Method and device for evaluating three-phase robust bearing capacity of distributed power supply of power distribution network, terminal equipment and storage medium

Abstract

The invention discloses a power distribution network distributed power supply three-phase robust bearing capacity evaluation method and device, terminal equipment and a storage medium. According to the method, a distributed power supply access-oriented three-phase robust bearing capacity description method is provided, that is, on the basis of distributed power supply bearing capacity evaluation, the access position, the access phase and the access capacity of the most robust in the whole system are selected. Meanwhile, a three-phase robust bearing capacity evaluation model considering the output uncertainty of the distributed power supply is constructed, namely, a Wasserstein fuzzy set is constructed to process the output uncertainty of the distributed power supply in a three-phase unbalanced network, and the three-phase robust bearing capacity evaluation model is established by considering the influence of soft switching regulation and network reconstruction on the robust bearing capacity. The method specifically comprises the steps of processing a nonlinear problem, finding an innermost-layer min model of a worst scene in a fuzzy set to be equivalent and dual as a max model, re-expressing the whole model as double-layer optimization after merging, and solving the distributed robustness of the double-layer optimization.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed power supply carrying capacity assessment, and in particular relates to a method, device, terminal equipment and storage medium for assessing the three-phase robust carrying capacity of distributed power supplies in a distribution network. Background Art

[0002] Distributed power sources, particularly photovoltaics, have experienced rapid growth in recent years. However, the intermittent and random nature of these large-scale integrations can negatively impact the planning and operation of distribution networks. These can cause fluctuations in power flow direction and magnitude, relay protection failures, voltage distortion, and increased network losses, limiting the capacity of these networks. Therefore, research is urgently needed to examine the capacity of distributed power sources in distribution networks under uncertainty.

[0003] Traditional robust load-carrying capacity assessments of distributed generation (DG) are typically based on a three-phase symmetrical network extended from a single-phase network, assessing the robust capacity required to ensure safe operation of the distribution network at any access location. However, these studies have significant shortcomings: First, these methods all investigate load-carrying capacity enhancement in a three-phase symmetrical network scenario, failing to fully account for the significant three-phase imbalances caused by unbalanced source-load distribution, line asymmetry, and asymmetric DG access to the distribution network. This results in a mismatch between the assessment results based on a three-phase symmetrical network and the actual asymmetric network, making them ineffective for practical application. Second, the robustness of existing load-carrying capacity is limited to its ability to cope with changes at any location. In today's three-phase distribution networks, which are subject to significant imbalances, differences in access phase can also affect the capacity of DG access. Against this backdrop, research on robust load-carrying capacity that can effectively cope with changes at any location and at any phase is essential. Summary of the Invention

[0004] The present invention provides a method, apparatus, terminal device, and storage medium for evaluating the robust three-phase carrying capacity of distributed power sources in a distribution network. The details are as follows:

[0005] A method for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network comprises the following steps:

[0006] (1) Constructing a three-phase robust carrying capacity evaluation model for distributed power sources in a distribution network, wherein the constraints corresponding to the three-phase robust carrying capacity evaluation model for distributed power sources in the distribution network include: power flow constraints, line current carrying capacity constraints, soft switching power constraints, and network reconfiguration constraints;

[0007] A Wasserstein distance-based evaluation model for the robust three-phase load-carrying capacity of distributed power sources in distribution networks is constructed to accurately calculate the robust three-phase load-carrying capacity of distributed power sources in distribution networks.

[0008] (2) Under various constraints, a corresponding three-phase robust access configuration scheme for distributed power sources is generated under a network reconstruction scheme and a soft switch control strategy that maximizes the carrying capacity. The three-phase robust access configuration scheme for distributed power sources includes: a network reconstruction switch action strategy, a soft switch control strategy, and the three-phase robust access capacity, access phase, and access position of the distributed power sources;

[0009] The three-phase robust carrying capacity evaluation model of distributed power sources in the distribution network is solved, and a solution method for the three-phase robust carrying capacity evaluation model of distributed power sources in the distribution network based on Wasserstein distance is proposed. By dealing with model nonlinear problems, equivalent duality and model merging, the distributed robust model is solved;

[0010] (3) According to the configuration scheme, the three-phase robust carrying capacity assessment of the distributed power source of the distribution network is configured.

[0011] Moreover, the three-phase robust carrying capacity assessment model of distributed power sources in the distribution network calculates the scheme with the smallest carrying capacity among the three-phase robust access configuration schemes of each distributed power source, which can effectively cope with changes in the access phase and access position of the distributed power source. For the scenario of single-point access of the distributed power source, the capacity can be used to access any node and any phase of the distribution network to ensure the safe operation of the distribution network. For the scenario of multi-point access of the distributed power source, the capacity can be arbitrarily distributed and connected to multiple nodes and multiple phases to ensure the safe operation of the distribution network.

[0012] Moreover, in order to consider the regulation effect of network reconstruction and soft switching, a three-phase robust carrying capacity solution model of distributed power sources in distribution network is constructed.

[0013] Furthermore, the three-phase robust carrying capacity assessment model for distributed power sources in the distribution network is a min-max two-layer carrying capacity assessment model. The inner max model aims to find the network reconstruction scheme and soft switching control strategy that maximizes the carrying capacity, while the outer min model aims to find the distributed power access scheme that makes the carrying capacity most robust, thereby obtaining a robust carrying capacity assessment result that takes into account the impact of soft switching control and network reconstruction.

[0014] The specific expression of the objective function of the optimization model is:

[0015]

[0016] Where: is a 0-1 variable. When , it means that node i is connected to the distributed power supply, otherwise it means that it is not connected to the distributed power supply; is the access capacity of the distributed generation at node i; N is the number of all optional access locations; are all optional access phases, u is all The set of network reconstruction schemes.

[0017] Moreover, the min-max two-layer load-carrying capacity evaluation model introduces the Wasserstein distance to describe the uncertainty characteristics of the distributed power output, and is further transformed into a min-max-min three-layer load-carrying capacity evaluation model, which specifically includes the following steps:

[0018] The specific expression of the objective function of the optimization model after taking into account the uncertainty of distributed generation output is:

[0019]

[0020] Where: Δξ is the set of deviations between the actual distribution of distributed generation output and the reference distribution; is a 0-1 variable. When , it means that node i is connected to the distributed power supply, otherwise it means that it is not connected to the distributed power supply; is the access capacity of the distributed generation at node i; N is the number of all optional access locations; are all optional access phases, u is all The set of network reconstruction schemes.

[0021] Furthermore, the method for solving the three-phase robust load-carrying capacity evaluation model of the distributed power supply in the distribution network in step (2) specifically includes the following steps:

[0022] (1) Convert the Wasserstein distance constraint into the following form:

[0023]

[0024] Where: L is the number of historical samples of distributed power output; Δξ l,t is the deviation between the actual output coefficient of the distributed generation with sample l as reference at time t and the sample output coefficient;

[0025] Before the duality, the uncertainty variable Δξ l,t The relevant objective functions and constraints are organized into the following form:

[0026]

[0027] Where: F is the coefficient matrix of the objective function; J1 is the coefficient matrix 1 of the equality constraint, and the result obtained by multiplying it with the vector x composed of the decision variables does not include the variable of distributed power access capacity; J2 is the coefficient matrix 2 of the equality constraint, and the result obtained by multiplying it with the vector x composed of the decision variables only includes the variable of distributed power access capacity; J3 is the constant term matrix of the equality constraint; G1 is the coefficient matrix 1 of the inequality constraint, and the result obtained by multiplying it with the vector x composed of the decision variables does not include the variable of distributed power access capacity; G2 is the coefficient matrix 2 of the equality constraint, and the result obtained by multiplying it with the vector x composed of the decision variables only includes the variable of distributed power access capacity; G3 is the constant term matrix of the inequality constraint; π l,t contains both decision variables x and uncertain variables Δξ l,t The Lagrange dual multiplier vector of the equality constraints; μ l,t contains both decision variables x and uncertain variables Δξ l,t The Lagrange dual multiplier vector of the inequality constraints; χ t is the Lagrange dual multiplier of the Wasserstein constraint;

[0028] (2) Construct the Lagrangian function of the optimization problem, which is as follows:

[0029]

[0030] To seek Demand and Δξ l,t The minimum value of the relevant terms, that is:

[0031]

[0032] Due to χ t , Are greater than 0, then The second term in the above formula is l,t =0 takes the minimum value 0, so that the first term of the above formula is in Δξ l,t =0 to get the minimum value, which needs to be satisfied Right now At this time, the value of the first item is 0;

[0033] (3) Construct the dual problem, which is as follows:

[0034]

[0035] (4) McCormick relaxation

[0036] Through the above transformation, the middle layer and the inner layer have been merged into a max problem, but at this time π l,t T x, μl,t T Since there are nonlinear terms such as x, McCormick relaxation is used to transform them. The objective function and constraints after transformation are both in linear form. The transformation formula of McCormick relaxation is as follows:

[0037]

[0038] In the formula: z and y are two variables before transformation; w is the variable after transformation;

[0039] Since linear transformation has been performed in the previous step, the above model is a mixed integer linear programming problem, which can be quickly solved using a commercial solver.

[0040] A three-phase robust carrying capacity assessment device for distributed power sources in a distribution network, comprising: a data acquisition module, an assessment model construction module, a configuration solution module, and a three-phase robust carrying capacity assessment configuration module for distributed power sources in a distribution network;

[0041] The data acquisition module is used to acquire basic data of the distribution network; wherein the basic data includes: active load demand and reactive load demand of each node in the distribution network, impedance of each branch line, and historical output data of distributed power sources;

[0042] The evaluation model construction module is used to construct a three-phase robust carrying capacity evaluation model for characterizing the distributed power generation in the distribution network; wherein the constraints corresponding to the planning model include: power flow constraints, line current carrying capacity constraints, soft switching power constraints, and network reconfiguration constraints;

[0043] The configuration solution solving module is used to solve the evaluation model under various constraints to generate a corresponding three-phase robust access configuration solution for distributed power sources under a network reconstruction solution and soft switch control strategy that maximizes the carrying capacity; wherein the configuration solution includes: a network reconstruction switch action strategy, a soft switch control strategy, and the three-phase robust access capacity, access phase, and access position of the distributed power sources;

[0044] The distribution network distributed power three-phase robust carrying capacity evaluation configuration module is used to configure the distribution network distributed power three-phase robust carrying capacity evaluation according to the configuration scheme.

[0045] A terminal device comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned methods for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network.

[0046] A storage medium comprising a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute any one of the above-mentioned methods for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network.

[0047] The advantages and effects of the present invention are:

[0048] A method, device, terminal equipment and storage medium for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network using this solution can ensure the safe operation of the distribution network by connecting this capacity to any node or any phase of the distribution network in a scenario with single-point access of the distributed power source; and by arbitrarily distributing this capacity and connecting it to multiple nodes or multiple phases in a scenario with multiple-point access of the distributed power source. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Attachment Figure 1 This is a solution flow chart of a method, device, terminal equipment and storage medium for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network in the present invention. DETAILED DESCRIPTION

[0050] As attached Figure 1 As shown, a method, apparatus, terminal device and storage medium for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network are provided. The method comprises the following steps:

[0051] Step 1: A three-phase robust load-carrying capacity description method for distributed generation access is proposed. Based on the distributed generation load-carrying capacity assessment, the most robust access location, access phase, and access capacity within the entire system are selected. A three-phase robust load-carrying capacity assessment model is constructed that considers the uncertainty of distributed generation output. Specifically, a Wasserstein fuzzy set is constructed to handle the uncertainty of distributed generation output in three-phase unbalanced networks. The impact of soft switching regulation and network reconfiguration on robust load-carrying capacity is considered, and a three-phase robust load-carrying capacity assessment model is established.

[0052] Step 2: A solution to the three-tiered min-max-min load-carrying capacity assessment model based on the Wasserstein distance is proposed. This involves addressing nonlinear issues, finding the innermost min model equivalent to the max model for the worst-case scenario within the fuzzy set, and reformulating the overall model as a two-tier optimization and distributed robust solution process.

[0053] Step 3: According to the configuration scheme, configure the three-phase robust carrying capacity assessment of the distributed power generation in the distribution network.

[0054] Specifically include:

[0055] (1) Constructing a three-phase robust carrying capacity evaluation model for distributed power sources in a distribution network; wherein the constraints corresponding to the evaluation model include: power flow constraint, line current carrying capacity constraint, soft switching power constraint, and network reconfiguration constraint; constructing a three-phase robust carrying capacity evaluation model for distributed power sources in a distribution network based on Wasserstein distance to achieve accurate calculation of the three-phase robust carrying capacity of distributed power sources in the distribution network;

[0056] (2) Under various constraints, a corresponding three-phase robust access configuration scheme for distributed power sources is generated under the network reconstruction scheme and soft switch control strategy that maximizes the carrying capacity; wherein, the configuration scheme includes: network reconstruction switch action strategy, soft switch control strategy, and distributed power source three-phase robust access capacity, access phase and access position; the evaluation model is solved, and a solution method for the three-phase robust carrying capacity evaluation model of distributed power sources in the distribution network based on Wasserstein distance is proposed. By processing the nonlinear problems of the model, equivalent duality and model merging, the distributed robust model is solved.

[0057] (3) According to the configuration scheme, the three-phase robust carrying capacity assessment of the distributed power source of the distribution network is configured.

[0058] The specific process of step (1) is as follows:

[0059] 1) Three-phase robust load capacity analysis

[0060] In order to adapt to the large amount of three-phase imbalance caused by the uneven distribution of sources and loads, asymmetric lines, and asymmetric access of distributed power sources to the distribution network, and to actively respond to the national policy requirements on clean energy consumption to accept as much distributed power generation as possible, this application proposes the concept of three-phase robust carrying capacity of distributed power sources, which refers to the maximum grid-connected capacity of distributed power sources that can be connected to any phase at any position in the distribution network. Robustness is reflected in the ability to effectively respond to changes in access position and access phase. For single-point access, connecting distributed power sources to any phase of any node with this capacity can ensure the safe operation of the distribution network; for multi-point access, arbitrarily distributing this capacity and connecting it to multiple phases of multiple nodes can ensure the safe operation of the distribution network.

[0061] Essentially, robust carrying capacity is the smallest of the carrying capacities corresponding to various access schemes. This is because, under any other access scheme, more distributed generation resources can be supported. Connecting distributed generation resources to any location with the capacity corresponding to the robust carrying capacity can ensure the safe operation of the distribution network. In traditional distribution networks that do not consider soft switching or network reconfiguration, the robust carrying capacity is easy to calculate due to the radial network and the known actual operating topology. This is because when the line has no branches, the location corresponding to the robust carrying capacity often appears at the end. Even if the line has multiple ends due to branches, the robust carrying capacity can be calculated by calculating the carrying capacities of multiple ends and comparing them.

[0062] However, in flexible interconnected distribution networks that consider network reconfiguration, the diverse network topology possibilities and the regulatory effects of soft switches must be considered during actual operation. On the one hand, the inability to intuitively assess network reconfiguration results makes it impossible to determine the "end point." On the other hand, the regulation of active and reactive power by soft switches affects the voltage at each node during operation, making it difficult to determine the "end point." Therefore, a novel, systematic, and robust three-phase load-carrying capacity assessment method is urgently needed.

[0063] 2) Construct a three-phase robust load-carrying capacity evaluation model

[0064] In order to obtain the robust carrying capacity of the flexible interconnected distribution network considering the effects of network reconfiguration and soft switching regulation, a min-max two-layer carrying capacity evaluation model was constructed. The inner max model aims to find the network reconfiguration scheme and soft switching regulation strategy that maximizes the carrying capacity, while the outer min model aims to find the distributed generation access scheme that makes the carrying capacity most robust. The robust carrying capacity evaluation result considering the effects of soft switching and network reconfiguration is obtained. The objective function expression is:

[0065]

[0066] Where: is a 0-1 variable. When , it means that node i is connected to the distributed power supply, otherwise it means that it is not connected to the distributed power supply; is the access capacity of the distributed generation at node i; N is the number of all optional access locations; u is the number of all The set of network reconstruction schemes.

[0067] The constraints are expressed as:

[0068] (a) Power flow constraints

[0069] V j,t =V i,t -Z ij I ij,t(2)

[0070]

[0071] Where: V i,t is the three-phase voltage column vector of node i at time t; Z ij is the three-way mutual impedance matrix of line ij; I ij,t is the three-phase current vector flowing on line ij at time t; is the injected power vector of node j at time t; diag(·) represents the column vector consisting of all elements of the matrix; (·) H represents the conjugate transpose.

[0072] At the same time, substation node voltage constraints, line thermal stability limit constraints, and other node voltage constraints are considered in the power flow model.

[0073] V min ≤V i,t ≤V max (4)

[0074] 0≤I ij ≤I max (5)

[0075] V0=[1 1∠-120° 1∠120°] T (6)

[0076] Where: V min is the lower limit of node voltage; V max is the upper limit of node voltage; I max is the thermal stability limit of each phase current of line ij.

[0077] According to formula (7), the Hermitian matrix variable X is defined as ij , the original variable V to be determined i and I ij Convert to the newly defined quadratic variable W i , L ij and S ij .

[0078]

[0079] Where: V min is the lower limit of node voltage; V max is the upper limit of node voltage; I max is the thermal stability limit of each phase current of line ij.

[0080] Under the above definition, the original voltage and current vectors are replaced by the newly defined quadratic variables, and the original power flow equations (2) to (6) can be reconstructed into the following equivalent form.

[0081]

[0082] Where, is the semi-positive definite symbol of the matrix; rank[·] represents the rank of the matrix. Among them, Equation (13) is a convex constraint, and Equation (14) is a rank-1 constraint, which is a non-convex constraint. At this point, the original non-convex optimization model Equations (2) to (6) are converted into SDP optimization models with rank-1 constraints, Equations (8) to (14). Based on the convex relaxation idea, the non-convex rank-1 constraint Equation (14) is relaxed, and the remaining parts of the optimization model, Equations (8) to (13), are all convex constraints. The above SDP model can be directly solved using mature commercial solvers to obtain the SDP relaxation solution of the original model. Finally, the SDP relaxation solution is substituted into the rank-1 constraint Equation (14) to check whether it satisfies the rank-1 constraint. If the SDP relaxation solution satisfies the rank-1 constraint, it means that the SDP relaxation solution is accurate, and the solution is the optimal solution of the original model before relaxation; otherwise, it means that the solution is infeasible.

[0083] (b) Line current carrying capacity constraints

[0084]

[0085] Where: is the active power transmitted by line ij in phase φ; is the reactive power transmitted by line ij in phase φ; is the maximum complex power allowed to flow through line ij.

[0086] Equation (15) can be transformed into the following semi-positive programming form:

[0087]

[0088] (c) Soft switching power constraints

[0089]

[0090] Where: are the active powers injected by the soft switch into nodes i and j at time t respectively; are the reactive powers injected by the soft switch to node i and node j at time t; S VSC is the capacity of the soft-switching converter, which is half of the soft-switching capacity.

[0091] Equations (17) and (18) can be transformed into the following semi-positive programming form:

[0092]

[0093] (d) Network Reconfiguration Constraints

[0094] Since the total number of closed branches does not change after reconstruction, the following constraints apply:

[0095]

[0096] Where: θ ij is a constant, always equal to 1; E SW is the set of branches containing switches (including both tie switches and section switches); E is the set of all branches; E\E SW represents the set of branches without switches; γ ij,t It 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 is the total number of branches; N SW is the number of tie switches.

[0097] To ensure that the active power and reactive power on the disconnected branch line are both 0, the following constraints are introduced:

[0098] -γ ij,t M1≤P ij,t ≤γ ij,t M1 (23)

[0099] -γ ij,t M2≤Q ij,t ≤γ ij,t M2 (24) Where: M1 and M2 are large enough positive numbers, which are generally taken as the maximum value of each variable. They should not be too large, otherwise it will affect the solution efficiency.

[0100] When the branch line is disconnected, the voltages of the two nodes do not satisfy Equation (8). To ensure the universality of the constraint, Equation (8) is rewritten as:

[0101]

[0102] Where: M3 is a sufficiently large positive number, which is generally taken as the maximum value of each variable. It should not be too large, otherwise it will affect the solution efficiency.

[0103] Since the nonlinearity of the tidal current equation will cause the model to be non-convex, it is very difficult to solve it. This application constructs a dynamic reconstruction model by simplifying the three-phase tidal current model into a linear three-phase linear tidal current equation. This constraint can be expressed as:

[0104]

[0105] 3) Construct a three-phase robust load capacity assessment model considering the uncertainty of distributed power output

[0106] (a) Constructing Wasserstein distance fuzzy sets

[0107] Wasserstein distance is a modeling method of fuzzy sets in distributed robust optimization. It is used to describe the distance between the true distribution and the reference distribution and is suitable for processing the uncertainty of distributed power output. Therefore, this application establishes a probability distribution fuzzy set based on Wasserstein distance to describe the uncertainty of distributed power output.

[0108] Define a set of random variables It is the historical observation data of distributed power generation (L is the total number of samples), constructed using the Dirac function The reference distribution of

[0109]

[0110] Where: for The Dirac function of is the lth historical observation data of the distributed power supply.

[0111] The reference distribution can be regarded as an estimate of the true distribution. In order to express the gap between the true distribution and the reference distribution, the Wasserstein distance is introduced as follows:

[0112]

[0113] Where: d w (·) represents the Wasserstein distance; ξ is the true distribution P * The set of random variables; Denotes dξ and The joint probability distribution of ||·|| means to find the norm, here we use the first-order norm; dξ and are ξ and The differential of Ω 2 represents the support space of random variables; inf{·} represents the infimum function.

[0114] Based on the above definition, a reference distribution is constructed As the center, with ε DG Is the Wasserstein sphere with radius, that is:

[0115]

[0116] Where: Γ represents support P * All value spaces of .

[0117] The above formula indicates that the distance between the actual distribution of distributed power output and the reference distribution is no greater than the radius of the sphere.

[0118] Wasserstein sphere radius ε DG is the sample size L and the confidence level α DG The function of , the relationship can be expressed as follows:

[0119]

[0120]

[0121] Where: P(·) is the probability function; S is Ω 2 The diameter of Ω 2 The maximum Wasserstein distance between any pair of elements in .

[0122]

[0123] Where: is the sample mean; μ is a variable greater than 0.

[0124] (b) Construct a three-phase distributed power supply three-phase load capacity assessment model taking into account uncertainty

[0125] Based on the established deterministic robust carrying capacity assessment model, taking into account the impact of distributed power output uncertainty on the accuracy of the assessment results, a min-max-min three-layer carrying capacity assessment model is further established. The added inner min model uses the distributed power output deviation as the decision variable, aiming to find the worst scenario in the fuzzy set.

[0126] The objective function taking uncertainty into account is expressed as:

[0127] After considering the uncertainty of distributed generation output and the role of soft switching regulation, the objective function becomes the following form:

[0128]

[0129] Where: Δξ is the set of deviations between the actual distribution of distributed generation output and the reference distribution.

[0130] The constraints taking into account uncertainty are expressed as:

[0131] Since the three-layer model is built on the basis of the two-layer model, the various constraints considered in the two-layer model also need to be considered in the three-layer model, including equations (8) to (14), (16), and (19) to (31).

[0132] In addition, since the three-layer model uses fuzzy sets based on Wasserstein distance to deal with uncertainty, the actual distribution and reference distribution of distributed generation output must satisfy the Wasserstein distance constraint, that is, the constraints shown in Equations (15) to (17).

[0133] At the same time, the output of distributed power generation meets the following constraints:

[0134]

[0135] Where: is the actual output of the distributed generation at node j at time t when sample l is used as a reference; l,t is the actual output coefficient of the distributed generation at time t when sample l is used as a reference; is the output coefficient of sample l at time t; Δξ l,t is the deviation between the actual output coefficient of the distributed generation and the sample output coefficient at time t when sample l is used as a reference.

[0136] In summary, equations (8) to (14), (16), (19) to (31), and (35) to (37) together constitute a distributed generation capacity assessment model that takes uncertainty into account.

[0137] The specific process of step (2) is as follows:

[0138] 1) Propose a conversion method for the min-max-min three-layer load-bearing capacity assessment model based on Wasserstein distance

[0139] (a) Wasserstein distance constraint transformation

[0140] The Wasserstein distance constraints in equations (35) to (37) are transformed into the following form:

[0141]

[0142] Where: L is the number of historical samples of distributed power output; Δξ l,t is the deviation between the actual output coefficient of the distributed generation with sample l as the reference at time t and the sample output coefficient.

[0143] (b) Inner Function Duality

[0144] Before the duality, the uncertainty variable Δξ l,t The relevant objective functions and constraints are organized into the following form:

[0145]

[0146] Where: F is the coefficient matrix of the objective function; J1 is the coefficient matrix 1 of the equality constraint, and the result obtained by multiplying it with the vector x composed of the decision variables does not include the variable of distributed power access capacity; J2 is the coefficient matrix 2 of the equality constraint, and the result obtained by multiplying it with the vector x composed of the decision variables only includes the variable of distributed power access capacity; J3 is the constant term matrix of the equality constraint; G1 is the coefficient matrix 1 of the inequality constraint, and the result obtained by multiplying it with the vector x composed of the decision variables does not include the variable of distributed power access capacity; G2 is the coefficient matrix 2 of the equality constraint, and the result obtained by multiplying it with the vector x composed of the decision variables only includes the variable of distributed power access capacity; G3 is the constant term matrix of the inequality constraint; π l,t contains both decision variables x and uncertain variables Δξ l,t The Lagrange dual multiplier vector of the equality constraints; μ l,t contains both decision variables x and uncertain variables Δξ l,t The Lagrange dual multiplier vector of the inequality constraints; χ t is the Lagrange dual multiplier of the Wasserstein constraint.

[0147] First, construct the Lagrangian function of the optimization problem. The specific form is as follows:

[0148]

[0149] Since it neutralizes Δξ l,t The relevant formula contains absolute values. To eliminate the absolute values, according to Δξ l,t The relationship between Δξ and 0 is as follows: l,t Divided into two categories, then and Δξ l,t The relevant formula becomes the following form:

[0150]

[0151] To seek Demand and Δξ l,t The minimum value of the relevant terms, that is:

[0152]

[0153] Due to χ t , Are greater than 0, then The second term in the above formula is l,t =0 takes the minimum value 0. In order to make the first term of the above formula in Δξ l,t =0 to get the minimum value, which needs to be satisfied Right now At this time, the value of the first item is 0.

[0154] So the dual function is:

[0155]

[0156] The dual problem becomes:

[0157]

[0158] Since the inner layer becomes a max problem, the inner layer can be merged with the middle layer into one max problem.

[0159] (c) McCormick relaxation

[0160] Through the above transformation, the middle layer and the inner layer have been merged into a max problem, but at this time π l,t T x, μ l,t T Since there are nonlinear terms such as x, McCormick relaxation is used to transform them. After transformation, the objective function and constraints are both in linear form. The transformation formula of McCormick relaxation is as follows:

[0161]

[0162] Where: z and y are two variables before transformation; w is the variable after transformation.

[0163] 2) Propose a solution method for the min-max-min three-layer load-bearing capacity assessment model based on Wasserstein distance

[0164] (a) Transformation of the distributed robust model

[0165] This application proposes a two-stage distributed blue-rod model. The first stage is to determine the access location of the distributed power source. In this stage, the access location, access phase and capacity of the distributed power source are selected. The second stage is to determine the operation strategy, including the operation strategy of the soft switch and network reconstruction. In this paper, the variables of the first stage are represented by vector c, including the access location, access phase and capacity of the distributed power source. The variables of the second stage, including the variables such as the transmission active power and reactive power of the soft switch and the network reconstruction strategy, are represented by vector d. The distributed blue-rod model is shown in Equation (46).

[0166]

[0167] Where: δ is the predicted value deviation of the distributed power output; P(δ) represents the distribution function of the parameter with uncertainty; ψ is the set of distribution functions; Gc is the access capacity of the first stage; E p [Hd+Kδ] is the mathematical expectation of the maximum carrying capacity under the optimal control scheme in the second stage.

[0168] In order to obtain a probability distribution that meets the conditions, a distributed robustness model based on historical data is further established, as shown in Equation (47).

[0169]

[0170] Where: The maximum term is to obtain the actual probability distribution p of each discrete scene s , and maximize the objective function in the second stage.

[0171] (b) Solution of the distributed blue stick model

[0172] The CCG algorithm is commonly used to solve distributed robust models designed for optimization. This algorithm decomposes the model into a main problem (MP) and subproblems (SP), then iterates to find the solution. MP aims to obtain an optimal allocation plan that satisfies the constraints of a known probability distribution under finite extreme conditions. The objective function of MP is as follows.

[0173]

[0174] Where r is the number of iterations.

[0175] SP aims to obtain a solution based on the optimization result c* of MP and find the worst probability distribution p s Then, the optimization results of SP will be provided to MP for further iterative calculation. The objective function of SP is shown below.

[0176]

[0177] Since the linear transformation has been performed in the previous step, the objective function (45) of SP is a linear function. SP is a MILP problem and can be solved quickly using a commercial solver. The optimal result of SP is p s Will be passed to MP for iterative calculation.

[0178] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0179] An embodiment of the present invention provides a three-phase robust carrying capacity assessment and configuration device for distributed power sources in a distribution network, comprising: a data acquisition module, an assessment model construction module, a configuration solution solution module, and a three-phase robust carrying capacity assessment and configuration module for distributed power sources in a distribution network;

[0180] The data acquisition module is used to acquire basic data of the distribution network; wherein the basic data includes: active load demand and reactive load demand of each node in the distribution network, impedance of each branch line, and historical output data of distributed power sources;

[0181] The evaluation model construction module is used to construct a three-phase robust carrying capacity evaluation model for characterizing the distributed power generation in the distribution network; wherein the constraints corresponding to the planning model include: power flow constraints, line current carrying capacity constraints, soft switching power constraints, and network reconfiguration constraints;

[0182] The configuration solution solving module is used to solve the evaluation model under various constraints to generate a corresponding three-phase robust access configuration solution for distributed power sources under a network reconstruction solution and soft switch control strategy that maximizes the carrying capacity; wherein the configuration solution includes: a network reconstruction switch action strategy, a soft switch control strategy, and the three-phase robust access capacity, access phase, and access position of the distributed power sources;

[0183] The distribution network distributed power three-phase robust carrying capacity evaluation configuration module is used to configure the distribution network distributed power three-phase robust carrying capacity evaluation according to the configuration scheme.

[0184] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0185] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network as described in the above-mentioned embodiment of the invention.

[0186] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment.

[0187] Another embodiment of the present invention provides a storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a method for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network as described in the above-mentioned embodiment of the invention.

[0188] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0189] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of code, and the part of the module, program segment or code comprises one or more executable instructions for realizing the logical function of the specification. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the function or action of the specification, or can be implemented by a combination of dedicated hardware and computer instructions.

[0190] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for evaluating the robust three-phase load capacity of distributed power sources in a distribution network, characterized by: The following steps are involved: (1) Constructing a three-phase robust carrying capacity evaluation model for distributed power sources in a distribution network, wherein the constraints corresponding to the three-phase robust carrying capacity evaluation model for distributed power sources in the distribution network include: power flow constraints, line current carrying capacity constraints, soft switching power constraints, and network reconfiguration constraints; A Wasserstein distance-based evaluation model for the robust three-phase load-carrying capacity of distributed power sources in distribution networks is constructed to accurately calculate the robust three-phase load-carrying capacity of distributed power sources in distribution networks. (2) Under various constraints, a corresponding three-phase robust access configuration scheme for distributed power sources is generated under a network reconstruction scheme and a soft switch control strategy that maximizes the carrying capacity. The three-phase robust access configuration scheme for distributed power sources includes: a network reconstruction switch action strategy, a soft switch control strategy, and the three-phase robust access capacity, access phase, and access position of the distributed power sources; The three-phase robust carrying capacity evaluation model of distributed power sources in the distribution network is solved, and a solution method for the three-phase robust carrying capacity evaluation model of distributed power sources in the distribution network based on Wasserstein distance is proposed. By dealing with model nonlinear problems, equivalent duality and model merging, the distributed robust model is solved; (3) According to the configuration scheme, the three-phase robust carrying capacity assessment of the distributed power source of the distribution network is configured.

2. A method for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network according to claim 1, characterized in that: The distribution network distributed power three-phase robust carrying capacity assessment model calculates the scheme with the smallest carrying capacity among the three-phase robust access configuration schemes of each distributed power source, and can effectively cope with changes in the access phase and access location of the distributed power source. For the scenario of single-point access of the distributed power source, the capacity can be used to access any node and any phase of the distribution network to ensure the safe operation of the distribution network. For the scenario of multi-point access of the distributed power source, the capacity can be arbitrarily distributed and connected to multiple nodes and multiple phases to ensure the safe operation of the distribution network.

3. A method for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network according to claim 2, characterized in that: Specifically, a three-phase robust carrying capacity solution model for distributed power sources in distribution networks is constructed considering the regulatory effects of network reconstruction and soft switching.

4. A method for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network according to claim 3, characterized in that: The three-phase robust carrying capacity assessment model for distributed power sources in the distribution network is constructed as a min-max two-layer carrying capacity assessment model. The inner max model aims to find the network reconstruction scheme and soft switching control strategy that maximizes the carrying capacity, while the outer min model aims to find the distributed power access scheme that makes the carrying capacity most robust, thereby obtaining a robust carrying capacity assessment result that takes into account the impact of soft switching control and network reconstruction. The specific expression of the objective function of the optimization model is: Where: is a 0-1 variable. When , it means that node i is connected to the distributed power supply, otherwise it means that it is not connected to the distributed power supply; is the access capacity of the distributed generation at node i; N is the number of all optional access locations; are all optional access phases, u is all The set of network reconstruction schemes.

5. A method for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network according to claim 4, characterized in that: The min-max two-layer load-carrying capacity evaluation model introduces Wasserstein distance to describe the uncertainty characteristics of distributed power output, and is further transformed into a min-max-min three-layer load-carrying capacity evaluation model. The following steps are involved: The specific expression of the objective function of the optimization model after taking into account the uncertainty of distributed generation output is: Where: Δξ is the set of deviations between the actual distribution of distributed generation output and the reference distribution; is a 0-1 variable. When , it means that node i is connected to the distributed power supply, otherwise it means that it is not connected to the distributed power supply; is the access capacity of the distributed generation at node i; N is the number of all optional access locations; are all optional access phases, u is all The set of network reconstruction schemes.

6. A method for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network according to claim 1, characterized in that: The method for solving the three-phase robust load-carrying capacity evaluation model of the distributed power supply in the distribution network in step (2) specifically includes the following steps: (1) Convert the Wasserstein distance constraint into the following form: Where: L is the number of historical samples of distributed power output; Δξ l,t is the deviation between the actual output coefficient of the distributed generation with sample l as reference at time t and the sample output coefficient; Before the duality, the uncertainty variable Δξ l,t The relevant objective functions and constraints are organized into the following form: Where: F is the coefficient matrix of the objective function; J1 is the coefficient matrix 1 of the equality constraint, and the result obtained by multiplying it with the vector x composed of the decision variables does not include the variable of distributed power access capacity; J2 is the coefficient matrix 2 of the equality constraint, and the result obtained by multiplying it with the vector x composed of the decision variables only includes the variable of distributed power access capacity; J3 is the constant term matrix of the equality constraint; G1 is the coefficient matrix 1 of the inequality constraint, and the result obtained by multiplying it with the vector x composed of the decision variables does not include the variable of distributed power access capacity; G2 is the coefficient matrix 2 of the equality constraint, and the result obtained by multiplying it with the vector x composed of the decision variables only includes the variable of distributed power access capacity; G3 is the constant term matrix of the inequality constraint; π l,t contains both decision variables x and uncertain variables Δξ l,t The Lagrange dual multiplier vector of the equality constraints; μ l,t contains both decision variables x and uncertain variables Δξ l,t The Lagrange dual multiplier vector of the inequality constraints; χ t is the Lagrange dual multiplier of the Wasserstein constraint; (2) Construct the Lagrangian function of the optimization problem, which is as follows: To seek Demand and Δξ l,t The minimum value of the relevant terms, that is: Due to χ t , Are greater than 0, then The second term in the above formula is l,t =0 takes the minimum value 0, so that the first term of the above formula is in Δξ l,t =0 to get the minimum value, which needs to be satisfied Right now At this time, the value of the first item is 0; (3) Construct the dual problem, which is as follows: (4) McCormick relaxation Through the above transformation, the middle layer and the inner layer have been merged into a max problem, but at this time π l,t T x, μ l,t T Since there are nonlinear terms such as x, McCormick relaxation is used to transform them. The objective function and constraints after transformation are both in linear form. The transformation formula of McCormick relaxation is as follows: In the formula: z and y are two variables before transformation; w is the variable after transformation; Since linear transformation has been performed in the previous step, the above model is a mixed integer linear programming problem, which can be quickly solved using a commercial solver.

7. A three-phase robust carrying capacity assessment device for distributed power supply in a distribution network, characterized in that: include: Data acquisition module, evaluation model construction module, configuration solution module, and distribution network distributed power three-phase robust carrying capacity evaluation configuration module; The data acquisition module is used to acquire basic data of the distribution network; wherein the basic data includes: active load demand and reactive load demand of each node in the distribution network, impedance of each branch line, and historical output data of distributed power sources; The evaluation model construction module is used to construct a three-phase robust carrying capacity evaluation model for characterizing the distributed power generation in the distribution network; wherein the constraints corresponding to the planning model include: power flow constraints, line current carrying capacity constraints, soft switching power constraints, and network reconfiguration constraints; The configuration solution solving module is used to solve the evaluation model under various constraints to generate a corresponding three-phase robust access configuration solution for distributed power sources under a network reconstruction solution and soft switch control strategy that maximizes the carrying capacity; wherein the configuration solution includes: a network reconstruction switch action strategy, a soft switch control strategy, and the three-phase robust access capacity, access phase, and access position of the distributed power sources; The distribution network distributed power three-phase robust carrying capacity evaluation configuration module is used to configure the distribution network distributed power three-phase robust carrying capacity evaluation according to the configuration scheme.

8. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network as described in any one of claims 1 to 6 is implemented.

9. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method for evaluating the three-phase robust carrying capacity of distributed power sources in a distribution network according to any one of claims 1 to 6.

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