Multi-feeder distributed power supply bearing capacity evaluation method, device, equipment and medium

Abstract

The invention relates to a multi-feeder distributed power supply bearing capacity assessment method, device and equipment and a medium, and the method comprises the steps: determining a to-be-assessed region, and collecting all data needed by assessment; constructing a multi-feeder robust bearing capacity evaluation model considering network reconstruction and flexible soft switching power regulation of a to-be-evaluated area by taking the maximum installation capacity of the multi-feeder distributed power supply as a target; carrying out linear constraint and chance constraint conversion on the multi-feeder robust bearing capacity evaluation model to obtain a mixed integer linear programming model; and based on the data, solving the mixed integer linear programming model by using a solver to obtain the bearing capacity of the multi-feeder distributed power supply. The evaluation efficiency and accuracy can be improved.

Description

Technical Field

[0001] The present invention relates to the field of power system and new energy access technology, and in particular to a method, device, equipment and medium for evaluating the carrying capacity of a multi-feeder distributed power source. Background Art

[0002] With the advancement of the dual carbon goals, the large-scale grid connection of distributed power sources represented by distributed photovoltaics and distributed wind power has brought huge challenges to the planning and operation of distribution networks. In some areas, voltage rise and reverse load rate exceeding the standard have occurred. Accurately assessing the upper limit of the capacity of distributed generation (DG) that the distribution network can carry is an important basis for supporting the coordinated development of new energy and distribution networks.

[0003] The spatiotemporal uncertainty of DG and the coupling effects of multi-feeder interconnection and active management measures of distribution networks are two aspects that need to be focused on in carrying capacity assessment. The spatiotemporal uncertainty of DG mainly refers to the difficulty in accurately predicting the time sequence output of DG and the difficulty in predicting its grid-connected location in advance. Dealing with this problem is one of the key difficulties in carrying capacity assessment. In recent years, experts and scholars have achieved a series of results in this field. For example, uncertain models are constructed using probability distributions and uncertain sets, and evaluations are performed based on robust optimization or stochastic optimization methods, 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 the evaluation of DG carrying capacity considering location uncertainty, it is usually assumed that multiple candidate locations have the possibility of DG grid connection, and these locations are used as decision variables in 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 the evaluation results under different scenarios can be obtained, its main shortcomings in large-scale distribution systems are low computational efficiency and long solution time.

[0005] In terms of network reconstruction's impact on DG carrying capacity, existing research mainly focuses on analyzing the effect of network reconstruction on the DG carrying capacity of a single feeder. However, there are few studies on multi-feeder scenarios, especially the lack of systematic analysis of the impact of inter-feeder reconstruction and flexible soft open point (SOP) regulation. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a method, device, equipment and medium for evaluating the carrying capacity of a multi-feeder distributed power source, so as to improve the evaluation efficiency and accuracy.

[0007] The technical solution adopted by the present invention to solve the technical problem is: to provide a method for evaluating the carrying capacity of a multi-feeder distributed power source, comprising the following steps:

[0008] Determine the area to be assessed and collect the data required for the assessment;

[0009] Taking the maximum installed capacity of multi-feeder distributed generation as the goal, a multi-feeder robust carrying capacity assessment model taking into account network reconstruction and flexible soft switch power regulation in the area to be assessed is constructed;

[0010] Performing linearization constraint and chance constraint transformation on the multi-feeder robust carrying capacity assessment model to obtain a mixed integer linear programming model;

[0011] 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 source.

[0012] 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; 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 composed of; S represents the set composed of the access capacity of distributed generation at all nodes; N S represents the number of all optional access locations; u i is a 0-1 variable. 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 source; is the access capacity of distributed generation at node i; N F Indicates the total number of feeder lines.

[0013] The constraints of the multi-feeder robust carrying capacity evaluation model include:

[0014] The voltage safety opportunity constraint is expressed as:

[0015] The line current carrying capacity opportunity constraint is expressed as:

[0016] The power flow constraint is expressed as:

[0017] The soft switching power constraint is expressed as:

[0018] The network reconstruction constraint is expressed as:

[0019] 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 represents the on / off state of branch ij at time t, N BR N is the total number of branches including the branch where the tie switch is located; SW is the number of tie switches.

[0020] 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, which specifically includes:

[0021] The distributed generation time sequence output is represented as a mixed probability density random variable;

[0022] According to the affine relationship between the distributed power generation time sequence output and the node voltage and the line current carrying capacity, the 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 by using the probability distribution function;

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

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

[0025] 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;

[0026] 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;

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

[0028] The technical solution adopted by the present invention to solve the technical problem is: to provide a multi-feeder distributed power supply carrying capacity evaluation device, comprising:

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

[0030] A construction module is used to construct a multi-feeder robust carrying capacity evaluation model taking into account network reconstruction and flexible soft switch power regulation in the area to be evaluated, with the goal of maximizing the installed capacity of multi-feeder distributed power sources;

[0031] A conversion module, used for performing linearization constraint and chance constraint conversion on the multi-feeder robust carrying capacity evaluation model to obtain a mixed integer linear programming model;

[0032] 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.

[0033] The objective function of the multi-feeder robust carrying capacity evaluation model constructed by the building 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; ij,t represents the on / off state of branch ij at time t; F represents the set of all feeders; u represents all u iThe set composed of; S represents the set composed of the access capacity of distributed generation at all nodes; N S represents the number of all optional access locations; u i is a 0-1 variable. 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 source; is the access capacity of distributed generation at node i; N F Indicates the total number of feeder lines.

[0034] The constraints of the multi-feeder robust carrying capacity evaluation model constructed by the building module include:

[0035] The voltage safety opportunity constraint is expressed as:

[0036] The line current carrying capacity opportunity constraint is expressed as:

[0037] The power flow constraint is expressed as:

[0038] The soft switching power constraint is expressed as:

[0039] The network reconstruction constraint is expressed as:

[0040] 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 represents the on / off state of branch ij at time t, N BR N is the total number of branches including the branch where the tie switch is located; SW is the number of tie switches.

[0041] The conversion module comprises:

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

[0043] A first conversion unit is used to obtain a probability distribution function of node voltage and line capacity according to an affine relationship between the distributed power generation time sequence output and the node voltage and 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 linear 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 disconnecting the 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 executable on the processor, wherein the processor implements the steps of the above-mentioned multi-feeder distributed power supply carrying capacity evaluation method when executing the computer program.

[0050] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned multi-feeder distributed power supply carrying capacity evaluation method are implemented.

[0051] Beneficial Effects

[0052] Due to the adoption of the above technical scheme, the present invention has the following advantages and positive effects compared with the prior art: the present invention considers the influence of network reconstruction and SOP adjustment on DG carrying capacity, establishes a multi-feeder DG carrying capacity evaluation model, and before solving the evaluation model, uses a Gaussian mixture model to process the uncertainty of DG timing output, and uses a linearization method to solve it to obtain the multi-feeder distributed power supply carrying capacity. In this way, the carrying capacity of DG in a complex power grid structure can be more accurately evaluated, the overall operating efficiency and safety of the distribution network can be improved, and the efficient access and utilization of new energy can be supported. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of a method for evaluating the carrying capacity of a multi-feeder distributed power source according to a first embodiment of the present invention;

[0054] Figure 2 Schematic diagram of a three-feeder interconnected distribution network in an embodiment of the present invention. DETAILED DESCRIPTION

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

[0056] The first embodiment of the present invention relates to a method for evaluating the carrying capacity of a multi-feeder distributed power source, such as Figure 1 As shown, the following steps are included:

[0057] Step 1: Determine the area to be evaluated and collect various data required for the evaluation, including distribution system topology, line equipment parameters, transformer equipment parameters, distributed power access capacity and access location, load demand, distributed power timing characteristic curve, and load timing characteristic curve.

[0058] Step 2, with the goal of maximizing the installed capacity of multi-feeder distributed power sources, construct a multi-feeder robust carrying capacity assessment model that takes into account network reconstruction and flexible soft switch power regulation in the area to be assessed.

[0059] In this step, the maximum installed capacity of distributed power supply is taken as the objective function, and the power flow constraint, SOP constraint, network reconstruction constraint, etc. are taken as constraints, and a multi-feeder robust carrying capacity evaluation model considering network reconstruction and flexible soft switching power regulation is constructed.

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

[0061]

[0062] 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; ij,t Represents the on / off state of branch ij at time t, which is a 0-1 variable. ij,t = 0, it means that branch ij is disconnected. ij,t =1, it means that the branch ij is closed; F represents the set of all feeders; u represents all u i The set composed of; S represents the set composed of the access capacity of distributed generation at all nodes; N S represents the number of all optional access locations; u i is a 0-1 variable. 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 source; is the access capacity of distributed generation at node i; N F Indicates the total number of feeder lines.

[0063] The constraints of the multi-feeder robust carrying capacity evaluation model include:

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

[0065]

[0066] 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 that the node voltage constraint is established, and Pr{} represents the chance constraint.

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

[0068]

[0069] Among them, P ij,t is the active power flowing through branch ij at time t, 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,trepresents the on / off state of branch ij at time t, N BR N is the total number of branches including the branch where the tie switch is located; SW is the number of tie switches.

[0080] Step 3: Perform linearization constraint and chance constraint transformation on the multi-feeder robust carrying capacity assessment model to obtain a mixed integer linear programming model. In this step, chance constraint transformation and linearization constraint transformation are performed before solving the model, as follows:

[0081] DG mixed probability density processing: After a series of uncertainty processing, the DG time series output is represented as a mixed probability density random variable. To support the carrying capacity assessment, it is necessary to obtain the DG quantiles at any confidence level. For any given confidence level, the DG output quantile at any time can be approximately expressed as:

[0082]

[0083] Among them, F(x) is the output quantile of DG 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 sequential output scenario, is the expression of Gaussian density function, ω is the probability distribution in the mixed Gaussian model, u i is the cluster center value of each category obtained by scene clustering, σ i 2 They are the cluster center value and cluster variance of each category obtained based on scene clustering.

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

[0085]

[0086] According to the affine relationship between DG sequential output, node voltage and line current carrying capacity, the probability distribution functions of node voltage and line capacity can be expressed as F′(V i,t ) and F′((P ij,t ) 2 +(Q ij,t ) 2 ), and then transform the opportunity constraint into:

[0087]

[0088] And then:

[0089]

[0090] In order to simplify the solution, the power flow constraint is linearized and converted into the following form:

[0091]

[0092] In addition, since network reconstruction is taken into consideration, when a branch is disconnected, its power flow is 0, and the node voltage and line capacity constraints no longer meet the constraints. This implementation method uses the big M method to transform the relevant constraints.

[0093] To ensure that the active power and reactive power on the disconnected branch line 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] Where M1 and M2 are large enough positive numbers, which are generally taken as the maximum values ​​of each variable. They should not be too large, otherwise it will affect the solution efficiency.

[0097] When the branch line is disconnected, the voltages of the two nodes do not meet the voltage safety opportunity constraint. To ensure the universality of the constraint, the power flow constraint of the voltage safety opportunity constraint is transformed into:

[0098]

[0099] Among them, M3 is a sufficiently large positive number.

[0100] The power flow constraint of the unbroken branch line is transformed into:

[0101] Step 4: 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 source. When solving, the CPLEX solver can be used in this embodiment.

[0102] Use Figure 2 The three-feeder interconnected distribution network shown in the figure is used as a case study, and the case analysis is carried out on the MATLAB 2018a simulation platform with built-in YALMIP toolbox and CPLEX solver. The three-feeder interconnected distribution network contains a total of 3715kW active load, 2300kVar reactive load, 26 nodes, and 23 branches.

[0103] Set up the following 3 scenarios:

[0104] Scenario 1: Network reconstruction is not considered.

[0105] Scenario 2: Consider network reconstruction.

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

[0107] The robust carrying capacity of multiple feeders under three scenarios is calculated, and the results are shown in Table 1.

[0108] Table 1 Robust carrying capacity of multiple feeders in different scenarios

[0109]

[0110] Through the above analysis, the method of this embodiment is verified to be effective in accurately evaluating the robust carrying capacity of multiple feeders under different network structures and adjustment measures.

[0111] It can be seen from Table 1 that the multi-feeder robust carrying capacity and its corresponding position in the three scenarios are different. This is because after the network reconstruction, the topology of the network during operation will change, and after the SOP is connected, since the SOP has the ability to control the flow, it will affect the flow distribution of the network and thus affect the network's robust carrying capacity and its corresponding position. This also proves the necessity of the method for obtaining the multi-feeder robust carrying capacity of this implementation.

[0112] It is not difficult to find that the present invention considers the impact of network reconstruction and SOP adjustment on the DG carrying capacity, establishes a multi-feeder DG carrying capacity evaluation model, and before solving the evaluation model, uses a Gaussian mixture model to process the uncertainty of the DG timing output, and uses a linearization method to solve it to obtain the multi-feeder distributed power supply carrying capacity. This method can more accurately evaluate the carrying capacity of DG in a complex power grid structure, improve the overall operating efficiency and safety of the distribution network, and support the efficient access and utilization of new energy.

[0113] A second embodiment of the present invention relates to a multi-feeder distributed power supply carrying capacity evaluation device, comprising:

[0114] The collection module is used to determine the area to be evaluated and collect various data required for the evaluation;

[0115] A construction module is used to construct a multi-feeder robust carrying capacity evaluation model taking into account network reconstruction and flexible soft switch power regulation in the area to be evaluated, with the goal of maximizing the installed capacity of multi-feeder distributed power sources;

[0116] A conversion module, used for performing linearization constraint and chance constraint conversion on the multi-feeder robust carrying capacity evaluation model to obtain a mixed integer linear programming model;

[0117] 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.

[0118] The objective function of the multi-feeder robust carrying capacity evaluation model constructed by the building 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; 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 composed of; S represents the set composed of the access capacity of distributed generation at all nodes; N S represents the number of all optional access locations; u i is a 0-1 variable. 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 source; is the access capacity of distributed generation at node i; N F Indicates the total number of feeder lines.

[0119] The constraints of the multi-feeder robust carrying capacity evaluation model constructed by the building module include:

[0120] The voltage safety opportunity constraint is expressed as:

[0121] The line current carrying capacity opportunity constraint is expressed as:

[0122] The power flow constraint is expressed as:

[0123] The soft switching power constraint is expressed as:

[0124] The network reconstruction constraint is expressed as:

[0125] 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,tis 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 represents the on / off state of branch ij at time t, N BR N is the total number of branches including the branch where the tie switch is located; SW is the number of tie switches.

[0126] The conversion module comprises:

[0127] A characterization unit, used for characterizing the distributed power generation time sequence output in the form of a mixed probability density random variable;

[0128] A first conversion unit is used to obtain a probability distribution function of node voltage and line capacity according to an affine relationship between the distributed power generation time sequence output and the node voltage and 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;

[0129] The second conversion unit is used to perform linear processing on the power flow constraint to obtain a mixed integer linear programming model.

[0130] The second conversion unit comprises:

[0131] 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;

[0132] The first constraint conversion subunit is used to convert the power flow constraint of the voltage safety opportunity constraint of disconnecting the branch circuit into: Among them, M3 is a sufficiently large positive number;

[0133] The second constraint conversion subunit is used to convert the power flow constraint of the unbroken branch circuit into:

[0134] A third embodiment of the present invention 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] A fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, and 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 of the first embodiment are implemented.

[0136] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt 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.) that contain computer-usable program code.

[0137] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0138] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0140] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for evaluating the carrying capacity of a multi-feeder distributed power source, characterized in that: The following steps are involved: Determine the area to be assessed and collect the data required for the assessment; Taking the maximum installed capacity of multi-feeder distributed generation as the goal, a multi-feeder robust carrying capacity assessment model taking into account network reconstruction and flexible soft switch power regulation in the area to be assessed is constructed; Performing linearization constraint and chance constraint transformation on the multi-feeder robust carrying capacity assessment 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 source.

2. The method for evaluating the carrying capacity of a multi-feeder distributed power source according to claim 1, characterized in that: 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; 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 composed of; S represents the set composed of the access capacity of distributed generation at all nodes; N S represents the number of all optional access locations; u i is a 0-1 variable. 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; S i dg is the access capacity of distributed generation at node i; N F Indicates the total number of feeder lines.

3. The method for evaluating the carrying capacity of a multi-feeder distributed power source according to claim 1, characterized in that: The constraints of the multi-feeder robust carrying capacity evaluation model include: The voltage safety 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 represents the on / off state of branch ij at time t, N BR N is the total number of branches including the branch where the tie switch is located; SW is the number of tie switches.

4. The method for evaluating the carrying capacity of a multi-feeder distributed power source according to claim 3, characterized in that: 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, which specifically includes: The distributed 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 the line current carrying capacity, the 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 by using the probability distribution function; The power flow constraints are linearized to obtain a mixed integer linear programming model.

5. The method for evaluating the carrying capacity of a multi-feeder distributed power source according to claim 4, characterized in that: 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 line is transformed into:

6. 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 various 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 switch power regulation in the area to be evaluated, with the goal of maximizing the installed capacity of multi-feeder distributed power sources; 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.

7. The multi-feeder distributed power supply carrying capacity evaluation device according to claim 6, characterized in that: The objective function of the multi-feeder robust carrying capacity evaluation model constructed by the building 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; 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 composed of; S represents the set composed of the access capacity of distributed generation at all nodes; N S represents the number of all optional access locations; u i is a 0-1 variable. 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 source; is the access capacity of distributed generation at node i; N F Indicates the total number of feeder lines.

8. The multi-feeder distributed power supply carrying capacity evaluation device according to claim 6, characterized in that: The constraints of the multi-feeder robust carrying capacity evaluation model constructed by the building module include: The voltage safety 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 represents the on / off state of branch ij at time t, N BR N is the total number of branches including the branch where the tie switch is located; SW is the number of tie switches.

9. The multi-feeder distributed power supply carrying capacity evaluation device according to claim 8, characterized in that: The conversion module comprises: A characterization unit, used for characterizing the distributed power generation time sequence output in the form of a mixed probability density random variable; A first conversion unit is used to obtain a probability distribution function of node voltage and line capacity according to an affine relationship between the distributed power generation time sequence output and the node voltage and line current carrying capacity, and to convert the voltage safety opportunity constraint and the line current carrying capacity opportunity constraint by using the probability distribution function; The second conversion unit is used to perform linear processing on the power flow constraint to obtain a mixed integer linear programming model.

10. The multi-feeder distributed power supply carrying capacity evaluation device according to claim 9, 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 disconnecting the 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:

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: 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 described in any one of claims 1-5 are implemented.

12. 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 5 are implemented.

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