Multi-level coordinated compensation reactive power optimization method and device based on second-order cone relaxation

Abstract

The invention provides a multi-level coordinated compensation reactive power optimization method and device based on second-order cone relaxation, and relates to the technical field of power distribution network loss reduction. The method comprises the steps that a reactive power optimization power flow mathematical model of a power distribution network is acquired, and the power distribution network comprises a main station side, a switching station and a power distribution room; an objective function of the reactive power optimization power flow mathematical model is constructed based on power data of a master station side, a switching station and a power distribution room, and penalty factors in the objective function comprise node voltage out-of-limit parameters and charging reactive power reverse transmission parameters; and optimizing the reactive power optimization power flow mathematical model based on a second-order cone relaxation algorithm to obtain a multi-level coordinated compensation reactive power optimization strategy of the power distribution network. According to the method, the reactive power optimization power flow mathematical model of the power distribution network is obtained, reactive power compensation is carried out on the power distribution network at the main station side, the switching station and the power distribution station, the distribution uniformity of reactive power in the power distribution network is improved, and the voltage stability of the power distribution network is further improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution network loss reduction, and in particular relates to a multi-level coordinated compensation reactive power optimization method and device based on second-order cone relaxation. Background Art

[0002] Cables are increasingly used in urban distribution networks because of their small size, light weight, and easy laying. Cables will generate more capacitive reactive power during use. When the load of the full cable distribution network is reduced, these capacitive reactive powers cannot be consumed in time and will be transmitted to the urban power grid. In other words, the full cable distribution network will cause a large amount of capacitive reactive power backfeed when the load is low. Due to the presence of inductive components in the circuit, inductive reactive power backfeed will also occur when capacitive reactive power backfeed occurs. Reactive power backfeed will cause voltage fluctuations in the power supply line, a decrease in power factor, and an increase in line losses.

[0003] The master station side of the distribution network is an important part of the distribution network, responsible for real-time monitoring, control, protection and data analysis of the entire distribution network. In order to solve the problem of reactive power reverse transmission in the existing technology, capacitive reactive power will be compensated on the master station side to reduce the power loss caused by the transmission of reactive power in the line and transformer.

[0004] Reactive power compensation can improve the power factor of the power grid, reduce the loss of power transformers and transmission lines, improve power supply efficiency, and improve the power supply environment in the power supply system. Power grid reactive power optimization compensation should try to overcome the shortcomings of traditional reactive power optimization methods, and use technical means to strengthen the awareness of economic operation of the power grid and the level of reactive power voltage management. Due to the nonlinearity of the reactive power optimization problem itself, nonlinear programming was first applied to reactive power optimization of power systems. The most representative ones are simplified gradient method, Newton method, quadratic programming method and interior point method. Nonlinear programming is the most direct method to deal with reactive power optimization. The mathematical model of this method is relatively intuitive, the physical concept is clear, and the calculation accuracy is high. However, the existing algorithms have different degrees of large calculation amount, large memory requirement, poor convergence, poor stability, and certain difficulties in handling inequalities, and their application is subject to certain restrictions.

[0005] Therefore, the local linearization method can be used to linearize the nonlinear objective function and safety constraints one by one. The more classic linear programming method is the sensitivity analysis method, which uses the Jacobian matrix in the power flow calculation to obtain the sensitivity relationship between the system state variables and the control variables. When performing reactive power optimization, various constraints can be easily introduced using the sensitivity matrix. The mathematical model of the linear programming method is simple and intuitive, the physical concepts are clear, the calculation speed is fast, and it is insensitive to the calculation scale. However, because it makes a linear approximation to the system optimization model and makes a continuous processing of discrete variables, the calculation results often have a large error from the actual situation of the power system.

[0006] Second-order cone programming is a very special nonlinear optimization with a very efficient solution algorithm. Many problems can be converted into second-order cone programming for solution. Second-order cone programming (SOCP) is mainly used to solve the problem of finding the minimum value of a linear objective function on the intersection of the Cartesian product of a finite number of second-order cones and the affine subspace. Second-order cone programming is a branch of cone programming. It is both a generalization of linear programming and a special case of semidefinite programming. It is a symmetric cone programming with a beautiful structure. This type of planning has a wide range of applications, such as facility site selection, graph theory control optimization, antenna array design, investment portfolio problems, as well as finance, engineering design, digital signal processing, acoustics, mechanics, civil aviation, electrical engineering and other fields. Therefore, studying the theory and algorithm of second-order cone programming problems has important theoretical significance and application value.

[0007] In recent years, many places have frequently seen high voltages caused by excess reactive power. Strengthening inductive reactive compensation to suppress excessive voltage has gradually attracted attention. However, the existing literature on reactive compensation optimization planning methods still mainly focuses on the study of capacitive compensation issues. In addition, long-term centralized compensation of a large amount of reactive power on the master station side will cause excess reactive power in the power grid, further leading to an increase in voltage in the power grid. Due to the uneven distribution of reactive power in the power grid, the voltage stability of the power grid will be reduced. When the voltage stability is reduced, the power grid may face the risk of voltage collapse, thereby threatening the safety of the power grid. Summary of the invention

[0008] The embodiment of the present invention provides a multi-level coordinated compensation reactive power optimization method and device based on second-order cone relaxation to solve the problem of excess reactive power in the distribution network caused by centralized compensation of a large amount of reactive power on the master station side, which further leads to reduced voltage stability in the distribution network.

[0009] In a first aspect, an embodiment of the present invention provides a multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation, comprising:

[0010] Obtaining a reactive power flow mathematical model of a distribution network, wherein the distribution network includes a master station side, a switch station, and a distribution room; an objective function of the reactive power flow mathematical model is constructed based on power data of the master station side, the switch station, and the distribution room, and a penalty factor in the objective function includes a node voltage over-limit parameter and a charging reactive reverse transmission parameter;

[0011] The reactive power optimization flow mathematical model is optimized based on a second-order cone relaxation algorithm to obtain a multi-level coordinated compensation reactive power optimization strategy for the distribution network.

[0012] In a possible implementation, obtaining a reactive power optimization flow mathematical model of a distribution network includes:

[0013] Acquiring power data of the master station side, the switch station and the distribution room;

[0014] Based on the power data of the master station side, the switch station and the distribution room, the objective function of the reactive power optimization power flow mathematical model is constructed with the minimum reactive power compensation capacity and the minimum active power loss as the goal;

[0015] Based on the power data of the master station side, the switch station and the distribution room, constraint conditions are constructed; the objective function and the constraint conditions form the reactive power optimization flow mathematical model.

[0016] In a possible implementation, the power data of the master station side, the switch station and the power distribution room include equipment power parameters of the distribution network and operating power parameters of the distribution network;

[0017] Acquiring power data of the master station side, the switch station and the power distribution room, including:

[0018] Obtaining equipment records and topological relationships of the master station side, the switch station, and the power distribution room;

[0019] Acquire device power parameters of each device in the distribution network based on the device ledger and the topological relationship;

[0020] The operating power parameters of the distribution network during operation are collected.

[0021] In a possible implementation, the objective function is:

[0022]

[0023] Q lossh =Q tranh +Q lineh ;

[0024]

[0025] Among them, Q Ci is the capacitance compensation capacity of node i in the distribution network, Q Li is the reactance compensation capacity of node i in the distribution network, N C ' is the number of nodes with capacitors installed in the distribution network, N L ' is the number of nodes in the distribution network where reactors are installed, P loss is the active network loss of the distribution network, U N is the node rated voltage of the distribution network, U i is the voltage of node i in the distribution network, N D ' is the number of nodes where voltage exceeds the limit in the distribution network, Q tranh is the reactive power loss of the transformer in the hth branch of the distribution network, Q lineh is the reactive power loss of the hth branch in the distribution network, Q lossh is the total reactive power loss of the hth branch in the distribution network, Q bh is the charging power of the hth branch in the distribution network, B h is the branch susceptance of the hth branch in the distribution network, Q LDh is the terminal reactive load of the hth branch in the distribution network, N T ' is the number of branches with branch reverse transmission in the distribution network, λ 1 is the node voltage over-limit parameter in the distribution network, λ 2 is a dynamic parameter of reactive power reverse transmission in the distribution network.

[0026] In a possible implementation, the active network loss of the distribution network is:

[0027]

[0028] Among them, P loss is the active network loss of the distribution network, α ij is the switch state of branch (i, j), 1 means the switch is open, 0 means the switch is closed, r ij is the resistance of branch (i, j), P ij is the active power at the head end of branch (i, j), Q ij is the reactive power at the head end of branch (i, j), V i is the voltage value of node i;

[0029] The reactive power loss of the transformer in the hth branch of the distribution network is:

[0030]

[0031] Among them, Q tranh is the reactive power loss of the transformer in the hth branch of the distribution network, I0 % is the no-load current percentage of the transformer on the hth branch, S N is the rated capacity of the transformer on the hth branch, U k % is the short-circuit voltage percentage of the transformer on the h-th branch, and S is the operating load capacity of the transformer on the h-th branch;

[0032] The total reactive power loss of the hth branch in the distribution network is:

[0033]

[0034] Among them, Q lossh is the total reactive power loss of the hth branch, P ij is the active power at the head end of branch (i, j), Q ij is the reactive power at the head end of branch (i, j), V i is the voltage value of node i; and branch (i, j) and the h-th branch are the same branch.

[0035] In a possible implementation, the constraint condition includes a power flow equation constraint of the distribution network, and the power flow equation constraint is:

[0036]

[0037] Q j =Q Cj +Q Lj -Q LDj ;

[0038]

[0039] Where k is the node connected to node j, j→k means the active power flows from node j to node k, Q jk is the reactive power flowing from node j to node k, P ij is the active power at the head end of branch (i, j), Q ij is the reactive power at the head end of branch (i, j), is the current between branches (i, j), P j is the injected active power of node j except branch (i, j), Q j is the injected reactive power of node j except branch (i, j), Q Cj The reactive power injected into the capacitor bank at node j, Q Lj is the reactive power injected by the reactor group at node j, Q LDj is the load reactive power of node j, r ij is the resistance of branch (i, j), x ij is the reactance of branch (i, j), Vi is the voltage at node i, V j is the voltage at node j.

[0040] In a possible implementation, the constraint condition includes an operating variable constraint of the distribution network, and the operating variable constraint is:

[0041] V imin ≤V i ≤V imax ,i∈N B ;

[0042] Q Cimin ≤Q Ci ≤Q Cimax ,i∈N C ;

[0043] Q Limin ≤Q Li ≤Q Limax ,i∈N L ;

[0044] T nmin ≤T n ≤T nmax ,i∈N T ;

[0045]

[0046] Among them, N B For all node sets, N C is the capacitor reactive power compensation node set, N L is the set of reactive power compensation nodes, N T is the transformer branch set with load voltage regulation, V i is the voltage at node i, V imax is the upper limit of the voltage at node i, V imin is the lower limit of the voltage at node i, Q Ci is the capacitance compensation capacity of node i, Q Cimax is the upper limit of the capacitance compensation capacity of node i, Q Cimin is the lower limit of the capacitance compensation capacity of node i, Q Li is the reactance compensation capacity of node i, Q Limax is the upper limit of the reactance compensation capacity of node i, Q Limin is the lower limit of the reactance compensation capacity of node i, T n is the tap position of the nth on-load tap-changing transformer, T nmax is the upper limit of the tap position of the nth on-load tap-changing transformer, T nmin is the lower limit of the tap position of the nth on-load tap-changing transformer, is the average power factor of the balanced node A, is the upper limit of the average power factor of the balancing node A, is the lower limit of the average power factor of the balancing node A.

[0047] In a possible implementation, the value of the node voltage over-limit parameter and the value of the charging reactive power reverse transmission parameter are set according to the operation requirements of the distribution network.

[0048] In a possible implementation, the reactive power optimization flow mathematical model is optimized based on a second-order cone relaxation algorithm to obtain a multi-level coordinated compensation reactive power optimization strategy for the distribution network, including:

[0049] Optimizing the reactive power optimization mathematical model based on a second-order cone relaxation algorithm, and converting the reactive power optimization mathematical model into a reactive power optimization mathematical model in a convex optimization form;

[0050] The reactive power optimization mathematical model based on the convex optimization form is solved to obtain the multi-level coordinated compensation reactive power optimization strategy of the distribution network.

[0051] In a second aspect, an embodiment of the present invention provides a multi-level coordinated compensation reactive power optimization device based on second-order cone relaxation, comprising:

[0052] A model building module is used to obtain a reactive power optimization flow mathematical model of a distribution network, wherein the distribution network includes a master station side, a switch station and a distribution room; an objective function of the reactive power optimization flow mathematical model is constructed based on power data of the master station side, the switch station and the distribution room, and a penalty factor in the objective function includes a node voltage over-limit parameter and a charging reactive reverse transmission parameter;

[0053] The optimization module is used to optimize the reactive power optimization flow mathematical model based on a second-order cone relaxation algorithm to obtain a multi-level coordinated compensation reactive power optimization strategy for the distribution network.

[0054] The embodiment of the present invention provides a multi-level coordinated compensation reactive optimization method and device based on second-order cone relaxation. By obtaining the reactive optimization flow mathematical model of the distribution network composed of the main station side, the switch station and the distribution room, and setting the node voltage over-limit parameter and the charging reactive reverse transmission parameter in the objective function of the flow mathematical model, the flow mathematical model is optimized by the second-order cone relaxation algorithm to obtain the multi-level coordinated compensation reactive optimization strategy of the distribution network. By constructing a multi-level coordinated compensation reactive optimization objective function, reactive compensation is performed at the three levels of the main station side, the switch station and the distribution network to improve the uniformity of reactive power distribution in the power grid, and further improve the voltage stability of the power grid. Adding the node voltage over-limit as a penalty item to the objective function reduces the difficulty of restoring the voltage of the distribution network, further reduces the impact on the inverter, and enhances the effect of reactive riding. Adding the charging reactive reverse transmission as a penalty item to the objective function, while compensating for the capacitive reactive power, it also compensates for the inductive reactive power, thereby improving the efficiency and stability of the power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0056] Figure 1 It is a flow chart of a multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation provided by an embodiment of the present invention;

[0057] Figure 2 It is a specific implementation flow chart of a second-order cone relaxation algorithm in a multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation provided by an embodiment of the present invention;

[0058] Figure 3 It is a schematic diagram of a specific implementation process of a multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation provided by an embodiment of the present invention;

[0059] Figure 4 It is a structural schematic diagram of a multi-level coordinated compensation reactive power optimization device based on second-order cone relaxation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0061] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0062] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0063] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0064] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0065] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0066] The inventors found that the existing literature on reactive power compensation optimization planning methods is still mainly focused on the study of capacitive compensation problems, and there are fewer research documents on inductive reactive power compensation. In addition, the reactive power optimization strategy of the distribution network mainly focuses on the reactive power optimization of a single level of the distribution network. The reactive power optimization of substations and substations that are closely related to the distribution network has not been comprehensively considered. It is necessary to comprehensively consider the inductive and capacitive reactive power optimization methods at multiple levels of substations, lines, and substations.

[0067] Therefore, a multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation is proposed. This method studies the hierarchical reactive power optimization method of the full-cable urban distribution network, carries out reactive power optimization for the three levels of the main station side-switch station-distribution room, introduces the second-order cone programming optimal power flow model of the distribution network, and uses the convex optimization algorithm package to efficiently solve the model and obtain the global optimal solution, thereby obtaining the optimal access location and capacity of reactive power compensation.

[0068] Figure 1 is a flow chart of a multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation provided by an embodiment of the present invention, with reference to Figure 1 The multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation is described in detail as follows:

[0069] Step 100, obtaining a reactive power optimization flow mathematical model of the distribution network, wherein the distribution network includes a master station side, a switch station and a distribution room; an objective function of the reactive power optimization flow mathematical model is constructed based on power data of the master station side, the switch station and the distribution room, and a penalty factor in the objective function includes a node voltage over-limit parameter and a charging reactive reverse transmission parameter.

[0070] Among them, the reactive power optimization flow mathematical model is the mathematical model of the optimal flow, which consists of an objective function and a large number of constraints. The optimal flow means that when the structural parameters and load conditions of the system are given, through the optimization of control variables, the flow distribution that can meet all the specified constraints and make the system performance indicators or objective functions reach the optimal state is found.

[0071] This embodiment adopts the principles of bottom-up, layered and zoned, and on-site balancing to rationally plan the locations of reactive compensation points at the substation level, distribution line level, and distribution area level of the power grid, optimize the compensation capacity, and improve the utilization efficiency of existing reactive compensation devices. These reactive balance optimization measures reduce the reactive power flowing on the transmission lines, effectively reduce the losses of the distribution network, and help improve the quality and efficiency of the power grid.

[0072] In an optional embodiment, step 100 may include:

[0073] Step 110, obtaining power data of the master station side, switch station and distribution room.

[0074] Step 120, based on the power data of the main station side, the switch station and the distribution room, with the minimum reactive power compensation capacity and the minimum active power loss as the goal, construct the objective function of the reactive power optimization power flow mathematical model.

[0075] Step 130, based on the power data of the main station side, the switch station and the distribution room, construct constraint conditions; the objective function and the constraint conditions form a mathematical model of reactive power optimization flow.

[0076] Optionally, the power data may include but is not limited to the number of capacitors and reactors installed in all nodes on the master station side, switch station and distribution room, the voltage, rated voltage, capacitance compensation, reactance compensation of each node, the number of nodes with voltage exceeding the limit, the loss data, susceptance, terminal reactive load of each branch, the number of nodes with reverse transmission and other power data. Among them, the loss data of each branch includes the reactive loss of the transformer on each branch and the reactive loss of the transmission line.

[0077] Among them, the minimum reactive power compensation capacity and the minimum active power loss are the objectives of the objective function. It should be noted that the objective function can also be other objectives, such as the minimum total system loss, the minimum total system cost, etc. The objective function can have one, two or more objectives, and the specific needs should be adjusted according to the actual situation.

[0078] Optionally, the constraints include but are not limited to distribution network power flow equation constraints and operating variable constraints.

[0079] By performing reactive power compensation on the distribution network at three levels, namely the main station side, the switch station and the distribution station, rationally planning the location of reactive power compensation points at each level of the distribution network and optimizing the compensation capacity, the optimization means of the existing reactive power compensation device are effectively improved, the reactive power flowing on the transmission line is reduced, and the voltage stability of the distribution network is further improved.

[0080] In some embodiments, the objective function is:

[0081]

[0082] Q lossh =Q tranh +Q lineh ;

[0083]

[0084] Among them, Q Ci is the capacitance compensation capacity of node i in the distribution network, Q Li is the reactance compensation capacity of node i in the distribution network, N C ' is the number of nodes with capacitors installed in the distribution network, N L ' is the number of nodes in the distribution network where reactors are installed, Ploss is the active network loss of the distribution network, U N is the node rated voltage of the distribution network, U i is the voltage of node i in the distribution network, N D ' is the number of nodes where voltage exceeds the limit in the distribution network, Q tranh is the reactive power loss of the transformer in the hth branch of the distribution network, Q l neh is the reactive power loss of the hth branch in the distribution network, Q lossh is the total reactive power loss of the hth branch in the distribution network, Q Bh is the charging power of the hth branch in the distribution network, B h is the branch susceptance of the hth branch in the distribution network, Q LDh is the terminal reactive load of the hth branch in the distribution network, N T ' is the number of branches with branch reverse transmission in the distribution network, λ 1 is the node voltage over-limit parameter in the distribution network, λ 2 It is the dynamic parameter of reactive power reverse transmission in the distribution network.

[0085] The active network loss of the distribution network is:

[0086]

[0087] Among them, P loss is the active network loss of the distribution network, α ij is the switch state of branch (i, j), 1 means the switch is open, 0 means the switch is closed, r ij is the resistance of branch (i, j), P ij is the active power at the head end of branch (i, j), Q j is the reactive power at the head end of branch (i, j), V i is the voltage value of node i;

[0088] The reactive power loss of the transformer in the hth branch of the distribution network is:

[0089]

[0090] Among them, Q tranh is the reactive power loss of the transformer in the hth branch of the distribution network, I 0 % is the no-load current percentage of the transformer on the hth branch, S N is the rated capacity of the transformer on the hth branch, U k % is the short-circuit voltage percentage of the transformer on the h-th branch, and S is the operating load capacity of the transformer on the h-th branch;

[0091] The total reactive power loss of the hth branch in the distribution network is:

[0092]

[0093] Among them, Q lossh is the total reactive power loss of the hth branch, P ij is the active power at the head end of branch (i, j), Q ij is the reactive power at the head end of branch (i, j), V i is the voltage value of node i; and branch (i, j) and the h-th branch are the same branch.

[0094] Exemplarily, the constraint condition includes a power flow equation constraint of the distribution network, and the power flow equation constraint is:

[0095]

[0096] Q j =Q Cj +Q Lj -Q LDj ;

[0097]

[0098] Where k is the node connected to node j, j→k means the active power flows from node j to node k, Q jk is the reactive power flowing from node j to node k, P ij is the active power at the head end of branch (i, j), Q ij is the reactive power at the head end of branch (i, j), is the current between branches (i, j), P j is the injected active power of node j except branch (i, j), Q j is the injected reactive power of node j except branch (i, j), Q Cj The reactive power injected into the capacitor bank at node j, Q Lj is the reactive power injected by the reactor group at node j, Q LDj is the load reactive power of node j, r ij is the resistance of branch (i, j), x ij is the reactance of branch (i, j), V i is the voltage at node i, V j is the voltage at node j.

[0099] Exemplarily, the constraint condition includes an operating variable constraint of the distribution network, and the operating variable constraint is:

[0100] V imin ≤V i ≤V imax ,i∈N B ;

[0101] Q Cimin ≤Q Ci ≤Q Cimax ,i∈N C ;

[0102] Q Limin ≤Q Li ≤Q Limax ,i∈N L ;

[0103] T nmin ≤T n ≤T nmax ,i∈N T ;

[0104]

[0105] Among them, N B For all node sets, N C is the capacitor reactive power compensation node set, N L is the set of reactive power compensation nodes, N T is the transformer branch set with load voltage regulation, V i is the voltage at node i, V imax is the upper limit of the voltage at node i, V imin is the lower limit of the voltage at node i, Q Ci is the capacitance compensation capacity of node i, Q Cimax is the upper limit of the capacitance compensation capacity of node i, Q Cimin is the lower limit of the capacitance compensation capacity of node i, Q Li is the reactance compensation capacity of node i, Q Limax is the upper limit of the reactance compensation capacity of node i, Q Limin is the lower limit of the reactance compensation capacity of node i, T n is the tap position of the nth on-load tap-changing transformer, T nmax is the upper limit of the tap position of the nth on-load tap-changing transformer, T nmin is the lower limit of the tap position of the nth on-load tap-changing transformer, is the average power factor of the balanced node A, is the upper limit of the average power factor of the balancing node A, is the lower limit of the average power factor of the balancing node A.

[0106] In some embodiments, the power data of the master station side, the switch station and the distribution room include equipment power parameters of the distribution network and operating power parameters of the distribution network;

[0107] Step 110 may include:

[0108] Step 111, obtain the equipment ledger and topological relationship of the master station side, switch station and distribution room.

[0109] Step 112, obtaining the equipment power parameters of each equipment in the distribution network based on the equipment ledger and the topological relationship.

[0110] Step 113, collecting operating power parameters of the distribution network during operation.

[0111] In this embodiment, the equipment ledger is used to record the details of the power equipment on the main station side, switch station and distribution room, and records in detail the key information of the power equipment, such as name, model, specification, manufacturer, purchase date, user department, maintenance record, etc. This information is mainly used to let the staff understand the current status of the power equipment.

[0112] The topological relationship refers to the adjacency, association, inclusion and connectivity relationship between entities represented by nodes, arcs and polygons. In this embodiment, it refers to the connection relationship between each node and each power device in the distribution network.

[0113] In this embodiment, the equipment power parameters are obtained from the equipment ledger and the topological relationship. For example, the rated voltage, rated current and other information of the equipment can be obtained through the equipment ledger. For example, the number of nodes, the equipment models used in the distribution network, and the connection relationship between devices can be obtained through the topological relationship.

[0114] Optionally, the operating power parameters refer to parameters such as current, voltage, power, etc. of each branch and each power equipment in the distribution network during operation.

[0115] It should be noted that in addition to obtaining power data from equipment records, topological relationships, and during operation as described in this embodiment, data collection can also be performed in other ways. For example, operating power parameters can be obtained by training neural networks on historical operating data and estimating them.

[0116] In an optional embodiment, the penalty factors in the objective function include a node voltage over-limit parameter and a charging reactive power reverse transmission parameter, and the values ​​of the node voltage over-limit parameter and the charging reactive power reverse transmission parameter are set according to the operating requirements of the distribution network.

[0117] Step 200, optimizing the reactive power optimization flow mathematical model based on the second-order cone relaxation algorithm to obtain a multi-level coordinated compensation reactive power optimization strategy for the distribution network.

[0118] The second-order cone relaxation algorithm is a solution method for non-convex quadratic programming problems. By transforming the non-convex nonlinear reactive power optimization flow model into a linear model, it reduces the difficulty of solution and improves the efficiency of solution without losing accuracy.

[0119] The embodiment of the present invention constructs a multi-level coordinated compensation reactive optimization flow mathematical model, sets node voltage over-limit parameters and charging reactive reverse transmission parameters in the objective function of the flow mathematical model, and then optimizes and solves the flow mathematical model through a second-order cone relaxation algorithm to obtain a multi-level coordinated compensation reactive optimization strategy for the distribution network. By comprehensively considering the reactive power optimization of the three levels of the main station side, the switch station, and the distribution network, and performing reactive power compensation on the distribution network at three levels, the uniformity of reactive power distribution in the power grid is improved, and the voltage stability is further improved. Adding node voltage over-limit and charging reactive reverse transmission as parameters to the objective function takes into account the compensation of inductive reactive power and reduces the difficulty of voltage recovery in the distribution network, thereby improving the efficiency and stability of power grid operation.

[0120] In an optional embodiment, step 200 may include:

[0121] Step 210: Optimize the reactive power optimization flow mathematical model based on a second-order cone relaxation algorithm, and transform the reactive power optimization flow mathematical model into a reactive power optimization mathematical model in a convex optimization form.

[0122] Step 220, solving a reactive power optimization mathematical model based on a convex optimization form to obtain a multi-level coordinated compensation reactive power optimization strategy for the distribution network.

[0123] Among them, the second-order cone relaxation algorithm can transform the non-convex constraints in the original nonlinear or non-convex optimization problem into second-order cone constraints by introducing auxiliary variables and relaxation variables, thereby transforming the entire problem into a second-order cone programming problem.

[0124] In this embodiment, the specific implementation process is as follows Figure 2 , as detailed below:

[0125] Firstly, the phase angle relaxation method is used to remove the phase angle of decision variables in the objective function, power flow equation constraints and operation variable constraints.

[0126] Secondly, the quadratic equality constraint is transformed into a norm form by performing second-order cone relaxation, so that it becomes a second-order cone constraint equation;

[0127] Finally, the power flow constraints formed in the second-order cone form after processing transform the original reactive power optimization power flow mathematical model solution problem into a second-order cone programming problem, and the commercial solver is directly called for solution.

[0128] This embodiment converts the reactive power optimization flow mathematical model into a second-order cone programming model through a second-order cone relaxation algorithm, and obtains a multi-level coordinated compensation reactive power optimization strategy for the distribution network through the second-order cone programming model. The second-order cone relaxation algorithm can handle various complex constraints in the distribution network and ensure calculation accuracy, thereby effectively improving calculation efficiency.

[0129] In this embodiment, a specific implementation method of a multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation is as follows: Figure 3 As shown, the details are as follows:

[0130] The equipment records, topological relationships and data collected during the operation of all power equipment in the distribution network are integrated into multi-source data.

[0131] Taking the minimum reactive power compensation capacity and the minimum active power loss as the goal, the objective function is constructed based on the economic efficiency of power grid operation.

[0132] From the perspective of power supply reliability and reducing reactive power ride-through, the node voltage over-limit parameters and charging reactive power reverse transmission parameters are added to the objective function as penalty factors.

[0133] Based on the operation requirements of safe, stable and economical operation of the power grid, a multi-level coordinated compensation reactive power optimization flow mathematical model is constructed.

[0134] The three-level reactive power optimization mathematical model of the main station side-switch station-distribution room is optimized through the second-order cone relaxation algorithm. After the mathematical model of the power flow is converted into a convex optimization form, the second-order cone programming problem is solved, and a multi-level coordinated compensation reactive power optimization loss reduction method is obtained.

[0135] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0136] Corresponding to the above embodiment, a multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation is provided. Figure 4 A schematic structural diagram of a multi-level coordinated compensation reactive power optimization device based on second-order cone relaxation provided by an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown.

[0137] See also Figure 4 , a multi-level coordinated compensation reactive power optimization device 4 based on second-order cone relaxation in an embodiment of the present invention may include a model building module 41 and an optimization module 42;

[0138] The model building module 41 is used to obtain a reactive power optimization flow mathematical model of the distribution network, wherein the distribution network includes a master station side, a switch station and a distribution room; the objective function of the reactive power optimization flow mathematical model is constructed based on the power data of the master station side, the switch station and the distribution room, and the penalty factor in the objective function includes a node voltage over-limit parameter and a charging reactive reverse transmission parameter;

[0139] The optimization module 42 is used to optimize the reactive power optimization flow mathematical model based on the second-order cone relaxation algorithm to obtain a multi-level coordinated compensation reactive power optimization strategy for the distribution network.

[0140] In some embodiments, the model building module 41 is specifically used to:

[0141] Obtain power data from the main station side, switch station and distribution room;

[0142] Based on the power data of the main station, switch station and distribution room, the objective function of the reactive power optimization flow mathematical model is constructed with the minimum reactive power compensation capacity and the minimum active power loss as the goal;

[0143] Based on the power data of the main station, switch station and distribution room, the constraints are constructed; the objective function and the constraints form a mathematical model of reactive power optimization flow.

[0144] In some embodiments, the power data of the master station side, the switch station and the distribution room include equipment power parameters of the distribution network and operating power parameters of the distribution network;

[0145] The model building module 41 is specifically used for:

[0146] Obtain equipment records and topological relationships at the master station, switch station, and distribution room;

[0147] Obtain the equipment power parameters of each device in the distribution network based on the equipment ledger and topological relationship;

[0148] Collect the operating power parameters of the distribution network during operation.

[0149] In some embodiments, the objective function is:

[0150]

[0151] Q lossh =Q tranh +Q lineh ;

[0152]

[0153] Among them, Q Ci is the capacitance compensation capacity of node i in the distribution network, Q Li is the reactance compensation capacity of node i in the distribution network, N C ' is the number of nodes with capacitors installed in the distribution network, N L ' is the number of nodes in the distribution network where reactors are installed, P loss is the active network loss of the distribution network, U N is the node rated voltage of the distribution network, U i is the voltage of node i in the distribution network, N D ' is the number of nodes where voltage exceeds the limit in the distribution network, Q tranh is the reactive power loss of the transformer in the hth branch of the distribution network, Qlineh is the reactive power loss of the hth branch in the distribution network, Q lossh is the total reactive power loss of the hth branch in the distribution network, Q Bh is the charging power of the hth branch in the distribution network, B h is the branch susceptance of the hth branch in the distribution network, Q LDh is the terminal reactive load of the hth branch in the distribution network, N T ' is the number of branches with branch reverse transmission in the distribution network, λ 1 is the node voltage over-limit parameter in the distribution network, λ 2 It is the dynamic parameter of reactive power reverse transmission in the distribution network.

[0154] In some embodiments, the active network loss of the distribution network is:

[0155]

[0156] Among them, P loss is the active network loss of the distribution network, α ij is the switch state of branch (i, j), 1 means the switch is open, 0 means the switch is closed, r ij is the resistance of branch (i, j), P ij is the active power at the head end of branch (i, j), Q ij is the reactive power at the head end of branch (i, j), V i is the voltage value of node i;

[0157] The reactive power loss of the transformer in the hth branch of the distribution network is:

[0158]

[0159] Among them, Q tranh is the reactive power loss of the transformer in the hth branch of the distribution network, I 0 % is the no-load current percentage of the transformer on the hth branch, S N is the rated capacity of the transformer on the hth branch, U k % is the short-circuit voltage percentage of the transformer on the h-th branch, and S is the operating load capacity of the transformer on the h-th branch;

[0160] The total reactive power loss of the hth branch in the distribution network is:

[0161]

[0162] Among them, Q lossh is the total reactive power loss of the hth branch, P ij is the active power at the head end of branch (i, j), Q ij is the reactive power at the head end of branch (i, j), Vi is the voltage value of node i; and branch (i, j) and the h-th branch are the same branch.

[0163] In some embodiments, the constraint condition includes a power flow equation constraint of the distribution network, and the power flow equation constraint is:

[0164]

[0165] Q j =Q Cj +Q Lj -Q LDj ;

[0166]

[0167] Where k is the node connected to node j, j→k means the active power flows from node j to node k, Q jk is the reactive power flowing from node j to node k, P ij is the active power at the head end of branch (i, j), Q ij is the reactive power at the head end of branch (i, j), is the current between branches (i, j), P j is the injected active power of node j except branch (i, j), Q j is the injected reactive power of node j except branch (i, j), Q Cj The reactive power injected into the capacitor bank at node j, Q Lj is the reactive power injected by the reactor group at node j, Q LDj is the load reactive power of node j, r ij is the resistance of branch (i, j), x ij is the reactance of branch (i, j), V i is the voltage at node i, V j is the voltage at node j.

[0168] In some embodiments, the operating variable constraints of the distribution network are:

[0169] V imin ≤V i ≤V imax ,i∈N B ;

[0170] Q Cimin ≤Q Ci ≤Q Cimax ,i∈N C ;

[0171] Q Limin ≤Q Li ≤QLimax ,i∈N L ;

[0172] T nmin ≤T n ≤T nmax ,i∈N T ;

[0173]

[0174] Among them, N B For all node sets, N C is the capacitor reactive power compensation node set, N L is the set of reactive power compensation nodes, N T is the transformer branch set with load voltage regulation, V i is the voltage at node i, V imax is the upper limit of the voltage at node i, V imin is the lower limit of the voltage at node i, Q Ci is the capacitance compensation capacity of node i, Q Cimax is the upper limit of the capacitance compensation capacity of node i, Q Cimin is the lower limit of the capacitance compensation capacity of node i, Q Li is the reactance compensation capacity of node i, Q Limax is the upper limit of the reactance compensation capacity of node i, Q Limin is the lower limit of the reactance compensation capacity of node i, T n is the tap position of the nth on-load tap-changing transformer, T nmax is the upper limit of the tap position of the nth on-load tap-changing transformer, T nmin is the lower limit of the tap position of the nth on-load tap-changing transformer, is the average power factor of the balanced node A, is the upper limit of the average power factor of the balancing node A, is the lower limit of the average power factor of the balancing node A.

[0175] In some embodiments, the value of the node voltage over-limit parameter and the value of the charging reactive power reverse transmission parameter are set according to the operation requirements of the distribution network.

[0176] In some embodiments, the optimization module 42 is specifically configured to:

[0177] The reactive power optimization flow mathematical model is optimized based on the second-order cone relaxation algorithm, and the reactive power optimization flow mathematical model is transformed into a reactive power optimization flow mathematical model in convex optimization form;

[0178] The reactive power optimization flow mathematical model based on convex optimization form is solved to obtain the multi-level coordinated compensation reactive power optimization strategy of distribution network.

[0179] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0180] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. 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.

[0181] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of each of the above-mentioned multi-level coordinated compensation reactive power optimization method embodiments based on second-order cone relaxation. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium.

[0182] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation, characterized in that: include: Obtaining a reactive power optimization flow mathematical model of a distribution network, wherein the distribution network includes a master station side, a switch station and a distribution room; The objective function of the reactive power optimization flow mathematical model is constructed based on the power data of the master station side, the switch station and the distribution room, and the penalty factors in the objective function include node voltage over-limit parameters and charging reactive reverse transmission parameters; The reactive power optimization flow mathematical model is optimized based on a second-order cone relaxation algorithm to obtain a multi-level coordinated compensation reactive power optimization strategy for the distribution network.

2. A multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation as claimed in claim 1, characterized in that: Obtain the reactive power optimization flow mathematical model of the distribution network, including: Acquiring power data of the master station side, the switch station and the distribution room; Based on the power data of the master station side, the switch station and the distribution room, the objective function of the reactive power optimization power flow mathematical model is constructed with the minimum reactive power compensation capacity and the minimum active power loss as the goal; Based on the power data of the master station side, the switch station and the distribution room, constraint conditions are constructed; the objective function and the constraint conditions form the reactive power optimization flow mathematical model.

3. A multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation as claimed in claim 2, characterized in that: The power data of the master station side, the switch station and the power distribution room include equipment power parameters of the distribution network and operating power parameters of the distribution network; Acquiring power data of the master station side, the switch station and the power distribution room, including: Obtaining equipment records and topological relationships of the master station side, the switch station, and the power distribution room; Acquire device power parameters of each device in the distribution network based on the device ledger and the topological relationship; The operating power parameters of the distribution network during operation are collected.

4. The multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation according to claim 2 is characterized in that: The objective function is: Q lossh =Q tranh +Q lineh ; Among them, Q Ci is the capacitance compensation capacity of node i in the distribution network, Q Li is the reactance compensation capacity of node i in the distribution network, N C ' is the number of nodes with capacitors installed in the distribution network, N L ' is the number of nodes in the distribution network where reactors are installed, P loss is the active network loss of the distribution network, U N is the node rated voltage of the distribution network, U i is the voltage of node i in the distribution network, N D ' is the number of nodes where voltage exceeds the limit in the distribution network, Q tranh is the reactive power loss of the transformer in the hth branch of the distribution network, Q lineh is the reactive power loss of the hth branch in the distribution network, Q lossh is the total reactive power loss of the hth branch in the distribution network, Q Bh is the charging power of the hth branch in the distribution network, B h is the branch susceptance of the hth branch in the distribution network, Q LDh is the terminal reactive load of the hth branch in the distribution network, N T ' is the number of branches with branch reverse transmission in the distribution network, λ1 is the node voltage over-limit parameter in the distribution network, and λ2 is the reactive reverse transmission dynamic parameter in the distribution network.

5. A multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation as claimed in claim 4, characterized in that: The active network loss of the distribution network is: Among them, P loss is the active network loss of the distribution network, α ij is the switch state of branch (i, j), 1 means the switch is open, 0 means the switch is closed, r ij is the resistance of branch (i, j), P ij is the active power at the head end of branch (i, j), Q ij is the reactive power at the head end of branch (i, j), V i is the voltage value of node i; The reactive power loss of the transformer in the hth branch of the distribution network is: Among them, Q tranh is the reactive power loss of the transformer on the hth branch of the distribution network, I0% is the percentage of the no-load current of the transformer on the hth branch, S N is the rated capacity of the transformer on the hth branch, U k % is the short-circuit voltage percentage of the transformer on the h-th branch, and S is the operating load capacity of the transformer on the h-th branch; The total reactive power loss of the hth branch in the distribution network is: Among them, Q lossh is the total reactive power loss of the hth branch, P ij is the active power at the head end of branch (i, j), Q ij is the reactive power at the head end of branch (i, j), V i is the voltage value of node i; and branch (i, j) and the h-th branch are the same branch.

6. A multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation as claimed in claim 2, characterized in that: The constraint condition includes the power flow equation constraint of the distribution network, and the power flow equation constraint is: Q j =Q Cj +Q Lj -Q LDj ; Where k is the node connected to node j, j→k means the flow from node j to node k, P jk is the active power flowing from node j to node k, Q jk is the reactive power flowing from node j to node k, P ij is the active power at the head end of branch (i, j), Q ij is the reactive power at the head end of branch (i, j), is the current between branches (i, j), P j is the injected active power of node j except branch (i, j), Q j is the injected reactive power of node j except branch (i, j), Q Cj The reactive power injected into the capacitor bank at node j, Q Lj is the reactive power injected by the reactor group at node j, Q LDj is the load reactive power of node j, r ij is the resistance of branch (i, j), x ij is the reactance of branch (i, j), V i is the voltage at node i, V j is the voltage at node j.

7. The multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation according to claim 2, characterized in that: The constraint condition includes an operating variable constraint of the distribution network, and the operating variable constraint is: In imin ≤V i ≤V imax ,i∈N B ; Q Cimin ≤Q Ci ≤Q Cimax ,i∈N C ; Q Limin ≤Q Li ≤Q Limax ,i∈N L ; T nmin ≤T n ≤T nmax ,i∈N T ; Among them, N B For all node sets, N C is the capacitor reactive power compensation node set, N L is the set of reactive power compensation nodes, N T is the transformer branch set with load voltage regulation, V i is the voltage at node i, V imax is the upper limit of the voltage at node i, V imin is the lower limit of the voltage at node i, Q Ci is the capacitance compensation capacity of node i, Q Cimax is the upper limit of the capacitance compensation capacity of node i, Q Cimin is the lower limit of the capacitance compensation capacity of node i, Q Li is the reactance compensation capacity of node i, Q Limax is the upper limit of the reactance compensation capacity of node i, Q Limin is the lower limit of the reactance compensation capacity of node i, T n is the tap position of the nth on-load tap-changing transformer, T nmax is the upper limit of the tap position of the nth on-load tap-changing transformer, T nmim is the lower limit of the tap position of the nth on-load tap-changing transformer, is the average power factor of the balanced node A, is the upper limit of the average power factor of the balancing node A, is the lower limit of the average power factor of the balancing node A.

8. The multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation according to claim 1, characterized in that: The value of the node voltage over-limit parameter and the value of the charging reactive power reverse transmission parameter are set according to the operation requirements of the distribution network.

9. The multi-level coordinated compensation reactive power optimization method based on second-order cone relaxation according to claim 1, characterized in that: The reactive power optimization flow mathematical model is optimized based on a second-order cone relaxation algorithm to obtain a multi-level coordinated compensation reactive power optimization strategy for the distribution network, including: Optimizing the reactive power optimization flow mathematical model based on a second-order cone relaxation algorithm, and converting the reactive power optimization flow mathematical model into a reactive power optimization flow mathematical model in a convex optimization form; The reactive power optimization flow mathematical model based on the convex optimization form is solved to obtain the multi-level coordinated compensation reactive power optimization strategy of the distribution network.

10. A multi-level coordinated compensation reactive power optimization device based on second-order cone relaxation, characterized in that: include: A model building module is used to obtain a reactive power optimization flow mathematical model of a distribution network, wherein the distribution network includes a master station side, a switch station and a distribution room; an objective function of the reactive power optimization flow mathematical model is constructed based on power data of the master station side, the switch station and the distribution room, and a penalty factor in the objective function includes a node voltage over-limit parameter and a charging reactive reverse transmission parameter; The optimization module is used to optimize the reactive power optimization flow mathematical model based on a second-order cone relaxation algorithm to obtain a multi-level coordinated compensation reactive power optimization strategy for the distribution network.

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