Photovoltaic bearing capacity improving method considering reactive power flexibility of converter

Abstract

The invention relates to the technical field of power distribution networks, in particular to a photovoltaic bearing capacity improving method considering reactive flexibility of a converter. Dynamic topology reconstruction considering active regulation and control of mobile energy storage is carried out on the power distribution network; by analyzing the influence of high-proportion photovoltaic access on the aspects of reverse heavy overload, voltage out-of-limit, N-1 security constraint and the like of the power distribution network, the flexibility potential of mobile energy storage in consumption of distributed photovoltaic and improvement of capacity expansion of a distribution network area is fully excavated; constraint conditions such as load transfer, main transformer capacity, a network topology structure, a main transformer short-time allowable overload coefficient and connection limit capacity are considered, active regulation and control of mobile energy storage and real-time dynamic reconstruction of network topology are further considered on the basis, and a new energy maximum admitting ability evaluation model considering optical storage reactive aggregation flexibility is established; and the new energy accepting capability of the power distribution network is improved by using mobile energy storage charging and discharging and space transferable characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a method for improving photovoltaic load carrying capacity by taking into account the reactive flexibility of a converter. Background Art

[0002] Distributed photovoltaics are characterized by being widely connected to the grid and locally connected at high density. Distributed photovoltaics have obvious volatility and intermittent characteristics. Large-scale access to the grid makes it less compatible with the distribution network load in time and space, which in turn leads to increasing uncertainty in the operation of the distribution network, and is prone to problems such as voltage over-limit, reverse overload of distribution transformers, and excessive power quality. In order to ensure the coordinated development of distributed photovoltaics, loads, and power grids, it is necessary to evaluate the capacity margin of distributed photovoltaics that can be connected to each node in the future based on the stable operation boundary and actual operation status of the distribution network, and thus provide guidance for the planning and construction of distributed photovoltaics and distribution networks.

[0003] In the actual operation of the distribution network, there are situations where some operating indicators reach the upper limit due to insufficient reactive power, while the remaining indicators have a large margin, resulting in serious limitations on the photovoltaic acceptance capacity. At present, the technical solution of using flexible resources to actively control and improve the distributed photovoltaic acceptance capacity of the distribution network only considers the flexibility space of the active power of flexible resources; there is a lack of quantification of the adjustable space of reactive power flexibility from the perspective of improving photovoltaic acceptance capacity, which makes it difficult to solve the problem of insufficient photovoltaic acceptance capacity caused by insufficient reactive power in the distribution network. Summary of the invention

[0004] The main purpose of the present invention is to provide a method for improving photovoltaic carrying capacity taking into account the reactive power flexibility of the converter, aiming to solve the problem of insufficient photovoltaic acceptance capacity caused by insufficient reactive power in the distribution network.

[0005] The technical solution proposed by the present invention is:

[0006] A method for improving photovoltaic load capacity considering the reactive power flexibility of a converter, comprising:

[0007] The reactive power feasible domain of mobile energy storage is derived according to the active power constraint and apparent power of mobile energy storage, and the reactive power feasible domain of photovoltaic inverter is derived according to the active power constraint and apparent power of photovoltaic inverter, so as to construct the reactive power flexibility aggregation model of distributed multi-converter in distribution network.

[0008] Considering multiple constraints, the dynamic topology reconstruction of the distribution network is carried out with consideration of active regulation of mobile energy storage;

[0009] Based on the reactive power flexibility aggregation model, the distribution network after dynamic topology reconstruction, safe operation constraints and photovoltaic output volatility, with the maximum total installed capacity of distributed photovoltaics connected to the distribution network as the optimization goal, a new energy maximum acceptance capacity evaluation model considering the flexibility of photovoltaic storage and reactive power aggregation is established to obtain a new energy maximum acceptance capacity improvement strategy considering the flexibility of photovoltaic storage and reactive power aggregation.

[0010] Preferably, the reactive feasible domain of the mobile energy storage is derived according to the active power constraint and apparent power of the mobile energy storage, and the reactive feasible domain of the photovoltaic inverter is derived according to the active power constraint and apparent power of the photovoltaic inverter, so as to construct a reactive flexibility aggregation model of the distributed multi-converter of the distribution network, including:

[0011] The operation constraints of mobile energy storage are obtained, where the operation constraints of mobile energy storage include maximum charge and discharge power limit constraints, energy timing constraints and mobile state constraints, and the maximum charge and discharge power limit constraints are:

[0012]

[0013]

[0014]

[0015] In the formula, is the charging state variable of the mobile energy storage at node i, is the discharge state variable of the mobile energy storage at node i, which takes a value of 0 or 1 to ensure that the energy storage cannot be charged and discharged at the same time; is the upper limit of the mobile energy storage charging power of node i, is the upper limit of the mobile energy storage discharge power of node i; is the charging power of the mobile energy storage at node i during period t, is the discharge power of the mobile energy storage at node i during period t;

[0016] The energy timing constraint of mobile energy storage is:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] In the formula, is the maximum capacity value of the mobile energy storage of node i in period t, is the minimum capacity value of the mobile energy storage of node i in period t, is the current remaining capacity value of the mobile energy storage at node i in period t; is the charging efficiency of mobile energy storage at node i, is the discharge efficiency of mobile energy storage at node i; t0 is the initial time, t d is the termination time, Δt is the unit time period; is the initial capacity of mobile energy storage at node i, is the termination capacity of the mobile energy storage at node i, and the initial capacity of the mobile energy storage is equal to the termination capacity; is the maximum capacity of the mobile energy storage at node i, is the minimum capacity of mobile energy storage at node i; is the energy limit of the mobile energy storage of node i from the starting period to the time t, is the lower limit of the energy of the mobile energy storage of node i from the starting period to time t, is the upper limit of energy at time t pushed forward from the end period, It is the lower limit of energy at time t pushed forward from the end period.

[0027] Preferably, the mobile state constraint of the mobile energy storage is:

[0028]

[0029]

[0030]

[0031] κ m,i,t+1 +κ m,i,t ≤1, j≠i(16),

[0032]

[0033]

[0034] In the formula, κ m,i,t is the state variable of mobile energy storage access, when κ m,i,tWhen it is 1, it means that the mobile energy storage m is connected to the distribution network node i in time period t; m is the mobile energy storage number, ranging from 1 to n; i is the distribution network node number; i∈Ω is the candidate access node set; Z i is the maximum number of mobile energy storage connected to the distribution network node i; γ m,t is the driving state variable of mobile energy storage, when γ m,t When γ is 1, it means that the mobile energy storage m is in the driving state during the period t. m,t When it is 0, it means that the mobile energy storage m is in the connected state during the period t; c,m,t is the charging power of mobile energy storage m during period t, and P d,m,t is the discharge power of mobile energy storage m in period t.

[0035] Preferably, the step of obtaining the operating constraints of the mobile energy storage further includes:

[0036] Characterize the flexibility area of ​​PV-storage reactive power aggregation, including:

[0037] The box uncertainty set is used to characterize the error of photovoltaic power generation prediction value, where the box uncertainty set U is expressed as:

[0038]

[0039] Where u is the set element in the box uncertainty set U; τ is the uncertainty; is the predicted value of photovoltaic output of node i at time t; is the photovoltaic active power of node i at time t;

[0040] Get the operation mode of the photovoltaic generator set with a constant power factor:

[0041]

[0042]

[0043]

[0044] In the formula, is the maximum active output of the photovoltaic node i at time t; is the photovoltaic reactive power of node i at time t; is the upper limit of distributed photovoltaic power factor, is the lower limit of distributed photovoltaic power factor; is the maximum photovoltaic reactive power of node i at time t, is the minimum photovoltaic reactive power of node i at time t;

[0045] Based on the power limitation and electric energy limitation of mobile energy storage itself, the feasible domain of active power of mobile energy storage at multiple times is depicted through the passage of time:

[0046]

[0047]

[0048] In the formula, is the upper limit of the active power of the mobile energy storage at node i during period t, is the lower limit of the active power of the mobile energy storage at node i during period t.

[0049] Preferably, based on the power limitation and electric energy limitation of the mobile energy storage itself, the active power feasible domain of the mobile energy storage at multiple times is depicted through the passage of time, and then it also includes:

[0050] According to the apparent power constraint of mobile energy storage, the feasible region of reactive power of mobile energy storage is derived:

[0051]

[0052]

[0053] In the formula, is the apparent power of the mobile energy storage at node i in period t, is the maximum apparent power of the mobile energy storage at node i; is the active power of the mobile energy storage at node i in period t; is the upper limit of reactive power of mobile energy storage at node i during period t, is the lower limit of reactive power of mobile energy storage at node i during period t;

[0054] Based on the reactive power feasible domain of mobile energy storage, the characterization model of active and reactive power flexibility of distributed energy storage resources is obtained:

[0055]

[0056]

[0057] In the formula, is the reactive power of the mobile energy storage at node i during period t.

[0058] Preferably, the reactive power feasible domain based on mobile energy storage is used to obtain a distributed energy storage resource active and reactive flexibility characterization model, which then includes:

[0059] Determine the VB model of electric vehicle monomer:

[0060]

[0061] In the formula, is the power of the i-th electric car at time t; is the battery energy of the i-th electric car at time t; is the upper limit of the power of the i-th electric vehicle at time t, is the lower power limit of the i-th electric vehicle at time t; is the upper limit of battery energy of the i-th electric vehicle at time t, is the lower limit of battery energy of the i-th electric vehicle at time t;

[0062] Using Minkowski sum, we can get the distribution network flexibility feasible set Z pro for:

[0063]

[0064] Where P1 is the sum of the reserve capacity of distributed energy storage, cluster electric vehicles and load under demand response at distribution network node 1, P2 is the sum of the reserve capacity of distributed energy storage, cluster electric vehicles and load under demand response at distribution network node 2, and P j is the sum of the reserve capacity of distributed energy storage, cluster electric vehicles and demand response load at distribution network node j, P ES is the backup capacity of distributed energy storage, P EV is the reserve capacity of the electric vehicles in the cluster, P D load reserve capacity for demand response; represents the Minkowski sum;

[0065] Preferably, the consideration of multiple constraints to perform dynamic topology reconstruction of the distribution network taking into account active regulation of mobile energy storage includes:

[0066] The distribution network is regarded as a tree with node 0 as the root, and two variables β are introduced for each line. ij,t and β ji,t , and use the variable α ij,t Indicates the connection status of each line at time t to ensure that the distribution network is connected to the main substation node through a spanning tree, and determines the spanning tree constraints satisfied by the dynamic topology reconstruction process of the distribution network:

[0067] β ij,t +β ji,t =α ij,t ,l=1,...,m L (31),

[0068]

[0069] β 0j,t =0, j∈N L (0)(33),

[0070] β ij,t ∈{0,1},i=1,...,NL ,j∈N L (i)(34),

[0071] 0≤α ij,t ≤1,l=1,...,m L (35),

[0072] In the formula, α ij,t is the connectivity variable of branch (i, j) at time t, α ij,t =1 means that the line switch of branch (i, j) is in the closed state at time t, α ij,t =0 means that the line switch of branch (i, j) is in the disconnected state at time t; β ij,t is the node level identification variable at time t, β ij,t =1 means that at time t, node i is the parent node of node j, β ij,t =0 means that at time t, node i is not the parent node of node j, β ji,t is the node level identification variable at time t, β ji,t =1 means that at time t, node j is the parent node of node i, β ji,t =0 means that node j is not the parent node of node i at time t; l represents the branch of the distribution network, m L is the total number of branches in the distribution network, n L is the total number of nodes in the distribution network; N L (i) represents the set of all nodes that may be associated with node i; N L (0) represents the set of all nodes that may be associated with the substation node; ij,t Set as a variable to indirectly ensure that α ij,t The result is 0 or 1;

[0073] In order to ensure that the distribution network remains radial in the system topology in any state, the spanning tree constraints are improved as follows:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] Where k = 0, 1, ..., N c ; N c is the number of N-1 faults, where k = 0, it means the distribution network is in normal operation, and k = 1, ..., N cWhen , it indicates that the distribution network is in a fault state; is the connectivity variable of branch (i, j) when the number of faults in the distribution network is k at time t; is the node level identification variable when the number of distribution network faults is k at time t, It is the node level identification variable when the number of distribution network faults is k at time t.

[0080] Preferably, in order to ensure that the distribution network remains radial in the system topology in any state, the spanning tree constraint is improved as follows, and then further includes:

[0081] The switching times limit constraint that the distribution network meets during dynamic topology reconstruction is determined as follows:

[0082]

[0083]

[0084] Where N ij,max S represents the maximum number of times the line switch of branch (i, j) operates during the entire system reconstruction period; L Represents the set of all branches; N SA,max Indicates the upper limit of the total number of switch operations in the entire period;

[0085] Introducing intermediate variables Change the switch count limit constraint to the following formula:

[0086]

[0087]

[0088]

[0089] Preferably, based on the reactive power flexibility aggregation model, the distribution network after dynamic topology reconstruction, the safety operation constraints and the volatility of photovoltaic output, the maximum total installed capacity of the distribution network connected to the distributed photovoltaic is taken as the optimization goal, and a new energy maximum acceptance capacity evaluation model considering the flexibility of photovoltaic storage reactive power aggregation is established to obtain a new energy maximum acceptance capacity improvement strategy considering the flexibility of photovoltaic storage reactive power aggregation, including:

[0090] Determine the objective function with the maximum total installed capacity of distributed photovoltaic connected to the distribution network as the optimization goal:

[0091]

[0092] Where N PV is the total number of nodes connected to photovoltaic in the system; is the photovoltaic capacity that can be connected to the i-th node;

[0093] Determine the network security boundary conditions under the normal operation of the new energy maximum acceptance capacity assessment model, where the network security boundary conditions include power balance constraints, node voltage upper and lower limit constraints, and line power flow limit constraints. The power balance constraint is:

[0094]

[0095]

[0096]

[0097]

[0098] In the formula, is the injected active power of substation node i at time t, is the injected active power of distributed photovoltaic power source at node i at time t; is the charging power of the energy storage station at node i at time t, is the discharge power of the energy storage power station at node i at time t; is the active power of the electrical load of node i at time t, is the active power of the electric vehicle load at node i at time t; is the injected reactive power of substation node i at time t, is the injected reactive power of distributed photovoltaic power source at node i at time t; is the reactive power of the electrical load at node i at time t, is the reactive power of the electric vehicle load at node i at time t; P ij,t is the active power flow of branch (i, j) at time t, Q ij,t is the reactive power flow of branch (i, j) at time t; r ij is the resistance on branch (i, j), x ij is the reactance value on branch (i, j); is the square value of the current at branch (i, j) at time t; is the original photovoltaic capacity of node i; is the theoretical output of distributed photovoltaic power per unit capacity at time t.

[0099] Preferably, the node voltage upper and lower limits are constrained as follows:

[0100] U i,min i,t i,max (48),

[0101] Where U i,t is the voltage of node i at time t; U i,min is the upper voltage limit at node i, U​​i,max is the lower limit of voltage at node i;

[0102] The line power flow limit constraint is:

[0103] 0≤S ij,t ij,max (49),

[0104] I ij,min ≤I ij,t ij,max (50),

[0105] -P g,srp,max ≤P 1,t <P g,srp,max (51),

[0106] In the formula, S ij,t is the apparent power of branch (i, j) at time t, S ij,max is the maximum apparent power of branch (i, j) and satisfies I ij,t is the current carrying capacity of branch (i, j) at time t, I ij,max is the maximum current carrying capacity of branch (i, j), I ij,min For I ij,max Negative value of P 1,t is the power of node 1; P g,srp,max The maximum power that the transformer can handle.

[0107] Through the above technical solution, the following beneficial effects can be achieved:

[0108] The photovoltaic carrying capacity improvement method considering the reactive power flexibility of the converter proposed in the present invention can solve the problem of insufficient photovoltaic acceptance capacity caused by insufficient reactive power in the distribution network; this scheme first reconstructs the dynamic topology of the distribution network considering the active regulation of mobile energy storage; by analyzing the impact of high-proportion photovoltaic access on the reverse heavy overload, voltage limit, N-1 safety constraints and other aspects of the distribution network, by fully exploring the flexibility potential of mobile energy storage in accommodating distributed photovoltaics and improving the expansion of distribution network areas, considering load transfer, main transformer capacity, network topology, main transformer short-term allowable overload coefficient and interconnection limit capacity and other constraints, on this basis, further considering the active regulation of mobile energy storage and real-time dynamic reconstruction of network topology, establish a new energy maximum acceptance capacity evaluation model considering the flexibility of photovoltaic storage reactive aggregation, so as to obtain a new energy maximum acceptance capacity improvement strategy considering the flexibility of photovoltaic storage reactive aggregation; the new energy acceptance capacity of the distribution network is improved by utilizing the charging and discharging and spatial transferability characteristics of mobile energy storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] ​​In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0110] Figure 1 A flowchart of a first embodiment of a method for improving photovoltaic load carrying capacity taking into account reactive power flexibility of a converter proposed by the present invention;

[0111] Figure 2 This is a calculation example structure diagram of the tenth embodiment of a method for improving photovoltaic load carrying capacity considering the reactive power flexibility of converters proposed by the present invention;

[0112] Figure 3 It is a simulation result diagram of four different schemes in the tenth embodiment of a method for improving photovoltaic load carrying capacity considering reactive power flexibility of converter proposed by the present invention;

[0113] Figure 4 This is a schematic diagram comparing the distributed photovoltaic acceptance capacity of distribution networks of four different schemes in the tenth embodiment of a method for improving photovoltaic carrying capacity considering the reactive flexibility of converters proposed by the present invention. DETAILED DESCRIPTION

[0114] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0115] The present invention proposes a method for improving photovoltaic load carrying capacity by taking into account the reactive flexibility of a converter.

[0116] As attached Figure 1 As shown, in a first embodiment of a method for improving photovoltaic load capacity considering reactive power flexibility of a converter proposed by the present invention, this embodiment includes the following steps:

[0117] Step S110: deriving the reactive feasible domain of the mobile energy storage according to the active power constraint and apparent power of the mobile energy storage, and deriving the reactive feasible domain of the photovoltaic inverter according to the active power constraint and apparent power of the photovoltaic inverter, so as to construct a reactive flexibility aggregation model of the distributed multi-converter of the distribution network;

[0118] Step S120: Considering various constraints, a dynamic topology reconstruction of the distribution network is performed taking into account active regulation of mobile energy storage;

[0119] Step S130: Based on the reactive power flexibility aggregation model, the distribution network after dynamic topology reconstruction, safe operation constraints and photovoltaic output volatility, with the maximum total installed capacity of distributed photovoltaics connected to the distribution network as the optimization goal, a new energy maximum acceptance capacity evaluation model considering the flexibility of photovoltaic storage and reactive power aggregation is established to obtain a new energy maximum acceptance capacity improvement strategy considering the flexibility of photovoltaic storage and reactive power aggregation.

[0120] The photovoltaic carrying capacity improvement method considering the reactive power flexibility of the converter proposed in the present invention can solve the problem of insufficient photovoltaic acceptance capacity caused by insufficient reactive power in the distribution network; this scheme first reconstructs the dynamic topology of the distribution network considering the active regulation of mobile energy storage; by analyzing the impact of high-proportion photovoltaic access on the reverse heavy overload, voltage limit, N-1 safety constraints and other aspects of the distribution network, by fully exploring the flexibility potential of mobile energy storage in accommodating distributed photovoltaics and improving the expansion of distribution network areas, considering load transfer, main transformer capacity, network topology, main transformer short-term allowable overload coefficient and interconnection limit capacity and other constraints, on this basis, further considering the active regulation of mobile energy storage and real-time dynamic reconstruction of network topology, establish a new energy maximum acceptance capacity evaluation model considering the flexibility of photovoltaic storage reactive aggregation, so as to obtain a new energy maximum acceptance capacity improvement strategy considering the flexibility of photovoltaic storage reactive aggregation; the new energy acceptance capacity of the distribution network is improved by utilizing the charging and discharging and spatial transferability characteristics of mobile energy storage.

[0121] In addition, the present invention also proposes a strategy for improving the maximum acceptance capacity of new energy sources by considering the flexibility of photovoltaic and storage reactive aggregation. Considering the influencing factors of the power feasible domain of distributed photovoltaic storage resources and the operating constraints including the maximum charging and discharging power constraints, the apparent power constraints and the timing constraints of the state of charge, the reactive feasible domains of mobile energy storage and photovoltaic inverters are derived according to the active constraints and apparent power of mobile energy storage and photovoltaic inverters, and a reactive flexibility aggregation model of distributed multi-converters in the distribution network is constructed. Further comprehensive consideration is given to the dynamic topology reconstruction of the distribution network, safe operation constraints and photovoltaic output volatility, and the maximum total installed capacity of distributed photovoltaics connected to the distribution network is taken as the optimization goal. The voltage quality is improved through the active-reactive coordinated scheduling of distributed flexible resources in the distribution network, and the photovoltaic absorption capacity of the distribution network is maximized.

[0122] In a second embodiment of a method for improving photovoltaic load capacity considering reactive power flexibility of a converter proposed by the present invention, based on the first embodiment, step S110 includes the following steps:

[0123] Step S210: Obtaining the operation constraints of the mobile energy storage, wherein the operation constraints of the mobile energy storage include maximum charge and discharge power limit constraints, energy timing constraints and mobile state constraints, and the maximum charge and discharge power limit constraints are:

[0124]

[0125]

[0126]

[0127] In the formula, is the charging state variable of the mobile energy storage at node i, is the discharge state variable of the mobile energy storage at node i, which takes a value of 0 or 1 to ensure that the energy storage cannot be charged and discharged at the same time; is the upper limit of the mobile energy storage charging power of node i, is the upper limit of the mobile energy storage discharge power of node i; is the charging power of the mobile energy storage at node i during period t, is the discharge power of the mobile energy storage at node i during period t.

[0128] The energy timing constraint of mobile energy storage is:

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138] In the formula, is the maximum capacity value of the mobile energy storage of node i in period t, is the minimum capacity value of the mobile energy storage of node i in period t, is the current remaining capacity value of the mobile energy storage at node i in period t; is the charging efficiency of mobile energy storage at node i, is the discharge efficiency of mobile energy storage at node i; t0 is the initial time, t d is the termination time, Δt is the unit time period; is the initial capacity of mobile energy storage at node i, is the termination capacity of the mobile energy storage at node i, and the initial capacity of the mobile energy storage is equal to the termination capacity; is the maximum capacity of the mobile energy storage at node i, is the minimum capacity of mobile energy storage at node i; is the energy limit of the mobile energy storage of node i from the starting period to the time t, is the lower limit of the energy of the mobile energy storage of node i from the starting period to time t, is the upper limit of energy at time t pushed forward from the end period, It is the lower limit of energy at time t pushed forward from the end period.

[0139] Specifically, mobile energy storage resources combine energy storage and mobile devices, which can be flexibly connected to the grid compared to traditional energy storage, improving operational flexibility. At present, mobile energy storage structures equipped with lithium batteries in electric vehicles are becoming increasingly mainstream. The operational constraints of mobile energy storage include maximum charge and discharge power limit constraints, energy timing constraints, and mobile state constraints.

[0140] In a third embodiment of a method for improving photovoltaic carrying capacity considering reactive power flexibility of a converter proposed by the present invention, based on the second embodiment, the mobile state constraint of the mobile energy storage is:

[0141]

[0142]

[0143]

[0144] κ m,i,t+1 +κ m,i,t ≤1, j≠i(16),

[0145]

[0146]

[0147] In the formula, κ m,i,t is the state variable of mobile energy storage access, when κ m,i,t When it is 1, it means that the mobile energy storage m is connected to the distribution network node i in time period t; m is the mobile energy storage number, ranging from 1 to n; i is the distribution network node number; i∈Ω is the candidate access node set; Z i is the maximum number of mobile energy storage connected to the distribution network node i; γ m,t is the driving state variable of mobile energy storage, when γ m,t When γ is 1, it means that the mobile energy storage m is in the driving state during the period t. m,t When it is 0, it means that the mobile energy storage m is in the connected state during the period t; c,m,t is the charging power of mobile energy storage m during period t, and P d,m,t is the discharge power of mobile energy storage m in period t.

[0148] In a fourth embodiment of a method for improving photovoltaic load carrying capacity considering reactive power flexibility of a converter proposed by the present invention, based on the third embodiment, step S210 further includes the following steps:

[0149] Step S410: characterizing the PV-storage reactive power aggregation flexibility region, including the following steps:

[0150] Step S411: using a box uncertainty set to characterize the error of the photovoltaic power generation prediction value, wherein the box uncertainty set U is expressed as:

[0151]

[0152] Where u is the set element in the box uncertainty set U; τ is the uncertainty, which is 0.35; is the predicted value of photovoltaic output of node i at time t; is the photovoltaic active power of node i at time t.

[0153] Specifically, the photovoltaic output has uncertainty, so the box-type uncertainty set is used to characterize the error of the photovoltaic power generation prediction value.

[0154] Step S412: Obtain the constant power factor operation mode adopted by the photovoltaic power generation group:

[0155]

[0156]

[0157]

[0158] In the formula, is the maximum active output of the photovoltaic node i at time t; is the photovoltaic reactive power of node i at time t; is the upper limit of distributed photovoltaic power factor, is the lower limit of distributed photovoltaic power factor; is the maximum photovoltaic reactive power of node i at time t, is the minimum photovoltaic reactive power of node i at time t.

[0159] Step S413: Based on the power limit and electric energy limit of the mobile energy storage itself, the feasible domain of active power of the mobile energy storage at multiple times is depicted through the passage of time:

[0160]

[0161]

[0162] In the formula, is the upper limit of the active power of the mobile energy storage at node i during period t, is the lower limit of the active power of the mobile energy storage at node i during period t.

[0163] In a fifth embodiment of a method for improving photovoltaic load carrying capacity considering reactive power flexibility of a converter proposed by the present invention, based on the fourth embodiment, step S413 further includes the following steps:

[0164] Step S510: According to the apparent power constraint of the mobile energy storage, derive the reactive power feasible region of the mobile energy storage:

[0165]

[0166]

[0167] In the formula, is the apparent power of the mobile energy storage at node i in period t, is the maximum apparent power of the mobile energy storage at node i; is the active power of the mobile energy storage at node i in period t; is the upper limit of reactive power of mobile energy storage at node i during period t, is the lower limit of reactive power of mobile energy storage at node i during period t.

[0168] Step S520: Obtaining a model for describing the active and reactive flexibility of distributed energy storage resources based on the reactive power feasible domain of mobile energy storage:

[0169]

[0170]

[0171] In the formula, is the reactive power of the mobile energy storage at node i during period t.

[0172] In a sixth embodiment of a method for improving photovoltaic load carrying capacity considering reactive power flexibility of a converter proposed by the present invention, based on the fifth embodiment, step S520 further includes the following steps:

[0173] Step S610: Determine the VB model of the electric vehicle unit:

[0174]

[0175] In the formula, is the power of the i-th electric car at time t; is the battery energy of the i-th electric car at time t; is the upper limit of the power of the i-th electric vehicle at time t, is the lower power limit of the i-th electric vehicle at time t; is the upper limit of battery energy of the i-th electric vehicle at time t, is the lower limit of the battery energy of the i-th electric car at time t.

[0176] Specifically, in actual systems, the scale of flexibility resources continues to expand. In order to improve the scheduling efficiency of distributed flexibility resources under a distributed architecture, an economic scheduling model for distributed cluster optimization is established. The distributed flexibility resources are regarded as a cluster as a whole, and each cluster is optimized and regulated. In addition, various flexibility resources are fully utilized to provide backup to achieve power balance.

[0177] Step S620: Use Minkowski sum to obtain the distribution network flexibility feasible set Z pro for:

[0178]

[0179] Where P1 is the sum of the reserve capacity of distributed energy storage, cluster electric vehicles and load under demand response at distribution network node 1, P2 is the sum of the reserve capacity of distributed energy storage, cluster electric vehicles and load under demand response at distribution network node 2, and P j is the sum of the reserve capacity of distributed energy storage, cluster electric vehicles and demand response load at distribution network node j; P ES is the backup capacity of distributed energy storage, P EV is the reserve capacity of the electric vehicles in the cluster, P D load reserve capacity for demand response; represents the Minkowski sum.

[0180] In a seventh embodiment of a method for improving photovoltaic load capacity considering reactive power flexibility of a converter proposed by the present invention, based on the sixth embodiment, step S120 includes the following steps:

[0181] Step S710: The distribution network is regarded as a tree with node 0 as the root node, and two variables β are introduced for each line. ij,t and β ji,t (both variables have values ​​between 0 and 1), and use the variable α ij,t Indicates the connection status of each line at time t to ensure that the distribution network is connected to the main substation node through a spanning tree, and determines the spanning tree constraints satisfied by the dynamic topology reconstruction process of the distribution network:

[0182] β ij,t +β ji,t =α ij,t ,l=1,...,m L (31),

[0183]

[0184] β 0j,t =0, j∈N L (0)(33),

[0185] β ij,t ∈{0,1},i=1,...,N L ,j∈N L (i)(34),

[0186] 0≤α ij,t ≤1,l=1,...,m L (35),

[0187] In the formula, α ij,t is the connectivity variable of branch (i, j) at time t, α ij,t =1 means that the line switch of branch (i, j) is in the closed state at time t, α ij,t =0 means that the line switch of branch (i, j) is in the disconnected state at time t; β ij,t is the node level identification variable at time t, β ij,t =1 means that at time t, node i is the parent node of node j, β ij,t =0 means that node i is not the parent node of node j at time t; β ji,t is the node level identification variable at time t, β ji,t =1 means that at time t, node j is the parent node of node i, β ji,t =0 means that node j is not the parent node of node i at time t; l represents the branch of the distribution network, m L is the total number of branches in the distribution network, n L is the total number of nodes in the distribution network; N L (i) represents the set of all nodes that may be associated with node i; N L (0) represents the set of all nodes that may be associated with the substation node; ij,t Set as a variable to indirectly ensure that α ij,t The result is 0 or 1; the above constraints ensure that no loop will occur during the reconstruction of the distribution network to meet the power supply demand.

[0188] Specifically, network reconstruction is a common strategy for optimizing the operation of distribution networks. By changing the switch state, the network topology is changed to improve the photovoltaic absorption capacity. In order to prevent the occurrence of island operation during the operation of the ring network, the topology of the distribution network needs to remain radial during reconstruction.

[0189] The network reconstruction model needs to adapt to the operation requirements under both normal and fault conditions. Under fault conditions, it is necessary to consider that after N-1 faults occur in the line, the node load can be transferred through fault reconstruction without causing capacity to exceed the limit. Therefore, the network reconstruction model needs to meet the following constraints:

[0190] Step S720: To ensure that the distribution network remains radial in the system topology in any state, the spanning tree constraint is improved as follows:

[0191]

[0192]

[0193]

[0194]

[0195]

[0196] Where k = 0, 1, ..., N c ; N c is the number of N-1 faults, where k = 0, it means the distribution network is in normal operation, and k = 1, ..., N c When , it indicates that the distribution network is in a fault state; is the connectivity variable of branch (i, j) when the number of faults in the distribution network is k at time t; is the node level identification variable when the number of distribution network faults is k at time t, It is the node level identification variable when the number of distribution network faults is k at time t.

[0197] In an eighth embodiment of a method for improving photovoltaic load carrying capacity considering reactive power flexibility of a converter proposed by the present invention, based on the seventh embodiment, step S720 further includes the following steps:

[0198] Step S810: When performing network reconstruction, consider limiting the number of switch operations to ensure efficiency and feasibility, so as to determine the switch number limit constraint satisfied by the dynamic topology reconstruction process of the distribution network as follows:

[0199]

[0200]

[0201] Where N ij,max S represents the maximum number of times the line switch of branch (i, j) operates during the entire system reconstruction period; L Represents the set of all branches; N SA,max Indicates the upper limit of the total number of switch operations in the entire period;

[0202] Step S820: Since the absolute value operator is not used in the constraint condition, an intermediate variable is introduced Change the switch count limit constraint to the following formula:

[0203]

[0204]

[0205]

[0206] In a ninth embodiment of a method for improving photovoltaic load carrying capacity considering reactive power flexibility of a converter proposed by the present invention, based on the eighth embodiment, step S130 includes the following steps:

[0207] Step S910: Determine an objective function with the maximum total installed capacity of distributed photovoltaics connected to the distribution network as the optimization target:

[0208]

[0209] Where N PV is the total number of nodes connected to photovoltaic in the system; is the photovoltaic capacity that can be connected to the i-th node.

[0210] Specifically, a strategy to improve the distribution network's new energy acceptance capacity by taking into account the flexibility of photovoltaic storage and reactive power aggregation is developed, and a distribution network photovoltaic maximum acceptance capacity assessment model is built.

[0211] Step S920: In addition to satisfying the distribution network topology dynamic reconstruction strategy constraints and energy storage operation constraints, the photovoltaic maximum acceptance capacity assessment model should also satisfy the network security under normal operation, and determine the network security boundary conditions under normal operation of the new energy maximum acceptance capacity assessment model, wherein the network security boundary conditions include power balance constraints, node voltage upper and lower limit constraints, and line power flow limit constraints, and the power balance constraints are:

[0212]

[0213]

[0214]

[0215]

[0216] In the formula, is the injected active power of substation node i at time t, is the injected active power of distributed photovoltaic power source at node i at time t; is the charging power of the energy storage station at node i at time t, is the discharge power of the energy storage power station at node i at time t; is the active power of the electrical load of node i at time t, is the active power of the electric vehicle load at node i at time t; is the injected reactive power of substation node i at time t, is the injected reactive power of distributed photovoltaic power source at node i at time t; is the reactive power of the electrical load at node i at time t, is the reactive power of the electric vehicle load at node i at time t; P ij,t is the active power flow of branch (i, j) at time t, Q ij,t is the reactive power flow of branch (i, j) at time t; r ij is the resistance on branch (i, j), x ij is the reactance value on branch (i, j); is the square value of the current at branch (i, j) at time t; is the original photovoltaic capacity of node i; is the theoretical output of distributed photovoltaic power per unit capacity at time t.

[0217] In a tenth embodiment of a method for improving photovoltaic load carrying capacity considering reactive power flexibility of a converter proposed by the present invention, based on the ninth embodiment, the upper and lower limits of the node voltage are constrained as follows:

[0218] U i,min i,t i,max (48),

[0219] Where U i,t is the voltage of node i at time t; U i,min is the upper voltage limit at node i, U i,max is the lower limit of the voltage at node i.

[0220] Specifically, the evaluation of the photovoltaic carrying capacity of the distribution network under the condition of high-proportion distributed photovoltaic access needs to take into account the upper and lower voltage constraints of each node. The national standard "Permissible Deviation of Power Quality Supply Voltage" GBT12325-2008 stipulates that the allowable deviation of the power supply voltage of 10kV and below is ±7% of the rated voltage.

[0221] The line power flow limit constraint is:

[0222] 0≤S ij,t ij,max (49),

[0223] I ij,min ≤I ij,t ij,max (50),

[0224] -P g,srp,max ≤P 1,t <P g,srp,max (51),

[0225] In the formula, S​​​​ij is the apparent power of branch (i, j), and satisfies S ij,t is the apparent power of branch (i, j) at time t, S ij,max is the maximum apparent power of branch (i, j) and satisfies I ij,t is the current carrying capacity of branch (i, j) at time t; I ij,max is the maximum current carrying capacity of branch (i, j), I ij,min For I ij,max Negative value of P 1,t is the power of node 1 (the power of the feeder head end substation node); P g,srp,max The maximum power that the transformer can handle.

[0226] Specifically, when evaluating the photovoltaic carrying capacity of the distribution network, it is necessary not only to consider the line flow limit constraints and branch current limits, but also the reverse power constraints of the power flow when the distributed photovoltaics reversely transmit power to the head end of the feeder. The "Guidelines for the Evaluation of the Carrying Capacity of Distributed Power Generation Access to the Grid" stipulates that the long-term working load rate of public distribution transformers should not be greater than 80%.

[0227] The following is an introduction to the calculation example:

[0228] A typical power distribution station is used as an example for simulation verification. The YALMIP toolbox is used to call the GUROBI software on the MATLAB software platform to solve the example. The time interval is 1 hour, and the whole day is divided into 24 periods. The example network structure is shown in the attached figure. Figure 1 As shown, Figure 2 In the figure, the solid line is the branch, the dotted line is the tie switch, the node number is marked in the figure, and ESS represents the energy storage node; among them, node 1 is the transformer node, the transformer capacity is 10MW, and the maximum reverse load it can bear is 8MW. The energy storage access nodes are nodes 15, 21, 24, and 32.

[0229] This embodiment sets up 4 different schemes to conduct comparative analysis on the evaluation methods of the distributed photovoltaic acceptance capacity of the distribution network: Scheme 1 is the method proposed in the present invention; Scheme 2 takes into account the active regulation of flexible resource energy storage and electric vehicles, considers the dynamic reconstruction of the network topology, is based on the normal operating state, and does not consider the N-1 expected fault state; Scheme 3 does not consider the dynamic reconstruction of the network topology, but only considers the active regulation of flexible resource energy storage and electric vehicles; Scheme 4 does not consider the active regulation of flexible resource energy storage and electric vehicles, but considers the dynamic reconstruction of the network topology as well as the normal operating state of the system and the N-1 expected fault state. The above schemes are used to evaluate the distributed photovoltaic acceptance capacity of the distribution network. The simulation result diagrams of the above 4 different schemes and the distributed photovoltaic acceptance capacity of the distribution network obtained respectively are shown in the attached figure. Figure 3 shown.

[0230] The final comparison diagram of the distributed photovoltaic acceptance capacity of the distribution network of the four schemes is shown in the attached figure. Figure 4 As shown in the attached Figure 4 It can be seen that the method proposed in the present invention (Scheme 1) has increased the overall distribution network photovoltaic acceptance capacity by 14.27% compared to the case where the dynamic reconstruction of the network topology is not considered (Scheme 3), and has increased by 4.92% compared to the case where the flexible resource storage and active regulation of electric vehicles are not considered (Scheme 4). Although the overall distribution network photovoltaic acceptance capacity is reduced compared to the case where the N-1 expected fault state is not considered (Scheme 2), the safe operation of the power grid in the N-1 fault state is guaranteed. Therefore, the method proposed in the present invention achieves the overall optimal distributed photovoltaic acceptance capacity, which shows that the combination of dynamic reconstruction of the distribution network topology and active regulation of flexible resources can not only improve the effective utilization of energy, but also improve the distributed photovoltaic acceptance capacity of the distribution network.

[0231] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0232] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for improving photovoltaic load capacity considering the reactive power flexibility of converters, characterized in that: include: The reactive power feasible domain of mobile energy storage is derived according to the active power constraint and apparent power of mobile energy storage, and the reactive power feasible domain of photovoltaic inverter is derived according to the active power constraint and apparent power of photovoltaic inverter, so as to construct the reactive power flexibility aggregation model of distributed multi-converter in distribution network. Considering multiple constraints, the dynamic topology reconstruction of the distribution network is carried out with consideration of active regulation of mobile energy storage; Based on the reactive power flexibility aggregation model, the distribution network after dynamic topology reconstruction, safe operation constraints and photovoltaic output volatility, with the maximum total installed capacity of distributed photovoltaics connected to the distribution network as the optimization goal, a new energy maximum acceptance capacity evaluation model considering the flexibility of photovoltaic storage and reactive power aggregation is established to obtain a new energy maximum acceptance capacity improvement strategy considering the flexibility of photovoltaic storage and reactive power aggregation.

2. A method for improving photovoltaic load capacity considering the reactive power flexibility of converter according to claim 1, characterized in that: The reactive feasible domain of the mobile energy storage is derived according to the active power constraint and apparent power of the mobile energy storage, and the reactive feasible domain of the photovoltaic inverter is derived according to the active power constraint and apparent power of the photovoltaic inverter, so as to construct a reactive flexibility aggregation model of the distributed multi-converter of the distribution network, including: The operation constraints of mobile energy storage are obtained, where the operation constraints of mobile energy storage include maximum charge and discharge power limit constraints, energy timing constraints and mobile state constraints, and the maximum charge and discharge power limit constraints are: In the formula, is the charging state variable of the mobile energy storage at node i, is the discharge state variable of the mobile energy storage at node i, which takes a value of 0 or 1 to ensure that the energy storage cannot be charged and discharged at the same time; is the upper limit of the mobile energy storage charging power of node i, is the upper limit of the mobile energy storage discharge power of node i; is the charging power of the mobile energy storage at node i during period t, is the discharge power of the mobile energy storage at node i during period t; The energy timing constraint of mobile energy storage is: In the formula, is the maximum capacity value of the mobile energy storage of node i in period t, is the minimum capacity value of the mobile energy storage of node i in period t, is the current remaining capacity value of the mobile energy storage at node i in period t; is the charging efficiency of mobile energy storage at node i, is the discharge efficiency of mobile energy storage at node i; t0 is the initial time, t d is the termination time, Δt is the unit time period; is the initial capacity of mobile energy storage at node i, is the termination capacity of the mobile energy storage at node i, and the initial capacity of the mobile energy storage is equal to the termination capacity; is the maximum capacity of the mobile energy storage at node i, is the minimum value of the mobile energy storage capacity of node i; is the energy limit of the mobile energy storage of node i from the starting period to the time t, is the lower limit of the energy of the mobile energy storage of node i from the starting period to time t, is the upper limit of energy at time t pushed forward from the end period, It is the lower limit of energy at time t pushed forward from the end period.

3. A method for improving photovoltaic load capacity considering the reactive power flexibility of converter according to claim 2, characterized in that: The mobile state constraints of the mobile energy storage are: k m,i,t+1 +k m,i,t ≤1, j≠i(16), In the formula, κ m,i,t is the state variable of mobile energy storage access, when κ m,i,t When it is 1, it means that the mobile energy storage m is connected to the distribution network node i during period t; m is the mobile energy storage number, ranging from 1 to n; i is the distribution network node number; i∈Ω is the candidate access node set; Z i is the maximum number of mobile energy storage connected to the distribution network node i; γ m,t is the driving state variable of mobile energy storage, when γ m,t When γ is 1, it means that the mobile energy storage m is in the driving state during the period t. m,t When it is 0, it means that the mobile energy storage m is in the connected state during the period t; c,m,t is the charging power of mobile energy storage m during period t, and P d,m,t is the discharge power of mobile energy storage m in period t.

4. A method for improving photovoltaic load capacity considering the reactive power flexibility of converter according to claim 3, characterized in that: The step of obtaining the operating constraints of the mobile energy storage further includes: Characterize the flexibility area of ​​PV-storage reactive power aggregation, including: The box uncertainty set is used to characterize the error of photovoltaic power generation prediction value, where the box uncertainty set U is expressed as: Where u is the set element in the box uncertainty set U; τ is the uncertainty; is the predicted value of photovoltaic output of node i at time t; is the photovoltaic active power of node i at time t; Get the operation mode of the photovoltaic generator set with a constant power factor: In the formula, is the maximum active output of the photovoltaic node i at time t; is the photovoltaic reactive power of node i at time t; is the upper limit of distributed photovoltaic power factor, is the lower limit of distributed photovoltaic power factor; is the maximum photovoltaic reactive power of node i at time t, is the minimum photovoltaic reactive power of node i at time t; Based on the power limitation and electric energy limitation of mobile energy storage itself, the feasible domain of active power of mobile energy storage at multiple times is depicted through the passage of time: In the formula, is the upper limit of the active power of the mobile energy storage at node i during period t, is the lower limit of the active power of the mobile energy storage at node i during period t.

5. A method for improving photovoltaic load capacity considering the reactive power flexibility of converter according to claim 4, characterized in that: Based on the power limitation and electric energy limitation of the mobile energy storage itself, the feasible domain of active power of the mobile energy storage at multiple times is depicted through the passage of time, and then it also includes: According to the apparent power constraint of mobile energy storage, the feasible region of reactive power of mobile energy storage is derived: In the formula, is the apparent power of the mobile energy storage at node i in period t, is the maximum apparent power of the mobile energy storage at node i; is the active power of the mobile energy storage at node i in period t; is the upper limit of reactive power of mobile energy storage at node i during period t, is the lower limit of reactive power of mobile energy storage at node i during period t; Based on the reactive power feasible domain of mobile energy storage, the characterization model of active and reactive power flexibility of distributed energy storage resources is obtained: In the formula, is the reactive power of the mobile energy storage at node i during period t.

6. A method for improving photovoltaic load capacity considering the reactive power flexibility of converter according to claim 5, characterized in that: The reactive power feasible domain based on mobile energy storage is used to obtain a distributed energy storage resource active and reactive flexibility characterization model, which then includes: Determine the VB model of electric vehicle monomer: In the formula, is the power of the i-th electric car at time t; is the battery energy of the i-th electric car at time t; is the upper limit of the power of the i-th electric vehicle at time t, is the lower power limit of the i-th electric vehicle at time t; is the upper limit of battery energy of the i-th electric vehicle at time t, is the lower limit of battery energy of the i-th electric vehicle at time t; Using Minkowski sum, we can get the distribution network flexibility feasible set Z pro for: Z pro =P1⊕P2⊕...⊕P j =P ES ⊕P EV ⊕P D (30), Where P1 is the sum of the reserve capacity of distributed energy storage, cluster electric vehicles and load under demand response at distribution network node 1, P2 is the sum of the reserve capacity of distributed energy storage, cluster electric vehicles and load under demand response at distribution network node 2, and P j is the sum of the reserve capacity of distributed energy storage, cluster electric vehicles and demand response load at distribution network node j, P ES is the backup capacity of distributed energy storage, P EV is the reserve capacity of the electric vehicles in the cluster, P D is the load reserve capacity under demand response; ⊕ represents the Minkowski sum.

7. A method for improving photovoltaic load capacity considering the reactive power flexibility of converter according to claim 6, characterized in that: The multiple constraints are considered to dynamically restructure the topology of the distribution network taking into account the active regulation of mobile energy storage, including: The distribution network is regarded as a tree with node 0 as the root, and two variables β are introduced for each line. ij,t and β ji,t , and use the variable α ij,t Indicates the connection status of each line at time t to ensure that the distribution network is connected to the main substation node through a spanning tree, and determines the spanning tree constraints satisfied by the dynamic topology reconstruction process of the distribution network: β ij,t +β ji,t ＝α ij,t ,l＝1,...,m L (31)， b 0j,t =0,j∈N L (0) (33), β ij,t ∈{0,1},i=1,...,N L ,j∈N L (i) (34), 0≤α ij,t ≤1,l＝1,...,m L (35)， In the formula, α ij,t is the connectivity variable of branch (i, j) at time t, α ij,t =1 means that the line switch of branch (i, j) is in the closed state at time t, α ij,t =0 means that the line switch of branch (i, j) is in the disconnected state at time t; β ij,t is the node level identification variable at time t, β ij,t =1 means that at time t, node i is the parent node of node j, β ij,t =0 means that at time t, node i is not the parent node of node j, β ji,t is the node level identification variable at time t, β ji,t =1 means that at time t, node j is the parent node of node i, β ji,t =0 means that node j is not the parent node of node i at time t; l represents the branch of the distribution network, m L is the total number of branches in the distribution network, n L is the total number of nodes in the distribution network; N L (i) represents the set of all nodes that may be associated with node i; N L (0) represents the set of all nodes that may be associated with the substation node; ij,t Set as a variable to indirectly ensure that α ij,t The result is 0 or 1; In order to ensure that the distribution network remains radial in the system topology in any state, the spanning tree constraints are improved as follows: Where k = 0, 1, ..., N c ; N c is the number of N-1 faults, where k = 0, it means the distribution network is in normal operation, and k = 1, ..., N c When , it indicates that the distribution network is in a fault state; is the connectivity variable of branch (i, j) when the number of faults in the distribution network is k at time t; is the node level identification variable when the number of distribution network faults is k at time t, It is the node level identification variable when the number of distribution network faults is k at time t.

8. A method for improving photovoltaic load capacity considering the reactive power flexibility of converter according to claim 7, characterized in that: In order to ensure that the distribution network remains radial in the system topology in any state, the spanning tree constraint is improved as follows, and then includes: The switching times limit constraint that the distribution network meets during dynamic topology reconstruction is determined as follows: Where N ij,max S represents the maximum number of times the line switch of branch (i, j) operates during the entire system reconstruction period; L represents the set of all branches; N SA,max Indicates the upper limit of the total number of switch operations in the entire period; Introducing intermediate variables Change the switch count limit constraint to the following formula:

9. A method for improving photovoltaic load capacity considering the reactive power flexibility of converter according to claim 8, characterized in that: Based on the reactive power flexibility aggregation model, the distribution network after dynamic topology reconstruction, the safety operation constraints and the volatility of photovoltaic output, the maximum total installed capacity of the distributed photovoltaic connected to the distribution network is taken as the optimization goal, and a new energy maximum acceptance capacity evaluation model considering the flexibility of photovoltaic storage reactive power aggregation is established to obtain a new energy maximum acceptance capacity improvement strategy considering the flexibility of photovoltaic storage reactive power aggregation, including: Determine the objective function with the maximum total installed capacity of distributed photovoltaic connected to the distribution network as the optimization goal: Where N PV is the total number of nodes connected to photovoltaic in the system; is the photovoltaic capacity that can be connected to the i-th node; Determine the network security boundary conditions under the normal operation of the new energy maximum acceptance capacity assessment model, where the network security boundary conditions include power balance constraints, node voltage upper and lower limit constraints, and line power flow limit constraints. The power balance constraint is: In the formula, is the injected active power of substation node i at time t, is the injected active power of distributed photovoltaic power source at node i at time t; is the charging power of the energy storage station at node i at time t, is the discharge power of the energy storage power station at node i at time t; is the active power of the electrical load of node i at time t, is the active power of the electric vehicle load at node i at time t; is the injected reactive power of substation node i at time t, is the injected reactive power of distributed photovoltaic power source at node i at time t; is the reactive power of the electrical load at node i at time t, is the reactive power of the electric vehicle load at node i at time t; P ij,t is the active power flow of branch (i, j) at time t, Q ij,t is the reactive power flow of branch (i, j) at time t; r ij is the resistance on branch (i, j), x ij is the reactance value on branch (i, j); is the square value of the current at branch (i, j) at time t; is the original photovoltaic capacity of node i; is the theoretical output of distributed photovoltaic power per unit capacity at time t.

10. A method for improving photovoltaic load capacity considering the reactive power flexibility of converter according to claim 9, characterized in that: The node voltage upper and lower limits are constrained as follows: IN i,min i,t i,max (48),​​ Where U i,t is the voltage of node i at time t; U i,min is the upper voltage limit at node i, U i,max is the lower limit of voltage at node i; The line power flow limit constraint is: 0≤S ij,t <S ij,max (49), I ij,min ≤I ij,t <I ij,max (50), -P g,srp,max ≤P 1,t <P g,srp,max (51), In the formula, S ij,t is the apparent power of branch (i, j) at time t, S ij,max is the maximum apparent power of branch (i, j) and satisfies I ij,t is the current carrying capacity of branch (i, j) at time t, I ij,max is the maximum current carrying capacity of branch (i, j), I ij,min For I ij,max Negative value of P 1,t is the power of node 1; P g,srp,max The maximum power that the transformer can handle.

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