Method, device and equipment for evaluating carrying capacity of multi-feed distributed power supply and medium

Abstract

The present application relates to a kind of multi-feed line distributed power carrying capacity evaluation method, device, equipment and medium, wherein, method includes: determining to be evaluated region, collection each data required for evaluation;With the maximum installation capacity of multi-feed line distributed power as target, the network reconfiguration and flexible soft switch power regulation of the multi-feed line robust carrying capacity evaluation model considering to be evaluated region are constructed;Linearization constraint and opportunity constraint conversion are carried out to the multi-feed line robust carrying capacity evaluation model, and mixed integer linear programming model is obtained;Based on each data, and using solver to solve the mixed integer linear programming model, the carrying capacity of multi-feed line distributed power is obtained.The present application can improve evaluation efficiency and accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems and new energy access, in particular to a multi-feed line distributed power supply carrying capacity evaluation method, device, equipment and medium. BACKGROUND

[0002] With the promotion of the double carbon target, the large-scale grid connection of distributed photovoltaic and distributed wind power as representatives of distributed power supply has brought great challenges to the planning and operation of distribution networks. In some areas, phenomena such as voltage rise and excessive reverse load rate have occurred. Accurate evaluation of the upper limit of the distributed power supply (DG) capacity that the distribution network can carry is an important foundation for supporting the coordinated development of new energy and distribution networks.

[0003] The spatio-temporal uncertainty of DG, multi-feed line interconnection and the coupling influence of active management measures of distribution network are two aspects that need to be focused on in carrying capacity evaluation. The spatio-temporal uncertainty of DG mainly refers to the difficulty in accurately predicting the timing output of DG and the difficulty in predicting the grid connection location in advance, and handling this problem is one of the key difficulties in carrying capacity evaluation. In recent years, experts and scholars have made a series of achievements in this field. For example, probabilistic distribution and uncertainty set are used to construct an uncertainty model, and robust optimization or stochastic optimization methods are used for evaluation, but these methods have their own limitations: robust optimization methods are usually too conservative, while stochastic optimization methods rely on scenario information and the results may be too optimistic.

[0004] In terms of DG carrying capacity evaluation considering location uncertainty, it is usually assumed that multiple candidate locations have the possibility of DG grid connection, and these locations are taken as decision variables of the evaluation model. However, these methods are limited to known DG access location scenarios and are difficult to extend to other locations. The Monte Carlo simulation method simulates all possible DG grid connection locations through random analysis, although it can obtain evaluation results under different scenarios, but in large-scale distribution systems, the main disadvantage is low computational efficiency and long solution time.

[0005] In terms of network reconfiguration for DG carrying capacity evaluation, existing research mainly focuses on analyzing the role of network reconfiguration in improving the carrying capacity of single-feed line DG. However, there are few studies on multi-feed line scenarios, especially lacking systematic analysis of the influence of inter-feed line reconfiguration and flexible soft open point (SOP) adjustment. SUMMARY

[0006] The technical problem to be solved by the present application is to provide a multi-feed line distributed power supply carrying capacity evaluation method, device, equipment and medium to improve evaluation efficiency and accuracy.

[0007] The technical scheme adopted by the present application to solve its technical problems is: a multi-feed line distributed power carrying capacity evaluation method is provided, comprising the following steps:

[0008] Determine the region to be evaluated, and collect various data required for evaluation;

[0009] With the maximum multi-feed line distributed power installation capacity as the target, a multi-feed line robust carrying capacity evaluation model considering network reconstruction and flexible soft switch power regulation of the region to be evaluated is constructed;

[0010] The multi-feed line robust carrying capacity evaluation model is linearized and converted into a chance constraint to obtain a mixed integer linear programming model;

[0011] Based on the various data, the mixed integer linear programming model is solved by using a solver to obtain the multi-feed line distributed power carrying capacity.

[0012] The objective function of the multi-feed line robust carrying capacity evaluation model is: Wherein, P is the active power injected by the flexible soft switch to node i at time t; Q is the reactive power injected by the flexible soft switch to node i at time t; γ ij,t Xij represents the on-off state of branch ij at time t; F represents a set composed of all feed lines; u represents a set composed of all u i S represents a set composed of all node distributed power access capacities; N S represents the number of all selectable access locations; u i is a 0-1 variable, when u i is 1, it represents that node i accesses the distributed power, when u i is 0, it represents that node i does not access the distributed power; N is the access capacity of the distributed power at node i; N F represents the total number of feed line numbers.

[0013] The constraint conditions of the multi-feed line robust carrying capacity evaluation model include:

[0014] The voltage safety chance constraint is represented as:

[0015] The line current carrying capacity chance constraint is represented as:

[0016] The power flow constraint is represented as:

[0017] The flexible soft switch power constraint is represented as:

[0018] Network reconfiguration constraints are expressed as:

[0019] where V i,t is the voltage of node i at time t; V max is the maximum value of the node voltage allowed by the power distribution network, 1-ε is the confidence level of the node voltage constraint, Pr{} represents the chance constraint, P ij,t is the active power flowing through branch ij at time t, Q ij,t is the reactive power flowing through branch ij at time t, is the maximum complex power allowed to flow through branch ij, 1-α is the confidence level of the line current carrying capacity constraint, V j,t is the voltage of node j at time t, r ij is the resistance of branch ij, x ij is the reactance of branch ij, P jk,t is the active power flowing through branch jk at time t, is the active power of the load at node j at time t, is the active power of the distributed power supply at node j at time t, Q jk,t is the reactive power flowing through branch jk at time t, is the reactive power of the load at node j at time t, is the active power injected by the flexible soft switch into node i at time t, is the reactive power injected by the flexible soft switch into node i at time t, is the active power injected by the flexible soft switch into node j at time t, is the reactive power injected by the flexible soft switch into node j at time t, S VSC is the capacity of the flexible soft switch converter, E SW is the set of branches where the tie switch is located, E is the set of all branches, θ ij,t is a constant, γ ij,t represents the on-off state of branch ij at time t, N BR is the total number of branches including the branch where the tie switch is located; N SW is the number of tie switches.

[0020] The linearization constraint and chance constraint transformation are performed on the multi-feed line robust carrying capacity evaluation model to obtain a mixed integer linear programming model, and the method specifically comprises the following steps:

[0021] The time sequence output of the distributed power supply is characterized as a mixed probability density random variable;

[0022] According to the affine relationship of the time sequence output of the distributed power supply, the node voltage and the line capacity, probability distribution functions of the node voltage and the line capacity are obtained, and the voltage safety opportunity constraint and the line load flow opportunity constraint are transformed by using the probability distribution functions;

[0023] The power flow constraint is linearized to obtain a mixed integer linear programming model.

[0024] The linearization of the power flow constraint is specifically:

[0025] The constraint that the active power and the reactive power of the disconnected branch are both 0 is introduced, and is expressed as: Wherein, M1 and M2 are large positive numbers;

[0026] The power flow constraint of the voltage safety opportunity constraint of the disconnected branch is transformed into: Wherein, M3 is a large positive number;

[0027] The power flow constraint of the disconnected branch is transformed into:

[0028] The technical scheme adopted by the application to solve the technical problems is to provide a multi-feed line distributed power supply carrying capacity evaluation device, comprising:

[0029] The collection module is used to determine the to-be-evaluated area and collect various data required for evaluation;

[0030] The construction module is used to construct a multi-feed line robust carrying capacity evaluation model considering network reconstruction and flexible soft switch power regulation of the to-be-evaluated area, with the maximum multi-feed line distributed power supply installation capacity as the target;

[0031] The transformation module is used to linearly constrain and opportunity-constrain the multi-feed line robust carrying capacity evaluation model to obtain a mixed integer linear programming model;

[0032] The solving module is used to solve the mixed integer linear programming model based on the various data and by using a solver to obtain the multi-feed line distributed power supply carrying capacity.

[0033] The objective function of the multi-feed line robust carrying capacity evaluation model constructed by the construction module is: Wherein, is the active power injected by the flexible soft switch into node i at time t; is the reactive power injected by the flexible soft switch into node i at time t; γ ij,t Indicates the on-off state of branch ij at time t; F indicates a set composed of all feed lines; u indicates all u iS represents a set of all distributed generation access capacities at nodes; N S represents the number of all optional access locations; u i is a 0-1 variable, when u i is 1, it represents that the node i accesses the distributed generation, when u i is 0, it represents that the node i does not access the distributed generation; is the access capacity of the distributed generation at the node i; N F represents the total number of feeder lines.

[0034] The constraint conditions of the multi-feeder robust carrying capacity evaluation model constructed by the construction module include:

[0035] The voltage safety chance constraint is represented as:

[0036] The line current carrying capacity chance constraint is represented as:

[0037] The power flow constraint is represented as:

[0038] The flexible soft switch power constraint is represented as:

[0039] The network reconstruction constraint is represented as:

[0040] Wherein, V i,t is the voltage of the node i at the time t; V max is the maximum value of the node voltage allowed by the distribution network, 1-ε is the confidence degree of the node voltage constraint being established, Pr{} represents the chance constraint, P ij,t is the active power flowing through the branch ij at the time t, Q ij,t is the reactive power flowing through the branch ij at the time t, is the maximum complex power allowed to flow through the branch ij, 1-α is the confidence degree of the line current carrying capacity constraint being established, V j,t is the voltage of the node j at the time t, r ij is the resistance of the branch ij, x ij is the reactance of the branch ij, P jk,t is the active power flowing through the branch jk at the time t, is the active power of the load at the node j at the time t, is the active power of the distributed generation at the node j at the time t, Q jk,t is the reactive power flowing through the branch jk at the time t, is the reactive power of the load at the node j at the time t, is the active power injected by the flexible soft switch to the node i at the time t, is the active power injected into node i by the flexible soft switch at time t, is the active power injected into node j by the flexible soft switch at time t, is the reactive power injected into node j by the flexible soft switch at time t, S VSC is the capacity of the flexible soft-switching converter, E SW is the set of branches where the tie switch is located, E is the set of all branches, θ ij,t is a constant, γ ij,t Indicates the on / off state of branch ij at time t, N BR is the total number of branches including the branch where the tie switch is located; N SW is the number of tie switches.

[0041] The conversion module comprises:

[0042] A characterization unit, used to characterize the distributed power generation time sequence output in the form of a mixed probability density random variable;

[0043] A first conversion unit is configured to obtain a probability distribution function of the node voltage and the line capacity according to an affine relationship between the distributed power generation time sequence output and the node voltage and the line current carrying capacity, and to convert the voltage safety opportunity constraint and the line current carrying capacity opportunity constraint using the probability distribution function;

[0044] The second conversion unit is used to perform linearization processing on the power flow constraint to obtain a mixed integer linear programming model.

[0045] The second conversion unit comprises:

[0046] A subunit is introduced to introduce the constraint that the active power and reactive power of the disconnected branch circuit are both 0, which is expressed as: Among them, M1 and M2 are sufficiently large positive numbers;

[0047] The first constraint conversion subunit is used to convert the power flow constraint of the voltage safety opportunity constraint of the disconnected branch circuit into: Among them, M3 is a sufficiently large positive number;

[0048] The second constraint conversion subunit is used to convert the power flow constraint of the unbroken branch circuit into:

[0049] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps of the above-mentioned multi-feeder distributed power supply carrying capacity evaluation method are implemented.

[0050] The technical scheme adopted by the present application to solve its technical problems is: a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to implement the steps of the multi-feed line distributed power supply carrying capacity evaluation method.

[0051] Advantages

[0052] Compared with the prior art, the present application has the following advantages and positive effects: the present application considers the influence of network reconstruction and SOP adjustment on DG carrying capacity, establishes a multi-feed line DG carrying capacity evaluation model, and before solving the evaluation model, the uncertainty of DG time sequence output is processed by a Gaussian mixture model, and the model is solved by linearization to obtain the multi-feed line distributed power supply carrying capacity. By this way, the carrying capacity of DG in complex power grid structure can be more accurately evaluated, the overall operation efficiency and safety of the distribution network are improved, and the efficient access and utilization of new energy are supported. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is a flow chart of the multi-feed line distributed power supply carrying capacity evaluation method of the first embodiment of the present application;

[0054] Figure 2 is a schematic diagram of a three-feed line interconnected distribution network in the embodiment of the present application. DETAILED DESCRIPTION

[0055] The present application will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present application and not used to limit the scope of the present application. In addition, it should be understood that after reading the content taught by the present application, those skilled in the art can make various modifications or changes to the present application, and these equivalent forms also fall within the scope of the appended claims of the present application.

[0056] The first embodiment of the present application relates to a multi-feed line distributed power supply carrying capacity evaluation method, as shown in Figure 1 , comprising the following steps:

[0057] Step 1, determine the region to be evaluated, and collect various data required for evaluation. The various data include the topology of the distribution system, the line equipment parameters, the transformer equipment parameters, the distributed power supply access capacity and the access position, the load demand condition, the distributed power supply time sequence characteristic curve, and the load time sequence characteristic curve, etc.

[0058] Step 2, taking the maximum installation capacity of the multi-feed line distributed power supply as the target, a multi-feed line robust carrying capacity evaluation model considering network reconstruction and flexible soft switch power adjustment of the region to be evaluated is constructed.

[0059] In this step, the maximum installation capacity of the distributed power supply is taken as the objective function, and the power flow constraint, SOP constraint, network reconstruction constraint and the like are taken as the constraint conditions, and a multi-feed line robust carrying capacity evaluation model considering network reconstruction and flexible soft switch power regulation is constructed.

[0060] The objective function of the multi-feed line robust carrying capacity evaluation model is expressed as:

[0061]

[0062] Among them, is the active power injected by the flexible soft switch to node i at t time; is the reactive power injected by the flexible soft switch to node i at t time; γ ij,t represents the on-off state of branch ij at t time, which is a 0-1 variable, when γ ij,t = 0, it means that the branch ij is disconnected, when γ ij,t = 1, it means that the branch ij is closed; F represents the set composed of all feed lines; u represents the set composed of all u i The set composed of all nodes; S represents the set composed of all the access capacity of the distributed power supply; N S represents the number of all optional access positions; u i is a 0-1 variable, when u i = 1, it means that the node i accesses the distributed power supply, when u i = 0, it means that the node i does not access the distributed power supply; is the access capacity of the distributed power supply at node i; N F represents the total number of feed lines.

[0063] The constraint conditions of the multi-feed line robust carrying capacity evaluation model include:

[0064] The voltage safety chance constraint is expressed as:

[0065]

[0066] Among them, V i,t is the voltage of node i at t time; V max is the maximum value of the node voltage allowed by the distribution network, 1-ε is the confidence degree of the node voltage constraint, and Pr{} represents the chance constraint.

[0067] The line current carrying capacity chance constraint is expressed as:

[0068]

[0069] Among them, P ij,t is the active power flowing through branch ij at t time, Q ij,tis the reactive power flowing through branch ij at time t, is the maximum complex power allowed to flow through branch ij, and 1-α is the confidence level that the line current carrying capacity constraint is established.

[0070] The power flow constraint is expressed as:

[0071] V j,t =V i,t -(r ij P ij,t +x ij Q ij,t )

[0072]

[0073] Among them, V j,t is the voltage of node j at time t, r ij is the resistance of branch ij, x ij is the reactance of branch ij, P jk,t is the active power flowing through branch jk at time t, is the active power of the load at node j at time t, is the active power of the distributed generation at node j at time t, Q jk,t is the reactive power flowing through branch jk at time t, is the reactive power of the load at node j at time t, is the active power injected into node j by the flexible soft switch at time t, is the reactive power injected into node j by the flexible soft switch at time t.

[0074] The soft switching power constraint is expressed as:

[0075]

[0076] in, is the active power injected into node i by the flexible soft switch at time t, is the active power injected into node i by the flexible soft switch at time t, S VSC is the capacity of the flexible soft switching converter, which is half of the flexible soft switching capacity.

[0077] The network reconstruction constraint is expressed as:

[0078]

[0079] Among them, E SW is the set of branches where the tie switch is located, E is the set of all branches, θ ij,t is a constant. In this embodiment, θ ij,t =1,γ ij,tN represents the on-off state of branch ij at time t, N BR N represents the total number of branches including the branch where the tie switch is located. SW N represents the number of tie switches.

[0080] Step 3, linearization constraint and opportunity constraint transformation is performed on the multi-feed robust carrying capacity evaluation model to obtain a mixed integer linear programming model. Before solving the model, opportunity constraint transformation and linearization constraint transformation are performed, as follows:

[0081] DG mixed probability density processing: after a series of uncertainty processing, the DG time series output is characterized as a mixed probability density random variable. In order to support the carrying capacity evaluation, the DG quantile point under any confidence level needs to be obtained. For any given confidence level, the DG output quantile point at any time can be approximately expressed as:

[0082]

[0083] where F(x) is the DG output quantile point at any time, x is any given confidence level, n is the number of cluster centers, ρ i is the probability value of the i th DG time series output scenario, is the expression form of the Gaussian density function, ω is the probability distribution in the mixed Gaussian model, u i is the cluster center value under each category obtained according to scenario clustering, σ i 2 are the cluster center value and cluster variance under each category obtained according to scenario clustering, respectively.

[0084] The corresponding DG output value under any confidence level can be approximately calculated according to the following equation.

[0085]

[0086] According to the affine relationship between the DG time series output and the node voltage and the line carrying capacity, the probability distribution functions of the node voltage and the line capacity can be expressed as F'(V i,t ) and F'(P ij,t ) 2 +(Q ij,t ) 2 respectively, and then the opportunity constraint is transformed as:

[0087]

[0088] Further, we have:

[0089]

[0090] For the convenience of solution, the power flow constraints are linearized and transformed into the following form:

[0091]

[0092] In addition, since the network reconfiguration is considered, when the branch is disconnected, the power flow is 0, and the node voltage and line capacity constraints no longer satisfy the constraints. The present embodiment uses the large M method to transform the related constraints.

[0093] In order to ensure that the active power and reactive power on the disconnected branch are both 0, the following constraints are introduced:

[0094] -γ ij,t M1≤P s,ij,t ≤γ ij,t M1

[0095] -γ ij,t M2≤Q s,ij,t ≤γ ij,t M2

[0096] In the formula, M1 and M2 are respectively a large positive number, generally taking the maximum value that each variable can take, and should not be too large, otherwise it will affect the solving efficiency.

[0097] On the disconnected branch, the voltages of the two nodes do not satisfy the voltage security opportunity constraint. In order to ensure the universality of the constraint, the power flow constraint of the voltage security opportunity constraint is transformed into:

[0098]

[0099] Where M3 is a large positive number.

[0100] The power flow constraint of the un-disconnected branch is transformed into:

[0101] Step 4, based on the data, and using a solver to solve the mixed integer linear programming model, the multi-feed line distributed power supply carrying capacity is obtained. When solving, the present embodiment can use CPLEX solver.

[0102] As shown in Figure 2 A three-feed line interconnected distribution network is used as a case, and the MATLAB 2018a simulation platform with built-in YALMIP toolbox and CPLEX solver is used for case analysis. The three-feed line interconnected distribution network contains a total of 3715kW active load, 2300kVar reactive load, 26 nodes and 23 branches.

[0103] The following three scenarios are set:

[0104] Scenario 1: without considering network reconfiguration.

[0105] Scenario 2: considering network reconfiguration.

[0106] Scenario 3: the SOP capacity is 600 kVA.

[0107] Through the calculation of the multi-feed line robust carrying capacity under the three scenarios, the results are shown in Table 1.

[0108] Table 1: Multi-feed line robust carrying capacity under different scenarios

[0109]

[0110] Through the above analysis, it is verified that the method of the embodiment has an accurate evaluation effect on the multi-feed line robust carrying capacity under different network structures and adjustment measures.

[0111] As can be seen from Table 1, the multi-feed line robust carrying capacity and its corresponding position under the three scenarios are different, because after considering network reconfiguration, the topology during network operation will change, and after the SOP is connected, the SOP has the ability to regulate power flow, which will affect the power flow distribution of the network and then affect the robust carrying capacity of the network and its corresponding position, which also proves the necessity of the multi-feed line robust carrying capacity calculation method of the embodiment.

[0112] It can be found that the present application considers the influence of network reconfiguration and SOP adjustment on DG carrying capacity, establishes a multi-feed line DG carrying capacity evaluation model, and before solving the evaluation model, the uncertainty of DG time sequence output is processed by using the Gaussian mixture model, and the linearization method is used for solving, to obtain the multi-feed line distributed power carrying capacity. Through this way, the carrying capacity of DG in complex power grid structure can be more accurately evaluated, the overall operation efficiency and safety of the distribution network are improved, and the efficient access and utilization of new energy are supported.

[0113] The second embodiment of the present application relates to a multi-feed line distributed power carrying capacity evaluation device, comprising:

[0114] The collection module is used for determining a to-be-evaluated area and collecting various data required for evaluation.

[0115] The construction module is used for constructing a multi-feed line robust carrying capacity evaluation model considering network reconfiguration and flexible soft switch power adjustment of the to-be-evaluated area, with the maximum multi-feed line distributed power installation capacity as the target.

[0116] The transformation module is used for linearization constraint and opportunity constraint transformation of the multi-feed line robust carrying capacity evaluation model, to obtain a mixed integer linear programming model.

[0117] A solving module, configured to solve the mixed integer linear programming model based on the data and by using a solver to obtain a multi-feeder distributed power carrying capacity.

[0118] The objective function of the multi-feeder robust carrying capacity evaluation model constructed by the constructing module is: Wherein, is the active power injected by the flexible soft switch to node i at time t; is the reactive power injected by the flexible soft switch to node i at time t; γ ij,t represents the on-off state of branch ij at time t; F represents a set composed of all feeders; u represents a set composed of all u i represents a set composed of all nodes; S represents a set composed of all distributed power access capacities; N S represents the number of all optional access locations; u i is a 0-1 variable, when u i is 1, it represents that node i accesses the distributed power, when u i is 0, it represents that node i does not access the distributed power; is the access capacity of the distributed power at node i; N F represents the total number of feeder lines.

[0119] The constraint conditions of the multi-feeder robust carrying capacity evaluation model constructed by the constructing module include:

[0120] The voltage safety chance constraint is represented as:

[0121] The line current carrying capacity chance constraint is represented as:

[0122] The power flow constraint is represented as:

[0123] The flexible soft switch power constraint is represented as:

[0124] The network reconfiguration constraint is represented as:

[0125] Wherein, V i,t is the voltage of node i at time t; V max is the maximum value of the node voltage allowed by the power distribution network, 1-ε is the confidence degree of the node voltage constraint, Pr{} represents the chance constraint, P ij,t is the active power flowing through branch ij at time t, Q ij,t is the reactive power flowing through branch ij at time t, is the maximum complex power allowed to flow through branch ij, 1-α is the confidence degree of the line current carrying capacity constraint, V j,tVj(t) is the voltage of node j at time t, r ij Rij is the resistance of branch ij, x ij Xij is the reactance of branch ij, P jk,t Pjk(t) is the active power flowing through branch jk at time t, Pj(t) is the active power of load at node j at time t, Pj(t) is the active power of distributed generator at node j at time t, Q jk,t Qjk(t) is the reactive power flowing through branch jk at time t, Qj(t) is the reactive power of load at node j at time t, Pfi(t) is the active power injected by flexible soft switch to node i at time t, Pfi(t) is the active power injected by flexible soft switch to node i at time t, Pfj(t) is the active power injected by flexible soft switch to node j at time t, Qfj(t) is the reactive power injected by flexible soft switch to node j at time t, S VSC S is the capacity of flexible soft switch converter, E SW E is the set of branches containing tie switches, E is the set of all branches, θ ij,t γ is a constant, γ ij,t N indicates the on-off state of branch ij at time t, N BR N is the total number of branches containing tie switches; N SW N is the number of tie switches.

[0126] The conversion module comprises:

[0127] The characterization unit is configured to characterize the time-series output of the distributed generator in the form of a mixed probability density random variable.

[0128] The first conversion unit is configured to obtain probability distribution functions of node voltages and line capacities according to the affine relationship between the time-series output of the distributed generator, the node voltage and the line capacity, and to convert the voltage safety opportunity constraint and the line capacity opportunity constraint by using the probability distribution functions.

[0129] The second conversion unit is configured to linearize the power flow constraint to obtain a mixed integer linear programming model.

[0130] The second conversion unit comprises:

[0131] The introduction subunit is configured to introduce a constraint that the active power and the reactive power of the disconnected branch are both 0, which is represented as: wherein M1 and M2 are large positive numbers;

[0132] The first constraint conversion subunit is configured to convert the power flow constraint of the voltage safety opportunity constraint of the disconnected branch into: wherein M3 is a sufficiently large positive number;

[0133] The second constraint conversion unit is configured to convert the power flow constraint of the unbroken branch into:

[0134] The third embodiment of the present application relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the multi-feeder distributed power supply carrying capacity evaluation method of the first embodiment when executing the computer program.

[0135] The fourth embodiment of the present application relates to a computer readable storage medium, which stores a computer program, wherein the computer program implements the steps of the multi-feeder distributed power supply carrying capacity evaluation method of the first embodiment when executed by a processor.

[0136] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage, etc.) containing computer-usable program code.

[0137] The present application is described with reference to the flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The means for implementing the functions specified in one or more flows and / or blocks.

[0138] These computer program instructions can also be stored in a computer-readable memory capable of guiding the computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product comprising an instruction method, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The means for implementing the functions specified in one or more flows and / or blocks.

[0139] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are generated to realize the computer-implemented processes in the computer or other programmable devices, and the instructions executed in the computer or other programmable devices provide the steps for realizing the functions specified in the flowchart Figure 1 one flow or multiple flows and / or the functions specified in the block Figure 1 one block or multiple blocks.

[0140] The above description is merely one specific implementation of the application. However, one of ordinary skill in the art should, in light of the above description, be able to devise modifications and alternatives to the application without departing from the scope of the application. Accordingly, the scope of the application is to be construed in accordance with the scope of the claims.

Claims

1. A method for evaluating the carrying capacity of a multi-feeder distributed power supply, characterized in that: The following steps are involved: Determine the area to be assessed and collect the data required for the assessment; With the goal of maximizing the installed capacity of multi-feeder distributed power sources, a multi-feeder robust carrying capacity evaluation model is constructed, taking into account the network reconstruction and flexible soft switching power regulation of the area to be evaluated. The objective function of the multi-feeder robust carrying capacity evaluation model is: in, is the active power injected into node i by the flexible soft switch at time t; is the reactive power injected into node i by the flexible soft switch at time t; γ ij,t represents the on / off state of branch ij at time t; F represents the set of all feeders; u represents all u i The set of distributed generation capacity at all nodes; S represents the set of distributed generation capacity at all nodes; N S Indicates the number of all optional access locations; u i is a 0-1 variable, when u i When it is 1, it means that node i is connected to the distributed power supply. i When it is 0, it means that node i is not connected to the distributed power supply; is the access capacity of distributed generation at node i; N F Indicates the total number of feeder lines; Performing linearization constraint and chance constraint transformation on the multi-feeder robust carrying capacity evaluation model to obtain a mixed integer linear programming model; Based on the data, the mixed integer linear programming model is solved by using a solver to obtain the carrying capacity of the multi-feeder distributed power supply.

2. The method for evaluating the carrying capacity of a multi-feeder distributed power supply according to claim 1, wherein: The constraints of the multi-feeder robust carrying capacity evaluation model include: The voltage security opportunity constraint is expressed as: The line current carrying capacity opportunity constraint is expressed as: The power flow constraint is expressed as: The soft switching power constraint is expressed as: The network reconstruction constraint is expressed as: Among them, V i,t is the voltage of node i at time t; V max is the maximum value of the node voltage allowed by the distribution network, 1-ε is the confidence level of the node voltage constraint, Pr{} represents the chance constraint, P ij,t is the active power flowing through branch ij at time t, Q ij,t is the reactive power flowing through branch ij at time t, is the maximum complex power allowed to flow through branch ij, 1-α is the confidence level of the line current carrying capacity constraint, V j,t is the voltage of node j at time t, r ij is the resistance of branch ij, x ij is the reactance of branch ij, P jk,t is the active power flowing through branch jk at time t, is the active power of the load at node j at time t, is the active power of the distributed generation at node j at time t, Q jk,t is the reactive power flowing through branch jk at time t, is the reactive power of the load at node j at time t, is the active power injected into node i by the flexible soft switch at time t, is the reactive power injected into node i by the flexible soft switch at time t, is the active power injected into node j by the flexible soft switch at time t, is the reactive power injected into node j by the flexible soft switch at time t, S VSC is the capacity of the flexible soft-switching converter, E SW is the set of branches where the tie switch is located, E is the set of all branches, θ ij,t is a constant, γ ij,t Indicates the on / off state of branch ij at time t, N BR is the total number of branches including the branch where the tie switch is located; N SW is the number of tie switches.

3. The method for evaluating the carrying capacity of a multi-feeder distributed power supply according to claim 2, wherein: The multi-feeder robust carrying capacity evaluation model is subjected to linearization constraint and chance constraint transformation to obtain a mixed integer linear programming model, specifically including: The distributed power generation time sequence output is represented as a mixed probability density random variable; According to the affine relationship between the distributed power generation time sequence output and the node voltage and line current carrying capacity, a probability distribution function of the node voltage and the line capacity is obtained, and the voltage safety opportunity constraint and the line current carrying capacity opportunity constraint are transformed using the probability distribution function; The power flow constraints are linearized to obtain a mixed integer linear programming model.

4. The method for evaluating the carrying capacity of a multi-feeder distributed power supply according to claim 3, wherein: The linearization process of the power flow constraint is specifically as follows: The constraint that the active power and reactive power of the disconnected branch circuit are both 0 is introduced, which can be expressed as: Among them, M1 and M2 are sufficiently large positive numbers; The power flow constraint of the voltage safety opportunity constraint of disconnecting the branch circuit is transformed into: Among them, M3 is a sufficiently large positive number; The power flow constraint of the unbroken branch circuit is transformed into:

5. A multi-feeder distributed power supply carrying capacity evaluation device, characterized in that: include: The collection module is used to determine the area to be evaluated and collect the data required for the evaluation; A construction module is used to construct a multi-feeder robust carrying capacity evaluation model taking into account network reconstruction and flexible soft switching power regulation in the area to be evaluated, with the goal of maximizing the installed capacity of multi-feeder distributed power sources; the objective function of the multi-feeder robust carrying capacity evaluation model constructed by the construction module is: in, is the active power injected into node i by the flexible soft switch at time t; is the reactive power injected into node i by the flexible soft switch at time t; γ ij,t represents the on / off state of branch ij at time t; F represents the set of all feeders; u represents all u i The set of distributed generation capacity at all nodes; S represents the set of distributed generation capacity at all nodes; N S Indicates the number of all optional access locations; u i is a 0-1 variable, when u i When it is 1, it means that node i is connected to the distributed power supply. i When it is 0, it means that node i is not connected to the distributed power supply; is the access capacity of distributed generation at node i; N F Indicates the total number of feeder lines; A conversion module is used to perform linearization constraint and chance constraint conversion on the multi-feeder robust carrying capacity evaluation model, Get the mixed integer linear programming model; A solution module is used to solve the mixed integer linear programming model based on the data and using a solver to obtain the carrying capacity of the multi-feeder distributed power supply.

6. The multi-feeder distributed power supply carrying capacity evaluation device according to claim 5, characterized in that: The constraints of the multi-feeder robust carrying capacity evaluation model constructed by the building module include: The voltage security opportunity constraint is expressed as: The line current carrying capacity opportunity constraint is expressed as: The power flow constraint is expressed as: The soft switching power constraint is expressed as: The network reconstruction constraint is expressed as: Among them, V i,t is the voltage of node i at time t; V max is the maximum value of the node voltage allowed by the distribution network, 1-ε is the confidence level of the node voltage constraint, Pr{} represents the chance constraint, P ij,t is the active power flowing through branch ij at time t, Q ij,t is the reactive power flowing through branch ij at time t, is the maximum complex power allowed to flow through branch ij, 1-α is the confidence level of the line current carrying capacity constraint, V j,t is the voltage of node j at time t, r ij is the resistance of branch ij, x ij is the reactance of branch ij, P jk,t is the active power flowing through branch jk at time t, is the active power of the load at node j at time t, is the active power of the distributed generation at node j at time t, Q jk,t is the reactive power flowing through branch jk at time t, is the reactive power of the load at node j at time t, is the active power injected into node i by the flexible soft switch at time t, is the active power injected into node i by the flexible soft switch at time t, is the active power injected into node j by the flexible soft switch at time t, is the reactive power injected into node j by the flexible soft switch at time t, S VSC is the capacity of the flexible soft-switching converter, E SW is the set of branches where the tie switch is located, E is the set of all branches, θ ij,t is a constant, γ ij,t Indicates the on / off state of branch ij at time t, N BR is the total number of branches including the branch where the tie switch is located; N SW is the number of tie switches.

7. The multi-feeder distributed power supply carrying capacity evaluation device according to claim 6, characterized in that: The conversion module comprises: A characterization unit, used to characterize the distributed power generation time sequence output in the form of a mixed probability density random variable; A first conversion unit is configured to obtain a probability distribution function of the node voltage and the line capacity according to an affine relationship between the distributed power generation time sequence output and the node voltage and the line current carrying capacity, and to convert the voltage safety opportunity constraint and the line current carrying capacity opportunity constraint using the probability distribution function; The second conversion unit is used to perform linearization processing on the power flow constraint to obtain a mixed integer linear programming model.

8. The multi-feeder distributed power supply carrying capacity evaluation device according to claim 7, characterized in that: The second conversion unit comprises: A subunit is introduced to introduce the constraint that the active power and reactive power of the disconnected branch circuit are both 0, which is expressed as: Among them, M1 and M2 are sufficiently large positive numbers; The first constraint conversion subunit is used to convert the power flow constraint of the voltage safety opportunity constraint of the disconnected branch circuit into: Among them, M3 is a sufficiently large positive number; The second constraint conversion subunit is used to convert the power flow constraint of the unbroken branch circuit into:

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for evaluating the carrying capacity of a multi-feeder distributed power source as claimed in any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating the carrying capacity of a multi-feeder distributed power source as claimed in any one of claims 1 to 4 are implemented.

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