Method and device for improving photovoltaic bearing capacity of power distribution network

Abstract

The invention discloses a method and a device for improving photovoltaic bearing capacity of a power distribution network, and relates to the technical field of renewable energy power distribution networks. By regulating and controlling the position of the movable distributed power supply under the normal working condition and the fault condition of the power distribution network, the movable distributed power supply is regulated and controlled, and the photovoltaic bearing capacity of the power distribution network under the normal working condition and the fault condition is improved; by adding the island constraint, it is ensured that the power distribution network can still avoid the island effect under different fault conditions, normal work can be rapidly recovered, and therefore the photovoltaic bearing capacity of the power distribution network under the condition that power supply of the power grid is emergently processed and recovered when the fault occurs is improved; according to the method, the problem that the photovoltaic bearing capacity is singly improved from the capacity of a photovoltaic generator in the prior art is avoided from the two aspects of dispatching of the movable distributed power supply and island constraint, the operation structure in the power distribution network is adjusted from the actual structure of the power distribution network, and the photovoltaic bearing capacity of the power distribution network is greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable energy distribution network, and in particular relates to a method and device for improving the photovoltaic carrying capacity of a distribution network. Background Art

[0002] As the research on renewable energy continues to advance, the proportion of renewable energy in the power system is gradually increasing, becoming an indispensable part of the power system. However, the power generation characteristics of renewable energy will affect the voltage and current constraints of the power network, limiting the amount of renewable energy inserted into the distribution network. For example, the randomness and volatility of photovoltaic power generation will cause fluctuations in the photovoltaic carrying capacity of the distribution network. Therefore, it is necessary to improve the photovoltaic carrying capacity of the distribution network to better adapt to the development trend of photovoltaic power generation in renewable energy.

[0003] Most of the existing technologies have adopted the method of distribution network reconstruction, which is the most commonly used method for improving the carrying capacity of distribution networks. It can change the topology of the network, simplify the network, improve the voltage distribution, and retain the radial structure of the network. However, the existing technology only considers the normal operating conditions in improving the photovoltaic carrying capacity of the power grid, and ignores the photovoltaic carrying capacity of the distribution network under the condition of emergency processing to restore the power supply of the power grid when a fault occurs; in addition, the existing technology is not comprehensive enough for the optimization of the photovoltaic carrying capacity of the distribution network, and is only limited to the capacity of photovoltaic generators, which leads to unsatisfactory optimization effect on the photovoltaic carrying capacity of the distribution network. Therefore, there is an urgent need for a new method and device for optimizing the photovoltaic carrying capacity of the distribution network to solve the defects of the existing technology. Summary of the invention

[0004] The present invention aims to provide a method and device for improving the photovoltaic carrying capacity of a distribution network to solve the above-mentioned technical problems, to perform position control on a movable distributed power source of the distribution network, to achieve multi-angle optimization of the photovoltaic carrying capacity of the distribution network, and to improve the photovoltaic carrying capacity of the distribution network.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a method for improving the photovoltaic carrying capacity of a distribution network, which is applicable to a distribution network including a mobile distributed power source, and specifically includes:

[0006] Acquire the rated operation data of the distribution network, construct the first control model of the distribution network, and obtain the rated position of the mobile distributed power source;

[0007] When a distribution network fault is detected, the fault information of the distribution network and the operation data of the current mobile distributed power source are obtained, and the island constraint and the power source positioning constraint are constructed in combination with the rated position of the mobile distributed power source;

[0008] According to the island constraint and the power source location constraint, combined with the first control model, a scheduling result of the mobile distributed power source is obtained;

[0009] According to the dispatching result, the mobile distributed power source is dispatched to improve the photovoltaic carrying capacity of the distribution network.

[0010] It can be understood that, compared with the prior art, the present invention achieves the improvement of the photovoltaic carrying capacity of the distribution network under normal working conditions and fault conditions by regulating the position of the mobile distributed power sources under normal working conditions and fault conditions of the distribution network; by adding island constraints, it is ensured that the distribution network can still avoid the island effect under different fault conditions and can quickly resume normal operation, thereby improving the photovoltaic carrying capacity of the distribution network under the condition of emergency processing to restore the power supply of the power grid when a fault occurs; from the two perspectives of scheduling of mobile distributed power sources and island constraints, it avoids the prior art of only improving the photovoltaic carrying capacity from the capacity of photovoltaic generators, and adjusts the operating structure in the distribution network based on the actual structure of the distribution network, which greatly improves the photovoltaic carrying capacity of the distribution network.

[0011] As a preferred solution, the step of acquiring the rated operation data of the distribution network, constructing the first control model of the distribution network, and obtaining the rated position of the mobile distributed power source specifically includes:

[0012] The distribution network includes: a substation, a generator, a plurality of nodes and a plurality of branches;

[0013] Acquire the rated operation data of the distribution network, wherein the rated operation data of the distribution network includes: power data of the generator, rated capacity of the generator, rated voltage of the distribution network and power data of the substation, operation data of the node and operation data of the branch;

[0014] According to the power data of the generator, the rated capacity of the generator, the rated voltage of the distribution network, the power data of the substation, the operation data of the node and the operation data of the branch, respectively construct the total installed capacity objective function of power generation, the power flow constraint, the control model of the substation, the generator capacity constraint, the photovoltaic carrying capacity model and the power source location constraint, and then construct the first control model of the distribution network;

[0015] The first control model is solved according to a preset solver to obtain the rated position of the mobile distributed power source.

[0016] This preferred solution constructs multiple constraints through the rated operating data of the distribution network, and then constructs the first control model of the distribution network. By solving the model, the optimal position of the mobile distributed power source of the distribution network can be obtained. By scheduling the mobile distributed power source, the photovoltaic carrying capacity of the distribution network under normal working conditions is improved.

[0017] As a preferred solution, according to the power data of the generator, the rated capacity of the generator, the rated voltage of the distribution network, the power data of the substation, the operation data of the node and the operation data of the branch, the total installed capacity objective function, the power flow constraint, the control model of the substation, the generator capacity constraint, the photovoltaic carrying capacity model and the power supply location constraint are respectively constructed, and then the first control model of the distribution network is constructed, which specifically includes:

[0018] Constructing a total installed capacity objective function and power flow constraints of power generation according to the power data of the generator, the rated capacity of the generator, the rated voltage of the distribution network, the power data of the substation, the operation data of the node and the operation data of the branch;

[0019] constructing a control model of the substation according to the power data of the node;

[0020] Constructing a generator capacity constraint and a photovoltaic load capacity model according to the power data of the generator and the rated capacity of the generator;

[0021] Constructing a power source location constraint according to the operation data of the node and the operation data of the branch;

[0022] A first control model of the distribution network is constructed according to the total installed capacity objective function, power flow constraints, substation control model, generator capacity constraints, photovoltaic carrying capacity model and power source location constraints.

[0023] This preferred solution constructs multiple constraints through the rated operating data of the distribution network, and then constructs the first control model of the distribution network, so that the detailed operation of the distribution network can be optimized, and the first control model can be solved to schedule the mobile distributed power sources; by scheduling the mobile distributed power sources, the photovoltaic carrying capacity of the distribution network under normal working conditions is improved.

[0024] As a preferred solution, the power location constraint is constructed according to the operation data of the node and the operation data of the branch, specifically including:

[0025] Obtaining the segmented position of the distribution network according to the operation data of the node and the operation data of the branch;

[0026] According to the segment location, construct the power supply location constraint:

[0027]

[0028] Where n represents the mobile distributed power source, represents the set of movable distributed power sources to be located, st represents the segmented position, is a binary variable used to define the nth mobile distributed generation on segment st. is the maximum predefined value.

[0029] This preferred solution constructs constraints on the mobile distributed power source, so that the optimal position of the mobile distributed power source can be obtained, and the photovoltaic carrying capacity of the distribution network under normal working conditions is improved by scheduling the mobile distributed power source.

[0030] As a preferred solution, the acquisition of the fault information of the distribution network and the current operation data of the mobile distributed power source, combined with the rated position of the mobile distributed power source, to construct the island constraint and the power source positioning constraint, specifically includes:

[0031] Acquire the fault information of the distribution network and the operation data of the current mobile distributed power source, wherein the fault information of the distribution network includes: the fault type and the fault information of the branch;

[0032] Building an island constraint according to the fault type and the fault information of the branch;

[0033] According to the operating data of the current movable distributed power source, a power source positioning constraint is constructed.

[0034] This preferred solution constructs island constraints and power source positioning constraints, so that the distribution network can still avoid the island effect under different fault conditions and can quickly resume normal operation, thereby improving the photovoltaic carrying capacity of the distribution network in the case of emergency processing to restore the power supply of the power grid when a fault occurs; starting from the two perspectives of scheduling of mobile distributed power sources and island constraints, it avoids the existing technology of only improving the photovoltaic carrying capacity from the capacity of photovoltaic generators.

[0035] As a preferred solution, the island constraint is constructed according to the fault type and the fault information of the branch, specifically including:

[0036] According to the fault type and the fault information of the branch, an island constraint is constructed:

[0037]

[0038] Among them, Λ F is the set of fault types, Auxiliary binary variable representing the line operation status with substation as reference, y ij,f is the binary variable of branch ij when fault f occurs. ij,f =1, indicating that branch ij is closed when fault f occurs, otherwise it is in operation state; is a collection of virtual lines connected to the virtual substation; A collection of lines connecting two substations.

[0039] This preferred solution constructs island constraints so that the distribution network can still avoid island effects under different fault conditions and can quickly resume normal operation, thereby improving the photovoltaic carrying capacity of the distribution network in the event of an emergency and restore grid power supply.

[0040] As a preferred solution, the power source positioning constraint is constructed according to the operation data of the current mobile distributed power source, specifically including:

[0041] According to the operation data of the current mobile distributed power source, constructing a power source positioning constraint, wherein the power source positioning constraint includes: positioning optimization constraint, time constraint and power constraint;

[0042] The positioning optimization constraints include:

[0043]

[0044] Where n represents the mobile distributed power source, A collection of nodes for installing mobile distributed power generation. represents the set of movable distributed power sources to be located, st represents the segmented location, is a binary variable used to define the nth mobile distributed generation on segment st. is a binary variable, indicating the displacement of n mobile DGs from st to i under fault f;

[0045] The time constraints include:

[0046]

[0047] in, is the total time demand for mobile distributed generation scheduling, is the congestion factor between positions st and i, For the replacement time, is the connection time, is a binary variable, indicating the displacement of n mobile DGs from st to i under fault f;

[0048] The power constraints include:

[0049]

[0050] in, and are the injected active power and reactive power of the mobile distributed generation when a fault f occurs at node i in scenario c and time period t, respectively; is the upper limit of the injected power of the mobile distributed power source, u i,n,t,fis the binary variable of the nth mobile distributed power source; and are the predefined upper and lower limits of the power factor respectively.

[0051] This preferred solution constructs power location constraints to schedule the mobile distributed power sources in the distribution network under different fault conditions, so that the mobile distributed power sources can be in the optimal position, thereby allowing the distribution network to resume normal working conditions more quickly, thereby improving the photovoltaic carrying capacity of the distribution network under fault conditions.

[0052] As a preferred solution, the dispatching result of the mobile distributed power source is obtained according to the island constraint and the power source location constraint in combination with the first control model, specifically including:

[0053] Reconstructing the power flow constraint of the first control model according to the fault information of the distribution network and the current operation data of the mobile distributed power source to obtain a second control model;

[0054] The second control model is solved according to the island constraint and the power source location constraint to obtain a scheduling result of the mobile distributed power source.

[0055] This preferred solution reconstructs the first control model to obtain a control model of the distribution network that meets the fault conditions. Based on the island constraint and the power source positioning constraint, the optimal position of the mobile distributed power source in the distribution network under the fault condition is obtained, thereby improving the photovoltaic carrying capacity of the distribution network under the fault condition.

[0056] As a preferred solution, the power flow constraint of the first control model is reconstructed according to the fault information of the distribution network and the current operation data of the mobile distributed power source to obtain the second control model, which specifically includes:

[0057] Reconstructing the power flow constraint of the first control model according to the fault information of the distribution network and the current operation data of the mobile distributed power source to obtain a first power flow constraint;

[0058] According to the first power flow constraint, a second control model is obtained by combining the total installed capacity objective function of power generation, the control model of the substation, the generator capacity constraint, the photovoltaic carrying capacity model and the power source location constraint.

[0059] This preferred solution reconstructs the first control model to obtain a control model of the distribution network that meets the fault conditions, and obtains the optimal position of the movable distributed power source in the distribution network under fault conditions, thereby improving the photovoltaic carrying capacity of the distribution network under fault conditions.

[0060] Accordingly, an embodiment of the present invention provides a device for optimizing the photovoltaic carrying capacity of a distribution network, comprising: a power supply location calculation module, a constraint construction module, a scheduling solution module, and a power supply scheduling module;

[0061] The power source position calculation module is used to obtain the rated operation data of the distribution network, construct a first control model of the distribution network, and obtain the rated position of the mobile distributed power source;

[0062] The constraint building module is used to obtain the fault information of the distribution network and the operation data of the current mobile distributed power source when a distribution network fault is detected, and to build the island constraint and the power source positioning constraint in combination with the rated position of the mobile distributed power source;

[0063] The scheduling solution module is used to obtain a scheduling result of the mobile distributed power source according to the island constraint and the power source location constraint in combination with the first control model;

[0064] The power scheduling module is used to schedule the mobile distributed power source according to the scheduling result to improve the photovoltaic carrying capacity of the distribution network.

[0065] It can be understood that, compared with the prior art, the present device improves the photovoltaic carrying capacity of the distribution network under normal working conditions and fault conditions by regulating the position of the mobile distributed power source under normal working conditions and fault conditions of the distribution network; by adding island constraints, it ensures that the distribution network can still avoid the island effect under different fault conditions and can quickly resume normal operation, thereby improving the photovoltaic carrying capacity of the distribution network under emergency processing to restore the power supply of the power grid when a fault occurs; from the two perspectives of scheduling of mobile distributed power sources and island constraints, it avoids the prior art of only improving the photovoltaic carrying capacity from the capacity of photovoltaic generators, and adjusts the operating structure in the distribution network based on the actual structure of the distribution network, which greatly improves the photovoltaic carrying capacity of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 : A schematic diagram of the structure of a power distribution network provided by an embodiment of the present invention;

[0067] Figure 2 : A flow chart of the steps of a method for improving the photovoltaic carrying capacity of a distribution network provided by an embodiment of the present invention;

[0068] Figure 3 : Another flow chart for improving the photovoltaic carrying capacity of a distribution network provided by an embodiment of the present invention;

[0069] Figure 4 : A schematic diagram of the structure of a device for improving the photovoltaic carrying capacity of a distribution network provided by an embodiment of the present invention;

[0070] Among them, 201: power supply location calculation module; 202: constraint construction module; 203: scheduling solution module; 204: power supply scheduling module. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] The generation characteristics of renewable energy will affect the voltage and current constraints of the power network, limiting the amount of renewable energy inserted into the distribution network. Therefore, it is necessary to improve the carrying capacity of the distribution network and increase the number of renewable energy sources that can be accommodated. The existing technology mostly adopts the method of distribution network reconstruction, which is also one of the most commonly used methods for improving the carrying capacity of the distribution network. It can change the topology of the network, simplify the network, improve the voltage distribution, and retain the radial structure of the network. Related technologies also include a two-stage optimization method, which reduces the impedance of the connection point to obtain a greater photovoltaic carrying capacity. The first stage is to design the optimal configuration of the distribution network, and then determine the photovoltaic carrying capacity in the second stage. Voltage and reactive power control is also another common method to improve the carrying capacity of the distribution network. The carrying capacity is improved by determining the operating constraints of the on-load tap changer and voltage regulator of the substation. And improve the resilience and photovoltaic carrying capacity of the power grid by optimizing the location of the mobile distributed power source in the power grid.

[0073] The prior art only considers normal operating conditions in improving the photovoltaic carrying capacity of the power grid, while ignoring the emergency treatment to restore the power supply of the power grid when a fault occurs. It does not locate the mobile distributed power source in the power grid and optimize the positioning after the fault occurs to deal with the problem. In addition, the prior art does not combine voltage and reactive power control when using the method of distribution network reconstruction to constrain the operation of the on-load tap changer and voltage regulator of the substation. In addition, the prior art does not apply the ZIP model when considering the load, which deviates from the actual situation. Therefore, the method for improving the photovoltaic load of the distribution network proposed in the embodiment of the present invention solves the defects of the prior art.

[0074] Embodiment 1

[0075] Please refer to Figure 1 , is a schematic diagram of a structure of a distribution network provided by an embodiment of the present invention, which includes: a substation, a plurality of dispatchable distributed generators (DGs), a plurality of mobile distributed power sources (MDGs), a plurality of nodes and a plurality of branches;

[0076] The upper and lower limits of the voltage amplitude of the distribution network are 1.05 pu and 0.95 pu respectively;

[0077] The nodes include: load nodes, photovoltaic power generation standby nodes and mobile distributed power supply connection nodes;

[0078] The branch route is composed of any two nodes;

[0079] The distribution network also includes two temporary storage locations, which are used to temporarily store mobile distributed power sources; the mobile distributed power source has a power of 0.20MVA and a power factor of 0.8, and can be connected to the mobile distributed power source connection node (such as Figure 1 The nodes 7, 12, 17, 22, 25 and 33) shown in the figure are transposed and connected; the number of dispatchable distributed generators (DG) in the distribution network is three, which are located at nodes 16, 22 and 29 respectively.

[0080] It should be noted that please refer to Table 1, which is a distribution network fault type table provided in an embodiment of the present invention; as described in Table 1, there are three operating scenarios of the distribution network described in the present invention, the first is to simultaneously perform photovoltaic carrying capacity assessment and mobile distributed power optimization scheduling; the second is to perform photovoltaic carrying capacity assessment without considering mobile distributed power optimization scheduling; the third is to first calculate the photovoltaic installed capacity during normal operation, and then perform mobile distributed power optimization scheduling based on the fault scenario and consider island constraints. The embodiment of the present invention improves the photovoltaic carrying capacity of the distribution network through the improvement of the third situation compared with the first two situations. The specific method implementation process and verification process are described as follows.

[0081] Table 1

[0082]

[0083] Please refer to Figure 2 , which is a flow chart of the steps of a method for improving the photovoltaic carrying capacity of a distribution network provided by an embodiment of the present invention, including steps S101-S104.

[0084] Step S101: Acquire rated operation data of the distribution network, construct a first control model of the distribution network, and obtain the rated position of the mobile distributed power source.

[0085] In this embodiment, the steps of acquiring the rated operation data of the distribution network, constructing the first control model of the distribution network, and obtaining the rated position of the mobile distributed power source specifically include:

[0086] The distribution network includes: a substation, a generator, a plurality of nodes and a plurality of branches;

[0087] Acquire the rated operation data of the distribution network, wherein the rated operation data of the distribution network includes: power data of the generator, rated capacity of the generator, rated voltage of the distribution network and power data of the substation, operation data of the node and operation data of the branch;

[0088] According to the power data of the generator, the rated capacity of the generator, the rated voltage of the distribution network, the power data of the substation, the operation data of the node and the operation data of the branch, respectively construct the total installed capacity objective function of power generation, the power flow constraint, the control model of the substation, the generator capacity constraint, the photovoltaic carrying capacity model and the power source location constraint, and then construct the first control model of the distribution network;

[0089] The first control model is solved according to a preset solver to obtain the rated position of the mobile distributed power source.

[0090] In this embodiment, according to the power data of the generator, the rated capacity of the generator, the rated voltage of the distribution network, the power data of the substation, the operation data of the node and the operation data of the branch, the total installed capacity objective function, the power flow constraint, the control model of the substation, the generator capacity constraint, the photovoltaic carrying capacity model and the power supply location constraint are respectively constructed, and then the first control model of the distribution network is constructed, which specifically includes:

[0091] Constructing a total installed capacity objective function and power flow constraints of power generation according to the power data of the generator, the rated capacity of the generator, the rated voltage of the distribution network, the power data of the substation, the operation data of the node and the operation data of the branch;

[0092] constructing a control model of the substation according to the power data of the node;

[0093] Constructing a generator capacity constraint and a photovoltaic load capacity model according to the power data of the generator and the rated capacity of the generator;

[0094] Constructing a power source location constraint according to the operation data of the node and the operation data of the branch;

[0095] A first control model of the distribution network is constructed according to the total installed capacity objective function, power flow constraints, substation control model, generator capacity constraints, photovoltaic carrying capacity model and power source location constraints.

[0096] In this embodiment, constructing the power location constraint according to the operation data of the node and the operation data of the branch specifically includes:

[0097] Obtaining the segmented position of the distribution network according to the operation data of the node and the operation data of the branch;

[0098] According to the segment location, construct the power supply location constraint:

[0099]

[0100] Where n represents the mobile distributed power source, represents the set of movable distributed power sources to be located, st represents the segmented location, is a binary variable used to define the nth mobile distributed generation on segment st. is the maximum predefined value.

[0101] In an optional embodiment, the total installed capacity objective function of power generation includes:

[0102]

[0103] in, represents the total installed capacity objective function, i represents the node, Λ PV Used to represent the set of spare nodes where photovoltaic generators are installed. Used to represent the photovoltaic installed capacity of node i.

[0104] In an optional embodiment, the power flow constraint includes: active and reactive power balance constraint and voltage constraint.

[0105] In an optional embodiment, based on the ZIP model to represent the load, the active and reactive power balance constraints include:

[0106]

[0107] Among them, Λ B is a set of branches, t represents the time period, c represents the running scenario, P ji,t,c and Q ji,t,c are the active power and reactive power flowing through branch ij in scenario c and time period t, respectively. and are the active and reactive power injected into the substation, respectively; and are the active and reactive power injected by the distributed generators, respectively; and are the active and reactive power injected into the photovoltaic generator, respectively; and are the active and reactive loads at rated voltage respectively; is the square of the voltage value; V i,t,c is the voltage value; V N The rated voltage for grid operation; and They are constant impedance, constant current and constant power factor for active power demand respectively; and are the constant impedance, constant current and constant power factor of reactive power demand respectively; the subscripts i, t, c all represent the data of node i in scene c and time period t.

[0108] In an optional embodiment, according to the effect of Kirchhoff's voltage law in the network, the voltage constraint specifically includes:

[0109]

[0110] in, is the square of the voltage value at node i; is the auxiliary variable of the voltage difference on the voltage regulator on branch ij or on-load tap changer of the substation; γ ij,t,c is the relaxation variable for calculating the voltage drop between branches; R ij , X ij and Z ij are the resistance, reactance and impedance on branch ij respectively; P ij,t,c and Q ij,t,c are the active power and reactive power flowing through branch ij in scenario c and time period t respectively; is the square of the current value on branch ij; and are the estimated voltages at nodes i and j respectively; θ i,t,c and θ j,t,c are the voltage phase angles at node i and node j respectively; μ ij,t,c is the relaxation variable for calculating the phase angle difference between branches; is the square of the voltage at node j; are the parameters used to calculate the relaxation variables associated with the decrease in branch voltage amplitude; are the parameters used to calculate the relaxation variables related to the branch voltage phase angle difference; is the operating status of branch ij.

[0111] In an optional embodiment, constructing a control model of a substation according to the power data of the node specifically includes:

[0112] The transformation ratio of the ideal transformer connected to node i in the transformer equivalent circuit is The impedance of the transformer R ij +jX ij Connect node j in series, and record the node in the middle as j, so as to calculate:

[0113]

[0114] Among them, V k is the voltage value of node k; is the transformation ratio parameter; It is an auxiliary variable for the voltage difference on the voltage regulator on branch ij or on-load tap-changer of the substation.

[0115] The control model of the substation is the model of the on-load tap changer and voltage regulator of the substation:

[0116]

[0117] Among them, Λ TC It is a branch group with voltage regulator and on-load tap changer; is the voltage V at node i i The square of .

[0118] In an optional embodiment, the generator capacity constraint specifically includes:

[0119]

[0120] in, is the apparent power capacity of the dispatchable distributed generation at node i; DG is the set of nodes with dispatchable distributed generators; and are the active and reactive power injected by the distributed generators, respectively; and are the power factor limits of the dispatchable distributed generation at node i respectively.

[0121] In an optional embodiment, the photovoltaic carrying capacity model specifically includes:

[0122]

[0123] in, and are the active and reactive power injected into the photovoltaic generator, respectively; is the power generation factor of the photovoltaic generator set; Power cuts on photovoltaic generators; is the photovoltaic installed capacity of node i; and are the power factor limits of the PV units at node i respectively.

[0124] Step S102: when a distribution network fault is detected, the fault information of the distribution network and the current operation data of the mobile distributed power source are obtained, and the island constraint and the power source positioning constraint are constructed in combination with the rated position of the mobile distributed power source.

[0125] In this embodiment, the acquisition of the fault information of the distribution network and the current operation data of the mobile distributed power source, combined with the rated position of the mobile distributed power source, to construct the island constraint and the power source positioning constraint specifically includes:

[0126] Acquire the fault information of the distribution network and the operation data of the current mobile distributed power source, wherein the fault information of the distribution network includes: the fault type and the fault information of the branch;

[0127] Building an island constraint according to the fault type and the fault information of the branch;

[0128] According to the operating data of the current movable distributed power source, a power source positioning constraint is constructed.

[0129] In this embodiment, the island constraint is constructed according to the fault type and the fault information of the branch, specifically including:

[0130] According to the fault type and the fault information of the branch, an island constraint is constructed:

[0131]

[0132] Among them, Λ F is the set of fault types, Auxiliary binary variable representing the line operation status with substation as reference, y ij,f is the binary variable of branch ij when fault f occurs. ij,f =1, indicating that branch ij is closed when fault f occurs, otherwise it is in operation state; is a collection of virtual lines connected to the virtual substation; A collection of lines connecting two substations.

[0133] It should be noted that after a distribution network failure occurs, considering the island operation constraints, it is assumed that a virtual substation is built, which can be connected to the disabled nodes to ensure the radiality of the power grid under each fault condition and avoid interconnection or loop formation between substations. Normally operating lines cannot be connected to lines that have not been restored to normal status, and only disabled lines can be connected to virtual substations, so:

[0134]

[0135] Among them, x i,f is the binary variable of node i when fault f occurs, and x of the non-faulty line i,f =0.

[0136] In this embodiment, constructing the power source positioning constraint according to the current operating data of the mobile distributed power source specifically includes:

[0137] According to the operation data of the current mobile distributed power source, constructing a power source positioning constraint, wherein the power source positioning constraint includes: positioning optimization constraint, time constraint and power constraint;

[0138] The positioning optimization constraints include:

[0139]

[0140] Where n represents the mobile distributed power source, A collection of nodes for installing mobile distributed power generation. represents the set of movable distributed power sources to be located, st represents the segmented location, is a binary variable used to define the nth mobile distributed generation on segment st. is a binary variable, indicating the displacement of n mobile DGs from st to i under fault f;

[0141] The time constraints include:

[0142]

[0143] in, is the total time demand for mobile distributed generation scheduling, is the congestion factor between positions st and i, For the replacement time, is the connection time, is a binary variable, indicating the displacement of n mobile DGs from st to i under fault f;

[0144] The power constraints include:

[0145]

[0146] in, and are the injected active power and reactive power of the mobile distributed generation when a fault f occurs at node i in scenario c and time period t, respectively; is the upper limit of the injected power of the mobile distributed power source, u i,n,t,f is the binary variable of the nth mobile distributed power source; and are the predefined upper and lower limits of the power factor respectively.

[0147] Step S103: obtaining a scheduling result of the mobile distributed power source according to the island constraint and the power source location constraint in combination with the first control model.

[0148] In this embodiment, the scheduling result of the mobile distributed power source is obtained according to the island constraint and the power source location constraint in combination with the first control model, specifically including:

[0149] Reconstructing the power flow constraint of the first control model according to the fault information of the distribution network and the current operation data of the mobile distributed power source to obtain a second control model;

[0150] The second control model is solved according to the island constraint and the power source location constraint to obtain a scheduling result of the mobile distributed power source.

[0151] In this embodiment, the power flow constraint of the first control model is reconstructed according to the fault information of the distribution network and the current operation data of the mobile distributed power source to obtain the second control model, which specifically includes:

[0152] Reconstructing the power flow constraint of the first control model according to the fault information of the distribution network and the current operation data of the mobile distributed power source to obtain a first power flow constraint;

[0153] According to the first power flow constraint, a second control model is obtained by combining the total installed capacity objective function of power generation, the control model of the substation, the generator capacity constraint, the photovoltaic carrying capacity model and the power source location constraint.

[0154] In an optional embodiment, the first power flow constraint specifically includes: a first active and reactive power balance constraint and a first voltage constraint.

[0155] In an optional embodiment, the first active and reactive power balance constraint includes:

[0156]

[0157]

[0158] Where f represents the fault condition, is the injected active power of the mobile distributed generation when the node i has a fault f in scenario c and time period t; the subscripts i, t, c, and f all represent the data of the node i when the fault f occurs in scenario c and time period t; x i,f is a binary variable for node i when fault f occurs.

[0159] In an optional embodiment, the first voltage constraint includes:

[0160]

[0161] Among them, the subscripts ij,t,c,f all represent the data of branch ij when fault f occurs in scenario c and time period t; is the auxiliary variable of the voltage difference on the voltage regulator on branch ij or on-load tap changer of the substation; γ ij,t,c,f is the relaxation variable for calculating the voltage drop between branches; μ ij,t,c,fis the relaxation variable for calculating the phase angle difference between branches; ε ij,t,c,f is the slack variable related to the working state of branch ij; Parameters for calculating relaxation variables associated with branch voltage amplitude drops; are the parameters used to calculate the relaxation variables related to the branch voltage phase angle difference; is the operating status of branch ij; y ij,f is the binary variable of branch ij when fault f occurs. ij,f = 0, the slack variable ε ij,t,c,f Satisfy the above constraints, otherwise ε ij,t,c,f =0.

[0162] In an optional embodiment, the preset solver is a CPLEX solver, the first control model and the second control model are both mixed integer linear programming (MILP) models, and the first control model is solved by the CPLEX solver to obtain the rated position of the mobile distributed power source; the second control model is solved by the CPLEX solver to obtain the scheduling result of the mobile distributed power source.

[0163] Step S104: According to the scheduling result, the mobile distributed power source is scheduled to improve the photovoltaic carrying capacity of the distribution network.

[0164] In an optional embodiment, the mobile distributed power source is dispatched according to the dispatch result to improve the photovoltaic carrying capacity of the distribution network, specifically including:

[0165] The scheduling result includes: a target node of a mobile distributed power source;

[0166] According to the target node of the mobile distributed power source, the mobile distributed power source is transposed and connected to the corresponding target node to complete the scheduling of the mobile distributed power source, so as to improve the photovoltaic carrying capacity of the distribution network.

[0167] Please refer to Table 2, which is a table of the recovery process of the distribution network under the first operation scenario (C1) and the second operation scenario (C2) provided by the embodiment of the present invention, including the percentage of power demand after the fault occurs, the fault node, the disconnected and connected lines, and the scheduling of the mobile distributed power source. As shown in the table, in the first operation scenario, only two mobile distributed power sources are pre-deployed in the temporary storage location S1. When fault type 1 occurs, 77.66% of the power demand is restored through the recovery process. For fault type 2, after the recovery process, there are still 2 nodes that stop working, and 83.31% of the power demand is restored. Under fault type 3, 80.89% of the power demand is restored, all dispatchable distributed generators are in island operation mode, and only one mobile distributed power source is dispatched from S1 to 12 nodes for connection. In the second operating scenario (C2), the optimal scheduling of mobile distributed generation is ignored. Therefore, in the cases of fault type 1, fault type 2 and fault type 3, the power demand is 68.78%, 78.47% and 78.47% respectively; therefore, by comparing the recovery process of the distribution network under the first operating scenario (C1) and the second operating scenario (C2), it can be concluded that optimizing the scheduling of mobile distributed generation in the distribution network after a fault occurs can effectively restore the power supply of the power grid, thereby improving the photovoltaic carrying capacity of the distribution network.

[0168] Table 2

[0169]

[0170]

[0171] Please refer to Table 3, which shows the total photovoltaic installed capacity of a distribution network under different operation scenarios provided by an embodiment of the present invention. Compared with the first operation scenario (C1) and the second operation scenario (C3), the third operation scenario (C3) achieves a 300.16% and 315.05% increase in PV installed capacity, respectively. This shows that the solution proposed by the present invention can effectively improve the photovoltaic carrying capacity of the distribution network.

[0172] Table 3

[0173]

[0174]

[0175] Please refer to Figure 3, which is another flow chart for improving the photovoltaic carrying capacity of a distribution network provided by an embodiment of the present invention, wherein the test system data of a distribution network including 33 nodes is initialized, the total installed capacity of photovoltaic power generation is maximized as the objective function, a basic model is established by considering the flow constraints and the operation constraints of the on-load tap changer and the voltage regulator of the substation, the capacity constraints of the dispatchable distributed generators are added, the MDG is located and the photovoltaic carrying capacity model is added, the flow constraints after the fault are reconstructed, the MDG positioning is optimized and the island operation constraints are added, the model is solved by using the CPLEX solver, and the solution result is obtained to dispatch the distribution network, thereby improving the photovoltaic carrying capacity of the distribution network.

[0176] This embodiment improves the photovoltaic carrying capacity of the distribution network under normal working conditions and fault conditions by regulating the position of the mobile distributed power sources under normal working conditions and fault conditions of the distribution network; by adding island constraints, it is ensured that the distribution network can still avoid the island effect under different fault conditions and can quickly restore normal operation, thereby improving the photovoltaic carrying capacity of the distribution network under emergency processing to restore the power supply of the power grid when a fault occurs; from the two perspectives of scheduling of mobile distributed power sources and island constraints, it avoids the existing technology of only improving the photovoltaic carrying capacity from the capacity of photovoltaic generators, and adjusts the operating structure in the distribution network based on the actual structure of the distribution network, which greatly improves the photovoltaic carrying capacity of the distribution network.

[0177] Embodiment 2

[0178] Please refer to Figure 4 , which is a structural schematic diagram of a device for improving the photovoltaic carrying capacity of a distribution network provided by an embodiment of the present invention, including: a power supply location calculation module 201, a constraint construction module 202, a scheduling solution module 203 and a power supply scheduling module 204.

[0179] The power source position calculation module 201 is used to obtain the rated operation data of the distribution network, construct a first control model of the distribution network, and obtain the rated position of the mobile distributed power source.

[0180] In this embodiment, the power source position calculation module 201 includes: a power source position calculation unit;

[0181] The power supply position calculation unit is used for the distribution network including: a substation, a generator, a plurality of nodes and a plurality of branches;

[0182] Acquire the rated operation data of the distribution network, wherein the rated operation data of the distribution network includes: power data of the generator, rated capacity of the generator, rated voltage of the distribution network and power data of the substation, operation data of the node and operation data of the branch;

[0183] According to the power data of the generator, the rated capacity of the generator, the rated voltage of the distribution network, the power data of the substation, the operation data of the node and the operation data of the branch, respectively construct the total installed capacity objective function of power generation, the power flow constraint, the control model of the substation, the generator capacity constraint, the photovoltaic carrying capacity model and the power source location constraint, and then construct the first control model of the distribution network;

[0184] The first control model is solved according to a preset solver to obtain the rated position of the mobile distributed power source.

[0185] In this embodiment, the power source position calculation unit includes: a first control model building subunit;

[0186] The first control model building subunit is used to build a total installed capacity objective function and power flow constraints according to the power data of the generator, the rated capacity of the generator, the rated voltage of the distribution network, the power data of the substation, the operation data of the node and the operation data of the branch;

[0187] constructing a control model of the substation according to the power data of the node;

[0188] Constructing a generator capacity constraint and a photovoltaic load capacity model according to the power data of the generator and the rated capacity of the generator;

[0189] Constructing a power source location constraint according to the operation data of the node and the operation data of the branch;

[0190] A first control model of the distribution network is constructed according to the total installed capacity objective function, power flow constraints, substation control model, generator capacity constraints, photovoltaic carrying capacity model and power source location constraints.

[0191] In this embodiment, the first control model building subunit includes: a power location constraint building component;

[0192] The power source location constraint building component is used to obtain the segmented location of the distribution network according to the operation data of the node and the operation data of the branch;

[0193] According to the segment location, construct the power supply location constraint:

[0194]

[0195] Where n represents the mobile distributed power source, represents the set of movable distributed power sources to be located, st represents the segmented position, is a binary variable used to define the nth mobile distributed generation on segment st. is the maximum predefined value.

[0196] The constraint building module 202 is used to obtain the fault information of the distribution network and the current operation data of the mobile distributed power source when a distribution network fault is detected, and to build the island constraint and the power source positioning constraint in combination with the rated position of the mobile distributed power source.

[0197] In this embodiment, the constraint construction module 202 includes: a constraint construction unit;

[0198] The constraint construction unit is used to obtain the fault information of the distribution network and the operation data of the current mobile distributed power source, wherein the fault information of the distribution network includes: the fault type and the fault information of the branch;

[0199] Building an island constraint according to the fault type and the fault information of the branch;

[0200] According to the operating data of the current movable distributed power source, a power source positioning constraint is constructed.

[0201] In this embodiment, the constraint construction unit includes: an island constraint construction subunit;

[0202] The island constraint building subunit is used to build an island constraint according to the fault type and the fault information of the branch:

[0203]

[0204] Among them, Λ F is the set of fault types, Auxiliary binary variable representing the line operation status with substation as reference, y ij,f is the binary variable of branch ij when fault f occurs. ij,f =1, indicating that branch ij is closed when fault f occurs, otherwise it is in operation state; is a collection of virtual lines connected to the virtual substation; A collection of lines connecting two substations.

[0205] In this embodiment, the constraint construction unit includes: a power positioning constraint construction subunit;

[0206] The power source positioning constraint construction subunit is used to construct a power source positioning constraint according to the operation data of the current movable distributed power source, wherein the power source positioning constraint includes: positioning optimization constraint, time constraint and power constraint;

[0207] The positioning optimization constraints include:

[0208]

[0209]

[0210] Where n represents the mobile distributed power source, A collection of nodes for installing mobile distributed power generation. represents the set of movable distributed power sources to be located, st represents the segmented location, is a binary variable used to define the nth mobile distributed generation on segment st. is a binary variable, indicating the displacement of n mobile DGs from st to i under fault f;

[0211] The time constraints include:

[0212]

[0213] in, is the total time demand for mobile distributed generation scheduling, is the congestion factor between positions st and i, For the replacement time, is the connection time, is a binary variable, indicating the displacement of n mobile DGs from st to i under fault f;

[0214] The power constraints include:

[0215]

[0216] in, and are the injected active power and reactive power of the mobile distributed generation when a fault f occurs at node i in scenario c and time period t, respectively; is the upper limit of the injected power of the mobile distributed power source, u i,n,t,f is the binary variable of the nth mobile distributed power source; and are the predefined upper and lower limits of the power factor respectively.

[0217] The scheduling solution module 203 is used to obtain a scheduling result of the mobile distributed power source according to the island constraint and the power source location constraint in combination with the first control model.

[0218] In this embodiment, the scheduling solution module 203 includes: a scheduling solution unit;

[0219] The dispatching solution unit is used to reconstruct the power flow constraint of the first control model according to the fault information of the distribution network and the current operation data of the mobile distributed power source to obtain a second control model;

[0220] The second control model is solved according to the island constraint and the power source location constraint to obtain a scheduling result of the mobile distributed power source.

[0221] In this embodiment, the scheduling solution unit includes: a second control model building subunit;

[0222] The second control model construction subunit is used to reconstruct the power flow constraint of the first control model according to the fault information of the distribution network and the current operation data of the mobile distributed power source to obtain the first power flow constraint;

[0223] According to the first power flow constraint, a second control model is obtained by combining the total installed capacity objective function of power generation, the control model of the substation, the generator capacity constraint, the photovoltaic carrying capacity model and the power source location constraint.

[0224] The power scheduling module 204 is used to schedule the mobile distributed power source according to the scheduling result to improve the photovoltaic carrying capacity of the distribution network.

[0225] This embodiment improves the photovoltaic carrying capacity of the distribution network under normal working conditions and fault conditions by regulating the position of the mobile distributed power sources under normal working conditions and fault conditions of the distribution network; by adding island constraints, it is ensured that the distribution network can still avoid the island effect under different fault conditions and can quickly restore normal operation, thereby improving the photovoltaic carrying capacity of the distribution network under emergency processing to restore the power supply of the power grid when a fault occurs; from the two perspectives of scheduling of mobile distributed power sources and island constraints, it avoids the existing technology of only improving the photovoltaic carrying capacity from the capacity of photovoltaic generators, and adjusts the operating structure in the distribution network based on the actual structure of the distribution network, which greatly improves the photovoltaic carrying capacity of the distribution network.

[0226] In summary, this embodiment achieves the improvement of the photovoltaic carrying capacity of the distribution network under normal working conditions and fault conditions by regulating the position of the mobile distributed power source under normal working conditions and fault conditions of the distribution network; by adding island constraints, it is ensured that the distribution network can still avoid the island effect under different fault conditions and can quickly restore normal operation, thereby improving the photovoltaic carrying capacity of the distribution network under the condition of emergency processing to restore the power supply of the power grid when a fault occurs; from the two perspectives of scheduling of mobile distributed power sources and island constraints, it avoids the existing technology of only improving the photovoltaic carrying capacity from the capacity of photovoltaic generators, and adjusts the operating structure in the distribution network based on the actual structure of the distribution network, which greatly improves the photovoltaic carrying capacity of the distribution network.

[0227] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for improving the photovoltaic carrying capacity of a distribution network, characterized in that: Applicable to distribution networks containing mobile distributed generation, including: Acquire the rated operation data of the distribution network, construct the first control model of the distribution network, and obtain the rated position of the mobile distributed power source; When a distribution network fault is detected, the fault information of the distribution network and the operation data of the current mobile distributed power source are obtained, and the island constraint and the power source positioning constraint are constructed in combination with the rated position of the mobile distributed power source; According to the island constraint and the power source location constraint, combined with the first control model, a scheduling result of the mobile distributed power source is obtained; According to the dispatching result, the mobile distributed power source is dispatched to improve the photovoltaic carrying capacity of the distribution network.

2. A method for improving the photovoltaic carrying capacity of a distribution network as claimed in claim 1, characterized in that: The step of obtaining the rated operation data of the distribution network, constructing a first control model of the distribution network, and obtaining the rated position of the mobile distributed power source specifically includes: The distribution network includes: a substation, a generator, a plurality of nodes and a plurality of branches; Acquire the rated operation data of the distribution network, wherein the rated operation data of the distribution network includes: power data of the generator, rated capacity of the generator, rated voltage of the distribution network and power data of the substation, operation data of the node and operation data of the branch; According to the power data of the generator, the rated capacity of the generator, the rated voltage of the distribution network, the power data of the substation, the operation data of the node and the operation data of the branch, respectively construct the total installed capacity objective function of power generation, the power flow constraint, the control model of the substation, the generator capacity constraint, the photovoltaic carrying capacity model and the power source location constraint, and then construct the first control model of the distribution network; The first control model is solved according to a preset solver to obtain the rated position of the mobile distributed power source.

3. A method for improving the photovoltaic carrying capacity of a distribution network as claimed in claim 2, characterized in that: According to the power data of the generator, the rated capacity of the generator, the rated voltage of the distribution network, the power data of the substation, the operation data of the node and the operation data of the branch, the total installed capacity objective function, the power flow constraint, the control model of the substation, the generator capacity constraint, the photovoltaic load capacity model and the power supply location constraint are respectively constructed, and then the first control model of the distribution network is constructed, which specifically includes: Constructing a total installed capacity objective function and power flow constraints of power generation according to the power data of the generator, the rated capacity of the generator, the rated voltage of the distribution network, the power data of the substation, the operation data of the node and the operation data of the branch; constructing a control model of the substation according to the power data of the node; Constructing a generator capacity constraint and a photovoltaic load capacity model according to the power data of the generator and the rated capacity of the generator; Constructing a power source location constraint according to the operation data of the node and the operation data of the branch; A first control model of the distribution network is constructed according to the total installed capacity objective function, power flow constraints, substation control model, generator capacity constraints, photovoltaic carrying capacity model and power source location constraints.

4. A method for improving the photovoltaic carrying capacity of a distribution network as claimed in claim 2 or 3, characterized in that: The step of constructing a power source location constraint according to the operation data of the node and the operation data of the branch specifically includes: Obtaining the segmented position of the distribution network according to the operation data of the node and the operation data of the branch; According to the segment location, construct the power supply location constraint: Where n represents the mobile distributed power source, represents the set of movable distributed power sources to be located, st represents the segmented position, is a binary variable used to define the nth mobile distributed generation on segment st. is the maximum predefined value.

5. A method for improving the photovoltaic carrying capacity of a distribution network as claimed in claim 1 or 2, characterized in that: The acquisition of the fault information of the distribution network and the current operation data of the mobile distributed power source, combined with the rated position of the mobile distributed power source, to construct the island constraint and the power source positioning constraint, specifically includes: Acquire the fault information of the distribution network and the operation data of the current mobile distributed power source, wherein the fault information of the distribution network includes: the fault type and the fault information of the branch; Building an island constraint according to the fault type and the fault information of the branch; According to the operating data of the current movable distributed power source, a power source positioning constraint is constructed.

6. A method for improving the photovoltaic carrying capacity of a distribution network as claimed in claim 5, characterized in that: The step of constructing an island constraint according to the fault type and the fault information of the branch specifically includes: According to the fault type and the fault information of the branch, an island constraint is constructed: Among them, Λ F is the set of fault types, Auxiliary binary variable representing the line operation status with substation as reference, y ij,f is the binary variable of branch ij when fault f occurs. ij,f =1, indicating that branch ij is closed when fault f occurs, otherwise it is in operation state; is a collection of virtual lines connected to the virtual substation; A collection of lines connecting two substations.

7. A method for improving the photovoltaic carrying capacity of a distribution network as claimed in claim 5, characterized in that: The step of constructing power source positioning constraints according to the current operating data of the mobile distributed power source specifically includes: According to the operation data of the current mobile distributed power source, constructing a power source positioning constraint, wherein the power source positioning constraint includes: positioning optimization constraint, time constraint and power constraint; The positioning optimization constraints include: Where n represents the mobile distributed power source, A collection of nodes for installing mobile distributed power generation. represents the set of movable distributed power sources to be located, st represents the segmented position, is a binary variable used to define the nth mobile distributed generation on segment st. is a binary variable, indicating the displacement of n mobile DGs from st to i under fault f; The time constraints include: in, is the total time demand for mobile distributed generation scheduling, is the congestion factor between positions st and i, For the replacement time, is the connection time, is a binary variable, indicating the displacement of n mobile DGs from st to i under fault f; The power constraints include: in, and are the injected active power and reactive power of the mobile distributed generation when a fault f occurs at node i in scenario c and time period t, respectively; is the upper limit of the injected power of the mobile distributed power source, u i,n,t,f is the binary variable of the nth mobile distributed power source; and are the predefined upper and lower limits of the power factor respectively.

8. A method for improving the photovoltaic carrying capacity of a distribution network as claimed in claim 5, characterized in that: The step of obtaining a dispatch result of the mobile distributed power source according to the island constraint and the power source location constraint in combination with the first control model specifically includes: Reconstructing the power flow constraint of the first control model according to the fault information of the distribution network and the current operation data of the mobile distributed power source to obtain a second control model; The second control model is solved according to the island constraint and the power source location constraint to obtain a scheduling result of the mobile distributed power source.

9. A method for improving the photovoltaic carrying capacity of a distribution network as claimed in claim 8, characterized in that: The reconstructing the power flow constraint of the first control model according to the fault information of the distribution network and the current operation data of the mobile distributed power source to obtain the second control model specifically includes: Reconstructing the power flow constraint of the first control model according to the fault information of the distribution network and the current operation data of the mobile distributed power source to obtain a first power flow constraint; According to the first power flow constraint, a second control model is obtained by combining the total installed capacity objective function of power generation, the control model of the substation, the generator capacity constraint, the photovoltaic carrying capacity model and the power source location constraint.

10. A device for improving the photovoltaic carrying capacity of a distribution network, characterized in that: include: Power supply location calculation module, constraint building module, scheduling solution module and power supply scheduling module; The power source position calculation module is used to obtain the rated operation data of the distribution network, construct a first control model of the distribution network, and obtain the rated position of the mobile distributed power source; The constraint building module is used to obtain the fault information of the distribution network and the operation data of the current mobile distributed power source when a distribution network fault is detected, and to build the island constraint and the power source positioning constraint in combination with the rated position of the mobile distributed power source; The scheduling solution module is used to obtain a scheduling result of the mobile distributed power source according to the island constraint and the power source location constraint in combination with the first control model; The power scheduling module is used to schedule the mobile distributed power source according to the scheduling result to improve the photovoltaic carrying capacity of the distribution network.

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