Method for evaluating bearing capacity of multi-feeder distributed power supply

Abstract

The invention relates to a multi-feeder distributed power supply bearing capacity assessment method, which comprises the following steps: firstly, determining a to-be-assessed power distribution network area, and obtaining line equipment parameters, transformer equipment parameters, distributed power supply access capacity and access position, load requirements, a distributed power supply time sequence characteristic curve and load time sequence characteristic curve information of the power distribution network area; then, according to the acquired information data, constructing a multi-feeder robust bearing capacity evaluation model by considering an objective function of the maximum installation capacity of the multi-feeder distributed power supply of SOP and network reconstruction; finally, carrying out linear constraint and opportunity constraint conversion on the multi-feeder robust bearing capacity evaluation model, and solving the evaluation model; according to the method, the uncertainty of DG time sequence output is processed through the Gaussian mixture model, the influence of network reconstruction and SOP adjustment on the DG bearing capacity is combined, a bearing capacity evaluation model is established, solving is carried out in a linearization mode, and the evaluation efficiency and accuracy are improved.

Description

Technical Field

[0001] The invention relates to a method for evaluating the carrying capacity of a multi-feeder distributed power source, and belongs to the technical field of power systems and new energy access. 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 decentralized 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 DG capacity 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 are 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 switch SOP regulation, making it difficult to fully evaluate the maximum carrying capacity of the distribution network. Summary of the invention

[0006] In order to solve the problems existing in the prior art, the present invention provides a multi-feeder robust carrying capacity evaluation method which comprehensively considers network reconstruction and SOP power regulation.

[0007] In order to achieve the above object, the technical solution proposed by the present invention is: a method for evaluating the carrying capacity of a multi-feeder distributed power source, characterized in that it includes the following steps:

[0008] Step 1: Determine the distribution network area to be evaluated

[0009] Step 2: Obtain the 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 information in the distribution network area as the distribution network information data set;

[0010] Step 3: Based on the distribution network information dataset, a multi-feeder robust carrying capacity evaluation model is constructed with the objective function of maximizing the multi-feeder distributed generation installation capacity considering SOP and network reconstruction;

[0011] Step 4: Perform linearization constraint and chance constraint transformation on the multi-feeder robust carrying capacity evaluation model, and solve the evaluation model to obtain the multi-feeder robust carrying capacity.

[0012] A further design of the above technical solution is as follows: the objective function in step 3 is:

[0013]

[0014] In the formula, 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.

[0015] The constraints of the multi-feeder robust carrying capacity assessment model in step 3 include voltage safety opportunity constraints, line current carrying capacity opportunity constraints, power flow constraints, SOP power constraints and network reconstruction constraints.

[0016] The voltage safety opportunity constraint is

[0017]

[0018] Where V i,t is the voltage of node i at the next time t; V maxis 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.

[0019] The line current carrying capacity opportunity constraint is:

[0020]

[0021] Where P ij,t is the active power flowing through branches i and j 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 branches i and j.

[0022] The power flow constraint is:

[0023] V j,t =V i,t -(r ij P ij,t +x ij Q ij,t )

[0024]

[0025] Where 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 , are the active power flowing through branches j and k at time t, the active power of the load at node j at time t, and the active power of the DG at node j at time t; Q jk,t , are the reactive powers of branch jk and node j at time t respectively.

[0026] The SOP power constraint is:

[0027]

[0028] In the formula, The active power injected by SOP to node i and node j at time t respectively; are the reactive power injected by SOP to node i and node j at time t; S VSC is the capacity of the SOP converter, which is half of the SOP capacity.

[0029] The network reconstruction constraints are:

[0030]

[0031] In the formula, E SWis the set of branches where the tie switch is located; E is the set of all branches; γ ij,t is a 0-1 variable used to describe the on / off state of the branch. When it is 0, the branch is open, and when it is 1, the branch is closed. BR N is the total number of branches including the branch where the tie switch is located; SW is the number of tie switches.

[0032] The step 4 specifically includes:

[0033] Step 4.1: Get the quantile of DG at any confidence level:

[0034]

[0035] 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, no explanation is needed, 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;

[0036] The corresponding DG output value at any confidence level is:

[0037]

[0038] Step 4.2: Opportunity constraint transformation;

[0039] According to the affine relationship between DG sequential output, node voltage and line current carrying capacity, the opportunity constraint is transformed into:

[0040]

[0041] Step 4.3: Constraint transformation caused by linearization and network reconstruction;

[0042] The power flow constraints are linearized and converted into the following form:

[0043]

[0044] The power flow constraint of voltage security opportunity constraint is transformed into:

[0045]

[0046] Voltage safety and line current carrying capacity constraints translate into:

[0047] -γ ij,t M1≤P s,ij,t ≤γ ij,t M1

[0048] -γ ij,t M2≤Q s,ij,t ≤γ ij,t M2

[0049]

[0050] Where M1, M2, and M3 are positive numbers, and their values ​​are the maximum values ​​that may occur for each variable.

[0051] The beneficial effects of the present invention are:

[0052] 1. The present invention establishes a mathematical model of timing output uncertainty and position uncertainty, performs multiple uncertainty analysis, models and analyzes the DG output through a Gaussian mixture model and a clustering algorithm, and achieves comprehensive processing of DG output and position uncertainty, avoiding the evaluation bias caused by over-conservatism or over-optimism in traditional methods, and making the obtained multi-feeder robust carrying capacity more accurate.

[0053] 2. The present invention constructs a multi-feeder robust carrying capacity evaluation model taking into account network reconstruction and flexible soft switching power regulation, and models the objective function in the form of max-min{min-max}, optimizes the power flow path and load distribution, so that the DG carrying capacity of different feeders can be significantly improved.

[0054] 3. The present invention adopts an optimization solution method combining mixed integer linear programming and genetic algorithm, through chance constraint transformation and linearization processing, and uses CPLEX solver to significantly improve the solution efficiency of the model. At the same time, by introducing chance constraints, it is ensured that the system operates within a safe range under a certain confidence level, which significantly improves the operational safety of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flow chart of the method of the present invention;

[0056] Figure 2 Schematic diagram of a three-feeder interconnected distribution network. DETAILED DESCRIPTION

[0057] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] Example

[0059] like Figure 1 As shown, the method for evaluating the carrying capacity of a multi-feeder distributed power source in this embodiment includes the following steps:

[0060] Step 1: Determine the distribution network area to be evaluated;

[0061] Step 2: Obtain information such as the topological structure of the power distribution system, line equipment parameters, transformer equipment parameters, distributed power access capacity and access location, load demand, distributed power timing characteristic curve, load timing characteristic curve, etc.;

[0062] Step 3: Based on the data and information obtained in step 2, a multi-feeder robust carrying capacity assessment model is constructed;

[0063] The objective function of the evaluation model is to maximize the installed capacity of multi-feeder distributed generation considering SOP and network reconstruction, and the expression is as follows:

[0064]

[0065] In the formula, 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.

[0066] The model takes power flow constraints, SOP constraints, network reconstruction constraints, etc. as constraints, as follows:

[0067] (1) Voltage safety opportunity constraints

[0068]

[0069] Where V i,t is the voltage of node i at the next 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.

[0070] (2) Opportunistic constraints on line current carrying capacity

[0071]

[0072] Where 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.

[0073] (3) Power flow constraints

[0074] V j,t =V i,t -(r ij P ij,t +x ij Q ij,t )

[0075]

[0076] Where 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 , are the active power flowing through branch jk at time t, the active power of load at node j at time t, and the active power of DG at node j at time t; Q jk,t , are the reactive powers of branch jk and node j at time t respectively.

[0077] (4) SOP power constraints

[0078]

[0079] In the formula, The active power injected by SOP to node i and node j at time t respectively; are the reactive power injected by SOP to node i and node j at time t; S VSC is the capacity of the SOP converter, which is half of the SOP capacity.

[0080] (5) Network Reconfiguration Constraints

[0081] Since the total number of closed branches does not change after reconstruction, the following constraints apply:

[0082]

[0083] In the formula, E SW is the set of branches where the tie switch is located; E is the set of all branches; γ ij,t is a 0-1 variable used to describe the on / off state of the branch. When it is 0, the branch is open, and when it is 1, the branch is closed. BRN is the total number of branches including the branch where the tie switch is located; SW is the number of tie switches.

[0084] Step 4: Perform linearization constraint and chance constraint transformation on the optimization model in step 3, transform the original model into a mixed integer linear programming model, and solve it using the CPLEX solver to obtain the multi-feeder robust carrying capacity.

[0085] Step 4.1: DG mixed probability density processing;

[0086] 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 quantiles of the DG at any confidence level.

[0087] For any given confidence level, the output quantile of DG at any time can be approximately expressed as follows:

[0088]

[0089] 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, no explanation is needed, 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.

[0090] According to the following equation, the corresponding DG output value under any confidence level can be approximately obtained.

[0091]

[0092] Step 4.2: Opportunity constraint transformation;

[0093] 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 j,t ) and F((P ij,t ) 2 +(Q ij,t ) 2 ), and then transform the opportunity constraint into:

[0094]

[0095] And then:

[0096]

[0097] Step 4.3: Constraint transformation caused by linearization and network reconstruction

[0098] In order to simplify the solution, the power flow constraint is linearized and converted into the following form:

[0099]

[0100] In addition, since network reconstruction is considered, when a branch is disconnected, its power flow is 0, and the node voltage and line capacity constraints no longer meet the constraints. This paper adopts the big M method to transform the relevant constraints.

[0101] To ensure that the active power and reactive power on the disconnected branch line are both 0, the following constraints are introduced:

[0102] -γ ij,t M1≤P s,ij,t ≤γ ij,t M1

[0103] -γ ij,t M2≤Q s,ij,t ≤γ ij,t M2

[0104] 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:

[0105]

[0106] The line capacity constraint is transformed into:

[0107]

[0108] Where M1, M2 and M3 are respectively 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.

[0109] The method of this embodiment uses a Gaussian mixture model to deal with the uncertainty of DG timing output, and combines the impact of network reconstruction and SOP adjustment on DG carrying capacity to establish a multi-feeder DG carrying capacity evaluation model, which is solved using a linearization method to improve the evaluation efficiency and accuracy.

[0110] Application Examples

[0111] This example uses the attached Figure 2The 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.

[0112] Set up the following 3 scenarios:

[0113] Scenario 1: Network reconstruction is not considered.

[0114] Scenario 2: Consider network reconstruction.

[0115] Scenario 3: The capacity including SOP is 600kVA.

[0116] The robust carrying capacity of multiple feeders under three scenarios is calculated, and the results are shown in Table 1.

[0117] Table 1 Robust carrying capacity of multiple feeders in different scenarios

[0118]

[0119] Through the above analysis, it is verified that the method proposed in the present invention can accurately evaluate the robust carrying capacity of multiple feeders under different network structures and adjustment measures.

[0120] It can be seen from Table 1 that F1, F2 and F3 are the carrying capacities of a single feeder respectively, and the following multi-feeder robust carrying capacity is the multi-feeder robust carrying capacity of three feeders of the present invention.

[0121] The robust carrying capacity of multiple feeders and their corresponding positions in the three scenarios are different. This is because after considering the network reconstruction, the topology of the network will change during operation, and after the SOP is connected, since the SOP has the ability to regulate the flow, it will affect the flow distribution of the network and thus affect the robust carrying capacity of the network and its corresponding position, so that the robust carrying capacity in the multi-feeder interconnection scenario can be evaluated, and the distributed photovoltaic access can be scientifically guided. This also proves the necessity of the method for obtaining the robust carrying capacity of multiple feeders proposed in the present invention.

[0122] By comprehensively considering the uncertainty of DG output, location uncertainty, network reconstruction and the influence of SOP adjustment, the present invention not only improves the DG carrying capacity of the distribution network, but also ensures the safety and operation efficiency of the system, providing important guarantee for the efficient access and utilization of new energy.

[0123] The technical solutions of the present invention are not limited to the above-mentioned embodiments, and any technical solutions obtained by equivalent replacement methods fall within the scope of protection required by the present invention.

Claims

1. A method for evaluating the carrying capacity of a multi-feeder distributed power source, characterized in that: The following steps are involved: Step 1: Determine the distribution network area to be evaluated Step 2: Obtain the 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 information in the distribution network area as the distribution network information data set; Step 3: Based on the distribution network information dataset, a multi-feeder robust carrying capacity evaluation model is constructed with the objective function of maximizing the multi-feeder distributed generation installation capacity considering SOP and network reconstruction; Step 4: Perform linearization constraint and chance constraint transformation on the multi-feeder robust carrying capacity evaluation model, and solve the evaluation model to obtain the multi-feeder robust carrying capacity.

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 in step 3 is: In the formula, 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.

3. The method for evaluating the carrying capacity of a multi-feeder distributed power source according to claim 2, characterized in that: The constraints of the multi-feeder robust carrying capacity assessment model in step 3 include voltage safety opportunity constraints, line current carrying capacity opportunity constraints, power flow constraints, SOP power constraints and network reconstruction constraints.

4. The method for evaluating the carrying capacity of a multi-feeder distributed power source according to claim 3 is characterized in that: The voltage safety opportunity constraint is Where V i,t is the voltage of node i at the next 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.

5. The method for evaluating the carrying capacity of a multi-feeder distributed power source according to claim 4, characterized in that: The line current carrying capacity opportunity constraint is: Where P ij,t is the active power flowing through branches i and j 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 branches i and j.

6. The method for evaluating the carrying capacity of a multi-feeder distributed power source according to claim 5, characterized in that: The power flow constraint is: V j,t =V i,t -(r ij P ij,t +x ij Q ij,t ) Where 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 , are the active power flowing through branches j and k at time t, the active power of the load at node j at time t, and the active power of the DG at node j at time t; Q jk,t , are the reactive powers of branch jk and node j at time t respectively.

7. The method for evaluating the carrying capacity of a multi-feeder distributed power source according to claim 6, characterized in that: The SOP power constraint is: In the formula, The active power injected by SOP to node i and node j at time t respectively; are the reactive power injected by SOP to node i and node j at time t; S VSC is the capacity of the SOP converter, which is half of the SOP capacity.

8. The method for evaluating the carrying capacity of a multi-feeder distributed power source according to claim 7, characterized in that: The network reconstruction constraints are: In the formula, E SW is the set of branches where the tie switch is located; E is the set of all branches; γ ij,t is a 0-1 variable used to describe the on / off state of the branch. When it is 0, the branch is open, and when it is 1, the branch is closed. 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 method for evaluating the carrying capacity of a multi-feeder distributed power source according to claim 8, characterized in that: The step 4 specifically includes: Step 4.1: Get the quantile of DG at any confidence level: 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, no explanation is needed, 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; The corresponding DG output value at any confidence level is: Step 4.2: Opportunity constraint transformation; According to the affine relationship between DG sequential output, node voltage and line current carrying capacity, the opportunity constraint is transformed into: Step 4.3: Constraint transformation caused by linearization and network reconstruction; The power flow constraints are linearized and converted into the following form: The power flow constraint of voltage security opportunity constraint is transformed into: Voltage safety and line current carrying capacity constraints translate into: -c ij,t M1≤P s,ij,t ≤γ ij,t M1 -γ ij,t M2≤Q s,ij,t ≤γ ij,t M2 Where M1, M2, and M3 are positive numbers, and their values ​​are the maximum values ​​that may occur for each variable.

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