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CN-122026417-A - Distributed reactive voltage rapid optimization control method

CN122026417ACN 122026417 ACN122026417 ACN 122026417ACN-122026417-A

Abstract

The invention provides a rapid optimization control method of a distributed reactive voltage, which relates to the technical field of power system optimization control, and comprises the steps of firstly acquiring system data at the initial moment of a time period, and calling a reactive voltage optimization model of the time period for processing to obtain an optimal operation point of the time period; the method comprises the steps of measuring the active power of a new energy source at each moment in a period of time based on an optimal operation point, calculating a new energy source short-time fluctuation rate evaluation index and an amplitude evaluation index, judging whether to trigger the optimal control of the reactive voltage of the new energy source according to the fluctuation rate evaluation index, and selecting the distributed reactive voltage rapid optimal control based on linear sensitivity under small fluctuation or the distributed reactive voltage rapid optimal control based on nonlinear sensitivity under large fluctuation according to the amplitude evaluation index when the optimal control is triggered, so that the efficient dynamic tracking of the optimal operation point of the system is realized. The invention can effectively improve the operation safety and economy of the high-proportion new energy power system.

Inventors

  • ZHANG ZHAOYI

Assignees

  • 华侨大学

Dates

Publication Date
20260512
Application Date
20260414

Claims (9)

  1. 1. The rapid optimization control method for the distributed reactive voltage is characterized by comprising the following steps of: Acquiring time period initial time system data, and calling a preset time period reactive voltage optimization model to process the time period initial time system data to obtain a time period optimal operation point; Based on the time interval optimal operation point, measuring the active power of the new energy at each moment in the time interval, and calculating a fluctuation rate evaluation index and an amplitude evaluation index of the new energy in short time according to the active power of the new energy; Judging whether to trigger new energy reactive voltage optimization control according to the fluctuation rate evaluation index, and generating a judgment result; And when the judgment result is triggering, performing distributed reactive voltage rapid optimization control according to the amplitude evaluation index and a preset distributed reactive voltage optimization model, wherein the distributed reactive voltage rapid optimization control comprises linear sensitivity-based distributed reactive voltage rapid optimization control under small fluctuation of new energy and nonlinear sensitivity-based distributed reactive voltage rapid optimization control under large fluctuation of new energy.
  2. 2. The distributed reactive voltage rapid optimization control method according to claim 1, wherein the construction step of the period reactive voltage optimization model is as follows: Constructing an active power network loss objective function by taking the minimum active power network loss as the objective function: , wherein, In the case of a set of nodes of a system, For the voltage magnitude at node i, For the ground conductance of node i, For the set of system branches, For the magnitude of the voltage at node j, For the voltage phase angle difference between node i and node j, The conductance of branch l; constructing a voltage deviation objective function by taking the minimum voltage deviation as the objective function: , the reference voltage of the node i is obtained, and the active power network loss objective function and the voltage deviation objective function are subjected to weighted summation to obtain a total objective function , The weighting coefficients for the active power loss objective function, A weight coefficient as a voltage deviation objective function; Establishing an equality constraint, wherein the equality constraint is a power balance equation , wherein, Active power of the new energy source of the node i, Active power fluctuation amount of the new energy source of the node i, Is the active power of the generator at node i, Is the load active power of the node i, Indicating that node j is connected to node i, For node i and node j to be mutually conductive in the node admittance matrix, For node i and node j to each other susceptance in the node admittance matrix, Reactive power is the new energy source of node i, The SVC reactive power for node i, The reactive power of the generator for node i, Load reactive power for node i; Establishing inequality constraints, including branch transmission power constraints, voltage amplitude constraints, voltage phase angle constraints, generator reactive power constraints, new energy reactive power constraints and SVC reactive power constraints; establishing discrete variable constraints based on the discrete variables, wherein the allowable range is as follows: , , Wherein the discrete variables include transformer ratio and number of capacitor banks, As the lower limit of the transformer ratio of branch l, For the transformer transformation ratio of the branch l, As the upper limit of the transformer ratio of branch l, Is the lower limit of the reactive power of the capacitor bank of node i, The reactive power of the capacitor bank for node i, Is the upper limit of the reactive power of the capacitor bank of node i, For the number of groups of capacitor operation, Reactive power capacity of a single group of capacitors for node i; Programming in preset Matlab software based on a total objective function, an equality constraint, an inequality constraint and a discrete variable constraint, and calling Cplex functions to solve to obtain a reactive voltage optimization basic model; and on the basis of the reactive voltage optimization basic model, setting the fluctuation amount of the active power of the new energy, the fluctuation value of the voltage amplitude and the fluctuation value of the voltage phase angle to 0, and obtaining the period reactive voltage optimization model.
  3. 3. The distributed reactive voltage fast optimization control method according to claim 2, wherein the inequality constraint is specifically defined as: The branch transmission power does not exceed its maximum transmission capacity: , for the active power transmitted by the branch l, For the reactive power transmitted by the branch l, Maximum transmission capacity for leg l; The voltage amplitude and voltage phase angle of the node should be within the following ranges: , , , As a lower limit for the voltage magnitude at node i, Is the fluctuating value of the voltage amplitude at node i, As an upper limit for the voltage magnitude at node i, As a lower limit of the voltage phase angle of node i, For the voltage phase angle of node i, As the fluctuating value of the voltage phase angle of node i, As the upper limit of the voltage phase angle of node i, Is the functional relation between the voltage amplitude of the node i and the fluctuation quantity of the active power of each new energy, Is the functional relation between the voltage phase angle of the node i and the fluctuation quantity of active power of each new energy, m is the number of new energy nodes, Active power fluctuation amount of new energy for the 1 st node, Active power fluctuation amount of new energy for the 2 nd node, Active power fluctuation amount of new energy for the mth node; The reactive power of the generator, the reactive power of the new energy and the reactive power of SVC are in the following ranges: , , , Is the lower limit of the generator reactive power of node i, The reactive power of the generator for node i, Is the upper limit of the generator reactive power of node i, Is the lower limit of the reactive power of the new energy source of the node i, Reactive power is the new energy source of node i, Is the upper limit of the reactive power of the new energy source of the node i, Is the lower limit of the SVC reactive power of node i, The SVC reactive power for node i, Is the upper limit of the SVC reactive power of node i.
  4. 4. The distributed reactive voltage rapid optimization control method according to claim 3, wherein the construction step of the distributed reactive voltage optimization model is as follows: taking an active power network loss objective function of the reactive voltage optimization basic model as an original objective function, and carrying out linearization treatment to obtain an objective function of a short-time linear reactive power-voltage optimization control model , Is a new energy node set in the system, For the incremental rate of the original objective function to the change in active power of node j, For the short-term fluctuation of the new energy source of node j at time t, For the incremental rate of the original objective function to the change in reactive power at node j, The reactive power adjustment quantity of the new energy source at the moment t is used as the node j, Is a set of SVC nodes in the system, For the incremental rate of the original objective function to the reactive power variation of node k, The SVC reactive power adjustment quantity of the node k at the time t is used as the SVC reactive power adjustment quantity; Linearizing equation constraint of the reactive voltage optimization basic model to obtain a node voltage fluctuation value as follows: , for the fluctuating value of the voltage amplitude of node i at time t, For the fluctuating value of the voltage phase angle of node i at time t, The unit active power change for the new energy source of the node j results in the change amount of the voltage amplitude of the node i, The unit reactive power change for the new energy source of the node j causes the change quantity of the voltage amplitude of the node i, The unit active power change for the new energy source of the node j results in the change amount of the voltage phase angle of the node i, The unit reactive power change for the new energy source of node j results in the change of the voltage phase angle of node i, For the sensitivity of the node i voltage amplitude to the SVC reactive power variation at node k, Sensitivity of node i voltage phase angle to SVC reactive power change at node k; and constructing an inequality constraint condition of a short-time linear reactive power-voltage optimization control model, wherein the voltage threshold exceeding which does not occur at any node i is as follows: , the resulting voltage amplitude is optimized for the period reactive voltage of node i, The obtained voltage phase angle is optimized for the period reactive voltage of the node i, and the new energy reactive power and SVC reactive power are in the following range: , solving the obtained new energy reactive power for the period reactive voltage optimization model of the node j, Is the lower limit of the reactive power for the period of node j, An upper limit for the period reactive power of node j, Solving the SVC reactive power obtained by the time interval reactive voltage optimization model of the node k, The lower limit of the SVC reactive power for the period of node k, An upper limit of SVC reactive power for the period of node k; Based on objective functions And obtaining the short-time linear reactive power-voltage optimization control model by using the equation constraint after linearization processing and the inequality constraint condition of the short-time linear reactive power-voltage optimization control model.
  5. 5. The distributed reactive voltage fast optimization control method according to claim 4, further comprising: The objective function of the short-time linear reactive power-voltage optimization control model is expressed as the sum of M sub-objective functions according to the partition, and the formula is as follows: , is the sum of the M sub-objective functions, For the 1 st sub-objective function, For the 2 nd sub-objective function, For the mth sub-objective function, M is the number of sub-objective functions, Active power fluctuation amount of new energy of the whole system, The reactive power regulation quantity for the new energy source and SVC of the whole system, The fluctuation amount of the active power of the new energy source which is the 1 st sub-objective function, The fluctuation amount of the active power of the new energy source which is the 2 nd sub-objective function, The fluctuation amount of the active power of the new energy source which is the Mth sub-objective function, The new energy source and the reactive power regulation of the SVC as 1 st sub-objective function, The new energy source for the 2 nd sub-objective function and the reactive power regulation of the SVC, The new energy source of the Mth sub-objective function and the reactive power regulation quantity of SVC; carrying out partition representation on node voltage fluctuation values of the short-time linear reactive power-voltage optimization control model to obtain a partition matrix form , The voltage fluctuation value corresponding to the 1 st sub-objective function, For the voltage fluctuation value corresponding to the 2 nd sub-objective function, For the voltage fluctuation value corresponding to the Mth sub-objective function, The sensitivity block matrix of the voltage fluctuation of the Mth partition to the active power fluctuation of the new energy of the Mth partition is obtained; substituting the block matrix form into a voltage out-of-limit formula of a short-time linear reactive power-voltage optimization control model, and expressing the block matrix form into a plurality of sub-constraint forms to obtain a system voltage inequality constraint set , As the voltage fluctuation value of the whole system, In the form of the 1 st voltage inequality sub-constraint, In the form of the 2 nd voltage inequality sub-constraint, In the form of the Mth voltage inequality sub-constraint, wherein each partition has a corresponding voltage inequality sub-constraint; the new energy reactive power and SVC reactive power range of the short-time linear reactive power-voltage optimization control model are expressed as M sub-constraint forms, and a system reactive power inequality constraint set is obtained , In the form of the 1 st reactive power inequality sub-constraint, In the form of the 2 nd reactive power inequality sub-constraint, The method comprises the steps of taking an M th reactive power inequality sub-constraint form, wherein each partition has a corresponding reactive power inequality sub-constraint form; Based on the objective function after partition processing, the partition matrix form, the system voltage inequality constraint set and the system reactive power inequality constraint set, a space decoupling mechanism is obtained, and a distributed reactive voltage optimization model is constructed based on the space decoupling mechanism.
  6. 6. The rapid optimization control method of distributed reactive voltage according to claim 5, wherein based on the time period optimal operation point, new energy active power at each moment in the time period is measured, and a fluctuation rate evaluation index and an amplitude evaluation index of the new energy in short time are calculated according to the new energy active power, specifically: defining a fluctuation rate evaluation index of a single new energy in a short time as the offset degree of the system running state caused by the active power change of the new energy at adjacent time, wherein the formula is as follows: , for the moment of time The fluctuation rate under the condition is evaluated as an index, At the moment of time for node j The active power of the new energy source is generated, At the moment of time for node j The active power of the new energy source is generated, 、 Are all the coefficients of the two-dimensional space, In order to achieve the micro-increase rate of the network loss, Calculating an average value; calculating time of day using voltage sensitivity Grid-connected point voltage fluctuation value caused by new energy fluctuation , At the moment of time for node j The fluctuation of active power of the new energy, The unit active power change for the new energy source of the node j results in the change amount of the voltage amplitude of the node j, For node j in period The new energy standard active power; Calculating real voltage fluctuation value of grid-connected point And defining the amplitude evaluation index as the calculation error of the sensitivity analysis method , For the moment of time The following amplitude evaluation index.
  7. 7. The rapid optimization control method of distributed reactive voltage according to claim 6, wherein whether to trigger the new energy reactive voltage optimization control is judged according to the fluctuation rate evaluation index, and a judgment result is generated, specifically: Constructing a judgment index according to the fluctuation rate evaluation index To evaluate the reference moment of the last execution of the new energy reactive voltage optimization control By the time The new energy active power fluctuation of (1) causes the system running state deviation degree, For the fluctuation rate evaluation index at the time t, Optimizing and controlling a trigger threshold value of an event trigger mechanism for the reactive voltage of the new energy; When judging that the judgment index is greater than or equal to the trigger threshold, triggering the judgment result, performing distributed reactive voltage rapid optimization control, rapidly adjusting the reactive power of new energy, and simultaneously enabling the reference moment ; And when judging that the judging index is smaller than the triggering threshold value, keeping the reactive power of the new energy unchanged, and entering the next moment.
  8. 8. The method for rapidly optimizing and controlling the distributed reactive voltage according to claim 7, wherein the step of rapidly optimizing and controlling the distributed reactive voltage based on the linear sensitivity under small fluctuation of the new energy source is as follows: At a single new energy grid-connected node, a distributed reactive voltage optimization model constructed based on a space decoupling mechanism is used for carrying out distributed reactive voltage rapid optimization control, and voltage reference variables are introduced The system voltage after active power fluctuation and reactive power adjustment of the new energy source of the node is not more than a voltage reference variable; the objective function of the distributed reactive voltage optimization model under small fluctuation of the new energy is as follows: The constraint condition of the distributed reactive voltage optimization model under small fluctuation of new energy is that node voltage meets the following conditions: the allowable range of the reactive power of the new energy is as follows: 。
  9. 9. The rapid optimization control method for the distributed reactive voltage according to claim 8, wherein the step of rapid optimization control for the distributed reactive voltage based on nonlinear sensitivity under the condition that new energy greatly fluctuates is as follows: Based on the quick optimization control of the distributed reactive voltage based on the linear sensitivity under the small fluctuation of new energy, a nonlinear mapping algorithm based on Taylor series inversion is used, wherein an alternating current power flow equation is developed in a high order at any one time period optimal operation point to obtain a nonlinear equation of node injection power increment relative to voltage increment , The active power delta injected for the node, The reactive power delta injected for the node, In the form of a jacobian matrix, Is a factorial of 2 and is a product of, As the voltage amplitude increment of the node, As the voltage phase angle increment of the node, For the purpose of the transposition, In the form of a hessian matrix, Is a factorial of 3 and is a product of, Is an alternating current power flow equation third-order partial derivative matrix, Is a higher-order term; deriving a nonlinear mapping algorithm by analogical Taylor series inversion, fitting a nonlinear equation The analogy to Taylor series, the analogy formula is obtained: , as dependent variables, the active power increment and the reactive power increment injected by the nodes are represented, a, b and c are all coefficients, As independent variables, the voltage amplitude increment and the voltage phase angle increment of the node are represented; assume that the inverse function of the taylor series is , 、 、 All are coefficients, and the inverse function is substituted into the analog formula to obtain the related value Is represented by the expression: And making the coefficients of each order on both sides of the equal sign of the formula equal to obtain the coefficients of each order and the expression of the inverse function: , ; the inverse function expression is analogically returned to the power system, and the analysis is carried out by combining engineering practice to obtain an analytic expression of a third-order nonlinear mapping algorithm of voltage increment with respect to node injection power increment S is a sensitivity matrix, and the matrix is obtained by inverting a jacobian matrix; A second-order nonlinear mapping algorithm is adopted to carry out rapid optimization control on the distributed reactive voltage, wherein a second-order nonlinear sensitivity matrix is adopted The formula of (2) is: 。

Description

Distributed reactive voltage rapid optimization control method Technical Field The invention relates to the technical field of power system optimization control, in particular to a distributed reactive voltage rapid optimization control method. Background The construction of a novel power system mainly based on new energy is an important way for realizing the aim of double carbon at present. With the large-scale grid connection of new energy sources such as wind power, photovoltaic and the like, the operation characteristics of the power system are deeply changed. The new energy output has obvious fluctuation and uncertainty, and the short-time and large-amplitude power fluctuation causes frequent change of the system power flow distribution, so that the optimal operation point of the system is difficult to keep continuously. The method not only leads to the increase of active power loss and the decrease of operation economy, but also increases the risk of voltage out-of-limit, and seriously threatens the safe and stable operation of the system. In the prior art, a centralized reactive power optimization method is generally adopted for solving the problem of reactive voltage optimization control. The method is based on global information, realizes reactive power scheduling and voltage regulation of a system level by solving a nonlinear power flow model, and has the theoretical advantage of converging to a global optimal point. However, the centralized optimization method has extremely strong dependence on the communication network and the central computing node, and the required data acquisition, model solving and control instruction issuing period is usually in the order of hours or minutes (such as 15 minutes or 5 minutes), so that the control requirement of the large-scale power system on real-time performance and rapidity under the short-time fluctuation scene of new energy is difficult to meet. On the other hand, although the first-stage voltage control with the highest response speed in the power system can realize the second-stage reactive power quick response, the control range is limited to a local area, the overall coordination of the whole network voltage deviation and the reactive power economic distribution is lacked, and the global optimization target is difficult to realize. In addition, traditional reactive voltage regulation equipment such as capacitor banks, on-load voltage regulating transformers and the like are limited by mechanical action life and response delay, and are difficult to efficiently match with the regulation speed requirement of short-time fluctuation of new energy. In recent years, a distributed optimization control method has been attracting attention because of its advantages in terms of reducing communication dependency, improving control response speed, making full use of in-situ adjustment resources, and the like. However, the existing distributed control strategy is mostly adjusted based on a fixed time period or local voltage deviation, cannot fully account for the influence of the fluctuation rate and amplitude of the new energy on the running state of the system, and lacks a fine identification and self-adaptive control mechanism for the fluctuation characteristics. Meanwhile, in the aspect of voltage sensitivity modeling, the traditional method mostly adopts linearization approximation, has certain applicability under small fluctuation, but has obvious linearity sensitivity error under the scene of large fluctuation of new energy, and is difficult to accurately reflect the nonlinear coupling relation between voltage and power, thereby influencing the optimal control effect. In view of this, the present application has been proposed. Disclosure of Invention The invention provides a rapid optimization control method for distributed reactive voltage, which can at least partially improve the problems. In order to achieve the above purpose, the present invention adopts the following technical scheme: A rapid optimization control method for distributed reactive voltage comprises the following steps: Acquiring time period initial time system data, and calling a preset time period reactive voltage optimization model to process the time period initial time system data to obtain a time period optimal operation point; Based on the time interval optimal operation point, measuring the active power of the new energy at each moment in the time interval, and calculating a fluctuation rate evaluation index and an amplitude evaluation index of the new energy in short time according to the active power of the new energy; Judging whether to trigger new energy reactive voltage optimization control according to the fluctuation rate evaluation index, and generating a judgment result; And when the judgment result is triggering, performing distributed reactive voltage rapid optimization control according to the amplitude evaluation index and a preset distributed reactive voltage optimiz