of charging facilities.

[0061] The distribution network optimization strategy control framework of the present invention is shown in FIG. Figure 1 shown.

[0062] The goal is to minimize the sum of the active network loss and the node voltage deviation. Specifically, it is as shown in the following formula (1):

[0063]

[0064] In the above formula, the first term on the right is the active power loss of the distribution network, where is the branch l in period t ij The current flowing through; L is the branch set; r ij For branch l ij The second term on the right is the node voltage offset, where U j is the voltage amplitude of the distribution network node j; U max , U min They are the upper and lower limits of voltage amplitude respectively; is the expected voltage value of distribution network node j, which is set to 1.0pu.

[0065] 2) Predict data such as power load at different time periods in the future and charging and discharging requirements at each charging station.

[0066] This step is data input. The forecast data can be obtained in many ways, such as load forecasting based on Monte Carlo method, big data analysis and other methods.

[0067] 3) The tap position of the on-load tap-changing transformer, the number of switched-in shunt capacitor groups, and the reactive output of the distributed gas turbine units are set as control variables.

[0068] In a specific distribution network node, an on-load tap-changing transformer (OLTC) and a shunt capacitor bank (CB) with known parameters are connected. Figure 2 .

[0069] 2. Construct a collaborative optimization model for charging facilities

[0070] The specific process of constructing the collaborative optimization model of charging facilities is as follows:

[0071] The goal mentioned in the previous step is used as the objective function. The constraints are as follows:

[0072] ① Power flow safety constraints. The optimal power flow model of the distribution network and the second-order cone linearization conversion method are used, with the output of distributed gas turbines and the injection of external grid power from the balancing node, as shown below.

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] Formulas (2) to (6) are respectively the branch l from node i to node j ij The active power balance equation, the composition equation of the active load connected to the distribution network, the amplitude constraint of the branch complex power S, the branch l from node i to node j ij Reactive power balance equation and reactive output constraints of distributed gas turbine units. is the branch l in period t ij The meritorious trend; is the active power purchased from the external network at node j during period t; is the active power output of the distributed gas turbine at node j during period t; is the branch l in period tij The current flowing through is the active load at node j during period t; π(j) is the set of end nodes of all branches with the head end being j; L is the set of branches; is the basic load of access node j during period t; is the charging load of EV connected to node j during period t; is the branch l in period t ij Reactive power flow; r ij 、x ij For branch l ij Resistance and reactance; is the reactive power output of the distributed gas turbine at node j during period t; is the reactive power output of the parallel switching unit at node j during period t; is the reactive power output by charging station j at time t; is the reactive load at node j in period t, and the reactive load is the basic load reactive power. , where They are the upper and lower limits of the reactive output of the distributed gas turbine unit at node j respectively.

[0079] ② Active power constraint for coordinated flexible charging facilities. The specific formula is as follows.

[0080]

[0081]

[0082]

[0083] The above formulas (7) to (10) respectively indicate that the scheduling of flexible charging facilities is subject to the constraints of the orderly charging and discharging plan of EVs and the number of charging facilities at each charging station. is the orderly charging and discharging EV load connected to node j during period t; the system EV charging and discharging state is considered with charging facilities as the unit, where p j,num N is the number of charging facilities at each node; j,c,t is the number of EVs charged at node j during period t; P c is the average charging power of EV; N j,d,t is the number of EV discharges at node j in period t; P d is the average discharge power of EV; the number of EVs that can participate in the distribution network charging and discharging scheduling is known, η c , η d are the charging and discharging efficiency of charging facilities; Mp j,c,num 、Mp j,d,num are the number of flexible charging facilities required for each node j of the decision variables; Mp max There is a limit on the number of flexible charging facilities.

[0084] ③Reactive power output constraint of charging station, the specific formula is as follows.

[0085]

[0086] In the above formula, is the reactive power output by charging station node j at time t, is the upper and lower limits of reactive power output of the charging station corresponding to node j at time t, according to the formula The reactive power support capacity of the charging facility depends on the maximum apparent capacity S of the rectifier inverter. max The active power during charging and discharging.

[0087] ④ Constraints of on-load tap-changing transformers (OLTC). OLTC is an important voltage regulating device in the power system. It changes the voltage distribution of the power grid by adjusting the gear position of the tap to change its ratio information. OLTC is mainly restricted in terms of root node voltage constraints, gear voltage change values, and the upper limit of the number of operations within a dispatching cycle. It does not generate reactive power itself. However, the position of OLTC taps is limited, and it is also a discrete voltage regulating device, which is often used in conjunction with CB. The specific formula is as follows.

[0088]

[0089]

[0090]

[0091] In the above formulas (13) to (15), is the gear position of OLTC during period t; It is the maximum gear position of OLTC; It is the limit of the number of actions of OLTC in the scheduling cycle; is an auxiliary 0-1 variable that characterizes the OLTC action state. If the sum of the two is 0, it means that the gear position of OLTC in period t is consistent with that in period t-1, and OLTC is not in action. If the sum of the two is 1, it means that the OLTC gear position is adjusted in period t; k2 is the OLTC ratio; k0 is the reference ratio; k1 is the unit step size; V is the φ-phase voltage of node j associated with OLTC during period t; base It is the primary voltage reference value of OLTC.

[0092] ⑤ Parallel capacitor group CB constraint CB is composed of several single capacitors connected in series and in parallel. It is mainly used to adjust the voltage when the grid is short of reactive power. When connected to the grid, it can provide the reactive power consumed by the inductive load and reduce the flow of reactive power in the grid. CB operation and maintenance are flexible and convenient, with low investment costs, so it is widely used in reactive power compensation of power systems. At the same time, the use of CB has certain limitations, such as the discreteness of reactive power regulation, easy over-compensation or under-compensation, and cannot be frequently switched on and off.

[0093]

[0094]

[0095]

[0096] In the above formulas (16) to (18), are the action gear and maximum gear of CB at node j in period t respectively; It is a 0-1 auxiliary variable of the mth CB gear at node j in period t, indicating the switch state of the CB. It is an auxiliary 0-1 variable that characterizes the action status of CB. If the sum of the two is 0, it means that the gear position of CB in period t is consistent with that in period t-1, and CB has not acted. If the sum of the two is 1, it means that the gear position of CB has been adjusted in period t. is the action number limit of CB at node j within the scheduling period.

[0097] 3. The optimal power flow model is converted into a SOCP model, and the overall model is solved using the commercial solver Gurobi.

[0098] 4. The experimental verification of the above embodiment is as follows.

[0099] The parameters of a 33-node distribution network in a certain area are used as an example. Its structure diagram is shown in Figure 2 As shown. The reference voltage is 12.66kV, the reference power is 10MVA, and the basic load data for the whole day is shown in Table 3.2. 10 EV charging stations are located at distribution network nodes 1, 2, 12, 14, 16, 21, 23, 25, 28, and 32. Each charging station is equipped with 30 fixed intelligent charging facilities as the minimum number of charging facilities. The entire system contains 120 mobile charging facilities that can be dispatched, and all can participate in the orderly charging and discharging dispatch of EVs. The maximum apparent capacity of the charging facility converter is 7kVA, the EV charging power is 7kW, the discharge power is 3kW, and the charging and discharging efficiency is 0.95.

[0100] Set up 6 scenes as follows.

[0101] Scenario 1: Only the distribution network connected to the basic load is considered, and the EV charging load is not considered;

[0102] Scenario 2: Add 1,500 EV loads with disorderly charging;

[0103] Scenario 3: Guide 500 EVs to charge and discharge in an orderly manner. The orderly charging plan is obtained by the electric vehicle charging demand based on the load curve. At the same time, the disorderly charging load of 1,000 EVs is added. The disorderly charging load distribution curve is obtained by the load forecasting method.

[0104] Scenario 4: The distribution network is provided with active and reactive power by distributed gas turbines. Based on the orderly charging and discharging plan of EVs, the dynamic scheduling of mobile charging facilities is considered, and the distribution network is coordinated with fixed charging facilities to smooth the peak flow and fill the valley flow. The reactive power optimization of other reactive compensation devices is not considered.

[0105] Scenario 5: In addition to considering the reactive output of distributed gas turbines, two reactive compensation devices, OLTC and CB, are added on the basis of Scenario 4 to optimize the OLTC gear position, the number of CB switching groups and the reactive power of distributed gas turbine units. The reactive support capacity of the two-way four-quadrant smart charging facilities is not considered.

[0106] Scenario 6: Based on Scenario 5, a distribution network optimization model that takes into account the reactive power support capacity of charging facilities is used. The EVs connected to the charging facilities participating in reactive power optimization are 500 EVs participating in orderly charging and discharging. The simulation is based on each charging station, and the reactive power compensation capacity is the sum of the charging facilities.

[0107] Table 1 Distribution network indicator results for each scenario

[0108] Scenario Active network loss / kWh Node voltage offset / pu Node voltage deviation extreme value / pu one 2998.10 17.0381 0.0459 two 3635.87 20.5780 0.0624 three 3494.95 19.9441 0.0611 Four 2386.70 14.7001 0.0602 five 2269.44 11.1845 0.0425 six 1827.21 9.6192 0.0293

[0109] The access of large-scale EVs to disorderly charging loads will have an impact on the distribution network, increase network losses, and aggravate the peak-to-valley difference of the distribution network load. It may even cause the voltage of some nodes in the distribution network to exceed the limit, threatening the safe operation of the power system. Scenario three dispatches the 500 vehicles in scenario two to charge and discharge in an orderly manner, discharging during the peak period of the distribution network load and charging during the valley period, which can improve the voltage exceeding the limit of the distribution network node and reduce network losses and peak-to-valley differences. Compared with scenario two, scenario three has a 3.88% decrease in active network losses, a 3.08% decrease in node voltage offset, and a 2.08% decrease in node voltage offset extreme value. In scenario four, due to the large load and the lack of optimization of reactive power compensation devices, the node voltage will exceed the limit, causing the distribution network system to be in an unsafe state. After reactive power optimization by the reactive power compensation device in scenario five, the active network loss of the distribution network decreased by 2.69%, and the node voltage offset decreased by 19.11%. After considering the reactive power support capacity of the charging facilities in scenario six, the active network loss of the distribution network decreased by 21.65% compared with scenario four, and the node voltage offset decreased by 30.43%. This effectively proves that considering the reactive power support capacity of the charging facilities can further stabilize the operation status of the distribution network and improve the safety and economy of the distribution network.

[0110] For specific 24-hour active network loss changes, see Figure 3 , the node voltage change comparison of node 19 is shown in Figure 4 .

[0111] In summary, the present invention adjusts active and reactive power at the same time. In practical applications, the intelligent flexible charging pile can not only flexibly adjust the reactive power, but also has the characteristics of lightweight and mobility. It can be moved in space to reduce the charging peak queue and make charging more orderly. The flexible charging pile targeted by the present invention is a bidirectional four-quadrant intelligent flexible charging pile with V2G response capability. It only adjusts its reactive power during the charging process and improves the active load distribution through scheduling.

Claims

1. A distribution network optimization method taking into account the reactive power support capability of flexible charging facilities, characterized in that: The following steps are involved: S1. Propose a distribution network optimization strategy that takes into account the reactive power support capacity of charging facilities; S2. Construct a collaborative optimization model for charging facilities; S3. The optimal power flow model is converted into a second-order cone optimization model, and the overall model is solved using the commercial solver Gurobi.

2. A distribution network optimization method taking into account the reactive power support capability of flexible charging facilities according to claim 1, characterized in that: In step S1, the distribution network optimization strategy taking into account the reactive power support capability of charging facilities is: The goal is to minimize the total active network loss and node offset of the distribution network in 24 hours the next day. Based on the prediction and processing of data such as power load in future time periods in the distribution network and charging and discharging requirements of each charging station, the tap position of the on-load tap-changing transformer, the number of switching groups of parallel capacitor groups and the reactive output of distributed generators are used as control variables. The predetermined time period is used as the optimization time granularity to carry out segmented optimization control for each time period of the next day. The key decision is made on the number of dispatchable flexible charging facilities for each charging station in each time period. At the same time, the charging facilities of the coordinated charging type are considered, the reactive response capability of the intelligent charging facilities is considered, and the charging stations are regarded as reactive optimization devices to provide a certain reactive capacity.

3. A distribution network optimization method taking into account the reactive power support capability of flexible charging facilities according to claim 2, characterized in that: The distributed generator set described in step S1 is a distributed gas turbine set or a distributed diesel engine set.

4. A distribution network optimization method taking into account the reactive support capacity of flexible charging facilities according to claim 2, characterized in that: The predetermined time period in step S1 is 1 hour.

5. A distribution network optimization method taking into account the reactive power support capability of flexible charging facilities according to claim 1, characterized in that: In step S1, the goal is to minimize the total active network loss and node offset of the next day's 24-hour distribution network, as shown below: In the above formula, the first term on the right is the active power loss of the distribution network, where is the branch l in period t ij The current flowing through; L is the branch set; r ij For branch l ij resistance. is the node voltage offset, where U j is the voltage amplitude of the distribution network node j; U max , U min They are the upper and lower limits of voltage amplitude respectively; is the expected voltage value of distribution network node j, which is set to 1.0pu.

6. A distribution network optimization method taking into account the reactive power support capability of flexible charging facilities according to claim 1, characterized in that: In step S2, the charging facility collaborative optimization model is constructed, and the constraints are as follows: S2.1 Power flow safety constraints; S2.2 Active power constraints for flexible charging facility coordination; S2.3 Reactive power output constraints of charging stations; S2.4 On-load tap-changing transformer OLTC constraints; S2.5 Parallel capacitor bank CB constraints.

7. A distribution network optimization method taking into account the reactive power support capability of flexible charging facilities according to claim 6, characterized in that: S2.1 The power flow safety constraint is: The optimal power flow model of the distribution network and the second-order cone linearization conversion method are used, with the output of distributed gas turbines and the injection of external grid power from the balance node. The formula is as follows: The branches l from node i to node j are ij The active power balance equation, the composition equation of the active load connected to the distribution network, the amplitude constraint of the branch complex power S, the branch l from node i to node j ij Reactive power balance equation and reactive power output constraints of distributed gas turbine units; is the branch l in period t ij The meritorious trend; is the active power purchased from the external network at node j during period t; is the active power output of the distributed gas turbine at node j during period t; is the branch l in period t ij The current flowing through is the active load at node j during period t; π(j) is the set of end nodes of all branches with the head end being j; L is the set of branches; is the basic load of access node j during period t; is the charging load of EV connected to node j during period t; is the branch l in period t ij Reactive power flow; r ij 、x ij For branch l ij Resistance and reactance; is the reactive power output of the distributed gas turbine at node j during period t; is the reactive power output of the parallel switching unit at node j during period t; is the reactive power output by charging station j at time t; is the reactive load at node j in period t, and the reactive load is the basic load reactive power; They are the upper and lower limits of the reactive output of the distributed gas turbine unit at node j respectively.

8. A distribution network optimization method taking into account the reactive power support capability of flexible charging facilities according to claim 6, characterized in that: S2.2 Active power constraint for flexible charging facility coordination, the formula is as follows: The above formulas indicate that the scheduling of flexible charging facilities is subject to the orderly charging and discharging plan of EVs and the number of charging facilities at each charging station; is the orderly charging and discharging EV load connected to node j during period t; the system EV charging and discharging state is considered with charging facilities as the unit, where p j,num N is the number of charging facilities at each node; j,c,t is the number of EVs charged at node j during period t; P c is the average charging power of EV; N j,d,t is the number of EV discharges at node j in period t; P d is the average discharge power of EV; the number of EVs that can participate in the distribution network charging and discharging scheduling is known, η c , η d are the charging and discharging efficiency of charging facilities; Mp j,c,num 、Mp j,d,num are the number of flexible charging facilities required for each node j of the decision variables; Mp max There is a limit on the number of flexible charging facilities.

9. A distribution network optimization method taking into account the reactive power support capability of flexible charging facilities according to claim 6, characterized in that: S2.3 Reactive power output constraint of charging station, the formula is as follows: In the above formula, is the reactive power output by charging station node j at time t, is the upper and lower limits of reactive power output of the charging station corresponding to node j at time t, according to the formula The reactive power support capacity of the charging facility depends on the maximum apparent capacity S of the rectifier inverter. max The active power during charging and discharging.

10. A distribution network optimization method taking into account the reactive power support capability of flexible charging facilities according to claim 6, characterized in that: S2.4 The constraints of the on-load tap-changing transformer OLTC are: OLTC is an important voltage regulating device in the power system. It changes the voltage distribution of the power grid by adjusting the gear of the tap to change its transformation ratio information. OLTC is limited in terms of root node voltage constraint, gear voltage change value and the upper limit of the number of operations within a dispatch cycle. It does not generate reactive power itself. However, the position of OLTC tap is limited. It is also a discrete voltage regulating device and is often used in conjunction with CB. The formula is as follows: In the above formula, is the gear position of OLTC during period t; It is the maximum gear position of OLTC; It is the limit of the number of actions of OLTC in the scheduling cycle; is an auxiliary 0-1 variable that characterizes the OLTC action state. If the sum of the two is 0, it means that the gear position of OLTC in period t is consistent with that in period t-1, and OLTC is not in action. If the sum of the two is 1, it means that the OLTC gear position is adjusted in period t; k2 is the OLTC ratio; k0 is the reference ratio; k1 is the unit step size; V is the φ-phase voltage of node j associated with OLTC during period t; base It is the primary voltage reference value of OLTC.

11. A distribution network optimization method taking into account the reactive power support capability of flexible charging facilities according to claim 6, characterized in that: S2.5 Parallel capacitor bank CB constraint, that is, CB is composed of several single capacitors connected in series and in parallel; When the grid is short of reactive power, it can regulate the voltage. When connected to the grid, it can provide the reactive power consumed by the inductive load and reduce the flow of reactive power in the grid. In the above formula, are the action gear and maximum gear of CB at node j in period t respectively; is a 0-1 auxiliary variable of the mth CB gear position at node j in period t, indicating the switch state of the CB; It is an auxiliary 0-1 variable that characterizes the action state of CB. If the sum of the two is 0, it means that the gear position of CB in period t is consistent with that in period t-1, and CB is not in action. If the sum of the two is 1, it means that the gear position of CB is adjusted in period t. is the action number limit of CB at node j within the scheduling period.

12. A system involved in the distribution network optimization method taking into account the reactive power support capability of flexible charging facilities as claimed in claim 1, characterized in that: The invention comprises a computer device; the computer device is configured or programmed to execute the steps of the method of the invention.

13. A system involved in the distribution network optimization method taking into account the reactive power support capability of flexible charging facilities as claimed in claim 1, characterized in that: A computer-readable storage medium includes a program running on a processor; the program is configured or programmed to execute the steps of a distribution network optimization method taking into account the reactive support capacity of flexible charging facilities.

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