Optimization method and system for optimal power flow of power distribution network

Abstract

The invention discloses a power distribution network optimal power flow optimization method and system, and the method comprises the steps: carrying out the state evaluation of a simulation model of a target power distribution network through employing obtained measurement data, obtaining initial operation state information, and generating electrical island information; based on the electrical island information, modeling the optimal power flow problem of the power distribution network into a second-order cone programming model; the optimal power distribution network power flow state vector of the second-order cone programming model is solved by adopting an improved interior point optimization algorithm, the improved interior point optimization algorithm is defined to carry out iterative updating on variables of the second-order cone programming model according to the corrected original variable correction step length and dual variable correction step length, and then iterative optimization is carried out according to the iteratively updated variables, so that the optimal power distribution network power flow state vector of the second-order cone programming model is obtained. And outputting the optimal power flow state vector of the power distribution network until convergence or the maximum allowable number of iterations is reached, so as to optimize the optimal power flow parameter of the target power distribution network. The optimization method provided by the invention has better convergence and smaller error, and improves the reliability and accuracy of the optimal power flow calculation of the distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network optimization, and in particular to a method and system for optimizing optimal power flow in a distribution network. Background Art

[0002] With the increasing penetration of new energy, the randomness and volatility of its output lead to the intensification of distribution network power flow changes and voltage fluctuations, posing a threat to the safe operation of the distribution network. The need to optimize the distribution network power flow is becoming more urgent. The optimal power flow calculation of the distribution network helps to fully utilize and tap the flexibility resources of the distribution network, which is crucial to improving the resilience of the distribution network and ensuring the safe operation of the distribution network. It also helps to improve the voltage quality of the distribution network, reduce network losses, and improve the economic efficiency of the distribution network operation.

[0003] In the prior art, the optimal power flow problem of the distribution network is solved by performing second-order cone relaxation on the optimal power flow problem of the distribution network as a second-order cone programming model, and the dual interior point method is used to solve the second-order cone programming model. However, the dual interior point method has the problems of large number of iterations or frequent non-convergence, which leads to inaccurate final power flow calculation results and low calculation efficiency, affecting the safety and stability of the distribution network.

[0004] It can be seen that how to improve the accuracy and efficiency of power flow calculation results has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0005] The present invention provides a method and system for optimizing the optimal power flow of a distribution network to solve the technical problem of how to improve the accuracy and efficiency of power flow calculation results, thereby reducing the number of iterations and the frequency of non-convergence in the process of solving the optimal power flow of the distribution network, thereby improving the accuracy and efficiency of the power flow calculation results.

[0006] In a first aspect, the present invention provides a method for optimizing the optimal power flow of a distribution network, the method comprising: obtaining a simulation model of a target distribution network and measurement data of the simulation model, and using the measurement data to perform a state evaluation on the simulation model to obtain initial operation state information of the simulation model;

[0007] Based on the initial operation state information, a breadth-first search algorithm is used to start searching from the power supply node of the simulation model, and electrical island information is generated after traversing the graph;

[0008] Based on the electrical island information, the optimal power flow problem of the distribution network is modeled as a second-order cone programming model, and the variable of the second-order cone programming model is the power flow state vector of the distribution network;

[0009] An improved interior point optimization algorithm is used to solve the optimal power distribution network flow state vector of the second-order cone programming model. The improved interior point optimization algorithm is defined as iteratively updating the original variables of the second-order cone programming model with a modified original variable correction step size, and iteratively updating the dual variables of the second-order cone programming model with a modified dual variable correction step size, and then iteratively optimizing according to the iteratively updated original variables and the dual variables until convergence or a maximum allowed number of iterations is reached, and outputting the optimal power distribution network flow state vector;

[0010] The distribution network power flow state vector of the simulation model is updated according to the optimal distribution network power flow state vector, and the optimal power flow parameters of the target distribution network are optimized according to the updated simulation model.

[0011] Preferably, the initial operating status information of the distribution network includes: node power data, branch current carrying data, switch closing status and unit output data.

[0012] Preferably, based on the electrical island information, the optimal power flow problem of the distribution network is modeled as a second-order cone programming model, including:

[0013] Taking the minimization of active network loss of the target distribution network as the objective function, taking the node active and reactive power flow balance constraints, branch voltage constraints, branch power constraints, branch current safety constraints, node voltage safety constraints, distributed power source active and reactive power regulation capacity constraints and energy storage active and reactive power regulation capacity constraints of the target distribution network as constraint conditions, constructing the second-order cone programming model;

[0014] The functional expression of the second-order cone programming model is:

[0015] minf(x)

[0016]

[0017] Where x is the power flow state vector of the distribution network, and its dimension is n; minf(x) is the objective function, and f(x) is the linear or quadratic function of the power flow state vector x of the distribution network; i is the equality constraint number, and m is the number of equality constraints; h i (x) is the constraint function of the ith equality constraint, which is a linear or quadratic function of the distribution network power flow state vector x, j is the inequality constraint number; r is the number of inequality constraints; g j(x) is the constraint function of the jth inequality constraint, which is a linear or quadratic function of the distribution network flow state vector x; the equality constraint includes the node active and reactive power flow balance constraint and the branch voltage constraint, and the inequality constraint includes the branch power constraint, the branch current safety constraint, the node voltage safety constraint, the distributed power source active and reactive power regulation capability constraint and the energy storage active and reactive power regulation capability constraint.

[0018] Preferably, the method of using an improved interior point optimization algorithm to solve the optimal variable value of the second-order cone programming model includes:

[0019] Introducing a slack variable for each of the inequality constraints, and introducing a logarithmic barrier function term for each of the slack variables, so as to transform the objective function in the second-order cone programming model;

[0020] Constructing a Lagrangian function based on the transformed second-order cone programming model, wherein the original variables of the Lagrangian function include a first original variable and a second original variable, and the dual variables of the Lagrangian function include a first dual variable and a second dual variable, wherein the first original variable is the power flow state vector of the distribution network, the second original variable is the slack variable, the first dual variable is the Lagrangian multiplier vector corresponding to the equality constraint, and the second dual variable is the Lagrangian multiplier vector corresponding to the inequality constraint;

[0021] Taking partial derivatives of the original variable and the dual variable to obtain a Kuhn-Tucker optimality condition relation of the second-order cone programming model;

[0022] Solving the Kuhn-Tucker optimality condition relationship to obtain the optimal variable value of the first original variable, and using the optimal variable value as the optimal distribution network power flow state vector of the second-order cone programming model;

[0023] The solution process is defined as: based on the initialization parameters and the correction amounts corresponding to the first original variable, the second original variable, the third dual variable and the fourth dual variable introduced, a modified linear equation group is generated; based on the modified linear equation group, the convergence of the second-order cone programming model is judged, if converged, the optimal variable value of the first original variable is output, if not converged, iterative optimization is performed based on the introduced original variable correction step size and the dual variable correction step size.

[0024] Preferably, the iterative optimization based on the introduced original variable correction step size and the dual variable correction step size comprises:

[0025] Solving the modified linear equations to obtain the modified vector after iteration;

[0026] According to the interior point property of the Kuhn-Tucker optimality condition relation, determining the maximum correction step length of the original variable and the maximum correction step length of the dual variable;

[0027] Introducing the original variable correction step length and the dual variable correction step length, and correcting the correction vector with the original variable correction step length and the dual variable correction step length; the original variable correction step length includes the correction step length of the distribution network power flow state vector;

[0028] Based on the corrected correction vector, the maximum correction step of the original variable and the maximum correction step of the dual variable are used as range limits respectively to solve the correction step of the original variable and the correction step of the dual variable;

[0029] Based on the original variable correction step size and the dual variable correction step size obtained by solving, the original variables and the dual variables are corrected to re-judge the convergence of the second-order cone programming model until convergence or the maximum allowed number of iterations is reached, and the optimal variable value is output.

[0030] In a second aspect, the present invention further provides an optimization system for the optimal power flow of a distribution network, which implements the optimization method for the optimal power flow of a distribution network described above, and the system comprises: a data acquisition and processing unit, an electrical island information generation unit, a second-order cone programming model construction unit, a second-order cone programming model solution unit and a distribution network optimal power flow state update unit;

[0031] The data acquisition processing unit is used to obtain a simulation model of a target distribution network and measurement data of the simulation model, and use the measurement data to perform a state evaluation on the simulation model to obtain initial operation state information of the simulation model;

[0032] The electrical island information generating unit is used to generate electrical island information by traversing a graph based on the initial operation state information and starting the search from the power supply node of the simulation model by using a breadth-first search algorithm;

[0033] The second-order cone programming model construction unit is used to model the distribution network optimal power flow problem as a second-order cone programming model based on the electrical island information, and the variable of the second-order cone programming model is the distribution network power flow state vector;

[0034] The second-order cone programming model solving unit is used to solve the optimal power distribution network flow state vector of the second-order cone programming model by adopting an improved interior point optimization algorithm, wherein the improved interior point optimization algorithm is defined as iteratively updating the original variables of the second-order cone programming model with a modified original variable correction step size, and iteratively updating the dual variables of the second-order cone programming model with a modified dual variable correction step size, and then iteratively optimizing according to the iteratively updated original variables and the dual variables until convergence or a maximum allowed number of iterations is reached, and outputting the optimal power distribution network flow state vector;

[0035] The distribution network optimal power flow state updating unit is used to update the distribution network power flow state vector of the simulation model according to the optimal distribution network power flow state vector, and optimize the optimal power flow parameters of the target distribution network according to the updated simulation model.

[0036] Preferably, the initial operating status information of the distribution network includes: node power data, branch current carrying data, switch closing status and unit output data.

[0037] Preferably, based on the electrical island information, the optimal power flow problem of the distribution network is modeled as a second-order cone programming model, including:

[0038] Taking the minimization of active network loss of the target distribution network as the objective function, taking the node active and reactive power flow balance constraints, branch voltage constraints, branch power constraints, branch current safety constraints, node voltage safety constraints, distributed power source active and reactive power regulation capacity constraints and energy storage active and reactive power regulation capacity constraints of the target distribution network as constraint conditions, constructing the second-order cone programming model;

[0039] The functional expression of the second-order cone programming model is:

[0040] minf(x)

[0041]

[0042] Where x is the power flow state vector of the distribution network, and its dimension is n; minf(x) is the objective function, and f(x) is the linear or quadratic function of the power flow state vector x of the distribution network; i is the equality constraint number, and m is the number of equality constraints; h i (x) is the constraint function of the ith equality constraint, which is a linear or quadratic function of the distribution network power flow state vector x, j is the inequality constraint number; r is the number of inequality constraints; g j(x) is the constraint function of the jth inequality constraint, which is a linear or quadratic function of the distribution network flow state vector x; the equality constraint includes the node active and reactive power flow balance constraint and the branch voltage constraint, and the inequality constraint includes the branch power constraint, the branch current safety constraint, the node voltage safety constraint, the distributed power source active and reactive power regulation capability constraint and the energy storage active and reactive power regulation capability constraint.

[0043] Preferably, the method of using an improved interior point optimization algorithm to solve the optimal variable value of the second-order cone programming model includes:

[0044] Introducing a slack variable for each of the inequality constraints, and introducing a logarithmic barrier function term for each of the slack variables, so as to transform the objective function in the second-order cone programming model;

[0045] Constructing a Lagrangian function based on the transformed second-order cone programming model, wherein the original variables of the Lagrangian function include a first original variable and a second original variable, and the dual variables of the Lagrangian function include a first dual variable and a second dual variable, wherein the first original variable is the power flow state vector of the distribution network, the second original variable is the slack variable, the first dual variable is the Lagrangian multiplier vector corresponding to the equality constraint, and the second dual variable is the Lagrangian multiplier vector corresponding to the inequality constraint;

[0046] Taking partial derivatives of the original variable and the dual variable to obtain a Kuhn-Tucker optimality condition relation of the second-order cone programming model;

[0047] Solving the Kuhn-Tucker optimality condition relationship to obtain the optimal variable value of the first original variable, and using the optimal variable value as the optimal distribution network power flow state vector of the second-order cone programming model;

[0048] The solution process is defined as: based on the initialization parameters and the correction amounts corresponding to the first original variable, the second original variable, the third dual variable and the fourth dual variable introduced, a modified linear equation group is generated; based on the modified linear equation group, the convergence of the second-order cone programming model is judged, if converged, the optimal variable value of the first original variable is output, if not converged, iterative optimization is performed based on the introduced original variable correction step size and the dual variable correction step size.

[0049] Preferably, the iterative optimization based on the introduced original variable correction step size and the dual variable correction step size comprises:

[0050] Solving the modified linear equations to obtain the modified vector after iteration;

[0051] According to the interior point property of the Kuhn-Tucker optimality condition relation, determining the maximum correction step length of the original variable and the maximum correction step length of the dual variable;

[0052] Introducing the original variable correction step length and the dual variable correction step length, and correcting the correction vector with the original variable correction step length and the dual variable correction step length; the original variable correction step length includes the correction step length of the distribution network power flow state vector;

[0053] Based on the corrected correction vector, the maximum correction step of the original variable and the maximum correction step of the dual variable are used as range limits respectively to solve the correction step of the original variable and the correction step of the dual variable;

[0054] Based on the original variable correction step size and the dual variable correction step size obtained by solving, the original variables and the dual variables are corrected to re-judge the convergence of the second-order cone programming model until convergence or the maximum allowed number of iterations is reached, and the optimal variable value is output.

[0055] The present invention provides a method and system for optimizing the optimal power flow of a distribution network. Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0056] The original variables of the second-order cone programming model are iteratively updated with the modified original variable correction step size, and the dual variables of the second-order cone programming model are iteratively updated with the modified dual variable correction step size. Then, iterative optimization is performed according to the iteratively updated original variables and dual variables until convergence or the maximum allowed number of iterations is reached, and the optimal distribution network power flow state vector is output. It has better convergence and smaller error, and can effectively improve the reliability and accuracy of distribution network optimal power flow calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the steps of a method for optimizing the optimal power flow of a distribution network provided by a preferred embodiment of the present invention;

[0058] Figure 2 It is an error variation curve of the dual interior point algorithm in the simulation provided by a preferred embodiment of the present invention and the optimization method of the optimal power flow of the distribution network of the present application in the iteration process;

[0059] Figure 3 It is a structural schematic diagram of an optimization system for optimal power flow in a distribution network provided by a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following is a detailed explanation of the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided only for illustrative purposes and cannot be understood as limiting the present invention. The accompanying drawings are only for reference and illustration purposes and do not constitute a limitation on the scope of protection of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, the meaning of "multiple" is two or more.

[0061] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0062] In the description of the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0063] In an embodiment of the present invention, a method for optimizing the optimal power flow of a power distribution network is provided. Figure 1 , the method comprising:

[0064] S1. Acquire a simulation model of a target distribution network and measurement data of the simulation model, and use the measurement data to perform a state evaluation on the simulation model to obtain initial operation state information of the simulation model.

[0065] S2. Based on the initial operation status information, a breadth-first search algorithm is used to start searching from the power supply nodes of the simulation model, and electrical island information is generated after traversing the graph.

[0066] S3. Based on the electrical island information, the optimal power flow problem of the distribution network is modeled as a second-order cone programming model, and the variable of the second-order cone programming model is the power flow state vector of the distribution network.

[0067] S4. Using an improved interior point optimization algorithm to solve the optimal distribution network power flow state vector of the second-order cone programming model, the improved interior point optimization algorithm is defined as iteratively updating the original variables of the second-order cone programming model with a modified original variable correction step size, and iteratively updating the dual variables of the second-order cone programming model with a modified dual variable correction step size, and then iteratively optimizing according to the iteratively updated original variables and the dual variables until convergence or the maximum allowed number of iterations is reached, and outputting the optimal distribution network power flow state vector.

[0068] S5. Update the distribution network power flow state vector of the simulation model according to the optimal distribution network power flow state vector, and optimize the optimal power flow parameters of the target distribution network according to the updated simulation model.

[0069] In the optimization method for the optimal power flow of a distribution network disclosed in the preferred embodiment of the present invention, a simulation model of a target distribution network and measurement data of the simulation model are obtained through a data interface, and the simulation model is evaluated using the measurement data to obtain initial operation state information of the simulation model. The initial operation state information of the distribution network includes: node power data, branch current carrying data, switch closing state, and unit output data.

[0070] Based on the initial operating state information, a breadth-first search algorithm is used to start the search from the power supply node of the simulation model, and the electrical island information is generated after traversing the graph.

[0071] Graph traversal means starting from a starting node of the graph and visiting each node in turn according to certain rules until all nodes have been visited. The breadth-first search algorithm starts from the starting node, first visits all nodes adjacent to the starting node, then visits the nodes adjacent to these nodes, and so on, until all nodes have been visited.

[0072] After obtaining the data of each node of the simulation model through the breadth-first search algorithm, the electrical island information is generated, and the beginning and end of each branch of each electrical island are marked. An electrical island is an area in the power system that is interconnected but isolated from other systems. It is usually composed of electrical components such as generators, transformers, switchgear, protection devices and power loads. An electrical island can be an independent power system or a small power system that is connected to the main power system but can operate independently.

[0073] Furthermore, based on the electrical island information, the optimal power flow problem of the distribution network is modeled as a second-order cone programming model. In the optimal power flow problem of the distribution network, the objective function is to minimize the active network loss of the target distribution network. The objective function expression is:

[0074]

[0075] The subscript e represents the node number. is the active power output of the equivalent power source of the node e substation; is the active power output of the distributed power source at node e; is the active power output of the energy storage equivalent unit at node e; is the active power of the node e load, S N Indicates the total number of nodes in the distribution network.

[0076] The constraints in the optimal power flow problem of distribution network include: node active and reactive power flow balance constraints, branch voltage constraints, branch power constraints, branch current safety constraints, node voltage safety constraints, distributed generation active and reactive power regulation capability constraints and energy storage active and reactive power regulation capability.

[0077] 1) Node active and reactive power flow balance constraints

[0078] According to Kirchhoff's current law, for any node e, we have:

[0079]

[0080] Among them, Ω L represents a branch set; k(e,:) represents branch k with node e as the head node; k(:,e) represents branch k with node e as the terminal node; P k and Q k They represent the active power and reactive power flowing from the head end to the end of branch k respectively; R k and X k They represent the resistance and reactance of branch k respectively; is the square of the current amplitude of branch k; and are the active and reactive injection of node e respectively, so the function of active and reactive power flow balance constraint of the node is expressed as:

[0081]

[0082] in, is the reactive power output of the equivalent power source of the node e substation; is the reactive power output of the distributed power source at node e; The reactive power output of the energy storage equivalent unit at node e; is the reactive power of the load at node e.

[0083] 2) The function expression of branch voltage constraint is:

[0084]

[0085] Wherein, subscripts a and b are the head node number and the terminal node number of branch k respectively; and are the squares of the voltage amplitudes at the head node a and the end node b respectively.

[0086] 3) The function expression of branch power constraint is:

[0087]

[0088] 4) The function expression of branch current safety constraint is:

[0089]

[0090] in, is the upper limit of the allowable current of branch k.

[0091] 5) The function expression of node voltage safety constraint is:

[0092]

[0093] in, V e and are the lower and upper limits of the voltage amplitude allowed at node e respectively.

[0094] 6) The functional expression of the active and reactive regulation capability constraint of distributed generation is:

[0095]

[0096] in, and are the lower and upper limits of the DG active output at node e, respectively; and are the lower and upper limits of DG reactive output at node e respectively.

[0097] 7) The functional expression of the active and reactive regulation capability constraints of energy storage is:

[0098]

[0099] in, and They are the lower and upper limits of the active output of the energy storage equivalent unit at node e respectively; and They are respectively the lower and upper limits of reactive power output of the energy storage equivalent unit at node e.

[0100] According to the above objective function expression and constraint condition function expression, a second-order cone programming model for the optimal power flow of the target distribution network is constructed. For the convenience of explanation, the second-order cone programming model is mathematically simplified, and the first function expression of the second-order cone programming model is obtained as follows:

[0101] minf(x)

[0102]

[0103] Where x is the power flow state vector of the distribution network, and its dimension is n; minf(x) is the objective function, and f(x) is the linear or quadratic function of the power flow state vector x of the distribution network; i is the equality constraint number, and m is the number of equality constraints; h i (x) is the constraint function of the ith equality constraint, which is a linear or quadratic function of the distribution network power flow state vector x, j is the inequality constraint number; r is the number of inequality constraints; g j (x) is the constraint function of the jth inequality constraint, which is a linear or quadratic function of the distribution network power flow state vector x.

[0104] The equality constraints include node active and reactive power flow balance constraints and branch voltage constraints; the inequality constraints include branch power constraints, branch current safety constraints, node voltage safety constraints, distributed generation active and reactive power regulation capability constraints, and energy storage active and reactive power regulation capability constraints.

[0105] Furthermore, an improved interior point optimization algorithm is used to solve the second-order cone programming model to obtain the optimal variable values ​​of the second-order cone programming model, and the optimal variable values ​​are the optimal distribution network power flow state vector of the second-order cone programming model. The basic principle of the improved interior point optimization algorithm is the dual interior point method. The improved interior point optimization algorithm iteratively updates the original variables of the second-order cone programming model with the corrected original variable correction step size, and iteratively updates the dual variables of the second-order cone programming model with the corrected dual variable correction step size, and then iteratively optimizes the original variables and the dual variables after iterative update until convergence or the maximum allowed number of iterations is reached, and the optimal distribution network power flow state vector is output.

[0106] Specifically, a slack variable is introduced for each inequality constraint to transform the inequality constraint into an equality constraint and a variable inequality constraint. The first function expression of the second-order cone programming model after the slack variable is introduced is:

[0107] minf(x)

[0108]

[0109] Among them, lj Slack variables introduced for inequality constraint j.

[0110] Furthermore, a logarithmic barrier function term is introduced for each slack variable to transform the objective function in the second-order cone programming model, and the second function expression of the second-order cone programming model is obtained. The second function expression of the second-order cone programming model is:

[0111]

[0112] Where p is the penalty coefficient, which is a real number greater than 0.

[0113] Furthermore, a Lagrangian function is constructed based on the second function expression of the second-order cone programming model. The original variables of the Lagrangian function include the first original variable and the second original variable. The dual variables of the Lagrangian function include the first dual variable and the second dual variable. The first original variable is the power flow state vector of the distribution network, the second original variable is the slack variable, the first dual variable is the Lagrangian multiplier vector corresponding to the equality constraint, the second dual variable is the Lagrangian multiplier vector corresponding to the inequality constraint, and the Lagrangian function is:

[0114]

[0115] Where x represents the distribution network power flow state vector, y represents the Lagrange multiplier vector corresponding to the equality constraint, m-dimensional, l represents the slack variable vector, z represents the Lagrange multiplier vector corresponding to the inequality constraint, r-dimensional, and h(x) represents the function h i (x), g(x) represents the function g j (x) is a vector composed of

[0116] Furthermore, formula (13) is used to obtain partial derivatives of x, y, l, and z respectively, and the derivative vector is set to 0, and the Kuhn-Tucker optimality condition relationship of the second-order cone programming model is obtained. The Kuhn-Tucker optimality condition relationship is:

[0117]

[0118] Where Z represents a diagonal matrix with r×r dimensions and each element of vector z as diagonal elements, L represents a diagonal matrix with r×r dimensions and each element of l as diagonal elements, e is an r-dimensional all-1 vector, represents the gradient vector of the function f(x), represents the transpose of the derivative matrix of the function vector h(x), Represents the transpose of the derivative matrix of the function vector g(x).

[0119] The Kuhn-Tucker optimality condition relation is further rewritten as follows:

[0120]

[0121] in,

[0122] Furthermore, the rewritten Kuhn-Tucker optimality condition relation is solved to obtain the optimal variable value of the first original variable, and the optimal variable value is used as the optimal distribution network power flow state vector of the second-order cone programming model.

[0123] Specifically, the centripetal parameter, the allowable error, the maximum allowable number of iterations, the initial number of iterations, the first original variable, the second original variable, the first dual variable, and the second dual variable are initialized.

[0124] In the convergence judgment process, the dual gap is calculated based on the first dual variable and the second dual variable, and the penalty factor is calculated according to the dual gap. The calculation formulas of the dual gap and the penalty factor are as follows;

[0125]

[0126] The superscript k represents the number of iterations. is the duality gap, Represents vector l k The i-th element of Represents vector z k The i-th element of k represents the vector value of vector l at the kth iteration, z k Represents the vector value of vector z at the kth iteration.

[0127] Furthermore, the correction values ​​corresponding to the first original variable, the second original variable, the third dual variable and the fourth dual variable are introduced, which are respectively the first correction vector of each iteration of the first original variable, the second correction vector of each iteration of the second original variable, the third correction vector of each iteration of the first dual variable and the fourth correction vector of each iteration of the second dual variable. Then, the values ​​of the first original variable, the second original variable, the third dual variable and the fourth dual variable in the iteration process can be expressed as:

[0128]

[0129] Where Δx k , Δy k , Δl k and Δz k are the first correction vector, the third correction vector, the second correction vector and the fourth correction vector at the kth iteration, respectively, k ,y k , l k and z kare respectively the first primitive variable vector value, the first dual variable vector value, the second primitive variable vector value, and the second dual variable vector value at the k-th iteration.

[0130] Substitute formula (18) into formula (15) and at point (x k ,y k , l k 、z k ) is linearized to generate a modified linear equation, which is:

[0131]

[0132] in,

[0133]

[0134] Among them, H k represents the Hessian matrix at the kth iteration, Indicates F x The vector value at the kth iteration, Indicates F y The vector value at the kth iteration, Indicates F z The vector value at the kth iteration, F l k Indicates F l Vector value at the kth iteration.

[0135] The convergence of the second-order cone programming model is judged based on the modified linear equations. The convergence criterion is: if and If converged, the iteration ends and the optimal variable value of the first original variable is output; otherwise, the iterative optimization continues based on the modified step size of the introduced original variable and the modified step size of the dual variable.

[0136] The specific process of iterative optimization is as follows:

[0137] Solve the modified linear equations to obtain the first correction vector Δx after the kth iteration k , the second correction vector Δl k , the third correction vector Δy k and the fourth correction vector Δz k .

[0138] Furthermore, according to the interior point property of the Kuhn-Tucker optimality condition relation, the maximum correction step length of the original variable and the maximum correction step length of the dual variable are determined. The calculation formulas for the maximum correction step length of the original variable and the maximum correction step length of the dual variable are as follows:

[0139]

[0140] in, and They represent the maximum correction step size of the primal variable and the dual variable respectively.

[0141] The original variable correction step size and the dual variable correction step size are introduced to correct the correction vector with the original variable correction step size and the dual variable correction step size. Based on the corrected correction vector, the values ​​of the first original variable, the second original variable, the third dual variable and the fourth dual variable in the iteration process are changed to:

[0142]

[0143] in, and They represent the primal variable correction step size and the dual variable correction step size in the kth iteration respectively.

[0144] According to formula (26), vectors x and l are the original variable correction steps. Since the components of g(x) and h(x) are all linear or quadratic functions, the F in formula (15) is y and F z Each component is also a linear or quadratic function of the vector x. When equation (26) is substituted into equation (15), F y and F z They are also the original variable correction step lengths is a linear or quadratic function, so is the quartic function of the original variable with the corrected step length. The quartic function of the original variable with the corrected step length is analyzed to obtain the extreme points, and then we can get Use within the range Minimum original variable correction step size

[0145] According to formula (26), vectors y and z are dual variables with modified step lengths is a linear function, then F in formula (15) x and F l are also linear functions of vectors y and z. When equation (26) is substituted into equation (15), F x and F l They are also dual variable correction steps is a linear function of is the quadratic function of the dual variable correction step length. The quadratic function of the dual variable correction step length is analyzed to obtain the extreme point, and then we can get Use within the range Minimum dual variable modification step size

[0146] Based on the solved primitive variables, the step size is corrected and the dual variables are corrected. The primitive variables and dual variables are corrected. The corrected primitive variables and dual variables are:

[0147]

[0148] Based on the corrected original variables and dual variables, the convergence of the second-order cone programming model is re-judged until convergence or the maximum allowed number of iterations is reached, the optimal variable value is output, and the optimal distribution network power flow state vector of the second-order cone programming model is obtained, or the calculation failure mark is displayed.

[0149] In order to verify the optimization method of the optimal power flow of the distribution network in this application, a simulation calculation is performed on a distribution network in a certain place. The calculation scale of the constructed optimization problem is: number of variables = 69596, number of equality constraints = 59885, number of inequality constraints = 18168. The computer used for the simulation is 12th Gen Intel(R)Core(TM)i9-12900HX 2.30GHz, memory 64G. In the simulation, the centripetal parameter is set to 0.2, the maximum allowed number of iterations is set to 100, and the maximum allowed error is set to 10 -5 .

[0150] The dual interior point algorithm is used to solve the optimal power flow of the distribution network. The information generated by the iterative process is shown in Table 1.

[0151] Table 1

[0152]

[0153]

[0154]

[0155]

[0156] It can be seen from Table 1 that the dual interior point algorithm still cannot meet the convergence criterion after 100 iterations, so the optimization calculation fails. The calculation process takes a total of 9.173 seconds.

[0157] The optimization method for the optimal power flow of the distribution network disclosed in this application is used to solve the optimal power flow of the distribution network. The information generated by the iterative process is shown in Table 2.

[0158] Table 2

[0159]

[0160]

[0161] As shown in Table 2, after 23 iterations of the optimization method for the optimal power flow of the distribution network disclosed in this application, the maximum error is less than the set maximum allowable error of 10 -5 , so the algorithm converges, and the calculation process takes a total of 2.091 seconds.

[0162] like Figure 2 The error curves of the dual interior point algorithm and the optimization method of the distribution network optimal power flow of the present application in the iterative process are shown in FIG. Figure 2 It can be seen that the optimization method for the optimal power flow of the distribution network of the present application has better convergence and smaller error, and can effectively improve the reliability and accuracy of the calculation of the optimal power flow of the distribution network.

[0163] In a preferred embodiment of the present invention, a simulation model of a target distribution network and measurement data of the simulation model are obtained, and the simulation model is evaluated using the measurement data to obtain initial operation state information of the simulation model; based on the initial operation state information, a breadth-first search algorithm is used to search from the power supply node of the simulation model, and electrical island information is generated by traversing the graph; based on the electrical island information, the optimal power flow problem of the distribution network is modeled as a second-order cone programming model, and the variables of the second-order cone programming model are the distribution network power flow state vector; an improved interior point optimization algorithm is used to solve the optimal distribution network power flow state vector of the second-order cone programming model, and the improved interior point optimization algorithm is defined as iteratively updating the original variables of the second-order cone programming model with a modified original variable correction step size, and iteratively updating the dual variables of the second-order cone programming model with a modified dual variable correction step size, and then iteratively optimizing according to the iteratively updated original variables and dual variables until convergence or the maximum allowed number of iterations is reached, and the optimal distribution network power flow state vector is output; the distribution network power flow state vector of the simulation model is updated according to the optimal distribution network power flow state vector, and the optimal power flow parameters of the target distribution network are optimized according to the updated simulation model. The optimization method for the optimal power flow of the distribution network provided in this application has better convergence and smaller error, and can effectively improve the reliability and accuracy of the distribution network optimal power flow calculation.

[0164] Accordingly, if Figure 3 As shown, based on an optimization method for optimal power flow of a distribution network, an embodiment of the present invention further provides an optimization system for optimal power flow of a distribution network, which implements the optimization method for optimal power flow of a distribution network disclosed in an embodiment of the present invention, and the system includes: a data acquisition and processing unit 1, an electrical island information generation unit 2, a second-order cone programming model construction unit 3, a second-order cone programming model solution unit 4 and a distribution network optimal power flow state update unit 5;

[0165] The data acquisition and processing unit 1 is used to obtain a simulation model of a target distribution network and measurement data of the simulation model, and use the measurement data to perform a state evaluation on the simulation model to obtain initial operation state information of the simulation model;

[0166] The electrical island information generating unit 2 is used to generate electrical island information by traversing the graph based on the initial operation state information and starting the search from the power supply node of the simulation model by adopting a breadth-first search algorithm;

[0167] The second-order cone programming model construction unit 3 is used to model the optimal power flow problem of the distribution network as a second-order cone programming model based on the electrical island information, and the variable of the second-order cone programming model is the power flow state vector of the distribution network;

[0168] The second-order cone programming model solving unit 4 is used to solve the optimal power distribution network flow state vector of the second-order cone programming model by adopting an improved interior point optimization algorithm, wherein the improved interior point optimization algorithm is defined as iteratively updating the original variables of the second-order cone programming model with a modified original variable correction step size, and iteratively updating the dual variables of the second-order cone programming model with a modified dual variable correction step size, and then iteratively optimizing according to the iteratively updated original variables and the dual variables until convergence or a maximum allowed number of iterations is reached, and outputting the optimal power distribution network flow state vector;

[0169] The distribution network optimal power flow state updating unit 5 is used to update the distribution network power flow state vector of the simulation model according to the optimal distribution network power flow state vector, and optimize the optimal power flow parameters of the target distribution network according to the updated simulation model.

[0170] The distribution network initial operation status information includes: node power data, branch current carrying data, switch closing status and unit output data.

[0171] Wherein, based on the electrical island information, the optimal power flow problem of the distribution network is modeled as a second-order cone programming model, including:

[0172] Taking the minimization of active network loss of the target distribution network as the objective function, taking the node active and reactive power flow balance constraints, branch voltage constraints, branch power constraints, branch current safety constraints, node voltage safety constraints, distributed power source active and reactive power regulation capacity constraints and energy storage active and reactive power regulation capacity constraints of the target distribution network as constraint conditions, constructing the second-order cone programming model;

[0173] The functional expression of the second-order cone programming model is:

[0174] minf(x)

[0175]

[0176] Where x is the power flow state vector of the distribution network, and its dimension is n; minf(x) is the objective function, and f(x) is the linear or quadratic function of the power flow state vector x of the distribution network; i is the equality constraint number, and m is the number of equality constraints; h i (x) is the constraint function of the ith equality constraint, which is a linear or quadratic function of the distribution network power flow state vector x, j is the inequality constraint number; r is the number of inequality constraints; g j (x) is the constraint function of the jth inequality constraint, which is a linear or quadratic function of the distribution network flow state vector x; the equality constraint includes the node active and reactive power flow balance constraint and the branch voltage constraint, and the inequality constraint includes the branch power constraint, the branch current safety constraint, the node voltage safety constraint, the distributed power source active and reactive power regulation capability constraint and the energy storage active and reactive power regulation capability constraint.

[0177] Wherein, the method of using an improved interior point optimization algorithm to solve the optimal variable value of the second-order cone programming model includes:

[0178] Introducing a slack variable for each of the inequality constraints, and introducing a logarithmic barrier function term for each of the slack variables, so as to transform the objective function in the second-order cone programming model;

[0179] Constructing a Lagrangian function based on the transformed second-order cone programming model, wherein the original variables of the Lagrangian function include a first original variable and a second original variable, and the dual variables of the Lagrangian function include a first dual variable and a second dual variable, wherein the first original variable is the power flow state vector of the distribution network, the second original variable is the slack variable, the first dual variable is the Lagrangian multiplier vector corresponding to the equality constraint, and the second dual variable is the Lagrangian multiplier vector corresponding to the inequality constraint;

[0180] Taking partial derivatives of the original variable and the dual variable to obtain a Kuhn-Tucker optimality condition relation of the second-order cone programming model;

[0181] Solving the Kuhn-Tucker optimality condition relationship to obtain the optimal variable value of the first original variable, and using the optimal variable value as the optimal distribution network power flow state vector of the second-order cone programming model;

[0182] The solution process is defined as: based on the initialization parameters and the correction amounts corresponding to the first original variable, the second original variable, the third dual variable and the fourth dual variable introduced, a modified linear equation group is generated; based on the modified linear equation group, the convergence of the second-order cone programming model is judged, if converged, the optimal variable value of the first original variable is output, if not converged, iterative optimization is performed based on the introduced original variable correction step size and the dual variable correction step size.

[0183] The iterative optimization based on the introduced original variable correction step size and the dual variable correction step size includes:

[0184] Solving the modified linear equations to obtain the modified vector after iteration;

[0185] According to the interior point property of the Kuhn-Tucker optimality condition relation, determining the maximum correction step length of the original variable and the maximum correction step length of the dual variable;

[0186] Introducing the original variable correction step length and the dual variable correction step length, and correcting the correction vector with the original variable correction step length and the dual variable correction step length; the original variable correction step length includes the correction step length of the distribution network power flow state vector;

[0187] Based on the corrected correction vector, the maximum correction step of the original variable and the maximum correction step of the dual variable are used as range limits respectively to solve the correction step of the original variable and the correction step of the dual variable;

[0188] Based on the original variable correction step size and the dual variable correction step size obtained by solving, the original variables and the dual variables are corrected to re-judge the convergence of the second-order cone programming model until convergence or the maximum allowed number of iterations is reached, and the optimal variable value is output.

[0189] For the specific definition of an optimization system for optimal power flow in a distribution network, please refer to the above-mentioned definition of an optimization method for optimal power flow in a distribution network, which will not be repeated here. A person of ordinary skill in the art can appreciate that the modules and steps described in conjunction with the embodiments disclosed in the present invention can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0190] The present embodiment provides a method and system for optimizing the optimal power flow of a distribution network, which are used to solve the technical problem of how to improve the accuracy and efficiency of power flow calculation results. The simulation model of the target distribution network and the measurement data of the simulation model are obtained, and the state of the simulation model is evaluated using the measurement data to obtain the initial operation state information of the simulation model; based on the initial operation state information, a breadth-first search algorithm is used to start the search from the power supply node of the simulation model, and electrical island information is generated by traversing the graph; based on the electrical island information, the optimal power flow problem of the distribution network is modeled as a second-order cone programming model, and the variables of the second-order cone programming model are the distribution network power flow state vector; the improved interior point optimization algorithm is used to solve the optimal distribution network power flow state vector of the second-order cone programming model, and the improved interior point optimization algorithm is defined as iteratively updating the original variables of the second-order cone programming model with the modified original variable correction step size, and iteratively updating the dual variables of the second-order cone programming model with the modified dual variable correction step size, and then iteratively optimizing according to the iteratively updated original variables and dual variables until convergence or the maximum allowed number of iterations is reached, and the optimal distribution network power flow state vector is output; the distribution network power flow state vector of the simulation model is updated according to the optimal distribution network power flow state vector, and the optimal power flow parameters of the target distribution network are optimized according to the updated simulation model. The optimization method for the optimal power flow of the distribution network provided in this application has better convergence and smaller error, and can effectively improve the reliability and accuracy of the distribution network optimal power flow calculation.

[0191] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0192] The above-described embodiments only express several preferred implementations of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the invention. It should be pointed out that, for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for optimizing the optimal power flow of a distribution network, characterized in that: The method comprises: Acquire a simulation model of a target distribution network and measurement data of the simulation model, and use the measurement data to perform a state evaluation on the simulation model to obtain initial operation state information of the simulation model; Based on the initial operation state information, a breadth-first search algorithm is used to start searching from the power supply node of the simulation model, and electrical island information is generated after traversing the graph; Based on the electrical island information, the optimal power flow problem of the distribution network is modeled as a second-order cone programming model, and the variable of the second-order cone programming model is the power flow state vector of the distribution network; An improved interior point optimization algorithm is used to solve the optimal power distribution network flow state vector of the second-order cone programming model. The improved interior point optimization algorithm is defined as iteratively updating the original variables of the second-order cone programming model with a modified original variable correction step size, and iteratively updating the dual variables of the second-order cone programming model with a modified dual variable correction step size, and then iteratively optimizing according to the iteratively updated original variables and the dual variables until convergence or a maximum allowed number of iterations is reached, and outputting the optimal power distribution network flow state vector; The distribution network power flow state vector of the simulation model is updated according to the optimal distribution network power flow state vector, and the optimal power flow parameters of the target distribution network are optimized according to the updated simulation model.

2. The method for optimizing the optimal power flow of a distribution network according to claim 1, characterized in that: The distribution network initial operation status information includes: node power data, branch current carrying data, switch closing status and unit output data.

3. The method for optimizing the optimal power flow of a distribution network according to claim 1, characterized in that: Based on the electrical island information, the optimal power flow problem of the distribution network is modeled as a second-order cone programming model, including: Taking the minimization of active network loss of the target distribution network as the objective function, taking the node active and reactive power flow balance constraints, branch voltage constraints, branch power constraints, branch current safety constraints, node voltage safety constraints, distributed power source active and reactive power regulation capacity constraints and energy storage active and reactive power regulation capacity constraints of the target distribution network as constraint conditions, constructing the second-order cone programming model; The functional expression of the second-order cone programming model is: minf(x) Where x is the power flow state vector of the distribution network, and its dimension is n; minf(x) is the objective function, and f(x) is the linear or quadratic function of the power flow state vector x of the distribution network; i is the equality constraint number, and m is the number of equality constraints; h i (x) is the constraint function of the ith equality constraint, which is a linear or quadratic function of the distribution network power flow state vector x, j is the inequality constraint number; r is the number of inequality constraints; g j (x) is the constraint function of the jth inequality constraint, which is a linear or quadratic function of the distribution network flow state vector x; the equality constraint includes the node active and reactive power flow balance constraint and the branch voltage constraint, and the inequality constraint includes the branch power constraint, the branch current safety constraint, the node voltage safety constraint, the distributed power source active and reactive power regulation capability constraint and the energy storage active and reactive power regulation capability constraint.

4. The method for optimizing the optimal power flow of a distribution network according to claim 3, characterized in that: The method of using an improved interior point optimization algorithm to solve the optimal variable value of the second-order cone programming model includes: Introducing a slack variable for each of the inequality constraints, and introducing a logarithmic barrier function term for each of the slack variables, so as to transform the objective function in the second-order cone programming model; Constructing a Lagrangian function based on the transformed second-order cone programming model, wherein the original variables of the Lagrangian function include a first original variable and a second original variable, and the dual variables of the Lagrangian function include a first dual variable and a second dual variable, wherein the first original variable is the power flow state vector of the distribution network, the second original variable is the slack variable, the first dual variable is the Lagrangian multiplier vector corresponding to the equality constraint, and the second dual variable is the Lagrangian multiplier vector corresponding to the inequality constraint; Taking partial derivatives of the original variable and the dual variable to obtain a Kuhn-Tucker optimality condition relation of the second-order cone programming model; Solving the Kuhn-Tucker optimality condition relationship to obtain the optimal variable value of the first original variable, and using the optimal variable value as the optimal distribution network power flow state vector of the second-order cone programming model; The solution process is defined as: based on the initialization parameters and the correction amounts corresponding to the first original variable, the second original variable, the third dual variable and the fourth dual variable introduced, a modified linear equation group is generated; based on the modified linear equation group, the convergence of the second-order cone programming model is judged, if converged, the optimal variable value of the first original variable is output, if not converged, iterative optimization is performed based on the introduced original variable correction step size and the dual variable correction step size.

5. The method for optimizing the optimal power flow of a distribution network as claimed in claim 4, characterized in that: The iterative optimization is performed based on the introduced original variable correction step size and the dual variable correction step size, including: Solving the modified linear equations to obtain the modified vector after iteration; According to the interior point property of the Kuhn-Tucker optimality condition relation, determining the maximum correction step length of the original variable and the maximum correction step length of the dual variable; Introducing the original variable correction step length and the dual variable correction step length, and correcting the correction vector with the original variable correction step length and the dual variable correction step length; the original variable correction step length includes the correction step length of the distribution network power flow state vector; Based on the corrected correction vector, the maximum correction step of the original variable and the maximum correction step of the dual variable are used as range limits respectively to solve the correction step of the original variable and the correction step of the dual variable; Based on the original variable correction step size and the dual variable correction step size obtained by solving, the original variables and the dual variables are corrected to re-judge the convergence of the second-order cone programming model until convergence or the maximum allowed number of iterations is reached, and the optimal variable value is output.

6. A distribution network optimal power flow optimization system, which implements the distribution network optimal power flow optimization method according to any one of claims 1 to 5, characterized in that: The system comprises: a data acquisition and processing unit, an electrical island information generation unit, a second-order cone programming model construction unit, a second-order cone programming model solution unit and a distribution network optimal power flow state update unit; The data acquisition processing unit is used to obtain a simulation model of a target distribution network and measurement data of the simulation model, and use the measurement data to perform a state evaluation on the simulation model to obtain initial operation state information of the simulation model; The electrical island information generating unit is used to generate electrical island information by traversing a graph based on the initial operation state information and starting the search from the power supply node of the simulation model by using a breadth-first search algorithm; The second-order cone programming model construction unit is used to model the distribution network optimal power flow problem as a second-order cone programming model based on the electrical island information, and the variable of the second-order cone programming model is the distribution network power flow state vector; The second-order cone programming model solving unit is used to solve the optimal power distribution network flow state vector of the second-order cone programming model by adopting an improved interior point optimization algorithm, wherein the improved interior point optimization algorithm is defined as iteratively updating the original variables of the second-order cone programming model with a modified original variable correction step size, and iteratively updating the dual variables of the second-order cone programming model with a modified dual variable correction step size, and then iteratively optimizing according to the iteratively updated original variables and the dual variables until convergence or a maximum allowed number of iterations is reached, and outputting the optimal power distribution network flow state vector; The distribution network optimal power flow state updating unit is used to update the distribution network power flow state vector of the simulation model according to the optimal distribution network power flow state vector, and optimize the optimal power flow parameters of the target distribution network according to the updated simulation model.

7. The optimization system for optimal power flow in a distribution network as claimed in claim 6, characterized in that: The distribution network initial operation status information includes: node power data, branch current carrying data, switch closing status and unit output data.

8. The optimization system for optimal power flow in a distribution network as claimed in claim 6, characterized in that: Based on the electrical island information, the optimal power flow problem of the distribution network is modeled as a second-order cone programming model, including: Taking the minimization of active network loss of the target distribution network as the objective function, taking the node active and reactive power flow balance constraints, branch voltage constraints, branch power constraints, branch current safety constraints, node voltage safety constraints, distributed power source active and reactive power regulation capacity constraints and energy storage active and reactive power regulation capacity constraints of the target distribution network as constraint conditions, constructing the second-order cone programming model; The functional expression of the second-order cone programming model is: minf(x) Where x is the power flow state vector of the distribution network, and its dimension is n; minf(x) is the objective function, and f(x) is the linear or quadratic function of the power flow state vector x of the distribution network; i is the equality constraint number, and m is the number of equality constraints; h i (x) is the constraint function of the ith equality constraint, which is a linear or quadratic function of the distribution network power flow state vector x, j is the inequality constraint number; r is the number of inequality constraints; g j (x) is the constraint function of the jth inequality constraint, which is a linear or quadratic function of the distribution network flow state vector x; the equality constraint includes the node active and reactive power flow balance constraint and the branch voltage constraint, and the inequality constraint includes the branch power constraint, the branch current safety constraint, the node voltage safety constraint, the distributed power source active and reactive power regulation capability constraint and the energy storage active and reactive power regulation capability constraint.

9. The optimization system for optimal power flow in a distribution network as claimed in claim 8, characterized in that: The method of using an improved interior point optimization algorithm to solve the optimal variable value of the second-order cone programming model includes: Introducing a slack variable for each of the inequality constraints, and introducing a logarithmic barrier function term for each of the slack variables, so as to transform the objective function in the second-order cone programming model; Constructing a Lagrangian function based on the transformed second-order cone programming model, wherein the original variables of the Lagrangian function include a first original variable and a second original variable, and the dual variables of the Lagrangian function include a first dual variable and a second dual variable, wherein the first original variable is the power flow state vector of the distribution network, the second original variable is the slack variable, the first dual variable is the Lagrangian multiplier vector corresponding to the equality constraint, and the second dual variable is the Lagrangian multiplier vector corresponding to the inequality constraint; Taking partial derivatives of the original variable and the dual variable to obtain a Kuhn-Tucker optimality condition relation of the second-order cone programming model; Solving the Kuhn-Tucker optimality condition relationship to obtain the optimal variable value of the first original variable, and using the optimal variable value as the optimal distribution network power flow state vector of the second-order cone programming model; The solution process is defined as: based on the initialization parameters and the correction amounts corresponding to the first original variable, the second original variable, the third dual variable and the fourth dual variable introduced, a modified linear equation group is generated; based on the modified linear equation group, the convergence of the second-order cone programming model is judged, if converged, the optimal variable value of the first original variable is output, if not converged, iterative optimization is performed based on the introduced original variable correction step size and the dual variable correction step size.

10. The optimization system for optimal power flow in a distribution network as claimed in claim 9, characterized in that: The iterative optimization is performed based on the introduced original variable correction step size and the dual variable correction step size, including: Solving the modified linear equations to obtain the modified vector after iteration; According to the interior point property of the Kuhn-Tucker optimality condition relation, determining the maximum correction step length of the original variable and the maximum correction step length of the dual variable; Introducing the original variable correction step length and the dual variable correction step length, and correcting the correction vector with the original variable correction step length and the dual variable correction step length; the original variable correction step length includes the correction step length of the distribution network power flow state vector; Based on the corrected correction vector, the maximum correction step of the original variable and the maximum correction step of the dual variable are used as range limits respectively to solve the correction step of the original variable and the correction step of the dual variable; Based on the original variable correction step size and the dual variable correction step size obtained by solving, the original variables and the dual variables are corrected to re-judge the convergence of the second-order cone programming model until convergence or the maximum allowed number of iterations is reached, and the optimal variable value is output.

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