Collaborative planning method and device for power distribution network and charging station

Abstract

The invention relates to the technical field of power systems, in particular to a collaborative planning method and device for a power distribution network and a charging station, and the method comprises the steps: constructing a power distribution network planning model based on a target function and constraint conditions of target power distribution network planning; the objective function comprises investment cost and operation cost; the constraint conditions comprise a power generation power supply constraint, a power distribution network flow constraint and a bus power balance constraint; and according to a preset decision variable rule and the power distribution network planning model, optimizing an access position of the to-be-accessed charging station in the target power distribution network so as to complete collaborative planning between the target power distribution network and the to-be-accessed charging station. According to the invention, the access position of the to-be-accessed charging station in the target power distribution network can be optimized, so that the target power distribution network can configure a reasonable charging load to the to-be-accessed charging station.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of power systems, and in particular to a method and device for collaborative planning of distribution networks and charging stations. Background Art

[0002] In order to reduce carbon emissions and ease dependence on fossil energy, electric vehicles are developing and popularizing rapidly around the world, becoming an important force in promoting sustainable transportation and energy transformation. Electric vehicles can not only effectively improve the absorption capacity of renewable energy and promote the development of green energy, but also bring significant economic benefits. Its impact on the power grid is also becoming increasingly prominent, especially in high-density cities and areas with concentrated charging stations. The proportion of charging load in the power grid continues to rise, significantly increasing the overall power demand of the power grid.

[0003] Especially during the period of concentrated charging of electric vehicles, the distribution network may face the pressure of surge in load, causing local grid overload. Equipment such as distribution transformers may be overloaded, increasing the risk of grid failure. Therefore, in order to meet this challenge, how to optimize the access of charging stations to the distribution network has become an issue that needs to be studied urgently in the power system. Summary of the invention

[0004] The present application provides a method and device for collaborative planning of a distribution network and a charging station, which can optimize the access position of a charging station to be connected in a target distribution network, so that the target distribution network can reasonably allocate the charging load to the charging station to be connected.

[0005] In a first aspect, a method for collaborative planning of a distribution network and a charging station is provided, comprising: Based on the objective function and constraints of the target distribution network planning, a distribution network planning model is constructed; the objective function includes investment cost and operation cost; the constraints include power generation constraints, distribution network flow constraints and bus power balance constraints; According to the preset decision variable rules and the distribution network planning model, the access position of the charging station to be connected in the target distribution network is optimized to complete the coordinated planning between the target distribution network and the charging station to be connected.

[0006] Optionally, before constructing the distribution network planning model based on the objective function and constraint conditions of the target distribution network planning, the method further includes: Calculate the distributed generation investment cost according to the first maximum output, the first investment variable and the first investment unit cost corresponding to the distributed generation plan; Calculate the investment cost of the renewable distributed power source according to the second maximum output, the second investment variable and the second investment unit cost corresponding to the planned renewable distributed power source; Calculate the investment cost of the transmission line expansion according to the planned expansion route length, the third investment variable and the third investment unit cost corresponding to the transmission line expansion; Calculate the investment cost of the substation expansion according to the planned substation expansion capacity, the fourth investment variable and the fourth investment unit cost corresponding to the substation expansion; The investment cost is calculated based on the investment cost of distributed power generation, the investment cost of renewable power generation, the investment cost of transmission line expansion, and the investment cost of substation expansion.

[0007] Optionally, before constructing the distribution network planning model based on the objective function and constraint conditions corresponding to the target distribution network, the method further includes: The operating cost is calculated based on the power curve of distributed power sources, power abandonment penalty, typical day weight, and the distributed power output and renewable power abandonment corresponding to each time in a typical day.

[0008] Optionally, the power generation constraints include distributed power output constraints and renewable power output constraints; the distributed power output constraints are used to constrain the active output and reactive output of distributed power sources at each time during a typical day; the renewable power output constraints are used to constrain the active output and reactive output of renewable power sources at each time during a typical day.

[0009] Optionally, the power flow constraint of the distribution network is determined by: According to the voltage of the busbar on the target distribution network at each time in a typical day, the resistance and reactance of the target line, and the active output and reactive output of the target line at each time in a typical day, the initial distribution network power flow model is constructed; The large M method is used to linearize the nonlinear constraints in the initial power grid power flow model so that the transmission lines of the target distribution network expansion can meet the power flow. According to the upper and lower limits of the bus voltage, the upper and lower limits of the bus power flow, and the upper and lower limits of the bus power flow, the bus voltage and the upper and lower limits of the power flow in the initial power grid power flow model processed by the large M method are constrained to obtain the final distribution network power flow model; According to the distribution network power flow model, the distribution network power flow constraints corresponding to each time in a typical day are obtained.

[0010] Optionally, the bus power balance constraint is used to constrain the power generation, charging load, input value and output value of the flow, the charging load of the substation and other loads on each bus except the charging load on each bus in the target distribution network; the bus power balance constraint is determined according to the active load and reactive load other than the charging load corresponding to the bus of the target distribution network at each time during a typical day, the active charging load and reactive charging load of the charging station to be connected, and the initial upper limit and expansion variable of the charging load of the substation.

[0011] Optionally, the preset decision variable rule includes connecting the charging station to be connected to a position corresponding to the substation in the distribution network planning model; according to the preset decision variable rule and the distribution network planning model, optimizing the access position of the charging station to be connected in the target distribution network to complete the coordinated planning between the target distribution network and the charging station to be connected, including: The location corresponding to the substation in the distribution network planning model is used as the access location of the charging station to be connected, so as to optimize the access location corresponding to the charging station to be connected; According to the original access matrix corresponding to the original access position of the charging station to be connected, the matrix variables corresponding to the optimized access position of the charging station to be connected, and the before and after association matrices of the charging station to be connected before and after the optimized access to the target distribution station, it is determined whether the access matrix corresponding to the charging station to be connected after being connected to the optimized access position has changed; If there is a change, it is determined that the target distribution network needs to expand the transmission line; When it is determined that the transmission line needs to be expanded, the expansion state auxiliary variable is obtained according to the before and after association matrix of the charging station to be connected to the target distribution station in the optimized connection, and the association matrix between the busbar and the transmission line in the target distribution network; Based on the expansion state auxiliary variables, the target transmission line that needs to be expanded is determined, and the target transmission line is introduced into the distribution network planning model to obtain a new distribution network planning model; According to the target transmission line, the generation power constraint and the bus power balance constraint are updated, and the final distribution network planning model is obtained according to the updated generation power constraint and the bus power balance constraint; According to the preset decision variable rules and the final distribution network planning model, the access position of the charging station to be connected in the final target distribution network is determined to complete the collaborative planning between the target distribution network and the charging station to be connected.

[0012] In a second aspect, a coordinated planning device for a distribution network and a charging station is provided, comprising: The distribution network planning model building module is used to build a distribution network planning model based on the objective function and constraints of the target distribution network planning; the objective function includes investment cost and operation cost; the constraints include power generation constraint, distribution network flow constraint and bus power balance constraint; The model introduction module is used to optimize the access position of the charging station to be connected in the target distribution network according to the preset decision variable rules and the distribution network planning model, so as to complete the coordinated planning between the target distribution network and the charging station to be connected.

[0013] Optionally, the device further comprises: A first investment cost calculation module, used to calculate the investment cost of the distributed power source according to the first maximum output, the first investment variable and the first investment unit cost corresponding to the distributed power source plan; A second investment cost calculation module, used to calculate the investment cost of the renewable distributed power source according to the second maximum output, the second investment variable and the second investment unit cost corresponding to the renewable distributed power source; A third investment cost calculation module, used to calculate the transmission line expansion investment cost according to the planned expansion route length corresponding to the transmission line expansion, the third investment variable and the third investment unit cost; A fourth investment cost calculation module, used to calculate the substation expansion investment cost according to the substation expansion capacity planned corresponding to the substation expansion, the fourth investment variable and the fourth investment unit cost; The total investment cost calculation module is used to calculate the investment cost according to the distributed power source investment cost, the renewable power source investment cost, the transmission line expansion investment cost and the substation expansion investment cost.

[0014] Optionally, the device further comprises: The operation cost calculation module is used to calculate the operation cost based on the power curve of the distributed power source, the power abandonment penalty, the typical day weight, and the distributed power output and renewable power abandonment corresponding to each time in the typical day.

[0015] Optionally, the power generation constraints include distributed power output constraints and renewable power output constraints; the distributed power output constraints are used to constrain the active output and reactive output of distributed power sources at each time during a typical day; the renewable power output constraints are used to constrain the active output and reactive output of renewable power sources at each time during a typical day.

[0016] Optionally, the device further comprises: The power grid flow module is used to construct an initial power grid flow model according to the voltage of the busbar on the target distribution network at each time in a typical day, the resistance and reactance of the target line, and the active output and reactive output of the target line at each time in a typical day; A linearization module is used to linearize the nonlinear constraints in the initial power grid power flow model using the large M method so that the transmission lines of the target distribution network expansion meet the power flow; A power flow model updating module is used to constrain the bus voltage and the upper and lower limits of the power flow in the initial power flow model processed by the large M method according to the upper and lower limits of the bus voltage, the upper and lower limits of the power flow of the bus, and the upper and lower limits of the power flow of the bus, so as to obtain the final distribution network power flow model; The distribution network flow constraint determination module is used to obtain the distribution network flow constraints corresponding to each time in a typical day according to the distribution network flow model.

[0017] Optionally, the bus power balance constraint is used to constrain the power generation, charging load, input value and output value of the flow, the charging load of the substation and other loads on each bus except the charging load on each bus in the target distribution network; the bus power balance constraint is determined according to the active load and reactive load other than the charging load corresponding to the bus of the target distribution network at each time during a typical day, the active charging load and reactive charging load of the charging station to be connected, and the initial upper limit and expansion variable of the charging load of the substation.

[0018] Optionally, the model introduction module includes: An access position optimization unit, used to use the position corresponding to the substation in the distribution network planning model as the access position of the charging station to be connected, so as to optimize the access position corresponding to the charging station to be connected; A judgment unit, used to judge whether the access matrix corresponding to the charging station to be connected to the optimized access position changes according to the original access matrix corresponding to the charging station to be connected to the original access position, the matrix variables corresponding to the charging station to be connected to the optimized access position, and the before and after association matrices of the charging station to be connected to the target distribution station after the charging station to be connected is optimized; A transmission line expansion determination unit, used to determine the need to expand the transmission line of the target distribution network if a change occurs; The expansion state auxiliary variable determination unit is used to obtain the expansion state auxiliary variable according to the before and after association matrix of the charging station to be connected to the target distribution station and the association matrix between the busbar and the transmission line in the target distribution network when it is determined that the transmission line needs to be expanded; A distribution network planning model updating unit is used to determine the target transmission line that needs to be expanded in the end based on the expansion state auxiliary variable, and introduce the target transmission line into the distribution network planning model to obtain a new distribution network planning model; The distribution network planning model training unit is used to update the power generation source constraint and the bus power balance constraint according to the target transmission line, and obtain the final distribution network planning model according to the updated power generation source constraint and the bus power balance constraint; The model introduction unit is used to determine the access position of the charging station to be connected in the final target distribution network according to the preset decision variable rules and the final distribution network planning model, so as to complete the collaborative planning between the target distribution network and the charging station to be connected.

[0019] According to a third aspect, an electronic device is provided, comprising: a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method in the first aspect or its various implementations.

[0020] According to a fourth aspect, a computer-readable storage medium is provided for storing a computer program, wherein the computer program enables a computer to execute the method according to the first aspect or its various implementations.

[0021] Through the technical solution provided in the present application, by constructing a distribution network planning model with investment cost and operating cost as objective functions, the planning scheme of the target distribution network is optimized; then, by introducing power generation constraints, distribution network flow constraints and bus power balance constraints into the distribution network planning model, the distribution network planning model finally obtained can include the economic factors of the target distribution network during the expansion process, so that the access position of the charging station to be connected in the target distribution network obtained according to the preset decision variable rules and the distribution network planning model can enable the target distribution network to reasonably allocate the charging load of the charging station to be connected, and can also reduce the cost of the expansion of the target distribution network.

[0022] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 An application scenario diagram provided for an embodiment of the present application; Figure 2 A flowchart of a method for collaborative planning of a distribution network and a charging station provided in an embodiment of the present application; Figure 3 A schematic diagram of a 41-node road network in a city provided in an embodiment of the present application; Figure 4 A schematic diagram of a 56-node high-voltage distribution network in a city provided in an embodiment of the present application; Figure 5 A diagram showing the change in expansion costs before and after optimized access to different charging demands provided in an embodiment of the present application; Figure 6 A schematic diagram of a collaborative planning device for a distribution network and a charging station provided in an embodiment of the present application; Figure 7It is a schematic block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] As mentioned above, although a large number of scholars at home and abroad have conducted in-depth research on the coordinated planning of charging stations and distribution networks, especially in terms of how to achieve the optimal layout of charging stations and improve the power supply capacity of distribution networks, many effective solutions have been proposed. However, these studies mostly focus on the coordinated planning of charging stations and distribution networks, and pay less attention to the problem of local overload in certain areas of the distribution network due to the continuous increase in charging loads in existing charging stations.

[0028] With the rapid development of distributed renewable energy, its access has become one of the important ways to solve the load pressure of distribution network. Especially for clean energy such as wind and solar energy, the flexibility of distributed power sources makes it a key component to alleviate grid load fluctuations and improve the stability of power system.

[0029] In summary, the increase in electric vehicle charging load has brought unprecedented opportunities and challenges to the distribution network. Although existing research has made some progress, there are relatively few in-depth studies on the local overload problem of the distribution network caused by the continuous increase in charging load. Therefore, there is still room for improvement in the expansion of the distribution network and the optimization of charging station access considering distributed power sources.

[0030] In order to solve the above technical problems, the inventive concept of the present application is: based on the objective function and constraints of the target distribution network planning, a distribution network planning model is constructed; the objective function includes investment cost and operating cost; the constraints include power generation constraint, distribution network flow constraint and bus power balance constraint; according to the preset decision variable rules and the distribution network planning model, the access position of the charging station to be connected in the target distribution network is optimized to complete the coordinated planning between the target distribution network and the charging station to be connected; the present application optimizes the planning scheme of the target distribution network by constructing a distribution network planning model with investment cost and operating cost as the objective function; and then introduces power generation constraint, distribution network flow constraint and bus power balance constraint into the distribution network planning model, so that the distribution network planning model finally obtained can include the economic factors of the target distribution network in the expansion process, so that the access position of the charging station to be connected in the target distribution network obtained according to the preset decision variable rules and the distribution network planning model can enable the target distribution network to reasonably allocate the charging load of the charging station to be connected, while also reducing the cost of the target distribution network in terms of expansion.

[0031] It should be understood that the technical solution of the present application can be applied to the following scenarios, but is not limited to: In some possible implementations, Figure 1 An application scenario diagram provided for an embodiment of the present application, such as Figure 1 As shown, the application scenario may include an electronic device 110 and a network device 120. The electronic device 110 may establish a connection with the network device 120 via a wired network or a wireless network.

[0032] Exemplarily, the electronic device 110 may be a desktop computer, a laptop computer, a tablet computer, etc., but is not limited thereto. The network device 120 may be a terminal device or a server, but is not limited thereto. In one embodiment of the present application, the electronic device 110 may send a request message to the network device 120, and the request message may be used to request to obtain the target function. Further, the electronic device 110 may receive a response message sent by the network device 120, and the response message includes obtaining the target function.

[0033] also, Figure 1 An electronic device 110 and a network device 120 are provided as an example, but other numbers of electronic devices and network devices may actually be included, and the present application does not impose any limitation on this.

[0034] In other possible implementations, the technical solution of the present application may also be executed by the above-mentioned electronic device 110, or the technical solution of the present application may also be executed by the above-mentioned network device 120, and the present application does not impose any limitation on this.

[0035] After introducing the application scenarios of the embodiments of the present application, the technical solution of the present application will be described in detail below: Figure 2 A flowchart of a method for collaborative planning of a distribution network and a charging station provided in an embodiment of the present application, the method can be performed as follows Figure 1 The electronic device 110 shown in the figure performs, but is not limited to this. Figure 2 As shown, the method may include the following steps: S210. Construct a distribution network planning model based on the objective function and constraint conditions of the target distribution network planning.

[0036] Here, the objective function includes investment cost and operating cost; the constraints include power generation constraint, distribution network flow constraint and bus power balance constraint.

[0037] It should be noted that by selecting the investment cost and the operating cost as the objective function of the distribution network planning model, the planning scheme corresponding to the distribution network and the charging station with the lowest total cost can be obtained through the constructed distribution network planning model; the constraints of the distribution network planning model are selected as the power generation source constraint, the distribution network flow constraint and the bus power balance constraint, so that the distribution network planning model is constrained by the power generation source, the distribution network flow and the bus power balance during the construction process, so that the target distribution network constructed based on the distribution network planning model can have sufficient power supply while avoiding line overload, and can maintain the power balance of the distribution network after the charging station is connected to the target distribution network.

[0038] S220: Optimize the access position of the charging station to be connected in the target distribution network according to the preset decision variable rules and the distribution network planning model, so as to complete the coordinated planning between the target distribution network and the charging station to be connected.

[0039] By completing the coordinated planning between the target distribution network and the charging stations to be connected in step S220, the access of the charging stations to be connected in the target distribution network can be optimized, the reasonable expansion of the distribution network infrastructure can be achieved, and the stability of the operation of the target distribution network and the energy utilization efficiency can be improved, so as to support the coordinated development of electric vehicles and green energy and promote sustainable transportation and energy transformation.

[0040] By adopting the above method, a distribution network planning model with investment cost and operation cost as objective functions is constructed to optimize the planning scheme of the target distribution network; then, by introducing power generation constraints, distribution network flow constraints and bus power balance constraints into the distribution network planning model, the final distribution network planning model can include the economic factors of the target distribution network during the expansion process, so that the access position of the charging station to be connected in the target distribution network obtained according to the preset decision variable rules and the distribution network planning model can enable the target distribution network to reasonably allocate the charging load of the charging station to be connected, and can also reduce the cost of the expansion of the target distribution network.

[0041] In some possible embodiments, before constructing a distribution network planning model based on the objective function and constraint conditions of the target distribution network planning, the following steps are also included: S310, calculating the investment cost of the distributed power source according to the first maximum output, the first investment variable and the first investment unit cost corresponding to the distributed power source plan.

[0042] The formula for calculating the investment cost of distributed power generation is as follows: ; Where: Investment costs for distributed power generation; is the first investment variable; is the first investment unit cost; The first maximum output.

[0043] S320: Calculate the investment cost of the renewable distributed power source according to the second maximum output, the second investment variable and the second investment unit cost of the renewable distributed power source.

[0044] The formula used to calculate the investment cost of renewable distributed power generation is as follows: ; Where: the cost of investing in renewable distributed generation; is the second investment variable; is the unit cost of the second investment; The second largest output.

[0045] S330, calculating the transmission line expansion investment cost according to the planned expansion route length corresponding to the transmission line expansion, the third investment variable and the third investment unit cost.

[0046] The formula used to calculate the investment cost of transmission line expansion is as follows: ; Where: Investment costs for transmission line expansion; is the third investment variable; is the unit cost of the third investment; To expand the length of the route.

[0047] S340, calculating the substation expansion investment cost according to the substation expansion capacity planned corresponding to the substation expansion, the fourth investment variable and the fourth investment unit cost.

[0048] The formula used to calculate the investment cost of substation expansion is as follows: ; Where: Investment costs for substation expansion; is the fourth investment variable; is the fourth investment unit cost; Expand the capacity of the substation.

[0049] S350, calculating the investment cost according to the distributed power source investment cost, the renewable power source investment cost, the transmission line expansion investment cost and the substation expansion investment cost.

[0050] The formula used to calculate the investment cost is as follows: ; Where: For investment costs; They are respectively the investment cost of distributed power generation, the investment cost of renewable distributed power generation, the investment cost of transmission line expansion, and the investment cost of substation expansion.

[0051] Here, the investment cost includes the investment cost of distributed generation, the investment cost of renewable distributed generation, the investment cost of substation expansion and the substation expansion capacity, and the investment cost is calculated based on the equal annual value (20 years) to obtain a fixed amount that needs to be spent each year.

[0052] By adopting the above method, the objective function is selected as the sum of the investment cost of distributed power generation, the investment cost of renewable power generation, the investment cost of transmission line expansion and the investment cost of substation expansion. The distribution network planning model constructed by the objective function can be realized, and the minimum total cost of building the target distribution network can be determined, thereby optimizing the planning scheme of the target distribution network.

[0053] In some possible embodiments, before constructing the distribution network planning model based on the objective function and constraint conditions corresponding to the target distribution network, the following steps may also be included: The operating cost is calculated based on the power curve of distributed power sources, power abandonment penalty, typical day weight, and the distributed power output and renewable power abandonment corresponding to each time in a typical day.

[0054] It should be noted that the power generation cost of distributed power sources can be obtained based on the typical day weight, the power curve of distributed power sources, and the distributed power output corresponding to each time in a typical day; the power abandonment cost of renewable power sources can be obtained based on the typical day weight, power abandonment penalty, and the power abandonment cost of renewable power sources corresponding to each time in a typical day. Therefore, the operating cost obtained here is the sum of the power generation cost of distributed power sources and the power abandonment cost of renewable power sources.

[0055] The formula used to calculate the operating cost is as follows: ; Where: For operating costs; is the power curve of distributed power source; For Typical days of the year Middle The distributed power output corresponding to the hour is: For Typical days of the year Middle The amount of renewable energy abandoned corresponding to the hours, To punish the abandonment of electricity, is the typical day weight.

[0056] By adopting the above method, the operating cost can be quickly calculated according to the power curve of distributed power sources, power abandonment penalty, typical day weight, and the distributed power output and renewable power abandonment corresponding to each time in a typical day, that is, the sum of the power generation cost of distributed power sources and the power abandonment cost of renewable power sources, so as to ensure that when the power generation of renewable power sources exceeds the demand, the economic losses caused by the ineffective use of electric energy are also calculated into the operating cost, so as to ensure the accuracy of the operating cost.

[0057] In some possible embodiments, power generation constraints include distributed power output constraints and renewable power output constraints; distributed power output constraints are used to constrain the active output and reactive output of distributed power sources at each time during a typical day; renewable power output constraints are used to constrain the active output and reactive output of renewable power sources at each time during a typical day.

[0058] Here, the power generation constraints are selected as the distributed power output constraints and the renewable power output constraints, which can ensure that the target distribution network built according to the distribution network planning model can have sufficient power supply. Among them, when determining the power generation constraints, it is necessary to consider the upper and lower limits of the active output and reactive output of the distributed power and renewable power at the same time, and when the investment 0-1 variable is 1, the distributed power and renewable power can effectively output; here, the renewable power must also meet the sum of the output and the abandoned power to be the total power generation.

[0059] Among them, the output of distributed power generation can meet the constraints of the following formula: ; ; Where: For distributed power generation Typical days of the year Middle Active output corresponding to the hour; For distributed power generation Typical days of the year Middle Reactive power output corresponding to the hour; is the minimum active output corresponding to the distributed power source; is the maximum active output corresponding to the distributed power source; They are the minimum and maximum reactive outputs corresponding to the distributed power sources, respectively, indicating that the active and reactive outputs of the distributed power sources are within the limits.

[0060] Among them, the output of renewable power sources can meet the constraints of the following formula: ; ; ; Where: For renewable power Typical days of the year Middle Active output corresponding to the hour; For renewable power Typical days of the year Middle The reactive power output corresponding to the hour is and It is used to indicate that the output of renewable power sources is within the limit, and it can be obtained in Typical days of the year Middle The amount of wasted electricity corresponding to the hour; For renewable power Typical days of the year Middle The allowable output corresponding to the hour.

[0061] By adopting the above method, the active output and reactive output corresponding to each moment of a typical day of the distributed power sources in the distribution network planning model, as well as the active output and reactive output corresponding to each moment of a typical day of the renewable power sources are constrained respectively, so as to ensure that the target distribution network constructed based on the distribution network planning model can have sufficient power supply.

[0062] In some possible implementations, the power flow constraint of the distribution network is determined by: S410: construct an initial distribution network power flow model according to the voltage of the busbar on the target distribution network at each time in a typical day, the resistance and reactance of the target line, and the active output and reactive output of the target line at each time in a typical day.

[0063] Here, the linearized Dist-flow model is used to model the distribution network flow to obtain the initial distribution network flow model, where the initial distribution network flow model is constructed by the following formula: ; Where: Target bus exist Typical days of the year Middle The voltage corresponding to the hour is Target lines and lines Between Typical days of the year Middle Active power output and reactive power output corresponding to the hour; Target lines and lines The resistance and reactance between is the voltage reference value.

[0064] S420, using the large M method to linearize the nonlinear constraints in the initial power grid power flow model so that the transmission lines of the target distribution network expansion meet the power flow.

[0065] Here, considering that the target distribution network will expand the transmission line, and the newly expanded transmission line also needs to meet the power flow, therefore, the 0-1 variable is introduced and the big M method is used to rewrite the formula in step S410 as follows: ; ; In the formula, is a large normal number.

[0066] S430. According to the upper and lower limits of the bus voltage, the upper and lower limits of the bus current active power, and the upper and lower limits of the bus current active power, the bus voltage and the upper and lower limits of the current in the initial power grid current model processed by the large M method are constrained to obtain the final distribution network current model.

[0067] Among them, the voltage of the busbar in the initial power grid flow model processed by the large M method, as well as the upper and lower limits of the flow are constrained by the following formula: ; ; ; Where: Target bus exist Typical days of the year Middle The voltage corresponding to the hour; The target bus The corresponding voltage upper and lower limits, They are the upper limits of the power flow and reactive power, respectively. The negative sign indicates that the power flow direction is from the target bus. - Target Bus .

[0068] S440. According to the distribution network power flow model, obtain the distribution network power flow constraints corresponding to each time in a typical day.

[0069] By adopting the above method, the distribution network flow constraints corresponding to each time of a typical day can be quickly obtained according to the distribution network flow model. At the same time, the distribution network planning model constructed according to the distribution network flow constraints can avoid line overload.

[0070] In some possible embodiments, the bus power balance constraint is used to constrain the power generation, charging load, input value and output value of the flow, the charging load of the substation and other loads on each bus except the charging load on each bus in the target distribution network; the bus power balance constraint is determined according to the active load and reactive load other than the charging load corresponding to the bus of the target distribution network at each time of a typical day, the active charging load and reactive charging load of the charging station to be connected, and the initial upper limit and expansion variable of the charging load of the substation.

[0071] Here, the power generation of each bus, the charging load, the charging load of the substation, and other loads on each bus except the charging load, as well as the input and output of the power flow are considered. The power input and power output of the bus also need to be balanced, and the active and reactive balance are considered at the same time. Therefore, the bus power balance can be constrained by the following formula: ; ; Where: Busbar exist Typical days of the year Middle Other active loads and reactive loads except charging load per hour; Charging stations exist Typical days of the year Middle Active charging load and reactive charging load corresponding to the hour; For distributed power generation Typical days of the year Middle Active output corresponding to the hour; For distributed power generation Typical days of the year Middle Reactive power output corresponding to the hour; For renewable power Typical days of the year Middle Active output corresponding to the hour; For renewable power Typical days of the year Middle The reactive power output corresponding to the hour.

[0072] In addition, the bus power balance constraint here should also meet the substation charging load limit. Therefore, it is considered here that renewable distributed power generation can offset part of the charging load, as shown in the following formula: ; ; Where: is the initial upper limit of the substation’s charging load, is the expansion variable of the substation, Expand the substation with 0-1 variables.

[0073] By adopting the above method, the power generation, charging load, input and output values ​​of the flow, the charging load of the substation and other loads on each bus except the charging load in the distribution network planning model can be constrained respectively, and the restriction constraints on the charging load of the substation on the target distribution network can be met to ensure the power balance of the target distribution network.

[0074] In some possible embodiments, the preset decision variable rule includes connecting the charging station to be connected to the position corresponding to the substation in the distribution network planning model; according to the preset decision variable rule and the distribution network planning model, optimizing the access position of the charging station to be connected in the target distribution network to complete the coordinated planning between the target distribution network and the charging station to be connected, which may include the following steps: S510: Using the location corresponding to the substation in the distribution network planning model as the access location of the charging station to be connected, so as to optimize the access location corresponding to the charging station to be connected.

[0075] Here, by selecting the preset decision variable rule as the location corresponding to the substation in the distribution network planning model to connect the charging station to be connected, it is possible to reasonably configure the charging load location of the target distribution network by connecting the charging station to be connected to the substation location as a decision variable, thereby effectively reducing the total investment in the target distribution network.

[0076] S520. Determine whether the access matrix corresponding to the charging station to be connected to the optimized access position has changed based on the original access matrix corresponding to the charging station to be connected to the original access position, the matrix variables corresponding to the charging station to be connected to the optimized access position, and the before and after association matrices of the charging station to be connected when optimized to the target distribution station.

[0077] It should be noted that the initial access bus position of the charging station to be connected on the target distribution network is known. Therefore, a 0-1 original access matrix with a dimension of bus-node can be constructed. The node here can be a substation. Since the optimized access matrix corresponding to the access position of the charging station to be connected is a matrix variable, after determining the optimized access position of the charging station to be connected, according to the matrix variables corresponding to the access of the charging station to be connected to the optimized access position, the before and after association matrix of the charging station to be connected in the optimized access to the target distribution station, and the original access matrix corresponding to the access of the charging station to be connected to the original access position, it can be determined which lines need to be expanded.

[0078] S530: If a change occurs, it is determined that the target distribution network needs to expand the transmission line.

[0079] Here, if the substation after the optimized access position of the charging station to be connected is different from the substation initially connected, it can be determined that the transmission line needs to be expanded, wherein the transmission line that needs to be expanded is determined by the following formula: ; Where: Access the 0-1 original access matrix corresponding to the original access position of the charging station to be accessed; A 0-1 matrix variable corresponding to the optimized access position for the charging station to be connected; The before-and-after association matrix for optimizing the access to the target distribution station for the charging station to be connected is constructed.

[0080] S540: When it is determined that the transmission line needs to be expanded, the expansion state auxiliary variable is obtained according to the before and after association matrix of the charging station to be connected to the target distribution station in the optimized connection, and the association matrix between the busbar and the transmission line in the target distribution network.

[0081] Among them, the auxiliary variable of the expansion state is calculated by the following formula: ; ; Where: The before-and-after association matrix for optimizing the access of the charging station to the target distribution station is Auxiliary variables for line expansion status; is the correlation matrix between the busbars and transmission lines in the target distribution network; is a 0-1 matrix variable for the expansion of transmission lines.

[0082] S550: Based on the expansion state auxiliary variable, determine the target transmission line that needs to be expanded in the end, and introduce the target transmission line into the distribution network planning model to obtain a new distribution network planning model.

[0083] The formula used to determine the target transmission line that needs to be expanded is as follows: ; Where M is a large positive constant.

[0084] S560: Update the generation source constraint and the bus power balance constraint according to the target transmission line, and obtain the final distribution network planning model according to the updated generation source constraint and the bus power balance constraint.

[0085] Here, after determining the target transmission line, the generation power constraint and the bus power balance constraint also need to be updated respectively. The update formulas for the generation power constraint and the bus power balance constraint are as follows: ; ; .

[0086] In this step, since step S550 introduces the target transmission line into the distribution network planning model, the power generation constraints and bus power balance constraints in the new distribution network planning model obtained in step S550 change with the introduction of the target transmission line. Therefore, in this step, the power generation constraints and bus power balance constraints are updated according to the target transmission line, and then the distribution network planning model is constructed according to the updated power generation constraints and bus power balance constraints, so that the final distribution network planning model can simulate the target distribution network with the target transmission line introduced.

[0087] S570: Determine the access position of the charging station to be connected in the final target distribution network according to the preset decision variable rules and the final distribution network planning model, so as to complete the coordinated planning between the target distribution network and the charging station to be connected.

[0088] Since the final distribution network planning model here is constructed based on the updated power generation constraints and bus power balance constraints based on the target transmission line, the access position of the charging station to be connected in the target distribution network after the target transmission line expansion can be quickly determined according to the preset decision variable rules and the final distribution network planning model, so as to accurately obtain the coordinated planning between the target distribution network and the charging station to be connected.

[0089] By adopting the above method, after determining that the target distribution network needs to be expanded, the target transmission line can be introduced into the distribution network planning model to obtain a new distribution network planning model; then the power generation source constraints and bus power balance constraints are updated, and the final distribution network planning model is constructed; finally, according to the preset decision variable rules and the final distribution network planning model, the access position of the charging station to be connected in the final target distribution network is determined to quickly complete the coordinated planning between the target distribution network and the charging station to be connected.

[0090] The specific implementation mode of the present invention is described below with a specific example.

[0091] This example is based on a city in Northwest China, which consists of a 41-node road network and a 56-node high-voltage distribution network. The road network and distribution network are configured as follows: Figure 3 and Figure 4As shown. Due to the confidentiality of the real urban power grid data, four modified IEEE-14 distribution networks simulate the distribution networks representing the 110kV power supply areas in the north, south, east and west of the city. These distribution networks operate in a relatively independent manner and are connected by a 330kV transmission network, which is not considered in the present invention. The network adopted is intended to simulate a real urban area.

[0092] According to the distribution network planning model built in the above steps and the distribution network flow model, the above examples are calculated respectively. The expansion costs of the substation and the target transmission line are taken as 600,000 yuan / MW and 400,000 yuan / km respectively. The investment and expansion costs are calculated based on their equivalent annual values, with a lifespan of 20 years and a discount rate of 0.08. The calculation results are shown in Table 1: Table 1 Comparison of charging stations to be connected before and after optimization

[0093] It can be seen from Table 1 that, when the charging load is given, the access position of the charging station is fixed without optimization, and the load concentration cannot be fully considered, which affects the reasonable configuration of the charging load and leads to unreasonable selection of the expansion plan; and the collaborative planning method of the distribution network and the charging station provided in the present application is adopted to optimize the access of the charging station to be connected to the target distribution network, which can not only optimize the position of the charging station to be connected to the distribution network, but also make the charging load evenly distributed, flexibly select the expansion plan, and effectively reduce the cost.

[0094] For further comparison, the charging load is gradually increased from the original 80% to 130%, and then optimized and calculated using the above distribution network planning model and distribution network flow model. Figure 5 The figure shows the change in expansion cost before and after the optimized access of charging stations under different charging demands. Figure 5 It can be seen that when the charging demand is not large (80%), the expansion costs of the two methods are the same; and as the number of electric vehicles continues to increase, the charging demand continues to increase. The coordinated planning method of the distribution network and charging stations provided in this application will optimize the access of the charging stations to be connected to the target distribution network. The expansion cost is reduced more, up to 19.4% of the original cost, while the charging stations without optimized access cannot effectively adjust the access position of the charging stations, resulting in uneven load distribution and a large increase in expansion costs. Therefore, the distribution network planning model provided in this application can effectively reduce the expansion cost, and can provide a more economical and efficient charging station access solution in the context of the popularization of electric vehicles and the increase in charging demand.

[0095] Finally, the selection of expansion schemes for charging stations to be optimally connected to the target distribution network based on the coordinated planning method for distribution network and charging station provided by this application is further discussed under different substation and line expansion costs. The results are shown in Table 2: Table 2 Expansion plans under four different expansion costs

[0096] As can be seen from Table 2, for different situations, the costs of substation and line expansion are different, and the expansion options with the least investment are also different. The charging station optimization access model corresponding to the collaborative planning method of the distribution network and charging station provided in this application can be flexibly adjusted to find the expansion plan with the least investment in different situations, making the distribution network planning more economical and efficient.

[0097] Figure 6 is a schematic diagram of a collaborative planning device 600 for a distribution network and a charging station according to an embodiment of the present invention. The device 600 includes: The distribution network planning model building module 610 is used to build a distribution network planning model based on the objective function and constraints of the target distribution network planning; the objective function includes investment cost and operation cost; the constraints include power generation constraint, distribution network flow constraint and bus power balance constraint; The model introduction module 620 is used to optimize the access position of the charging station to be connected in the target distribution network according to the preset decision variable rules and the distribution network planning model, so as to complete the coordinated planning between the target distribution network and the charging station to be connected.

[0098] In some possible implementations, the apparatus 600 further includes: A first investment cost calculation module, used to calculate the investment cost of the distributed power source according to the first maximum output, the first investment variable and the first investment unit cost corresponding to the distributed power source plan; A second investment cost calculation module, used to calculate the investment cost of the renewable distributed power source according to the second maximum output, the second investment variable and the second investment unit cost corresponding to the renewable distributed power source; A third investment cost calculation module, used to calculate the transmission line expansion investment cost according to the planned expansion route length corresponding to the transmission line expansion, the third investment variable and the third investment unit cost; A fourth investment cost calculation module, used to calculate the substation expansion investment cost according to the substation expansion capacity planned corresponding to the substation expansion, the fourth investment variable and the fourth investment unit cost; The total investment cost calculation module is used to calculate the investment cost according to the distributed power source investment cost, the renewable power source investment cost, the transmission line expansion investment cost and the substation expansion investment cost.

[0099] In some possible implementations, the apparatus 600 further includes: The operation cost calculation module is used to calculate the operation cost based on the power curve of the distributed power source, the power abandonment penalty, the typical day weight, and the distributed power output and renewable power abandonment corresponding to each time in the typical day.

[0100] In some implementable embodiments, the power generation constraints include distributed power output constraints and renewable power output constraints; the distributed power output constraints are used to constrain the active output and reactive output of distributed power sources at each time during a typical day; the renewable power output constraints are used to constrain the active output and reactive output of renewable power sources at each time during a typical day.

[0101] In some possible implementations, the apparatus 600 further includes: The power grid flow module is used to construct an initial power grid flow model according to the voltage of the busbar on the target distribution network at each time in a typical day, the resistance and reactance of the target line, and the active output and reactive output of the target line at each time in a typical day; A linearization module is used to linearize the nonlinear constraints in the initial power grid power flow model using the large M method so that the transmission lines of the target distribution network expansion meet the power flow; A power flow model updating module is used to constrain the bus voltage and the upper and lower limits of the power flow in the initial power flow model processed by the large M method according to the upper and lower limits of the bus voltage, the upper and lower limits of the power flow of the bus, and the upper and lower limits of the power flow of the bus, so as to obtain the final distribution network power flow model; The distribution network flow constraint determination module is used to obtain the distribution network flow constraints corresponding to each time in a typical day according to the distribution network flow model.

[0102] In some implementable embodiments, the bus power balance constraint is used to constrain the power generation, charging load, input value and output value of the flow, the charging load of the substation and other loads on each bus except the charging load on each bus in the target distribution network; the bus power balance constraint is determined according to the active load and reactive load other than the charging load corresponding to the bus of the target distribution network at each time during a typical day, the active charging load and reactive charging load of the charging station to be connected, and the initial upper limit and expansion variable of the charging load of the substation.

[0103] In some implementations, the model introduction module 620 includes: An access position optimization unit, used to use the position corresponding to the substation in the distribution network planning model as the access position of the charging station to be connected, so as to optimize the access position corresponding to the charging station to be connected; A judgment unit, used to judge whether the access matrix corresponding to the charging station to be connected to the optimized access position changes according to the original access matrix corresponding to the charging station to be connected to the original access position, the matrix variables corresponding to the charging station to be connected to the optimized access position, and the before and after association matrices of the charging station to be connected to the target distribution station after the charging station to be connected is optimized; A transmission line expansion determination unit, used to determine the need to expand the transmission line of the target distribution network if a change occurs; The expansion state auxiliary variable determination unit is used to obtain the expansion state auxiliary variable according to the before and after association matrix of the charging station to be connected to the target distribution station and the association matrix between the busbar and the transmission line in the target distribution network when it is determined that the transmission line needs to be expanded; A distribution network planning model updating unit is used to determine the target transmission line that needs to be expanded in the end based on the expansion state auxiliary variable, and introduce the target transmission line into the distribution network planning model to obtain a new distribution network planning model; The distribution network planning model training unit is used to update the power generation source constraint and the bus power balance constraint according to the target transmission line, and obtain the final distribution network planning model according to the updated power generation source constraint and the bus power balance constraint; The model introduction unit is used to determine the access position of the charging station to be connected in the final target distribution network according to the preset decision variable rules and the final distribution network planning model, so as to complete the collaborative planning between the target distribution network and the charging station to be connected.

[0104] It should be understood that an embodiment of a collaborative planning device for a distribution network and a charging station and an embodiment of a collaborative planning method for a distribution network and a charging station may correspond to each other, and similar descriptions may refer to an embodiment of a collaborative planning method for a distribution network and a charging station. To avoid repetition, they will not be described here. Specifically, Figure 6 The device 600 shown can execute the above-mentioned embodiment of the collaborative planning method for distribution network and charging station, and the aforementioned and other operations and / or functions of each module in the device 600 are respectively for realizing the corresponding processes in the above-mentioned collaborative planning method for distribution network and charging station, which will not be described in detail here for the sake of brevity.

[0105] In the above, the device 600 of the embodiment of the present invention is described from the perspective of the functional module in conjunction with the accompanying drawings. It should be understood that the functional module can be implemented in hardware form, can be implemented by instructions in software form, and can also be implemented by a combination of hardware and software modules. Specifically, each step of the embodiment of a method for collaborative planning of a distribution network and a charging station and a detection method in the embodiment of the present invention can be completed by an integrated logic circuit of hardware and / or software in the processor, and the steps of the method for collaborative planning of a distribution network and a charging station and a detection method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or a combination of hardware and software modules in the decoding processor to execute. Optionally, the software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory, and combines its hardware to complete the steps in the embodiment of the above-mentioned method for collaborative planning of a distribution network and a charging station and a detection method.

[0106] Figure 7 is a schematic block diagram of an electronic device 110 according to an embodiment of the present invention.

[0107] like Figure 4 As shown, the electronic device 110 may include: The memory 111 and the processor 112, the memory 111 is used to store the computer program and transmit the program code to the processor 112. In other words, the processor 112 can call and run the computer program from the memory 111 to implement the method in the embodiment of the present invention.

[0108] For example, the processor 112 may be configured to execute the above method embodiments according to instructions in the computer program.

[0109] In some embodiments of the present invention, the electronic device 110 may include but is not limited to: General-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0110] In some embodiments of the present invention, the memory 111 includes but is not limited to: Volatile memory and / or non-volatile memory. Among them, the non-volatile memory can be read-only memory (ROM), programmable ROM (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct memory bus random access memory (Direct Rambus RAM, DR RAM).

[0111] In some embodiments of the present invention, the computer program may be divided into one or more modules, which are stored in the memory 111 and executed by the processor 112 to complete the method provided by the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the controller.

[0112] like Figure 7 As shown, the electronic device 110 may further include: The transceiver 113 may be connected to the processor 112 or the memory 111 .

[0113] The processor 112 may control the transceiver 113 to communicate with other devices, specifically, to send information or data to other devices, or to receive information or data sent by other devices. The transceiver 113 may include a transmitter and a receiver. The transceiver 113 may further include an antenna, and the number of antennas may be one or more.

[0114] It should be understood that the various components in the electronic device are connected via a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus and a status signal bus.

[0115] The present invention also provides a computer storage medium having a computer program stored thereon, which, when executed by a computer, enables the computer to perform the method of the above method embodiment. In other words, an embodiment of the present invention also provides a computer program product containing instructions, which, when executed by a computer, enables the computer to perform the method of the above method embodiment.

[0116] When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integration. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (Digital Video Disc, DVD)), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0117] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0118] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the module is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0119] The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. For example, each functional module in each embodiment of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0120] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in this application, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A collaborative planning method for distribution network and charging station, characterized in that: include: Based on the objective function and constraint conditions of the target distribution network planning, a distribution network planning model is constructed; the objective function includes investment cost and operation cost; the constraint conditions include power generation constraint, distribution network flow constraint and bus power balance constraint; According to the preset decision variable rules and the distribution network planning model, the access position of the charging station to be connected in the target distribution network is optimized to complete the collaborative planning between the target distribution network and the charging station to be connected.

2. The method according to claim 1, characterized in that Before constructing the distribution network planning model based on the objective function and constraint conditions of the target distribution network planning, the following is also included: Calculate the distributed generation investment cost according to the first maximum output, the first investment variable and the first investment unit cost corresponding to the distributed generation plan; Calculate the investment cost of the renewable distributed power source according to the second maximum output, the second investment variable and the second investment unit cost corresponding to the planned renewable distributed power source; Calculate the investment cost of the transmission line expansion according to the planned expansion route length, the third investment variable and the third investment unit cost corresponding to the transmission line expansion; Calculate the investment cost of the substation expansion according to the planned substation expansion capacity corresponding to the substation expansion, the fourth investment variable and the fourth investment unit cost; The investment cost is calculated based on the distributed power source investment cost, the renewable power source investment cost, the transmission line expansion investment cost and the substation expansion investment cost.

3. The method according to claim 1, characterized in that Before constructing the distribution network planning model based on the objective function and constraint conditions corresponding to the target distribution network, the method further includes: The operating cost is calculated according to the power curve of the distributed power source, the power abandonment penalty, the typical day weight, and the distributed power source output and the renewable power abandonment amount corresponding to each time in the typical day.

4. The method according to claim 1, characterized in that: The power generation constraints include distributed power output constraints and renewable power output constraints; the distributed power output constraints are used to constrain the active output and reactive output of distributed power sources at each time in a typical day; the renewable power output constraints are used to constrain the active output and reactive output of renewable power sources at each time in a typical day.

5. The method according to claim 1, characterized in that: The power flow constraint of the distribution network is determined by: According to the voltage of the busbar on the target distribution network at each time in a typical day, the resistance and reactance of the target line, and the active output and reactive output of the target line at each time in a typical day, the initial distribution network power flow model is constructed; The nonlinear constraint conditions in the initial power grid power flow model are linearized by using the large M method so that the transmission lines of the target distribution network expansion meet the power flow; According to the upper limit and lower limit of the voltage of the bus, the upper limit of the power flow active power of the bus, and the upper limit of the power flow active power of the bus, the voltage of the bus in the initial power grid power flow model processed by the large M method, and the upper limit and lower limit of the power flow are constrained to obtain the final distribution network power flow model; According to the distribution network power flow model, the distribution network power flow constraints corresponding to each time in a typical day are obtained.

6. The method according to claim 1, characterized in that The bus power balance constraint is used to constrain the power generation, charging load, input value and output value of the flow on each bus in the target distribution network, the charging load of the substation and other loads on each bus except the charging load; the bus power balance constraint is determined according to the active load and reactive load other than the charging load corresponding to the bus of the target distribution network at each time of a typical day, the active charging load and reactive charging load of the charging station to be connected, and the initial upper limit and expansion variable of the charging load of the substation.

7. The method according to claim 1, characterized in that The preset decision variable rule includes connecting the charging station to be connected to the position corresponding to the substation in the distribution network planning model; optimizing the access position of the charging station to be connected in the target distribution network according to the preset decision variable rule and the distribution network planning model to complete the collaborative planning between the target distribution network and the charging station to be connected, including: The location corresponding to the substation in the distribution network planning model is used as the access location of the charging station to be connected, so as to optimize the access location corresponding to the charging station to be connected; According to the original access matrix corresponding to the original access position of the charging station to be connected, the matrix variables corresponding to the access position of the charging station to be connected after being connected to the optimized access position, and the before and after association matrices of the charging station to be connected after being optimized to be connected to the target power distribution station, it is determined whether the access matrix corresponding to the access position of the charging station to be connected after being connected to the optimized access position has changed; If there is a change, it is determined that it is necessary to expand the transmission line of the target distribution network; In the case where it is determined that the transmission line needs to be expanded, the expansion state auxiliary variable is obtained according to the before and after association matrix of the charging station to be connected to the target distribution station in the optimized connection, and the association matrix between the busbar in the target distribution network and the transmission line; Based on the expansion state auxiliary variable, determining the target transmission line that ultimately needs to be expanded, and introducing the target transmission line into the distribution network planning model to obtain a new distribution network planning model; According to the target transmission line, the power generation constraint and the bus power balance constraint are updated, and a final distribution network planning model is obtained according to the updated power generation constraint and the bus power balance constraint; According to the preset decision variable rules and the final distribution network planning model, the access position of the charging station to be connected in the final target distribution network is determined to complete the collaborative planning between the target distribution network and the charging station to be connected.

8. A collaborative planning device for a distribution network and a charging station, characterized in that: include: A distribution network planning model building module is used to build a distribution network planning model based on the objective function and constraint conditions of the target distribution network planning; the objective function includes investment cost and operation cost; the constraint conditions include power generation constraint, distribution network flow constraint and bus power balance constraint; The model introduction module is used to optimize the access position of the charging station to be connected in the target distribution network according to the preset decision variable rules and the distribution network planning model, so as to complete the collaborative planning between the target distribution network and the charging station to be connected.

9. An electronic device, characterized in that: include: A processor and a memory, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, the computer program causing a computer to execute the method according to any one of claims 1 to 7.

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