A distribution network reconstruction method and device based on operation uncertainty

Abstract

The present invention discloses a distribution network reconstruction method and device based on operation uncertainty. An interval uncertainty set is constructed according to the uncertainty of distribution network operation. A linearized power flow constraint is established for the distribution network, and a maximum slack variable is used to replace the nonlinear power flow over-limit of the distribution network, so that the original nonlinear problem can be converted into a linear problem. Minimization of the over-limit frequency and degree of distribution network state variables is used as an objective function, and the objective function is solved in combination with the interval uncertainty set and the linearized power flow constraint to reconstruct the distribution network with the minimum risk. Therefore, the influence of the uncertainty of distribution network operation on the network reconstruction model can be considered, which is conducive to the rapid network reconstruction of the distribution network and is suitable for application in the scenario of distribution network network reconstruction.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation optimization, and in particular to a distribution network reconstruction method and device based on operation uncertainty. Background Art

[0002] The distribution network is an important part of the power system, and the network reconstruction of the distribution network is an effective way to improve the safety of the distribution system. Although network reconstruction can improve the economy and safety of the distribution network, the nonlinearity of the distribution network flow equation and the discrete variables introduced by the controllable switches make the network reconstruction of the distribution network a mixed and integer nonlinear programming problem that is difficult to solve.

[0003] In addition, the proportion of renewable energy in the distribution network increases year by year. In the distribution network reconstruction model, it is also necessary to consider the impact of renewable energy output uncertainty on the optimization results, which further complicates the network reconstruction problem.

[0004] Many existing works use heuristic methods to solve the network reconstruction problem. However, heuristic methods may be time-consuming and can only obtain a local optimal solution in most cases.

[0005] How to establish linear power flow constraints, how to describe the uncertainty of renewable energy output, and how to construct a robust power grid network reconstruction method are issues that need to be urgently addressed in the field of power system operation optimization technology. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a distribution network network reconstruction method and device based on operation uncertainty, which can consider the impact of distribution network operation uncertainty on the network reconstruction model and facilitate rapid network reconstruction of the distribution network.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0008] A distribution network reconstruction method based on operation uncertainty comprises the following steps:

[0009] Construct interval uncertainty sets based on the uncertainty of distribution network operation;

[0010] Establish linear power flow constraints for the distribution network, and use the maximum slack variable to replace the nonlinear power flow limit of the distribution network;

[0011] Taking the minimization of the frequency and degree of the distribution network state variables exceeding the limit as the objective function, the objective function is solved by combining the interval uncertainty set and the linearized power flow constraint to reconstruct the distribution network with the minimum risk.

[0012] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0013] A distribution network reconstruction device based on operation uncertainty, comprising:

[0014] An uncertainty set building module is used to construct an interval uncertainty set according to the uncertainty of distribution network operation;

[0015] The distribution network constraint establishment module is used to establish linearized power flow constraints for the distribution network and use the maximum slack variable to replace the nonlinear power flow limit of the distribution network;

[0016] The distribution network reconstruction module is used to take the minimization of the frequency and degree of the distribution network state variable exceeding the limit as the objective function, combine the interval uncertainty set and the linearized power flow constraint to solve the objective function, and reconstruct the distribution network with the minimum risk.

[0017] Furthermore, the uncertainty set building module is used to construct an interval uncertainty set according to the uncertainty of the distribution network operation, including:

[0018] The output uncertainty of renewable energy in the distribution network is constructed as an interval uncertainty set:

[0019]

[0020] Where P RE represents the new energy injection vector, and They represent the lower and upper limits of the uncertainty of the i-th renewable energy output interval, Φ RE Represents the set of nodes connected to new energy sources.

[0021] Furthermore, the distribution network constraint establishing module is further used for:

[0022] A linearized power flow constraint without considering line network loss is established, and the conditional constraints of active power flow and reactive power flow in the linearized power flow constraint are converted into inequality constraints.

[0023] Furthermore, the distribution network constraint establishing module is further used for:

[0024] Establish voltage safety constraints and branch power flow thermal constraints:

[0025]

[0026]

[0027] Where V l Indicates the lower limit of voltage safety operation; V h Indicates the upper limit of voltage safety operation; represents the safe operating apparent power of branch (i, j), i.e., thermal constraint;

[0028] Introducing slack variables to the voltage safety constraint;

[0029] The branch power flow thermal constraint is abstracted into a circular constraint, and after the abstracted circular constraint is converted into a plurality of square constraints, a slack variable is introduced to represent the degree of power flow exceeding the limit at the operating point.

[0030] Furthermore, the distribution network constraint establishing module is further used for:

[0031] Two slack variables are introduced for voltage safety constraints:

[0032]

[0033]

[0034] In the formula, and The slack variables represent the voltage upper and lower limits, respectively, and indicate the degree to which the node voltage deviates from the safe operating range.

[0035] Furthermore, the distribution network constraint establishing module is further used for:

[0036] The branch power flow thermal constraint is abstracted into a circle constraint, the abstract circle constraint is converted into four square constraints, and a slack variable is introduced to indicate the degree of power flow exceeding the limit at the operating point:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] In the formula, represents the power flow operation point (P ij ,Q ij ), represents the maximum value of all slack variables.

[0048] Furthermore, the distribution network reconstruction module is further used to:

[0049] Establish node injection power constraints, the relationship between new energy reactive power and active power, substation power constraints, distribution network radial topology constraints, and reference node constraints.

[0050] Furthermore, the distribution network network reconstruction module is used to minimize the frequency and degree of distribution network state variable crossing as the objective function, including:

[0051]

[0052] In the formula, c v represents the cost coefficient of node voltage exceeding the limit, c S Indicates the cost coefficient of line flow exceeding the limit.

[0053] Furthermore, the distribution network reconstruction module is used to solve the objective function by combining the interval uncertainty set and the linearized power flow constraint, and reconstruct the distribution network with the minimum risk, including:

[0054] Combining the interval uncertainty set, linearized power flow constraints, node injection power constraints, the relationship between reactive and active power of renewable energy and power constraints of substations, radial topology constraints of distribution networks and constraints of reference nodes, a robust network reconstruction model is obtained.

[0055] By solving the robust network reconstruction model, a network topology with the minimum robust risk of the distribution network is obtained.

[0056] The beneficial effects of the present invention are as follows: constructing an interval uncertainty set according to the uncertainty of distribution network operation; establishing a linearized power flow constraint for the distribution network, and using a maximum slack variable to replace the nonlinear power flow over-limit of the distribution network, so as to convert the original nonlinear problem into a linear problem; minimizing the frequency and degree of over-limit of distribution network state variables as the objective function, combining the interval uncertainty set and the linearized power flow constraint to solve the objective function, and reconstructing the distribution network with the minimum risk; therefore, the influence of the uncertainty of distribution network operation on the network reconstruction model can be considered, which is conducive to the rapid network reconstruction of the distribution network, and is suitable for application in the scenario of distribution network reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flow chart of a distribution network reconstruction method based on operation uncertainty according to an embodiment of the present invention;

[0058] Figure 2A schematic diagram of a distribution network reconstruction device based on operation uncertainty according to an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of a linearized power flow thermal constraint method according to an embodiment of the present invention;

[0060] Figure 4 The figure is a schematic diagram of a method for setting slack variables taking the first quadrant as an example according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in conjunction with the implementation modes and the accompanying drawings.

[0062] Please refer to Figure 1 The embodiment of the present invention provides a distribution network reconstruction method based on operation uncertainty, comprising the steps of:

[0063] Construct interval uncertainty sets based on the uncertainty of distribution network operation;

[0064] Establish linear power flow constraints for the distribution network, and use the maximum slack variable to replace the nonlinear power flow limit of the distribution network;

[0065] Taking the minimization of the frequency and degree of the distribution network state variables exceeding the limit as the objective function, the objective function is solved by combining the interval uncertainty set and the linearized power flow constraint to reconstruct the distribution network with the minimum risk.

[0066] From the above description, it can be seen that the beneficial effects of the present invention are: constructing an interval uncertainty set according to the uncertainty of distribution network operation; establishing linearized power flow constraints for the distribution network, and using the maximum slack variable to replace the nonlinear power flow over-limit of the distribution network, which can convert the original nonlinear problem into a linear problem; minimizing the frequency and degree of over-limit of distribution network state variables as the objective function, combining the interval uncertainty set and the linearized power flow constraints to solve the objective function, and reconstructing the distribution network with the least risk; therefore, the influence of the uncertainty of distribution network operation on the network reconstruction model can be considered, which is conducive to the rapid network reconstruction of the distribution network, and is suitable for application in the scenario of distribution network reconstruction.

[0067] Further, constructing an interval uncertainty set according to the uncertainty of distribution network operation includes:

[0068] The output uncertainty of renewable energy in the distribution network is constructed as an interval uncertainty set:

[0069]

[0070] Where P RE represents the new energy injection vector, and They represent the lower and upper limits of the uncertainty of the i-th renewable energy output interval, Φ RE Represents the set of nodes connected to new energy sources.

[0071] From the above description, it can be seen that by establishing the uncertainty set, the output of new energy can take any value in the interval, and for any new energy output in this interval, any other constraints in the subsequently established network reconstruction model should be satisfied.

[0072] Furthermore, establishing linearized power flow constraints on the distribution network includes:

[0073] A linearized power flow constraint without considering line network loss is established, and the conditional constraints of active power flow and reactive power flow in the linearized power flow constraint are converted into inequality constraints.

[0074] From the above description, it can be seen that a linearized power flow constraint is established, and the conditional constraints of active power flow and reactive power flow in the linearized power flow constraint are converted into inequality constraints, which facilitates solving the constraints.

[0075] Furthermore, the use of the maximum slack variable to replace the nonlinear power flow exceeding limit of the distribution network includes:

[0076] Establish voltage safety constraints and branch power flow thermal constraints:

[0077]

[0078]

[0079] Where V l Indicates the lower limit of voltage safety operation; V h Indicates the upper limit of voltage safety operation; represents the safe operating apparent power of branch (i, j), i.e., thermal constraint;

[0080] Introducing slack variables to the voltage safety constraint;

[0081] The branch power flow thermal constraint is abstracted into a circular constraint, and after the abstracted circular constraint is converted into a plurality of square constraints, a slack variable is introduced to represent the degree of power flow exceeding the limit at the operating point.

[0082] From the above description, it can be seen that using the maximum relaxation variable instead of the original nonlinear power flow limiter makes it easier to transform the original nonlinear problem into a linear problem.

[0083] Furthermore, introducing slack variables into the voltage safety constraint includes:

[0084] Two slack variables are introduced for voltage safety constraints:

[0085]

[0086]

[0087] In the formula, and The slack variables represent the voltage upper and lower limits, respectively, and indicate the degree to which the node voltage deviates from the safe operating range.

[0088] From the above description, it can be seen that since the distribution network is prone to not meeting the safety constraints during operation, and the goal of subsequent network reconstruction optimization is to quantify and minimize the degree and frequency of node voltage and branch power flow exceeding the limit, the introduction of slack variables facilitates the calculation of voltage safety constraints.

[0089] Furthermore, the branch power flow thermal constraint is abstracted into a circular constraint, and after the abstracted circular constraint is converted into a plurality of square constraints, a slack variable is introduced to indicate the degree of power flow exceeding the limit at the operating point, including:

[0090] The branch power flow thermal constraint is abstracted into a circle constraint, the abstract circle constraint is converted into four square constraints, and a slack variable is introduced to indicate the degree of power flow exceeding the limit at the operating point:

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] In the formula, represents the power flow operation point (P ij ,Q ij ), represents the maximum value of all slack variables.

[0102] From the above description, it can be seen that the branch power flow thermal constraint is a circular constraint, that is, a nonlinear constraint. Therefore, multiple square constraints are used to approximate the circular constraint, and then a slack variable is added to represent the degree of power flow exceeding the limit at the operating point, so that the branch power flow thermal constraint can be converted into a linear constraint.

[0103] Furthermore, the method of minimizing the frequency and degree of the distribution network state variable crossing the limit as the objective function includes:

[0104] Establish node injection power constraints, the relationship between new energy reactive power and active power, substation power constraints, distribution network radial topology constraints, and reference node constraints.

[0105] From the above description, it can be seen that the establishment of node injection power constraints, the relationship between new energy reactive and active power, substation power constraints, distribution network radial topology constraints and reference node constraints can ensure the integrity of the subsequent network reconstruction model.

[0106] Furthermore, the objective function of minimizing the frequency and degree of the distribution network state variable crossing the limit includes:

[0107]

[0108] In the formula, c v represents the cost coefficient of node voltage exceeding the limit, c S Indicates the cost coefficient of line flow exceeding the limit.

[0109] From the above description, it can be seen that by minimizing the frequency and degree of distribution network state variables exceeding the limit as the objective function, it is easy to find the optimal network reconstruction scheme with the smallest total slack variables of voltage and line flow in the subsequent process.

[0110] Further, solving the objective function by combining the interval uncertainty set and the linearized power flow constraint to reconstruct the distribution network with the minimum risk includes:

[0111] Combining the interval uncertainty set, linearized power flow constraints, node injection power constraints, the relationship between reactive and active power of renewable energy and power constraints of substations, radial topology constraints of distribution networks and constraints of reference nodes, a robust network reconstruction model is obtained.

[0112] By solving the robust network reconstruction model, a network topology with the minimum robust risk of the distribution network is obtained.

[0113] From the above description, it can be seen that by combining the uncertainty set of new energy intervals and the above constraints, a robust network reconstruction model can be obtained. By solving the robust network reconstruction model, the network topology with the minimum robust risk of the distribution network can be obtained.

[0114] Please refer to Figure 2Another embodiment of the present invention provides a distribution network reconstruction device based on operation uncertainty, comprising:

[0115] A distribution network reconstruction device based on operation uncertainty, comprising:

[0116] An uncertainty set building module is used to construct an interval uncertainty set according to the uncertainty of distribution network operation;

[0117] The distribution network constraint establishment module is used to establish linearized power flow constraints for the distribution network and use the maximum slack variable to replace the nonlinear power flow limit of the distribution network;

[0118] The distribution network reconstruction module is used to take the minimization of the frequency and degree of the distribution network state variable exceeding the limit as the objective function, and reconstruct the distribution network with the minimum risk by solving the objective function.

[0119] From the above description, it can be seen that an interval uncertainty set is constructed according to the uncertainty of distribution network operation; a linearized power flow constraint is established for the distribution network, and the maximum slack variable is used to replace the nonlinear power flow over-limit of the distribution network, which can transform the original nonlinear problem into a linear problem; the minimization of the frequency and degree of over-limit of the distribution network state variables is used as the objective function, and the objective function is solved by combining the interval uncertainty set and the linearized power flow constraint to reconstruct the distribution network with the least risk; therefore, the impact of the uncertainty of distribution network operation on the network reconstruction model can be considered, which is conducive to the rapid network reconstruction of the distribution network and is suitable for application in the scenario of distribution network reconstruction.

[0120] Furthermore, the uncertainty set building module is used to construct an interval uncertainty set according to the uncertainty of the distribution network operation, including:

[0121] The output uncertainty of renewable energy in the distribution network is constructed as an interval uncertainty set:

[0122]

[0123] Where P RE represents the new energy injection vector, and They represent the lower and upper limits of the uncertainty of the i-th renewable energy output interval, Φ RE Represents the set of nodes connected to new energy sources.

[0124] From the above description, it can be seen that by establishing the uncertainty set, the output of new energy can take any value in the interval, and for any new energy output in this interval, any other constraints in the subsequently established network reconstruction model should be satisfied.

[0125] Furthermore, the distribution network constraint establishing module is further used for:

[0126] A linearized power flow constraint without considering line network loss is established, and the conditional constraints of active power flow and reactive power flow in the linearized power flow constraint are converted into inequality constraints.

[0127] From the above description, it can be seen that a linearized power flow constraint is established, and the conditional constraints of active power flow and reactive power flow in the linearized power flow constraint are converted into inequality constraints, which facilitates solving the constraints.

[0128] Furthermore, the distribution network constraint establishing module is further used for:

[0129] Establish voltage safety constraints and branch power flow thermal constraints:

[0130]

[0131]

[0132] Where V l Indicates the lower limit of voltage safety operation; V h Indicates the upper limit of voltage safety operation; represents the safe operating apparent power of branch (i, j), i.e., thermal constraint;

[0133] Introducing slack variables to the voltage safety constraint;

[0134] The branch power flow thermal constraint is abstracted into a circular constraint, and after the abstracted circular constraint is converted into a plurality of square constraints, a slack variable is introduced to represent the degree of power flow exceeding the limit at the operating point.

[0135] From the above description, it can be seen that using the maximum relaxation variable instead of the original nonlinear power flow limiter makes it easier to transform the original nonlinear problem into a linear problem.

[0136] Furthermore, the distribution network constraint establishing module is further used for:

[0137] Two slack variables are introduced for voltage safety constraints:

[0138]

[0139]

[0140] In the formula, and The slack variables represent the voltage upper and lower limits, respectively, and indicate the degree to which the node voltage deviates from the safe operating range.

[0141] From the above description, it can be seen that since the distribution network is prone to not meeting the safety constraints during operation, and the goal of subsequent network reconstruction optimization is to quantify and minimize the degree and frequency of node voltage and branch power flow exceeding the limit, the introduction of slack variables facilitates the calculation of voltage safety constraints.

[0142] Furthermore, the distribution network constraint establishing module is further used for:

[0143] The branch power flow thermal constraint is abstracted into a circle constraint, the abstract circle constraint is converted into four square constraints, and a slack variable is introduced to indicate the degree of power flow exceeding the limit at the operating point:

[0144]

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154] In the formula, represents the power flow operation point (P ij ,Q ij ), represents the maximum value of all slack variables.

[0155] From the above description, it can be seen that the branch power flow thermal constraint is a circular constraint, that is, a nonlinear constraint. Therefore, multiple square constraints are used to approximate the circular constraint, and then a slack variable is added to represent the degree of power flow exceeding the limit at the operating point, so that the branch power flow thermal constraint can be converted into a linear constraint.

[0156] Furthermore, the distribution network reconstruction module is further used to:

[0157] Establish node injection power constraints, the relationship between new energy reactive power and active power, substation power constraints, distribution network radial topology constraints, and reference node constraints.

[0158] From the above description, it can be seen that the establishment of node injection power constraints, the relationship between new energy reactive and active power, substation power constraints, distribution network radial topology constraints and reference node constraints can ensure the integrity of the subsequent network reconstruction model.

[0159] Furthermore, the distribution network network reconstruction module is used to minimize the frequency and degree of distribution network state variable crossing as the objective function, including:

[0160]

[0161] In the formula, c v represents the cost coefficient of node voltage exceeding the limit, c S Indicates the cost coefficient of line flow exceeding the limit.

[0162] From the above description, it can be seen that by minimizing the frequency and degree of distribution network state variables exceeding the limit as the objective function, it is easy to find the optimal network reconstruction scheme with the smallest total slack variables of voltage and line flow in the subsequent process.

[0163] Furthermore, the distribution network reconstruction module is used to solve the objective function by combining the interval uncertainty set and the linearized power flow constraint, and reconstruct the distribution network with the minimum risk, including:

[0164] Combining the interval uncertainty set, linearized power flow constraints, node injection power constraints, the relationship between reactive and active power of renewable energy and power constraints of substations, radial topology constraints of distribution networks and constraints of reference nodes, a robust network reconstruction model is obtained.

[0165] By solving the robust network reconstruction model, a network topology with the minimum robust risk of the distribution network is obtained.

[0166] From the above description, it can be seen that by combining the uncertainty set of new energy intervals and the above constraints, a robust network reconstruction model can be obtained. By solving the robust network reconstruction model, the network topology with the minimum robust risk of the distribution network can be obtained.

[0167] The above-mentioned distribution network network reconstruction method and device based on operation uncertainty of the present invention are suitable for the scenario of distribution network network reconstruction, can consider the influence of distribution network operation uncertainty on the network reconstruction model, and are conducive to the rapid network reconstruction of the distribution network. The following is an explanation through specific implementation methods:

[0168] Embodiment 1

[0169] Please refer to Figure 1 , a distribution network reconstruction method based on operation uncertainty, comprising the steps of:

[0170] S1. Construct an interval uncertainty set based on the uncertainty of distribution network operation.

[0171] Specifically, since the output of renewable energy is uncertain, it is necessary to describe this uncertainty in the establishment of the network reconstruction model in order to consider the impact of uncertainty on the results. The robust network reconstruction model can consider the impact of uncertainty and has a low requirement on the amount of input data.

[0172] Therefore, a robust approach is used and an uncertainty set is constructed.

[0173] The uncertainty of new energy output is constructed as an interval uncertainty set:

[0174]

[0175] Where P RE represents the new energy injection vector, and represents the lower and upper limits of the uncertainty of the i-th renewable energy output interval, Φ RE Represents the set of nodes connected to new energy sources.

[0176] The interval uncertainty set indicates that the output of the renewable energy can take any value in the interval, and for any renewable energy output in this interval, any other constraints in the robust optimization model should be satisfied.

[0177] S2. Establish linear power flow constraints for the distribution network and use the maximum slack variable to replace the over-limit of the nonlinear power flow of the distribution network.

[0178] S21. The linearized power flow equation is as follows:

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185] Where P ij represents the active power flow from branch (i, j) from i to j; P ji P represents the active power flow from branch (i, j) to branch i; j represents the injected active power of node j; Q ijIt represents the reactive power flow from branch (i, j) from i to j; Q ij It represents the reactive power flow from branch (i, j) to branch i; Q j represents the injected reactive power of node j; G ij represents the conductance of branch (i, j); B ij represents the susceptance of branch (i, j); V i represents the voltage amplitude of node i; θ i represents the voltage phase angle of node i; Φ l represents the set of all branches; Φ all represents the set of all nodes; K(j) represents the set of all nodes directly connected to node j.

[0186] It can be seen that the established linearized power flow constraint does not consider the line network loss; and the first two equality constraints in the linearized power flow constraint are conditional constraints, which are not easy to solve. Therefore, the "big M method" can be introduced to transform the original two equality constraints into six inequality constraints:

[0187]

[0188]

[0189]

[0190]

[0191]

[0192]

[0193] In the formula, M is a large number that is set in advance. The meaning of the above inequality constraint is that if x ij =1, then the term containing M becomes 0, the first and second inequalities together degenerate into an equality constraint on active power, and the third and fourth inequalities together degenerate into an equality constraint on reactive power. ij = 0, then the term containing M becomes a large positive value or a large negative value, which is equivalent to P ij and Q ij Instead of following the equality constraint, we can take any value in [-M,M]. However, taking any value still does not conform to the actual situation of cutting out the line, so we introduce the fifth and sixth inequalities. The meaning of these two inequalities is that if x ij = 0, then the P of branch (i, j) ij and Q ij Should be strictly equal to 0, so that the constraint conforms to the actual situation.

[0194] S22, voltage safety constraints and branch power flow thermal constraints are generally expressed as follows:

[0195]

[0196]

[0197] Where V l Indicates the lower limit of voltage safety operation; V h Indicates the upper limit of voltage safety operation; It represents the safe operating apparent power of branch (i, j), i.e., thermal constraint.

[0198] Generally, during the operation of the distribution network, especially when the distribution network implements the maintenance plan, it is easy to fail to meet the safety constraints. The goal of the network reconstruction optimization method in this embodiment is to quantify and minimize the degree and frequency of node voltage and branch power flow exceeding the limit. Therefore, two slack variables are introduced for the voltage safety constraint, as shown in the following formula:

[0199]

[0200]

[0201] In the formula, and The slack variables that represent the voltage upper and lower limits respectively indicate the degree to which the node voltage deviates from the safe operating range. For the same node, these two slack variables will not be non-zero at the same time.

[0202] The original branch power flow thermal constraint can be mathematically abstracted as a circle constraint, that is, the operating point within the circle meets the constraint. It is easy to know that this constraint is a nonlinear constraint. If slack variables are directly added to the circle constraint, it will lead to difficulty in solving. Therefore, four square constraints are first used to approximate the circle constraint, and then slack variables are added to represent the degree of power flow exceeding the limit at the operating point. The specific constraints are as follows:

[0203]

[0204]

[0205]

[0206]

[0207]

[0208]

[0209]

[0210]

[0211]

[0212]

[0213] in, represents the power flow operation point (P ij ,Q ij ) 16 slack variables, these slack variables will not exist at the same time; Represents the maximum value of all slack variables. For the specific meaning of the above constraints, please refer to Figure 3 and Figure 4 .

[0214] Please refer to Figure 3 , use a regular hexadecagon surrounded by four squares to approximate the original circle constraint, so that the original circle is inscribed in the regular hexadecagon. For each branch (i, j) of the power flow operation point (P ij ,Q ij ), there are at most 5 slack variables at the same time, which is equal to the number of sides of the regular hexadecagon in its quadrant, such as Figure 4 In The distance of the extension of the side length of the regular hexadecagon in the quadrant where it is located, such as For (P ij ,Q ij ) to the edge The specific number of slack variables is determined by the operating point (P ij ,Q ij ) is determined by the positional relationship between the extension line of the regular hexadecimal deformation in the quadrant where it is located. ij ,Q ij ) When the constraint of a certain edge does not exceed the limit, the slack variable is 0, and it can be considered that the slack variable does not exist. Next, take the maximum value of all slack variables and set it as The maximum relaxation variable is used to replace the original nonlinear power flow over-limit. ij ,Q ij ) to the circle and the maximum value of the linearized slack variable, such as Figure 4 As shown, it can be seen that the lengths of the two are very close, so the approximation method has a high degree of accuracy.

[0215] S3. Taking the minimization of the frequency and degree of the distribution network state variables exceeding the limit as the objective function, the distribution network with the minimum risk is reconstructed by solving the objective function.

[0216] S31. After the above linearized power flow constraints, voltage safety constraints and branch power flow thermal constraints are established, for the completeness of the model, it is necessary to establish node injection power constraints, the relationship between new energy reactive power and active power, power constraints of substations, radial topology constraints of distribution networks and reference node constraints:

[0217] S311, the node injection power constraint is:

[0218]

[0219]

[0220] Where P i Sub represents the injected active power of the substation connected to node i; P i RE represents the injected active power of the new energy connected to node i; P i d represents the active power consumed by the load of node i; Q i Sub represents the injected reactive power of the substation connected to node i; Q i DG represents the injected reactive power of the new energy connected to node i; Q i d Represents the reactive power consumed by the load at node i; the net power of each node is equal to all injected power minus the power consumed by the load. Generally, only the root node is connected to the substation.

[0221] S312. The relationship between the reactive power and active power of new energy and the power constraint of the substation is:

[0222]

[0223]

[0224]

[0225] Where λ represents the fixed ratio of reactive power to active power when the distributed power source connected to node i operates in the maximum power point tracking mode; Φ RE Represents the collection of nodes connected to new energy sources; and They represent the minimum and maximum power limits of the active power of the transformer connected to node i respectively; and They represent the minimum and maximum power limits of the reactive power of the substation connected to node i respectively.

[0226] S313. When the new energy source operates in the maximum power tracking mode, the radial topology constraints of the distribution network are:

[0227]

[0228]

[0229] Where N node Indicates the total number of nodes; N root Indicates the total number of root nodes; FP ij Represents the virtual active power of branch (i, j).

[0230] Specifically, the following three constraints need to be met for radial network operation: The first equality constraint indicates that the sum of lines put into operation should be equal to the number of all nodes minus the total number of root nodes; the second condition indicates the power balance of each node, that is, satisfying Kirchhoff's first law, which is reflected in the node injection power constraint; the third condition indicates that except for the root node, that is, except for the node containing the substation, all nodes are load nodes. However, the distribution network may contain distributed power sources and zero injection nodes, which does not meet the third condition, so virtual injection power is introduced here. This constraint assumes that each node also has a power balance constraint with a virtual injected active power of -1. All three constraints are reflected, which can ensure that the distribution network maintains a radial operation state under any circumstances.

[0231] S314. The constraints of the reference node are:

[0232] V r =1;

[0233] θ r =0;

[0234] Where V r =1 means the voltage amplitude of the reference node r is 1, θ r =0 means that the voltage phase angle of the reference node r is 0.

[0235] S32. After all constraints are set, the objective function for minimizing the frequency and degree of over-limit of distribution network state variables can be established as follows:

[0236]

[0237] In the formula, c v is the cost coefficient of node voltage exceeding the limit; c Sis the cost coefficient of line flow over-limit. The purpose of this objective function is to find the optimal network reconstruction scheme with the minimum total slack variables of voltage and line flow. Although the objective function does not obey the definition of narrow risk indicators, it contains the sum of slack variables representing the severity of state variables and deviation frequency, which can reflect the safety of the system. Combining the uncertainty set of new energy intervals and the above constraints, a robust network reconstruction model can be obtained. By solving the robust network reconstruction model, the network topology with the minimum robust risk of the distribution network can be obtained.

[0238] Therefore, the uncertainty of the output of new energy can be considered in this embodiment; by constructing an interval uncertainty set to describe the possible upper and lower bounds of the injected power of new energy, a robust network reconstruction method is constructed. The robust method has low requirements on the amount of input data, and does not require a large amount of historical data to generate the corresponding upper and lower bounds of the interval, which is easier to operate and practice in engineering. In addition, this embodiment takes minimizing the frequency and degree of network state variable over-limit as the objective function, and can obtain a robust distribution network topology scheme with minimal risk; by establishing a linearized power flow constraint and using the maximum slack variable to replace the original nonlinear power flow over-limit, the original nonlinear problem is converted into a linear problem, and at the same time, by using the maximum slack variable to represent the degree of over-limit of the system state variable, an objective function of minimizing the frequency and degree of over-limit of the distribution network state variable is established, which is suitable for application in the scenario of distribution network reconstruction.

[0239] Embodiment 2

[0240] Please refer to Figure 2 , a distribution network reconstruction device based on operation uncertainty, comprising:

[0241] An uncertainty set building module is used to construct an interval uncertainty set according to the uncertainty of distribution network operation;

[0242] The distribution network constraint establishment module is used to establish linearized power flow constraints for the distribution network and use the maximum slack variable to replace the nonlinear power flow limit of the distribution network;

[0243] The distribution network reconstruction module is used to take the minimization of the frequency and degree of the distribution network state variable exceeding the limit as the objective function, and reconstruct the distribution network with the minimum risk by solving the objective function.

[0244] In some embodiments, the uncertainty set building module, for constructing an interval uncertainty set according to the uncertainty of the operation of the distribution network, comprises:

[0245] The output uncertainty of renewable energy in the distribution network is constructed as an interval uncertainty set:

[0246]

[0247] Where PRE represents the new energy injection vector, and They represent the lower and upper limits of the uncertainty of the i-th renewable energy output interval, Φ RE Represents the set of nodes connected to new energy sources.

[0248] In some embodiments, the power distribution network constraint establishing module is further used to:

[0249] A linearized power flow constraint without considering line network loss is established, and the conditional constraints of active power flow and reactive power flow in the linearized power flow constraint are converted into inequality constraints.

[0250] In some embodiments, the power distribution network constraint establishing module is further used to:

[0251] Establish voltage safety constraints and branch power flow thermal constraints:

[0252]

[0253]

[0254] Where V l Indicates the lower limit of voltage safety operation; V h Indicates the upper limit of voltage safety operation; represents the safe operating apparent power of branch (i, j), i.e., thermal constraint;

[0255] Introducing slack variables to the voltage safety constraint;

[0256] The branch power flow thermal constraint is abstracted into a circular constraint, and after the abstracted circular constraint is converted into a plurality of square constraints, a slack variable is introduced to represent the degree of power flow exceeding the limit at the operating point.

[0257] In some embodiments, the power distribution network constraint establishing module is further used to:

[0258] Two slack variables are introduced for voltage safety constraints:

[0259]

[0260]

[0261] In the formula, and The slack variables represent the voltage upper and lower limits, respectively, and indicate the degree to which the node voltage deviates from the safe operating range.

[0262] In some embodiments, the power distribution network constraint establishing module is further used to:

[0263] The branch power flow thermal constraint is abstracted into a circle constraint, the abstract circle constraint is converted into four square constraints, and a slack variable is introduced to indicate the degree of power flow exceeding the limit at the operating point:

[0264]

[0265]

[0266]

[0267]

[0268]

[0269]

[0270]

[0271]

[0272]

[0273]

[0274] In the formula, represents the power flow operation point (P ij ,Q ij ), represents the maximum value of all slack variables.

[0275] In some embodiments, the power distribution network reconstruction module is further used to:

[0276] Establish node injection power constraints, the relationship between new energy reactive power and active power, substation power constraints, distribution network radial topology constraints, and reference node constraints.

[0277] In some embodiments, the distribution network network reconstruction module is used to minimize the frequency and degree of distribution network state variable crossing as the objective function, including:

[0278]

[0279] In the formula, c v represents the cost coefficient of node voltage exceeding the limit, c S Indicates the cost coefficient of line flow exceeding the limit.

[0280] In some embodiments, the distribution network reconstruction module is used to solve the objective function by combining the interval uncertainty set and the linearized power flow constraint to reconstruct the distribution network with the minimum risk, including:

[0281] Combining the interval uncertainty set, linearized power flow constraints, node injection power constraints, the relationship between reactive and active power of renewable energy and power constraints of substations, radial topology constraints of distribution networks and constraints of reference nodes, a robust network reconstruction model is obtained.

[0282] By solving the robust network reconstruction model, a network topology with the minimum robust risk of the distribution network is obtained.

[0283] In summary, the present invention provides a distribution network reconstruction method and device based on operation uncertainty. According to the uncertainty of distribution network operation, an interval uncertainty set is constructed, and the uncertainty of new energy output can be considered. Moreover, by constructing an interval uncertainty set to describe the possible upper and lower bounds of new energy injection power, a robust network reconstruction method can be constructed, wherein the robust method has low requirements on the amount of input data, and does not require a large amount of historical data to generate the corresponding interval upper and lower bounds, which is easier to operate and practice in engineering. A linearized power flow constraint is established for the distribution network, and the maximum slack variable is used to replace the nonlinear power flow over-limit of the distribution network, so that the original nonlinear problem can be converted into a linear problem. The objective function is to minimize the frequency and degree of over-limit of the distribution network state variables, and the objective function is solved by combining the interval uncertainty set and the linearized power flow constraint to reconstruct the distribution network with the least risk. Therefore, the influence of the uncertainty of distribution network operation on the network reconstruction model can be considered, which is conducive to the rapid network reconstruction of the distribution network, and is suitable for application in the scenario of distribution network reconstruction.

[0284] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

[0285] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0286] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0287] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0288] 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.

[0289] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0290] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A distribution network reconstruction method based on operation uncertainty, characterized in that: Includes steps: Construct interval uncertainty sets based on the uncertainty of distribution network operation; Establish linear power flow constraints for the distribution network, and use the maximum slack variable to replace the nonlinear power flow limit of the distribution network; Taking the minimization of the frequency and degree of the distribution network state variable exceeding the limit as the objective function, the objective function is solved by combining the interval uncertainty set and the linearized power flow constraint to reconstruct the distribution network with the minimum risk; The use of the maximum slack variable to replace the nonlinear power flow exceeding limit of the distribution network includes: Establish voltage safety constraints and branch power flow thermal constraints: ； ； In the formula, V l Indicates the lower limit of voltage safe operation; V h Indicates the upper limit of voltage safety operation; Indicates a branch ( i , j )’s safe operating apparent power, i.e. thermal constraints; Represents the set of all nodes; P ij Indicates a branch ( i , j ) Active power flow from i to j; Q ij Indicates a branch ( i , j ) Reactive power flow from i to j; Introducing slack variables to the voltage safety constraint; The branch power flow thermal constraint is abstracted into a circular constraint, and after the abstracted circular constraint is converted into a plurality of square constraints, a slack variable is introduced to represent the degree of power flow exceeding the limit at the operating point; Introducing slack variables into the voltage safety constraint includes: Two slack variables are introduced for voltage safety constraints: ； ； In the formula, and The slack variables for voltages above the upper and lower limits, respectively, indicate the extent to which the node voltage deviates from the safe operating range; The branch power flow thermal constraint is abstracted into a circular constraint, and after the abstracted circular constraint is converted into multiple square constraints, a slack variable is introduced to indicate the degree of power flow exceeding the limit at the operating point, including: The branch power flow thermal constraint is abstracted into a circle constraint, the abstract circle constraint is converted into four square constraints, and a slack variable is introduced to indicate the degree of power flow exceeding the limit at the operating point: ； ； ； ； ； ； ； ； ； ； In the formula, Indicates a branch ( i , j ) of the power flow operation point ( P ij , Q ij ), represents the maximum value of all slack variables, Represents the set of all branches.

2. A distribution network reconstruction method based on operation uncertainty according to claim 1, characterized in that: The step of constructing an interval uncertainty set according to the uncertainty of the distribution network operation includes: The output uncertainty of renewable energy in the distribution network is constructed as an interval uncertainty set: ； In the formula, represents the new energy injection vector, and They represent the lower and upper limits of the uncertainty of the i-th renewable energy output interval, Represents the set of nodes connected to new energy sources.

3. A distribution network reconstruction method based on operation uncertainty according to claim 1, characterized in that: The establishment of linearized power flow constraints on the distribution network includes: A linearized power flow constraint without considering line network loss is established, and the conditional constraints of active power flow and reactive power flow in the linearized power flow constraint are converted into inequality constraints.

4. A distribution network reconstruction method based on operation uncertainty according to claim 1, characterized in that: The method of minimizing the frequency and degree of the distribution network state variable crossing the limit as the objective function includes: Establish node injection power constraints, the relationship between new energy reactive power and active power, substation power constraints, distribution network radial topology constraints, and reference node constraints.

5. A distribution network reconstruction method based on operation uncertainty according to claim 1, characterized in that: The objective function of minimizing the frequency and degree of the state variables of the distribution network exceeding the limit includes: ； In the formula, represents the cost coefficient of node voltage exceeding the limit, Indicates the cost coefficient of line flow exceeding the limit.

6. A distribution network reconstruction method based on operation uncertainty according to claim 4, characterized in that: Solving the objective function by combining the interval uncertainty set and the linearized power flow constraint to reconstruct the distribution network with the minimum risk includes: Combining the interval uncertainty set, linearized power flow constraints, node injection power constraints, the relationship between reactive and active power of renewable energy and power constraints of substations, radial topology constraints of distribution networks and constraints of reference nodes, a robust network reconstruction model is obtained. By solving the robust network reconstruction model, a network topology with the minimum robust risk of the distribution network is obtained.

7. A distribution network reconstruction device based on operation uncertainty, characterized in that: include: An uncertainty set building module is used to construct an interval uncertainty set according to the uncertainty of distribution network operation; The distribution network constraint establishment module is used to establish linearized power flow constraints for the distribution network and use the maximum slack variable to replace the nonlinear power flow limit of the distribution network; A distribution network reconstruction module is used to take the minimization of the frequency and degree of the distribution network state variable crossing the limit as the objective function, combine the interval uncertainty set and the linearized power flow constraint to solve the objective function, and reconstruct the distribution network with the minimum risk; The use of the maximum slack variable to replace the nonlinear power flow exceeding limit of the distribution network includes: Establish voltage safety constraints and branch power flow thermal constraints: ； ； In the formula, V l Indicates the lower limit of voltage safe operation; V h Indicates the upper limit of voltage safety operation; Indicates a branch ( i , j )’s safe operating apparent power, i.e. thermal constraints; Represents the set of all nodes; P ij Indicates a branch ( i , j ) Active power flow from i to j; Q ij Indicates a branch ( i , j ) Reactive power flow from i to j; Introducing slack variables to the voltage safety constraint; The branch power flow thermal constraint is abstracted into a circular constraint, and after the abstracted circular constraint is converted into a plurality of square constraints, a slack variable is introduced to represent the degree of power flow exceeding the limit at the operating point; Introducing slack variables into the voltage safety constraint includes: Two slack variables are introduced for voltage safety constraints: ； ； In the formula, and The slack variables for voltages above the upper and lower limits, respectively, indicate the extent to which the node voltage deviates from the safe operating range; The branch power flow thermal constraint is abstracted into a circular constraint, and after the abstracted circular constraint is converted into multiple square constraints, a slack variable is introduced to indicate the degree of power flow exceeding the limit at the operating point, including: The branch power flow thermal constraint is abstracted into a circle constraint, the abstract circle constraint is converted into four square constraints, and a slack variable is introduced to indicate the degree of power flow exceeding the limit at the operating point: ； ； ； ； ； ； ； ； ； ； In the formula, Indicates a branch ( i , j ) of the power flow operation point ( P ij , Q ij ), represents the maximum value of all slack variables, Represents the set of all branches.

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