Power distribution network voltage coordination control method based on event triggering mechanism

Abstract

The application discloses a power distribution network voltage coordination control method based on an event triggering mechanism and belongs to the technical field of power distribution control. The method comprises the following steps: a global optimization model is constructed; the power distribution network is divided into multiple sub-regions, coupling constraints of each region are established, and the global optimization problem is decomposed into regional optimization problems; a pre-designed ETM-DAL-ADMM algorithm is adopted, a Lagrange function of the regional optimization problem is linearly processed and dynamically adjusted in a penalty coefficient, local decision variables of each region including boundary variables are updated, information exchange between regions is carried out according to a pre-set event triggering mechanism, and final local decision variables are iteratively solved; and reactive power outputs of distributed generators in each region are adjusted according to the final local decision variables, so that the voltage of the power distribution network meets the constraints and branch loss is minimized. The application can solve the problem that the existing control method cannot guarantee the stability of the power distribution system when the communication capability is insufficient.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution control, and particularly relates to a power distribution network voltage coordination control method based on an event triggering mechanism. BACKGROUND

[0002] With large-scale access of photovoltaic (PV), wind power, energy storage and other distributed energy resources (DERs) in distribution networks (DNs), the traditional centralized scheduling mode is often difficult to meet the real-time VCC demand of high frequency and low delay due to the high concentration of calculation and communication load. The distributed optimization based on the augmented Lagrange multiplier method (ADMM) provides a feasible path for active distribution network (ADN) voltage control: the method divides the whole network into multiple autonomous sub-regions through network community discovery or modularity maximization, independently solves the local sub-problems of each region, and then only exchanges the "boundary variables" of adjacent regions at the boundary. The boundary variable refers to the node voltage and power flow information, so as to ensure the coordinated convergence of each region and finally realize the consistency of the whole network.

[0003] However, the existing distributed ADMM method still has obvious deficiencies in engineering application. First, since all boundary information of the sub-regions needs to be transmitted every iteration, the communication frequency and the number of iterations increase synchronously, which easily leads to congestion in the scene of limited network bandwidth or large scale, and significantly increases the energy consumption of system operation; second, the penalty factor ρ in ADMM for balancing the degree of constraint violation and convergence speed is often fixed by experience, which is difficult to quickly reduce the original residual and dual residual in the early stage of iteration, and also cannot be finely adjusted in the later stage to ensure high-precision convergence; third, the existing fixed-time or conditional triggering communication strategy often lacks rigorous threshold design and minimum triggering interval analysis, and cannot theoretically exclude the "Zeno phenomenon", that is, the infinite triggering behavior in a limited time, so the existing fixed-time or conditional triggering communication strategy cannot maintain the global convergence and system stability of the algorithm under the condition of significant communication sparsification. SUMMARY

[0004] The purpose of the present application is to overcome the deficiencies in the prior art and provide a power distribution network voltage coordination control method based on an event triggering mechanism, which solves the problem that the existing method cannot guarantee the stability of the power distribution system when the communication capacity is insufficient.

[0005] To solve the above technical problems, the present application is implemented by using the following technical solutions:

[0006] The application provides a power distribution network voltage coordination control method based on an event triggering mechanism, comprising:

[0007] Based on power distribution network operation data and equipment parameters, a global optimization model including distributed power flow equation constraints and safety constraints is constructed with the objective of minimizing branch loss;

[0008] According to the power distribution network topology and load distribution characteristics, the power distribution network is divided into multiple sub-regions, the coupling constraints of each region are established by copying the boundary variables of each region, and the global optimization problem is decomposed into regional optimization problems according to the coupling constraints;

[0009] An ETM-DAL-ADMM algorithm based on the global optimization model and the coupling constraints is used to update the local decision variables of each region including the boundary variables by linearizing the Lagrangian function of the regional optimization problem and dynamically adjusting the penalty coefficient according to the original residual and the dual residual, and according to the pre-set event triggering mechanism, the information exchange between regions is only carried out when the boundary variable changes exceed the pre-set threshold, and the final local decision variable is iteratively solved;

[0010] According to the final local decision variable, the reactive power output of the distributed generator in each region is adjusted so that the voltage of the power distribution network meets the constraints and the branch loss is minimized.

[0011] The aforementioned power distribution network voltage coordination control method based on an event triggering mechanism, the distributed power flow equation constraints of the global optimization model, comprising:

[0012] According to the DistFlow model, the quadratic loss term is removed and and are used as the voltage amplitude of node j and node i, respectively, to obtain the final distributed power flow equation:

[0013] ,

[0014] In the formula, node is the upstream node, and node is the downstream node; and are the active power and reactive power on the branch , respectively; and are the active power and reactive power of node , respectively; is the resistance of branch ; is the reactance of branch ;

[0015] Security constraints of the global optimization model, including: security constraints in the distribution network and distributed generator power constraints;

[0016] Security constraints in the distribution network:

[0017] ,

[0018] wherein, and are the lower and upper limits of the voltage amplitude of the node ; is the set of nodes; is the branch current, is the upper limit of the branch current; and are the lower and upper limits of the active power of the branch ; and are the lower and upper limits of the reactive power of the branch , is the set of branches;

[0019] Distributed generator power constraints:

[0020] ,

[0021] wherein, , and are the active power, reactive power and apparent power of the distributed generator at the node ; and are the lower and upper limits of the active power of the distributed generator at the node ; and are the lower and upper limits of the reactive power of the distributed generator at the node .

[0022] The aforementioned distribution network voltage coordination control method based on the event triggering mechanism, the coupling constraints of each region include:

[0023] The coupling constraints of the first region are:

[0024] ,

[0025] wherein, is the upstream region, is the downstream region; is the boundary variable of the first region, including the​ Boundary variables when the first region is an upstream region and the second region Boundary variables when a region is used as a downstream region; For the corresponding number The boundary variables of the adjacent regions of the nth region, when the nth region... When a region is an upstream region of an adjacent region For the boundary variable when the adjacent region is the downstream region, when the first... When a region is a downstream region of an adjacent region This is the boundary variable when the adjacent region is used as the upstream region; For the first Global variables for each region; , and The first The voltage magnitude, active power, and reactive power at the boundaries of each region, including the first... The boundary voltage magnitude, active power, and reactive power when the first region is considered as the upstream region, and the first... The boundary voltage magnitude, active power, and reactive power when each region is a downstream region; , and Corresponding to the first The voltage magnitude, active power, and reactive power of the adjacent region boundaries of each region, when the first region... When a region is an upstream region of an adjacent region , and These represent the boundary voltage magnitude, active power, and reactive power when the adjacent region is considered as the downstream region. When a region is a downstream region of an adjacent region , and These represent the boundary voltage magnitude, active power, and reactive power when the adjacent region is considered as the upstream region.

[0026] The aforementioned distribution network voltage coordination control method based on an event-triggered mechanism, wherein the decomposition of the global optimization problem into regional optimization problems according to coupling constraints includes:

[0027] Based on the coupling constraints, the global optimization problem is decomposed into several regional optimization problems, and the objective function of the global optimization model is designed as follows:

[0028] ,

[0029] In the formula, The total number of areas into which the power distribution network is divided; For the first Local decision variables for each region Including the Boundary variables of each region ; For the first The objective function for each region; For the first The feasible region of local decision variables for each region that meets the constraints of distributed power flow equations and security constraints.

[0030] The aforementioned event-triggered voltage coordination control method for distribution networks, in the pre-designed ETM-DAL-ADMM algorithm, includes the design of the Lagrangian function for each region's optimization problem, which involves: linearizing the Lagrangian function of each region's optimization problem based on the global optimization model and coupling constraints to reduce computational complexity.

[0031] Based on the objective function of the designed global optimization model, the Lagrangian function of the global optimization problem is constructed. The calculation formula is:

[0032] ,

[0033] In the formula, For the Lagrange multipliers corresponding to the consistency constraints between boundary variables and global variables, T represents the transpose operation; This is the penalty coefficient; Square of the 2-norm operation;

[0034] Based on the Lagrangian function of the global optimization problem and the domain partitioning, the Lagrangian functions of the optimization problems in each domain are obtained. The calculation formula is:

[0035] ,

[0036] For the Lagrangian function of the optimization problem in each region, a first-order Taylor expansion is performed on the objective function of each region to obtain the linearized Lagrangian function of the optimization problem in each region. The calculation formula is:

[0037] ,

[0038] In the formula, For the first The second iteration Objective function for each region In the Local decision variables for each region The linear term in the first-order Taylor expansion.

[0039] In the ETM-DAL-ADMM algorithm of the foregoing power distribution network voltage coordination control method based on an event triggering mechanism, the k+1 iteration formula of the local decision variable of the first region is:

[0040]

[0041] In the formula, xk+1 is the local decision variable of the first region in the k+1 iteration; xk+1 is the local decision variable of the first region in the k+1 iteration; xk+1 is the local decision variable of the first region in the k+1 iteration; xk+1 is the global variable of the first region in the k+1 iteration; xk+1 is the Lagrange multiplier of the first region in the k+1 iteration; and the argmin function is used to output the input parameter when the subsequent function reaches the minimum value. xk+1 is the local decision variable of the first region in the k+1 iteration; xk+1 is the local decision variable of the first region in the k+1 iteration; xk+1 is the global variable of the first region in the k+1 iteration; xk+1 is the Lagrange multiplier of the first region in the k+1 iteration; and the argmin function is used to output the input parameter when the subsequent function reaches the minimum value.

[0042] In the formula, xk+1 is the global variable of the first region in the k+1 iteration; xk+1 is the global variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration;

[0043] xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the local decision variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration;

[0044] xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration;

[0045] xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration;

[0046] xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration; xk+1 is the boundary variable of the first region in the k+1 iteration;

[0047] xk+1 is the boundary variable of the first region in the k+1 iteration; the Lagrange multiplier of the kth iteration of the jth region; the Lagrange multiplier of the kth iteration of the jth region; the Lagrange multiplier of the kth iteration of the jth region; the Lagrange multiplier of the kth iteration of the jth region. the Lagrange multiplier of the kth iteration of the jth region. the Lagrange multiplier of the kth iteration of the jth region.

[0048] The aforementioned power grid voltage coordination control method based on the event triggering mechanism, in the pre-designed ETM-DAL-ADMM algorithm, the design of the Lagrange function of the regional optimization problem also includes: dynamically adjusting the penalty coefficient according to the original residual error and the dual residual error to improve the convergence speed:

[0049] the kth iteration formula of the penalty coefficient of the jth region the kth iteration formula of the penalty coefficient of the jth region

[0050] ,

[0051] ,

[0052] In the formula, and are the minimum and maximum iteration indexes allowed to adjust, respectively; the penalty coefficient of the kth iteration of the jth region; the penalty coefficient of the kth iteration of the jth region; the penalty coefficient of the kth iteration of the jth region; the penalty coefficient of the kth iteration of the jth region; the penalty coefficient of the kth iteration of the jth region; the initial value of the penalty coefficient of the jth region; and are the preset penalty coefficient up and down parameters, respectively; and are the preset original residual error and dual residual error comparison parameters, respectively; is the 2-norm operation; and are the original residual error and the dual residual error of the kth iteration of the jth region, respectively; are the original residual error and the dual residual error of the kth iteration of the jth region, respectively; the original residual error of the kth iteration of the jth region the original residual error of the kth iteration of the jth region

[0053] the original residual error of the kth iteration of the jth region the original residual error of the kth iteration of the jth region the original residual error of the kth iteration of the jth region ,

[0054] the dual residual error of the kth iteration of the jth region the dual residual error of the kth iteration of the jth region the dual residual error of the kth iteration of the jth region ​​​​ .

[0055] The aforementioned power distribution network voltage coordination control method based on the event triggering mechanism, the inter-regional information exchange is only carried out when the boundary variable changes beyond the preset threshold according to the preset event triggering mechanism, comprising:

[0056] The time point of the inter-regional information exchange of the first region The calculation formula is:

[0057] ,

[0058] ,

[0059] ,

[0060] In the formula, is the next event triggering time of the first region; is the last event triggering time of the first region; is the maximum allowed time; is the event triggering condition of the first region; is the error of the first region in the nth iteration; is the boundary variable of the first region in the nth iteration; is the boundary variable of the first region in the nth iteration; is the event triggering function of the first region; is the weighted square of the error of the first region in the nth iteration is the preset weight matrix; is the preset threshold parameter of the control event triggering, ; is the weighted square of the boundary variable of the first region in the nth iteration ; The event triggering condition of the first region is: . The error of the first region in the nth iteration is the weighted square of the boundary variable of the first region in the nth iteration ; The event triggering condition of the first region is: .

[0061] The event triggering condition of the first region is:

[0062] .

[0063] ​​​​​​​​​​The aforementioned event-triggered distribution network voltage coordination control method employs a pre-designed ETM-DAL-ADMM algorithm to iteratively solve for the final local decision variables, including:

[0064] Lagrange multipliers for each region Initialize to 0, set global variables for each region. For the actual boundary variables of each region Set the iteration index ;

[0065] The following process is executed repeatedly until the iteration termination condition is met, outputting the final local decision variables for each region, which are used for distribution network voltage coordination control:

[0066] Each region according to the first Local decision variables for each region The iterative formula is used to solve the optimization problem in each region, and the boundary variables of each region are obtained. ;

[0067] Based on the event-triggered mechanism (ETM), the error of each region is determined according to the event triggering conditions. If the event triggering conditions are met, the corresponding region exchanges information with its neighboring regions.

[0068] Each region according to the first Global variables for each region The iterative formula updates the global variables for each region. ;

[0069] Each region according to the first Lagrange multipliers in each region The iterative formula updates the Lagrange multipliers for each region. ;

[0070] Each region according to the first Penalty coefficient for each region The iterative formula updates the penalty coefficient for each region. ;

[0071] Each region according to the first Original residuals of each region and dual residuals Calculation formula for calculating the original residuals of each region. and dual residuals .

[0072] The aforementioned event-triggered distribution network voltage coordination control method includes the following iteration termination conditions:

[0073] According to the convergence condition formula, whether the iteration termination condition is met is judged, if the convergence condition formula is met, the ETM-DAL-ADMM algorithm converges, and the iteration is terminated, and the convergence condition formula is:

[0074] ,

[0075] In the formula, It is an infinite norm operation; It is a preset tolerance, It is a boundary residual;

[0076] Or, according to the iteration number, whether the iteration termination condition is met is judged, and when the iteration number is greater than or equal to the preset upper limit of the iteration index, the iteration is terminated.

[0077] Compared with the prior art, the beneficial effects achieved by the present application are:

[0078] The power distribution network voltage coordination control method based on the event triggering mechanism improves the traditional ADMM algorithm, linearizes the Lagrange function of each regional optimization problem to reduce the calculation complexity, dynamically adjusts the penalty coefficient according to the original residual and the dual residual to improve the convergence speed, and adds the event triggering mechanism, which triggers the regional information exchange only at necessary moments, reduces the redundant data transmission and the communication frequency, and can solve the problem that the existing method cannot guarantee the stability of the power distribution system when the communication ability is insufficient.

[0079] The present application aims to divide the power distribution network into several autonomous sub-regions and virtually copy the boundary variables by regional decoupling based on network community discovery or modularity maximization, realize complete independent optimization at the model level, introduce first-order Taylor linearization and dynamic penalty coefficient ρ adjustment based on original / dual residual in the ADMM framework to significantly accelerate the iteration convergence, design an event-triggered communication strategy based on the quadratic threshold of the boundary variable increment, and completely eliminate the Zeno phenomenon through formal analysis of the minimum triggering interval, so as to greatly reduce the communication overhead and energy consumption, and guarantee the global convergence of the algorithm and the stability of the power distribution network operation.

[0080] Through complete mathematical modeling and constraint analysis, the present application not only guarantees the physical feasibility of the distributed power flow equation and the strict boundary of node voltage and unit output, but also lays a foundation for high-precision optimization; on this basis, flexible network regional division and coupling constraint design are adopted, so that each region can be operated in parallel and maintain global consistency; further, the DAL-ADMM algorithm with accelerated linearity is introduced to realize fast convergence of local update and global coordination; at the same time, the event triggering mechanism ETM only exchanges information when the boundary variable changes exceeds the threshold, greatly reducing the redundant data transmission. BRIEF DESCRIPTION OF DRAWINGS

[0081] Figure 1 is an ETM-DAL-ADMM algorithm construction process schematic diagram of a power distribution network voltage coordination control method based on an event triggering mechanism according to Embodiment One of the present application.

[0082] Figure 2 is an ADN radiation topology feature schematic diagram according to Embodiment One of the present application.

[0083] Figure 3 is a process schematic diagram of establishing coupling constraints of each region by copying boundary variables of each region according to Embodiment One of the present application.

[0084] Figure 4 is a radial topology and region division schematic diagram of an IEEE-33 power distribution network according to Embodiment One of the present application.

[0085] Figure 5 is a boundary residual change with iteration number comparison schematic diagram of five algorithms, i.e., an ETM-DAL-ADMM algorithm, a DAL-ADMM algorithm, an L-ADMM algorithm, an O-ADMM algorithm and a FALM algorithm according to Embodiment One of the present application. DETAILED DESCRIPTION

[0086] The technical solutions of the present application will be described in detail below with the accompanying drawings and specific embodiments. It should be understood that the specific features in the embodiments and the specific embodiments are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application. In the case of no conflict, the technical features in the embodiments and the specific embodiments can be combined with each other.

[0087] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not used to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as there is no conflict.

[0088] Embodiment One:

[0089] The present embodiment proposes a power distribution network voltage coordination control method based on an event triggering mechanism, Figure 1 is an ETM-DAL-ADMM algorithm construction process flow chart of a power distribution network voltage coordination control method based on an event triggering mechanism according to Embodiment One of the present application. The flow chart only shows the logical order of the method described in the present embodiment, and the steps shown or described can be completed in an order different from that shown in the present application under the premise of no conflict. The method comprises the following steps: Figure 1

[0090] ​S1: Based on the distribution network operation data and equipment parameters, a global optimization model including distributed power flow equation constraints and safety constraints is constructed to minimize branch loss;

[0091] S2: According to the distribution network topology and load distribution characteristics, the distribution network is divided into multiple sub-regions, the coupling constraints of each region are established by copying the boundary variables of each region, and the global optimization problem is decomposed into regional optimization problems according to the coupling constraints;

[0092] S3: The ETM-DAL-ADMM algorithm based on the global optimization model and the coupling constraint pre-design is used to update the local decision variables of each region including the boundary variables through the linearization processing of the Lagrange function of the regional optimization problem and the dynamic adjustment of the penalty coefficient according to the original residual and the dual residual, and according to the pre-set event triggering mechanism, the information exchange between regions is only carried out when the boundary variable changes more than the pre-set threshold, and the final local decision variable is iteratively solved;

[0093] S4: According to the final local decision variable, the reactive power output of the distributed generator in each region is adjusted to make the distribution network voltage meet the constraints and the branch loss minimized.

[0094] Step S1 includes: based on the system target of distributed voltage coordinated control VCC, a global optimization model is constructed: on the one hand, the distributed power flow equation is derived to ensure the physical feasibility of power flow, that is, to meet the power conservation, voltage constraint, current constraint and the like; on the other hand, combined with the node voltage safety range and the upper and lower limits of generator output and other constraints, a complete mathematical programming is formed to provide an accurate theoretical basis for subsequent algorithm design.

[0095] In step S1, the goal of the VCC problem is to reduce the loss caused by the current on each branch in the power grid. The constraints of the global optimization model include distributed power flow equation constraints and safety constraints; wherein the global optimization model aims to minimize branch loss, and the objective function is as follows:

[0096] , (1)

[0097] In the formula, is the branch loss function; is the resistance of the branch from node to node ; and are the active and reactive power of the branch , respectively; is the voltage amplitude of node ; denotes the set of all branches; is the set of nodes.

[0098] Distribution networks usually present a radial topology, and there are a large number of distributed power sources and complex loads, so the distributed power flow equation needs to reflect these characteristics, and the commonly used classical model is the DistFlow model, which describes the power flow characteristics through the recursive relationship between branch power and node voltage, including the following key equations:

[0099] 1) DistFlow branch constraint: as shown in the following equation, the Distflow branch equation is used to describe the radial topology characteristics of the ADN. Figure 2

[0100] Node power balance equation: for any node in the distribution network , the active / reactive power flowing into the node is equal to the active / reactive power flowing out of the node;

[0101] Branch power and voltage relationship equation: branch power loss is related to node voltage and branch impedance;

[0102] Branch power loss equation: the active / reactive loss of the branch can be directly calculated by power and impedance;

[0103] , (2)

[0104] , (3)

[0105] In the equation, node is the upstream node, and node is the downstream node; and are the active and reactive power of branch , respectively; and are the active power loss and reactive power loss of branch , which are the quadratic loss terms of power; and are the active power and reactive power of node , respectively; is the voltage amplitude of node ; is the voltage amplitude of node ; is the resistance of branch ; is the reactance of branch ; is the linear loss term, which is proportional to the branch power; is the quadratic loss term of line impedance, which is proportional to the square of branch power, reflecting the nonlinear effect of line impedance; and are the active power and reactive power of node ​active and reactive power at the nodes with distributed generators; and are the active and reactive power at the nodes with distributed generators, respectively; is the set of nodes with distributed generators; is the set of nodes without distributed generators.

[0106] The equation of the DistFlow branch constraint contains quadratic terms, which include: , , that is, contains non-convex terms, resulting in the non-convexity of the DistFlow model. Non-convex optimization problems have multiple local optimal solutions, which are difficult to solve and have low computational efficiency, and are difficult to be directly applied to distributed optimization algorithms.

[0107] To solve this problem, the linear approximation of the DistFlow model is adopted in the embodiment, the quadratic loss term is ignored, and the quadratic loss term of power and the quadratic loss term of line impedance are removed, so that the problem is easier to solve. The embodiment respectively adopts and as the voltage amplitude of node j and node i to ensure that the constraint no longer contains non-convex terms. The final DistFlow branch equation is as follows:

[0108] , (4)

[0109] The security constraints include: security constraints in the distribution network and distributed generator power constraints;

[0110] 2) Security constraints in the distribution network:

[0111] , (5)

[0112] In the formula, and are the lower limit and upper limit of the voltage amplitude of node , respectively; is the set of nodes; is the branch current, is the upper limit of the branch current; and are the lower limit and upper limit of the active power on the branch , respectively; and are the lower limit and upper limit of the reactive power on the branch , respectively, is the branch set.

[0113] 3) Distributed generator power constraints:

[0114] , (6)

[0115] wherein, , and are the active power, reactive power and apparent power of the distributed generator at node ; and are the lower and upper limits of the active power of the distributed generator at node ; and are the lower and upper limits of the reactive power of the distributed generator at node .

[0116] Step S2 comprises: on the basis of the existing model, according to the topology and load distribution characteristics of the distribution network, dividing the distribution network into a plurality of regions; for each region boundary, defining a power and voltage demarcation variable, and establishing a corresponding coupling constraint to ensure that the calculation results of each region after the division remain consistent with the global calculation results of the global optimization model.

[0117] In step S2, in order to cope with the inherent high communication pressure and slow calculation speed in ADN centralized optimization, according to the topology of the active distribution network ADN and the load distribution characteristics of the active distribution network ADN containing a plurality of distributed generators, the active distribution network ADN is divided into a plurality of regions. When solving the optimization problem, each region independently solves its distributed optimization problem. However, since there is an inherent coupling relationship between these regions, a decoupling method needs to be used.

[0118] In this case, a decoupling method as shown in Figure 3 is used. Specifically, the boundary bus of the upstream region is "copied" to the downstream region as a virtual bus, and the voltage of the virtual bus is the voltage of the boundary bus of the upstream region. At the same time, the power flowing through the boundary branch is "copied" to the upstream region as a virtual power source, and the power flow between regions is considered as the power flowing through the boundary branch. In order to ensure that the solution of the decoupled distributed model is consistent with the solution of the centralized model before decoupling, it is necessary to ensure that the constraint conditions of each region on the boundary variable are consistent. The coupling constraint of each region is established by copying the boundary variable of each region, and the coupling constraint of the first region is:

[0119] , (7)

[0120] wherein, is the upstream region, is the downstream region; is the boundary variable of the first region, including the the boundary variable of the upstream region when the i-th region is a downstream region; the boundary variable of the downstream region when the i-th region is an upstream region; the boundary variable of the i-th region, when the i-th region is an upstream region; the boundary variable of the i-th region, when the i-th region is a downstream region; the boundary variable of the i-th region, when the i-th region is an upstream region of a neighboring region; the boundary variable of the i-th region, when the i-th region is a downstream region of a neighboring region; the boundary variable of the i-th region, when the i-th region is a downstream region of a neighboring region; the boundary variable of the i-th region, when the i-th region is an upstream region of a neighboring region; the global variable of the i-th region, when the i-th region is an upstream region; the boundary variable of the i-th region, when the i-th region is a downstream region the boundary variable of the i-th region, when the i-th region is a downstream region the boundary variable of the i-th region, when the i-th region is a downstream region the boundary variable of the i-th region, when the i-th region is a downstream region the global variable of the i-th region, when the i-th region is a downstream region; the global variable of the i-th region, when the i-th region is a downstream region; the global variable of the i-th region, when the i-th region is a downstream region; , and are the voltage amplitude, active power and reactive power of the boundary of the i-th region, including the boundary voltage amplitude, active power and reactive power of the i-th region as an upstream region, and the boundary voltage amplitude, active power and reactive power of the i-th region as a downstream region; , and are the voltage amplitude, active power and reactive power of the boundary of the i-th region, including the boundary voltage amplitude, active power and reactive power of the i-th region as an upstream region, and the boundary voltage amplitude, active power and reactive power of the i-th region as a downstream region; , and are the voltage amplitude, active power and reactive power of the boundary of the i-th region, including the boundary voltage amplitude, active power and reactive power of the i-th region as an upstream region, and the boundary voltage amplitude, active power and reactive power of the i-th region as a downstream region; , and are the voltage amplitude, active power and reactive power of the boundary of the i-th region, including the boundary voltage amplitude, active power and reactive power of the i-th region as an upstream region, and the boundary voltage amplitude, active power and reactive power of the i-th region as a downstream region; , and are the voltage amplitude, active power and reactive power of the boundary of the i-th region, including the boundary voltage amplitude, active power and reactive power of the i-th region as an upstream region, and the boundary voltage amplitude, active power and reactive power of the i-th region as a downstream region; , and are the voltage amplitude, active power and reactive power of the boundary of the i-th region, including the boundary voltage amplitude, active power and reactive power of the i-th region as an upstream region, and the boundary voltage amplitude, active power and reactive power of the i-th region as a downstream region.

[0121] This embodiment uses a distributed approach to solve the optimization problem. Based on the regional division and decoupling of the power distribution network, the global optimization problem of the original global optimization model objective function (1) can be decomposed into several smaller regional optimization problems. By solving the regional optimization problems and coordinating between regions, the optimal solution of the global problem is finally obtained. According to the coupling constraints, the global optimization problem is decomposed into several regional optimization problems. The objective function (1) and its constraints are rewritten, and the objective function of the global optimization model is designed as follows:

[0122] (8)

[0123] In the formula, The total number of areas into which the power distribution network is divided; For the first Local decision variables for each region Including the Boundary variables of each region ; For the first The objective function for each region; For the first The feasible region of local decision variables for each region that meets the constraints of distributed power flow equations and security constraints.

[0124] In step S3, the pre-design of the ETM-DAL-ADMM algorithm includes: based on the global optimization model in step S1 and the coupling constraints in step S2, a DAL-ADMM algorithm combining linearization processing and acceleration techniques is constructed, and an event-triggered mechanism (ETM) is added to the DAL-ADMM algorithm. This algorithm enables each sub-region to independently update its local variables, while achieving cross-region coordination through global variables, thereby ensuring consistency between distributed solution results and centralized optimization results.

[0125] Traditional distributed active-mode generator (ADMM) methods suffer from slow convergence speed and high computational cost. To address this shortcoming, this embodiment proposes a distribution network voltage coordination control method based on Distributed Accelerated Linear-ADMM (DAL-ADMM) for optimizing the active-mode network (ADN). This algorithm minimizes branch losses while ensuring that the voltage at each node remains within acceptable limits by coordinating the reactive power of each distributed generator.

[0126] Based on the objective function of the global optimization model designed according to formula (8), the Lagrangian function of the global optimization problem is constructed. The calculation formula is:

[0127] (9)

[0128] In the formula, the system is divided into regions, the first region optimizes its objective function independently ; is the Lagrange multiplier corresponding to the consistency constraint of the boundary variable and the global variable, and is used to measure the difference between regions. T represents the transpose operation. is the penalty coefficient, which ensures that the boundary variables of each region and the global variable converge quickly to be consistent. is the square of the 2-norm operation.

[0129] In this embodiment, when the current region only has downstream adjacent regions, that is, the current region only has boundary variables as upstream regions, the coupling constraint of the current region objective function only constrains: the boundary variables of the current region as upstream regions need to be equal to the boundary variables of the downstream adjacent regions; when the current region only has upstream adjacent regions, that is, the current region only has boundary variables as downstream regions, the coupling constraint of the current region objective function only constrains: the boundary variables of the current region as downstream regions need to be equal to the boundary variables of the upstream adjacent regions; when the current region has upstream and downstream adjacent regions, that is, the current region has upstream and downstream boundary variables, the coupling constraint of the current region objective function has two types of constraints: the boundary variables of the current region as upstream regions need to be equal to the boundary variables of the downstream adjacent regions, and the boundary variables of the current region as downstream regions need to be equal to the boundary variables of the upstream adjacent regions; when the current region has two types of constraints or has multiple adjacent regions, the adjacent regions need to be distinguished and the of formula (9) needs to be calculated according to the constraint type, and finally the summation operation is performed.

[0130] According to the Lagrange function of the global optimization problem of formula (9) and the region division, the Lagrange function of each region optimization problem is obtained, and the calculation formula of the Lagrange function of each region optimization problem is

[0131] , (10)

[0132] Considering that the traditional ADMM method often takes a long time in optimization calculation, an improved method of linearizing and approximating the Lagrange function of each region is proposed in this embodiment. Specifically, instead of pursuing accurate minimization of the objective function in each iteration, an approximate method is used to speed up the solution. In the iteration process, the originally complex nonlinear objective function is replaced by its first-order Taylor expansion approximation at the current iteration point, that is, the linearized objective function. The purpose of this is to replace the complex nonlinear problem with a simpler linear form, thereby reducing the computational burden. Let the ​The point of the next iteration is So in the next iteration, that is, the th In the next iteration Will be determined by it Perform a first-order Taylor expansion, retaining only the linear terms, which means expanding the nonlinear terms to the first-order Taylor expansion. The objective function of the next iteration Replaced by a linear approximation function:

[0133] (11)

[0134] In the formula, the first Local decision variables for each region Indicates the first The vector composed of the node variables in each region, that is, the vector composed of the first node variables in each region. Treating all variables within a region as a single vector, formula (11) refers to the vector obtained in the current iteration. The value of this set of vectors is used to find the first... The objective function for each region The partial derivatives of each component of this vector form the Jacobian matrix, and then a linear approximation is made using "constant term + derivative × (increment of variable relative to the current value)";

[0135] To ensure consistency of boundary variables between adjacent regions, this method introduces a linear dual term and a quadratic penalty term into the objective function of each region. The linear dual term dynamically characterizes the inconsistencies between regions using Lagrange multipliers and continuously drives the boundary solutions to converge during iteration. The quadratic penalty term penalizes the square of the boundary differences, making larger deviations more costly, thus effectively constraining the regions to gradually reduce differences and ultimately achieving boundary consistency. The modified Lagrange function for each region is shown below:

[0136] (12)

[0137] In the formula, the linear term Use the current iteration The first-order Taylor expansion replaces the original objective function, retaining only the linear terms, transforming the nonlinear subproblem into a linear one, thus reducing the computational cost of each iteration; the linear dual term... Apply boundary consistency using Lagrange multipliers; For Lagrange multiplier vectors paired for consistency constraints; for Transpose; Secondary penalty item Further reduce boundary deviations to stabilize and accelerate uniform convergence; This represents the penalty coefficient for the secondary penalty term.

[0138] The DAL-ADMM algorithm solves the problem in the following way:

[0139] No. Local decision variables for each region The formula for the (k+1)th iteration is:

[0140] (13)

[0141] No. Global variables for each region The formula for the (k+1)th iteration is:

[0142] (14)

[0143] No. Lagrange multipliers in each region The formula for the (k+1)th iteration is:

[0144] (15)

[0145] In the formula, For the first The region in the first Local decision variables for the next iteration; For the first The region in the first Local decision variables for the next iteration; For the first The region in the first Global variables for this iteration; For the first The region in the first Global variables for this iteration; For the first The region in the first Lagrange multipliers in the next iteration; For the first The region in the first Lagrange multipliers in the next iteration; For the first The region in the first The boundary variable for the next iteration; the argmin function is used to output the input parameters that make the subsequent function reach its minimum value.

[0146] Due to the Global variables for each region Updates cannot be completed within a single region, therefore they require collection of the first... Boundary variables of each region and corresponding number Boundary variables of adjacent regions of each region To update the number Global variables for each region Therefore, by substituting formula (14), we obtain the final result. Global variables for each region The formula for the (k+1)th iteration is:

[0147] (16)

[0148] In the formula, For the first The region in the first Boundary variables for the next iteration; For the corresponding number The adjacent regions of the first region are in the second region. Boundary variables of the nth iteration; The region in the first Local decision variables in the next iteration Including the The region in the first Boundary variables of the next iteration ;

[0149] This invention improves the convergence of the DAL-ADMM model by adaptively adjusting the penalty coefficient within each region. Based on the magnitudes of the original and dual residuals, the penalty coefficient is adaptively adjusted to dynamically balance convergence speed and constraint satisfaction; that is, a larger original residual results in a larger penalty to accelerate consistency, while a larger dual residual results in a smaller penalty to avoid oscillations caused by over-convergence. Penalty coefficient for each region The iterative formula is:

[0150] (17)

[0151] (18)

[0152] In the formula, , These are the minimum and maximum iteration indices that are allowed to be adjusted, respectively; For the first The region in the first The penalty coefficient for the next iteration; For the first The region in the first The penalty coefficient for the next iteration; For the first The initial values ​​of the penalty coefficient for each region; , These are the preset penalty coefficient adjustment parameters, one for increasing and one for decreasing. , In this embodiment, the preset residual comparison parameters in the DAL-ADMM algorithm are: , ; For 2-norm operations; , The first The region in the first The primary residual and dual residual in the next iteration, the primary residual and dual residual The calculation formula is as follows:

[0153] (19)

[0154] (20)

[0155] To reduce frequent iterative communication in step S3, an event-triggered mechanism (ETM) is introduced: inter-regional information exchange only occurs when the boundary variable changes exceed a preset threshold.

[0156] The DAL-ADMM algorithm requires a complete global data communication in each iteration. To reduce communication overhead, this embodiment introduces an event-triggered mechanism (ETM) based on the DAL-ADMM algorithm, designing the ETM-DAL-ADMM algorithm. Information exchange between regions is only triggered when the difference between the current boundary variable and the boundary variable transmitted in the previous communication exceeds a set threshold, thus avoiding unnecessary communication.

[0157] According to the ETM-DAL-ADMM algorithm, the first The time point at which information is exchanged between regions The calculation formula is:

[0158] ,(twenty one)

[0159] ,(twenty two)

[0160] ,(twenty three)

[0161] In the formula, For the first The next event trigger time for each region; For the first The time when the previous event was triggered in the region; The maximum allowed time; For the first Event triggering conditions for each region; For the first The region in the first The error of the next iteration; For the first The region in the first Boundary variables for the next iteration; For the first The region in the first Boundary variables for the next iteration; For the first Event triggering functions for each region; For the first The region in the first Error of the next iteration The weighted square, This is a preset weight matrix used to control the impact of errors; To control the preset threshold parameters for event triggering, ; For the first The region in the first Boundary variables of the next iteration The weighted square.

[0162] When the event is triggered function When the value is greater than or equal to zero, the event is triggered. At this time, inter-regional information exchange includes: [The following is a continuation of the previous sentence, likely related to the event itself.] When a region is used as an upstream region, its boundary variables are transferred to the downstream adjacent region, replacing the corresponding variables previously stored in the downstream adjacent region. Boundary variables of the region; the first The region receives boundary variables transmitted from the upstream adjacent region, replacing the first region. The boundary variables of the corresponding upstream adjacent regions previously stored in each region;

[0163] If the event is not triggered, no information exchange occurs between regions, and each region retains the boundary variables from the last event trigger. Combining formula (21), the... The event triggering conditions for each region can be represented as follows:

[0164] ,(twenty four)

[0165] The following is the proof of formula (24):

[0166] According to the event triggering condition, when the first Error in each region The event will be triggered when the threshold defined in formula (21) is reached. After the event is triggered, the variable... Will be updated, error Reset to zero. In the absence of an event, according to formula (23), the distribution network must satisfy the following inequality constraint:

[0167] (25)

[0168] In the formula, is a preset positive definite diagonal matrix. Through the formula, it can be further obtained:

[0169] , (26)

[0170] In the formula, is the a-th eigenvalue of , and is an element of the vector ; s is the dimension of the vector . These formulas make the event triggering mechanism always run effectively in the optimization process and timely constrain and effectively control the error after each triggering.

[0171] Substitute formula (22) and formula (26) into formula (25), and further calculate:

[0172] , (27)

[0173] According to formula (27), the condition that the event does not trigger is:

[0174] , (28)

[0175] According to formula (28), the event triggering condition is formula (24).

[0176] In the ETM-DAL-ADMM algorithm of the embodiment, the Zeno phenomenon does not occur. The Zeno phenomenon refers to triggering an infinite number of events in a finite time frame. The reason why the Zeno phenomenon does not occur is that, as shown in formulas (21)-(23), the event triggering mechanism is only detected at the sampling points corresponding to the discrete time iteration steps, and each iteration optimization calculation and variable update requires time, so there must be a minimum interval between adjacent two triggers. Even in the extreme case of triggering each iteration, the number of triggers is only increasing with the number of iteration steps, and it is always bounded in a finite time, thereby avoiding the occurrence of the Zeno phenomenon.

[0177] The ETM-DAL-ADMM algorithm of the embodiment iteratively solves the final local decision variable, and the process is as follows:

[0178] 1. Initialization: initialize the Lagrange multiplier of each region to 0, set the global variable of each region to the actual boundary variable of each region , and set the initial iteration index .

[0179] 2. Solve the optimization problem: each region solves the optimization problem according to the local decision variable of the first region ​iterative formula of the local variable of the ith region, formula (13) solves the reactive power of the distributed generator of each region and the boundary variable .

[0180] 3. Boundary variable transmission: the event triggered mechanism ETM proposed based on formulas (21)-(23) judges whether the error of each region meets the event triggered condition according to the event triggered condition formula (24), if the event triggered condition is met, each region receives the data transmitted from the adjacent region, and the adjacent region refers to the adjacent region having a connection relationship with the power distribution network, rather than only the adjacent region in geography.

[0181] 4. Global variable update: each region updates the global variable of each region according to the iterative formula of the global variable of the ith region, formula (16) .

[0182] 5. Lagrange multiplier update: each region updates the Lagrange multiplier of each region according to the iterative formula of the Lagrange multiplier of the ith region, formula (15) .

[0183] 6. Penalty coefficient update: each region updates the penalty coefficient of each region according to the iterative formula of the penalty coefficient of the ith region, formulas (17)-(18) .

[0184] 7. Residual error calculation: each region calculates the original residual error and the dual residual error of each region according to the original residual error and the dual residual error of the ith region, formulas (19)-(20) . .

[0185] 8. Termination condition judgment: if one of the following conditions is met, the iteration is terminated, and the final local decision variable of each region is output, which is used for voltage coordination control of the power distribution network. In this embodiment, the reactive power output of the distributed generator of each region is adjusted according to the final local decision variable, so that the voltage of the power distribution network meets the constraint and the branch loss is minimized; otherwise, return to step 2:

[0186] 1) Judge whether the ETM-DAL-ADMM algorithm converges, if the following convergence condition formula is met, the ETM-DAL-ADMM algorithm converges:

[0187] , (29) ​​​​​​​​​

[0188] In the formula, Operations on infinity norms; This is the preset tolerance level; It is the boundary residual.

[0189] 2) The number of iterations is greater than or equal to the preset upper limit of the iteration index.

[0190] The following is a simulation experiment demonstrating the implementation effect of the distributed voltage control method for distribution networks in this embodiment:

[0191] like Figure 2 As shown, a radial distribution network topology with bus 0 as the root is depicted: nodes 1, ..., l are connected sequentially along the main trunk, and then branches are sent to... Nodes, each labeled with its voltage, active power, and reactive power injection, and branches. upper complex impedance and power flow The arrow indicates the direction from the upstream node. downstream nodes Transmission, and upstream nodes downstream nodes Transmission data, including the active and reactive power of each node, reflects the injection and absorption of distributed power sources and loads at each node.

[0192] Figure 3 The upper part is the original network: nodes in the upstream region. Nodes in the downstream region Active and reactive power flows are carried by branch circuits. ; Figure 3 The lower half of the diagram illustrates the "decoupling" process for this boundary: in the upstream region, the node... Its outflow power is equivalent to an injection source P represented by a ring in the diagram, with the voltage remaining constant. In the downstream region, the original upstream node will be... Copy as node Upstream node Voltage copy as voltage This parameter serves as an externally given parameter for the upstream region, replacing the coupling dependency on the upstream. In this way, the upstream and downstream sub-regions can independently complete power flow calculations and optimizations while ensuring the consistency of boundary injected power and voltage, achieving distributed decoupling and parallel solution.

[0193] Figure 4The radial topology of the IEEE-33 distribution network is given and divided into three sub-regions according to function: Region 1 includes nodes 1–9 and two branches, one branch includes nodes 19–22 and the other branch includes nodes 23–25; Region 2 includes nodes 26–33; Region 3 includes nodes 10–18; nodes connected by a green box marked DG indicate that the node has a distributed generator.

[0194] Figure 5 Comparing the boundary residual changes of the ETM-DAL-ADMM algorithm (the algorithm of this invention) in this embodiment with those of five other algorithms—DAL-ADMM (distributed accelerated linear ADMM), L-ADMM (linear ADMM), O-ADMM (over-relaxed ADMM), and FALM (fast alternating linearization)—with the number of iterations, both ETM-DAL-ADMM and DAL-ADMM show that, with significantly fewer iterations than the other three algorithms, the boundary residual decreases rapidly to a level significantly lower than the other three methods. The iteration terminates when the boundary residual decreases to a preset tolerance level. Therefore, both ETM-DAL-ADMM and DAL-ADMM can obtain the final calculation results faster.

[0195] like Figure 4 As shown, the IEEE-33 distribution network system used in this embodiment is configured with 6 distributed generators (DGs), located at node numbers {4, 8, 11, 16, 21, 30}. The initial active and reactive power outputs of each DG are shown in Table 1. The root bus reference voltage is assumed to be 1.0 pu. The upper and lower limits of reactive power for the DGs are set to 1 MVar and 0, respectively. Furthermore, the initial penalty coefficient of the algorithm... Set to 2.5, the preset tolerance. Preset threshold parameters for controlling event triggering Furthermore, the preset upper limit for iteration index is 150.

[0196] Table 1. Location and initial power of DG in IEEE-33 distribution network system

[0197]

[0198] Figure 4 The IEEE-33 distribution network system is shown in its regional division, which is divided into three regions, namely Region 1 to Region 3. Region 1 and Region 2 are adjacent to each other, and Region 3 is adjacent to each other. Each region contains at least one distributed generator (DG).

[0199] The ETM-DAL-ADMM algorithm of the embodiment is compared with several distributed algorithms: linear ADMM (L-ADMM), fast alternating linearization method (FALM), over-relaxation ADMM (O-ADMM) and DAL-ADMM. In the distributed method, the convergence is defined as the ability of the objective function to quickly reach the global optimal solution, which directly affects the performance of the overall solution. Figure 5 The boundary residuals of the proposed algorithm applied to the IEEE-33 distribution network system are shown, and the results are compared with those of other algorithms.

[0200] Table 2 shows the total running time of various algorithms under VCC, highlighting that the proposed ETM-DAL-ADMM and DAL-ADMM algorithms are significantly shorter in total running time than other methods. This advantage not only comes from the reduced number of iterations of the two algorithms, but also benefits from their lower single-optimization running time. The reason for the improved efficiency is that the DAL-ADMM framework significantly reduces the time required for each optimization through linearization of the objective function. In addition, the adaptive adjustment of the penalty coefficient in the DAL-ADMM framework effectively reduces the number of iterations required for convergence, further improving the overall running efficiency of the distributed algorithm.

[0201] Table 2 Total running time of different algorithms

[0202]

[0203] Table 3 shows the number of inter-regional communications of ETM-DAL-ADMM and DAL-ADMM under the same number of iterations. It can be seen that the ETM-DAL-ADMM algorithm with the event-triggering mechanism ETM has significantly fewer inter-regional communications than the DAL-ADMM algorithm. Compared with the DAL-ADMM algorithm, the ETM-DAL-ADMM algorithm integrated with ETM reduces the number of inter-regional communications in the IEEE-33 distribution network system from 600 to 204, a reduction of 66.00%. Therefore, the ETM-DAL-ADMM algorithm can effectively alleviate the communication congestion in the process of large-scale data transmission in distributed optimization, reduce the energy consumption of data transmission, and improve the stability of the distribution system.

[0204] Table 3 Number of inter-regional communications of different algorithms

[0205]

[0206] According to the above simulation test, it can be seen that the distribution network voltage coordination control method based on the event-triggering mechanism of the embodiment significantly reduces the computational and communication overhead through partitioned parallel computing, model linearization and intelligent triggering mechanism, and is suitable for real-time optimization control of large-scale distribution networks.

[0207] Those skilled in the art will appreciate that embodiments of the present application can be readily used as software, hardware, or a combination of software and hardware. In a software embodiment, the methods can be tangibly embodied in a machine-readable storage medium having stored thereon instructions that can be used to program a processing system to perform the methods. The machine-readable storage medium can be magnetic

[0208] The present application is described in reference to the drawings, which are as follows: Figure 1 Each flow and / or block in the flow and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing system, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow and / or block diagrams block or blocks. The computer program instructions can also be stored in a computer- readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the computer- readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement the function specified in the flow and / or block diagrams block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow and / or block diagrams block or blocks.

[0209] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow and / or block diagrams block or blocks. Figure 1 Each flow and / or block in the flow and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing system, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow and / or block diagrams block or blocks. The computer program instructions can also be stored in a computer- readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the computer- readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement the function specified in the flow and / or block diagrams block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow and / or block diagrams block or blocks.

[0210] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow and / or block diagrams block or blocks. Figure 1 Each flow and / or block in the flow and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing system, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow and / or block diagrams block or blocks. The computer program instructions can also be stored in a computer- readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the computer- readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement the function specified in the flow and / or block diagrams block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow and / or block diagrams block or blocks.

[0211] The above description is only preferred embodiments of the application. It is obvious that those skilled in the art can make some improvements and modifications without departing from the technical principles of the application.

Claims

1. A distribution network voltage coordination control method based on an event-triggered mechanism, characterized in that, include: Based on distribution network operation data and equipment parameters, a global optimization model is constructed with the goal of minimizing branch losses, including distributed power flow equation constraints and security constraints. Based on the distribution network topology and load distribution characteristics, the distribution network is divided into multiple sub-regions. By replicating the boundary variables of each region, coupling constraints are established for each region. Based on the coupling constraints, the global optimization problem is decomposed into optimization problems for each region. The ETM-DAL-ADMM algorithm, based on a global optimization model and pre-designed coupling constraints, is adopted. The Lagrangian function of each region optimization problem is linearized and the penalty coefficient is dynamically adjusted according to the original residual and dual residual. The local decision variables of each region, including boundary variables, are updated. According to the pre-set event triggering mechanism, information exchange between regions is only performed when the change of boundary variables exceeds the preset threshold. The final local decision variables are solved iteratively. Adjust the reactive power output of distributed generators in each region according to the final local decision variables so that the distribution network voltage meets the constraints and the branch losses are minimized. The distributed power flow equation constraints for constructing the global optimization model include: The secondary loss term was removed from the DistFlow model and then... and Using the voltage magnitudes of node j and node i as values, the final distributed power flow equations are obtained: ， In the formula, nodes For upstream nodes, node For downstream nodes; and They are branch roads Active power and reactive power; and These are nodes Active power and reactive power; It is a side road The resistance; branch road The reactance; The security constraints of the global optimization model include: security constraints in the power distribution network and power constraints of distributed generators; Safety constraints in power distribution networks: ， In the formula, and They are nodes The lower and upper limits of voltage amplitude; A set of nodes; branch road Current, branch road The upper limit of the current; and Branch roads It has a lower limit and an upper limit for active power; and Branch roads The lower and upper limits of reactive power. For branch set; Distributed generator power constraints: ， In the formula, , and Distributed generators at nodes Active power, reactive power, and apparent power; and These are nodes The lower and upper limits of the active power of distributed generators; and These are nodes The lower and upper limits of reactive power of distributed generators; In the pre-designed ETM-DAL-ADMM algorithm, the design of the Lagrangian function for each region's optimization problem includes: linearizing the Lagrangian function for each region's optimization problem based on the global optimization model and coupling constraints to reduce computational complexity. Based on the objective function of the designed global optimization model, the Lagrangian function of the global optimization problem is constructed. The calculation formula is: ， In the formula, For the Lagrange multipliers corresponding to the consistency constraints between boundary variables and global variables, T represents the transpose operation; This is the penalty coefficient; Square of the 2-norm operation; Based on the Lagrangian function of the global optimization problem and the domain partitioning, the Lagrangian functions of the optimization problems in each domain are obtained. The calculation formula is: ， For the Lagrangian function of the optimization problem in each region, a first-order Taylor expansion is performed on the objective function of each region to obtain the linearized Lagrangian function of the optimization problem in each region. The calculation formula is: ， In the formula, For the first The second iteration Objective function for each region In the Local decision variables for each region The linear term in the first-order Taylor expansion; The method of exchanging information between regions only when the boundary variable changes exceed a preset threshold, according to a pre-set event triggering mechanism, includes: No. The time point at which information is exchanged between regions The calculation formula is: ， ， ， In the formula, For the first The next event trigger time for each region; For the first The time when the previous event was triggered in the region; The maximum allowed time; For the first Event triggering conditions for each region; For the first The region in the first The error of the next iteration; For the first The region in the first Boundary variables for the next iteration; For the first The region in the first Boundary variables for the next iteration; For the first Event triggering functions for each region; For the first The region in the first Error of the next iteration The weighted square, This is a preset weight matrix; To control the preset threshold parameters for event triggering, ; For the first The region in the first Boundary variables of the next iteration The weighted square; No. The event triggering conditions for each region are: 。 2. The distribution network voltage coordination control method based on event triggering mechanism according to claim 1, characterized in that, The coupling constraints of each region include: No. The coupling constraints for each region are: ， In the formula, For the upstream area, For the downstream region; For the first Boundary variables for each region, including the first region. Boundary variables when the first region is an upstream region and the second region Boundary variables when a region is used as a downstream region; For the corresponding number The boundary variables of the adjacent regions of the nth region, when the nth region... When a region is an upstream region of an adjacent region For the boundary variable when the adjacent region is the downstream region, when the first... When a region is a downstream region of an adjacent region This is the boundary variable when the adjacent region is used as the upstream region; For the first Global variables for each region; , and The first The voltage magnitude, active power, and reactive power at the boundaries of each region, including the first... The boundary voltage magnitude, active power, and reactive power when the first region is considered as the upstream region, and the first... The boundary voltage magnitude, active power, and reactive power when each region is considered as a downstream region; , and Corresponding to the first The voltage magnitude, active power, and reactive power of the adjacent region boundaries of each region, when the first region... When a region is an upstream region of an adjacent region , and These represent the boundary voltage magnitude, active power, and reactive power when the adjacent region is considered as the downstream region. When a region is a downstream region of an adjacent region , and These represent the boundary voltage magnitude, active power, and reactive power when the adjacent region is considered as the upstream region.

3. The distribution network voltage coordination control method based on event triggering mechanism according to claim 2, characterized in that, The process of decomposing the global optimization problem into regional optimization problems based on coupling constraints includes: Based on the coupling constraints, the global optimization problem is decomposed into several regional optimization problems, and the objective function of the global optimization model is designed as follows: ， In the formula, This represents the total number of areas into which the power distribution network is divided. For the first Local decision variables for each region Including the Boundary variables of each region ; For the first The objective function for each region; For the first The feasible region of local decision variables for each region that meets the constraints of distributed power flow equations and security constraints.

4. The distribution network voltage coordination control method based on event triggering mechanism according to claim 1, characterized in that, In the ETM-DAL-ADMM algorithm No. Local decision variables for each region The formula for the (k+1)th iteration is: ， In the formula, For the first The region in the first Local decision variables for the next iteration; For the first The region in the first Local decision variables for the next iteration; For the first The region in the first Global variables for this iteration; For the first The region in the first The Lagrange multipliers for the next iteration; the argmin function is used to output the input parameters that minimize the subsequent function. No. Global variables for each region The formula for the (k+1)th iteration is: ， In the formula, For the first The region in the first Global variables for this iteration; For the first The region in the first Boundary variables for the next iteration; For the corresponding number The adjacent regions of the first region are in the second region. Boundary variables of the nth iteration; The region in the first Local decision variables in the next iteration Including the The region in the first Boundary variables of the next iteration ; No. Lagrange multipliers in each region The formula for the (k+1)th iteration is: ， In the formula, For the first The region in the first Lagrange multipliers in the next iteration; For the first The region in the first Lagrange multipliers in the next iteration.

5. The distribution network voltage coordination control method based on event triggering mechanism according to claim 4, characterized in that, In the pre-designed ETM-DAL-ADMM algorithm, the design of the Lagrangian function for each region's optimization problem also includes: dynamically adjusting the penalty coefficient based on the original residual and the dual residual to improve the convergence speed. No. Penalty coefficient for each region The formula for the (k+1)th iteration is: ， ， In the formula, and These are the minimum and maximum iteration indices that are allowed to be adjusted, respectively; For the first The region in the first The penalty coefficient for the next iteration; For the first The region in the first The penalty coefficient for the next iteration; For the first The initial values ​​of the penalty coefficient for each region; and These are the preset penalty coefficient adjustment parameters for increasing and decreasing; and These are the preset comparison parameters for the original residual and the dual residual, respectively; For 2-norm operations; and The first The region in the first The original residual and dual residual of the next iteration; No. The first region The original residual of the next iteration The calculation formula is: , No. The first region The dual residual of the next iteration The calculation formula is: .

6. The distribution network voltage coordination control method based on event triggering mechanism according to claim 1, characterized in that, The pre-designed ETM-DAL-ADMM algorithm is used to iteratively solve for the final local decision variables, including: Lagrange multipliers for each region Initialize to 0, set global variables for each region. For the actual boundary variables of each region Set the iteration index ; The following process is executed repeatedly until the iteration termination condition is met, outputting the final local decision variables for each region, which are used for distribution network voltage coordination control: Each region according to the first Local decision variables for each region The iterative formula is used to solve the optimization problem in each region, and the boundary variables of each region are obtained. ; Based on the event-triggered mechanism (ETM), the error of each region is determined according to the event triggering conditions. If the event triggering conditions are met, the corresponding region exchanges information with its neighboring regions. Each region according to the first Global variables for each region The iterative formula updates the global variables for each region. ; Each region according to the first Lagrange multipliers in each region The iterative formula updates the Lagrange multipliers for each region. ; Each region according to the first Penalty coefficient for each region The iterative formula updates the penalty coefficient for each region. ; Each region according to the first Original residuals of each region and dual residual Calculation formula for calculating the original residuals of each region. and dual residual .

7. The distribution network voltage coordination control method based on an event-triggered mechanism according to claim 6, characterized in that, The iteration termination conditions include: The iteration termination condition is determined based on the convergence condition formula. If the convergence condition formula is met, the ETM-DAL-ADMM algorithm converges and the iteration terminates. The convergence condition formula is as follows: ， In the formula, Operations on infinity norms; As a preset tolerance level, It is the boundary residual; Alternatively, the iteration termination condition can be determined based on the number of iterations. If the number of iterations is greater than or equal to the preset upper limit of the iteration index, the iteration will be terminated.

Images