A control method for frequency and voltage regulation of a power grid

Abstract

The application provides a power grid frequency and voltage regulating control method, comprising the following steps: S1, establishing a linearized power flow model and an active power compensation model of the power grid; S2, establishing an optimization model and constraints about active power and reactive power to ensure that the active power compensation error of voltage and frequency is within a set range; S3, solving the optimization model to obtain an active power and reactive power strategy; thereby coordinating the active and reactive power of the power grid, effectively compensating frequency fluctuation and regulating the voltage of the power grid node. The application considers the flexibility of the power grid energy in active and reactive power, excavates the potential of the power grid energy in coordinated regulation of frequency and voltage, can effectively compensate frequency fluctuation and regulate the voltage of each node of the network by coordinating the active and reactive power, solves the problem of effective regulation of the frequency and voltage of the power grid, and thereby ensures the safe operation of the power grid.

Description

Technical Field

[0001] This invention relates to the field of power grid control planning, and in particular to a control method for power grid frequency and voltage regulation. Background Technology

[0002] Currently, with the high penetration of renewable energy sources such as wind and solar power, the safe operation and effective control of the power grid face enormous challenges. On the one hand, traditional voltage regulation equipment lacks flexibility and cannot effectively adapt to the current intermittent, uncertain, and time-varying network environment. On the other hand, the inherent intermittency and uncertainty of renewable energy sources make it impossible for traditional power generation to stabilize the frequency within the standard range. Therefore, how to study a new coordinated control method to regulate the frequency and voltage of the power grid is an urgent problem to be solved.

[0003] Currently, most control strategies for regulating frequency and voltage employ a separate approach of "reactive power voltage regulation and active power frequency regulation," which ignores the significant resistance / reactance ratio at the distribution side due to renewable energy injection. In other words, in network systems with a high resistance / reactance ratio, both active power voltage regulation and reactive power frequency regulation play a crucial role. Therefore, the traditional separate control strategy of "reactive power voltage regulation and active power frequency regulation" is insufficient to cope with modern power systems with a high proportion of renewable energy penetration. Furthermore, traditional control methods based on time sampling and information transmission incur substantial computational and communication costs, leading to significant network resource depletion. Therefore, current control methods have considerable limitations. Summary of the Invention

[0004] The purpose of this invention is to solve the problem of effectively regulating the frequency and voltage of the power grid, and to provide a control method for power grid frequency and voltage regulation.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A control method for frequency and voltage regulation of a power grid includes the following steps:

[0007] S1. Establish a linearized power flow model and active power compensation model for the power grid;

[0008] S2. Based on the linearized power flow model and active power compensation model, establish optimization models and constraints for active and reactive power output to ensure that voltage and frequency active power compensation errors are within the set range.

[0009] S3. Solve the optimization model to obtain the active and reactive power output strategies; thereby coordinating the active and reactive power output of the power grid, effectively compensating for frequency fluctuations and regulating the voltage of power grid nodes.

[0010] In some embodiments, in step S3, the optimization model is solved by constructing the Lagrange function and KKT conditions of the constrained optimization problem, and the active power output and reactive power output strategies include Lagrange multipliers.

[0011] In some embodiments, step S3 includes: designing event triggering conditions so that each power grid node can transmit Lagrange multipliers non-periodically while ensuring convergence, thereby effectively saving the communication resources of the entire network.

[0012] In some embodiments, designing event triggering conditions specifically includes: designing event triggering conditions for the Lagrange multiplier. When the deviation of the Lagrange multiplier of the grid node meets the event triggering conditions, that is, when the designed triggering threshold is reached, the grid node transmits its local variable to the neighboring grid node, that is, updates its event sample value to a local value; otherwise, it does not transmit its real-time local value and keeps its sample value as the local value of the previous moment when the event triggering conditions were met.

[0013] In some embodiments, the bias of the Lagrange multiplier includes the bias of local harmonizing variables and the bias of harmonizing variables based on event sampling.

[0014] In some embodiments, the local coordination variable deviation is represented by the following formula:

[0015]

[0016] Where, λ i and Let represent the voltage-constrained Lagrange multipliers at grid node i locally and triggered by an event, respectively, where t is the sampling time;

[0017] The bias of the event-sampling-based harmonized variable is expressed by the following formula:

[0018]

[0019] Where, μ i and Let represent the compensation error constraint Lagrange multipliers at the local and event-triggered nodes i in the power grid, respectively.

[0020] In some embodiments, in step S2, the optimization model is expressed by the following formula:

[0021]

[0022] Among them, F P (p), F Q (q) represent the active power demand cost and reactive power demand cost, respectively, p = [p1, ..., p2]. n ] T ,q=[q1,…,qn ] T Let represent the vectors of active power and reactive power at each power grid node, respectively, and let Ω represent the set of allowable active and reactive power output ranges.

[0023] In some embodiments, the constraints of the optimization model are expressed by the following formula:

[0024]

[0025]

[0026] Where v(p,q) represents voltage constraint, e pf (p,q) represents the active power compensation error constraint. v and These represent the lower and upper bounds of the voltage constraint, respectively. e and These represent the lower and upper bounds of the frequency active power compensation error constraint, respectively.

[0027] In some embodiments, a weighted ball-based acceleration control algorithm is used to iterate the Lagrange multipliers to improve the convergence speed and smooth the active and reactive power outputs.

[0028] The present invention also provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0029] The present invention has the following beneficial effects:

[0030] This invention considers the flexibility of power grid energy in terms of active and reactive power output, and explores its potential in coordinating frequency and voltage regulation. By establishing optimization models and constraints for active and reactive power output based on linearized power flow and active power compensation models, and solving the above optimization models, the active and reactive power output strategies are set. By coordinating active and reactive power output, frequency fluctuations can be effectively compensated and the voltage of each node in the network can be regulated, thus solving the problem of effectively regulating the frequency and voltage of the power grid and ensuring the safe operation of the power grid.

[0031] In some embodiments, the present invention solves the optimization model by constructing the Lagrange function and KKT conditions of the constrained optimization problem, and designs event triggering conditions so that each local node of the power grid can transmit Lagrange multipliers non-periodically while ensuring convergence, thereby effectively saving the communication resources of the entire network.

[0032] In some embodiments, the present invention employs a weighted ball-based acceleration control algorithm to iterate the Lagrange multipliers in order to improve the convergence speed and smooth the active and reactive power output.

[0033] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0034] Figure 1 This is a flowchart of the power grid frequency and voltage regulation control method in an embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram of a radial power network in an embodiment of the present invention;

[0036] Figure 3 This is a schematic diagram of the power network topology in an embodiment of the present invention;

[0037] Figure 4 This is the frequency active power compensation response curve of TSO at PCC point within 12 hours of a day in the experimental example of this invention when the method of this embodiment is not used;

[0038] Figure 5 This is a graph showing the main node voltage curve over 12 hours in a day when the method of this embodiment was not used in the experimental example of this invention;

[0039] Figure 6 This is a schematic diagram of the voltage compensation error at each node after using the method of this embodiment in the experimental example of the present invention;

[0040] Figure 7 This is a schematic diagram of the frequency compensation error of each node after using the method of this embodiment in the experimental example of the present invention;

[0041] Figure 8 This is a trigger curve diagram of the control node response voltage adjustment in the experimental example of this invention;

[0042] Figure 9 This is the trigger curve diagram of the active power compensation of the control node frequency in the experimental example of the present invention;

[0043] Explanation of reference numerals in the attached figures:

[0044] 101-Non-mainstream load node, 102-Reactive power regulator, 103-Substation. Detailed Implementation

[0045] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and not intended to limit the scope and application of the present invention.

[0046] Currently, most control strategies for regulating frequency and voltage employ independent methods of "reactive power voltage regulation and active power frequency regulation." Furthermore, traditional control strategies have slow convergence speeds, which may affect the optimization level or even prevent convergence within the specified time, leading to system instability. Moreover, traditional control methods based on time sampling and information transmission incur significant computational and communication costs, resulting in substantial network resource consumption. Therefore, current control methods fail to simultaneously consider factors such as slow convergence speed and limited network resources, exhibiting significant limitations. To address the shortcomings of existing frequency and voltage regulation control methods, this invention proposes a power grid frequency and voltage regulation control method.

[0047] like Figure 1 As shown, the method of this embodiment of the invention includes the following steps:

[0048] S1. Establish a linearized power flow model and active power compensation model for the power grid;

[0049] S2. Based on the linearized power flow model and active power compensation model, establish optimization models and constraints for active and reactive power output to ensure that voltage and frequency active power compensation errors are within the set range.

[0050] S3. Solve the optimization model to obtain the active and reactive power output strategies; thereby coordinating the active and reactive power output of the power grid, effectively compensating for frequency fluctuations and regulating the voltage of power grid nodes.

[0051] In this embodiment, step S3 solves the optimization model by constructing the Lagrange function and KKT conditions for the constrained optimization problem. To improve the convergence speed of the method in this embodiment, unlike the traditional gradient descent iterative algorithm, this embodiment adopts a heavy-ball-based accelerated iterative algorithm. The specific principle is as follows: a momentum term is added to the traditional gradient descent-based iterative algorithm to accelerate the algorithm's convergence and smooth the control and response curves; that is, the heavy-ball-based accelerated control algorithm is used to iterate the Lagrange multipliers to improve the convergence speed and smooth the active and reactive power outputs. To reduce network communication costs, unlike the traditional periodic sampling and real-time communication, this embodiment designs an event-triggered sampling and communication strategy, which includes the following steps in step S3: designing event triggering conditions so that each grid node can transmit Lagrange multipliers non-periodically while ensuring convergence, thereby effectively saving the communication resources of the entire network; the specific design of event triggering conditions includes: for Lagrange multipliers... The Lagrange multiplier design employs an event triggering condition. When the deviation of the Lagrange multiplier at a grid node meets the event triggering condition, i.e., reaches the designed triggering threshold, the grid node transmits its local variables to neighboring grid nodes, updating its event sampled values ​​to local values ​​(sampled values ​​are the transmitted communication values ​​of each node; local values ​​are the real-time calculated values ​​of each node; sampled values ​​are generally collected in a zero-order hold, and are triggered to be updated to real-time calculated values ​​when certain conditions are met). Conversely, if the deviation does not meet the event triggering condition, its real-time local values ​​are not transmitted, and its sampled values ​​are maintained as the local values ​​at the previous moment when the event triggering condition was met. The deviation of the Lagrange multiplier includes the deviation of local coordination variables and the deviation of coordination variables based on event sampling. For example, when node A meets the triggering condition (i.e., has not reached the triggering threshold), it does not communicate, while other neighboring nodes use node A's sampled data from the previous moment for iteration. When node A violates the triggering condition, it transmits its latest data to neighboring nodes, and the neighboring nodes use its updated data for iteration. As can be seen from the event triggering principle, this aperiodic data transmission strategy can significantly reduce the communication cost of the entire network compared to periodic sampling.

[0052] The specific steps of the method in this embodiment are as follows:

[0053] S1: Establish a linearized power flow model and a frequency-based active power compensation model for the power grid. In this embodiment, the power grid is as follows: Figure 2 The radial power network shown is a network where 0,1,i,j,n represent node numbers, and p1 and q1, p i and q i p j and q j p n and q n These represent the active and reactive power injections at nodes 1, i, j, and n, respectively; while P 01 and Q 01This represents the active and reactive power flowing from node 0 into node 1, P. ij and Q ij These represent the active and reactive power flowing from node i into node j, respectively; furthermore, r 01 and x 01 Represent the resistance and reactance matrices of the line segment from node 0 to 1, respectively, r ij and x ij Let i and j represent the resistance and reactance matrices of the line segment from node i to j, respectively.

[0054] First, the linearized power flow model can be simplified as follows:

[0055]

[0056] In the formula, v = [v1, ... v2] n ] T p = [p1, ... p] n ] T ,q=[q1,…q n ] T Let represent the vectors of voltage, active power, and reactive power at each node, respectively, and [·]. T Represents the transpose of a vector or matrix. Additionally, it is defined as follows: R is a set of distributed network nodes. ij ] n×n and X = [x ij ] n×n Let these represent the resistance and reactance matrices of the line segment from node i to node j, respectively. This indicates the reference voltage.

[0057] On the other hand, according to the rules of power grid operation, the active power compensation required for frequency deviation is generally provided by the transmission end operator (TSO) at the PCC point (0 point) of the power network. Here, we assume ΔP f This is the active power compensation command issued by the TSO based on primary frequency fluctuations. This frequency compensation is transmitted at the PCC point through active and reactive power injections distributed across nodes in the distribution network. Therefore, linearizing the active power compensation at the PCC point yields:

[0058] ΔP f =Mp + Nq + ΔP0.

[0059] In the formula, ΔP f It is the active power compensation response given by TSO based on the frequency deviation. It is a linearization matrix for active and reactive power, and ΔP0 is the linearization constant.

[0060] S2: Based on the linearized power flow model and active power compensation model, establish optimization models and constraints for active and reactive power output to ensure that voltage and frequency active power compensation errors are within the set range.

[0061] Based on the linearized model established above, the following optimization model and constraints can be constructed:

[0062]

[0063] In the formula, These are the demand costs for active and reactive power, respectively. and For active power demand cost function coefficients, and and The coefficients of the reactive power demand cost function are generally assumed to be... and and Given a constant, Ω represents the set of allowable power output ranges for both active and reactive power. v and These are the upper and lower bounds of the voltage constraint, respectively; furthermore, as mentioned above, ΔP f The compensation provided by the TSO is based on the real-time response to frequency fluctuations. Therefore, compensation by distributed nodes in the distribution network may result in deviations. This is why e... pf =Mp+Nq+ΔP0-ΔP f Defined as active power compensation error, its allowable error range is: in e and These represent the lower and upper bounds of the frequency active power compensation error constraint, respectively.

[0064] S3. Based on the constructed optimization model, the active and reactive power output strategy is obtained by using a control algorithm based on heavy ball acceleration:

[0065] By constructing the Lagrangian function and KKT (Karush-Kuhn-Tucker) conditions for the constrained optimization problem, the formulas for calculating active and reactive power output can be obtained as follows:

[0066]

[0067] and

[0068]

[0069] in, and These are the Lagrange multipliers for voltage constraints and frequency active power compensation error constraints, respectively. λ and Let these represent the Lagrange multipliers for the lower and upper bounds of the voltage constraint, respectively. μ and These represent the Lagrange multipliers for the lower and upper bounds of the active power compensation error constraint, respectively. Furthermore... and and These represent the given ranges of active and reactive power output, where... p and These represent the lower and upper limits of contribution / effort, respectively; q and These represent the lower and upper bounds of no effort output, respectively. Furthermore, b P and b Q It is about and The set of n-dimensional vectors. Here, the iteration of the Lagrange multipliers λ and μ adopts a heavy ball-based acceleration algorithm to improve the convergence speed while smoothing the active and reactive power output curves.

[0070] In this embodiment, triggering conditions are designed for the Lagrange multipliers λ and μ during the solution process to save on the communication cost of the entire network. To this end, the deviations of the local coordination variables of each node and the deviations of the coordination variables based on event sampling are defined as follows: and Where λ i and Let μ represent the voltage-constrained Lagrange multipliers at node i locally and triggered by the event, respectively, where t is the sampling time; similarly, μ i and Let represent the compensation error constraint Lagrange multipliers at node i locally and during event triggering, respectively. As described above regarding the event triggering principle, when the deviation of each node's Lagrange multiplier meets the triggering condition, i.e., reaches the designed triggering threshold, each node transmits its local variables to its neighboring nodes, updating its event sampled value to a local value; otherwise, it does not transmit its real-time local value, maintaining its sampled value as the local value at the previous event triggering time, until the triggering condition is met.

[0071] The embodiments of the present invention have the following advantages:

[0072] 1. For new types of power grids with intermittent and uncertain grids where a large amount of renewable energy has penetrated, coordinating active and reactive power output can effectively compensate for frequency fluctuations and regulate the voltage of each node in the network.

[0073] 2. Acceleration algorithms based on heavy spheres with momentum terms can speed up convergence.

[0074] 3. By designing reasonable event triggering conditions for the Lagrange multipliers, and designing an event triggering mechanism for non-periodic sampling that guarantees global convergence for the Lagrange multipliers, the communication cost of the entire network can be greatly reduced by introducing appropriate triggering conditions.

[0075] This embodiment can accelerate convergence while reducing network communication costs, while achieving effective frequency and voltage regulation.

[0076] Experimental Example

[0077] To make the objectives, technical solutions, and beneficial effects of this invention clearer, experimental examples are given below to provide a more intuitive explanation of the effectiveness of the embodiments of this invention.

[0078] This experimental example uses an IEEE-123 node power network, whose topology is as follows: Figure 3 As shown, the system includes non-core load node 101, three-phase core load nodes 1 to 55, voltage regulator 56, reactive power regulator 102, and substation 103. First, the main parameters and relevant data of this experimental example are given to illustrate the effectiveness and universality of the embodiments of the present invention. In this experimental example, we assume that nodes 10, 15, 20, 32, and 38 are equipped with solar power generation devices (PVs) and control devices for regulating active and reactive power. We assume that the reactive power output (MVAR) range that the corresponding controllable nodes can provide is [-0.4, 0.4], [-0.4, 0.4], [-0.4, 0.4], [-0.6, 0.6], and [-0.2, 0.2], respectively; while the active power output (MW) range of all controllable nodes is [-0.1, 0.1]. In addition, the data sampling period is set to once every 5 seconds, the voltage constraint is set to [0.95, 1.95] pu, and the allowable range for frequency active power compensation error is ±5 × 10⁻⁶. -3 .

[0079] refer to Figure 4 Before implementing the method in this embodiment of the invention, the frequency active power compensation response curves given by TSO at the PCC point within 12 hours of a day are collected. Figure 4 The horizontal axis represents time, and the vertical axis represents active power compensation; (Reference) Figure 5 The figure shows the voltage curves of the backbone nodes before they are controlled. In the figure, the voltage curves of the five controllable nodes are indicated by arrows. The five controllable nodes are node 10, node 15, node 20, node 32 and node 38. The curves not marked with arrows are the voltage curves of other nodes in the network topology. Figure 5 The horizontal axis represents time, and the vertical axis represents node voltage.

[0080] By implementing the method proposed in the embodiments of the present invention, this experimental example obtains the corresponding voltage and frequency compensation errors at each node as follows: Figure 6 , Figure 7 As shown, Figure 6 , Figure 7 The horizontal axis represents time. Figure 6The vertical axis represents the node voltage. Figure 7 The ordinate represents the tracking error; similarly... Figure 6 Arrows are used to indicate the voltage curves of the five controllable nodes. Furthermore, to demonstrate the superiority of this embodiment, the performance of the method using this embodiment, a traditional time-triggered distributed method, and a method without control implementation are compared using four indicators: overvoltage time percentage, overvoltage root mean square error, over-frequency tracking error percentage, and over-tracking error variance. Table 1 below shows the performance comparison of control methods based on these four indicators:

[0081] Table 1

[0082]

[0083] As can be seen, the method of this embodiment can effectively regulate voltage while compensating for the active power required for frequency fluctuations. Compared with traditional methods, although the method proposed in this embodiment slightly sacrifices frequency tracking performance, this is negligible compared to its significant advantage in accelerating voltage regulation. To illustrate the overall network communication situation, as shown... Figure 8 , Figure 9 As shown, this experimental example presents the trigger curves for control node response voltage regulation and frequency active power compensation. Figure 8 , Figure 9 The horizontal axis represents time. Figure 8 The ordinate represents the communication triggering state with respect to the voltage-constrained Lagrange multiplier λ. Figure 9 The vertical axis represents the communication triggering state with respect to the Lagrange multiplier μ of the frequency active power compensation error. In the figure, the number "1" indicates triggering, and "0" indicates non-triggering. According to statistical calculations, compared with the traditional time-based triggering method which requires 8640 communication times, the event-triggered strategy proposed in this embodiment of the invention only requires 433 communication times with respect to the voltage constraint Lagrange multiplier and 352 communication times with respect to the active power compensation error Lagrange multiplier, which greatly reduces the communication cost of the entire network.

[0084] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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 processor, 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0086] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0088] The above description provides a further detailed explanation of the present invention in conjunction with specific / preferred embodiments, and it should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various substitutions or modifications can be made to these described embodiments without departing from the concept of the present invention, and all such substitutions or modifications should be considered within the scope of protection of the present invention. In the description of this specification, the reference to terms such as "an embodiment," "some embodiments," "preferred embodiment," "example," "specific example," or "some examples," etc., indicates that the specific features, structures, materials, or characteristics described in connection with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples. Although the embodiments of the present invention and their advantages have been described in detail, it should be understood that various changes, substitutions, and modifications can be made herein without departing from the scope of protection of the patent application.

Claims

1. A control method for frequency and voltage regulation of a power grid, characterized in that, Includes the following steps: S1. Establish a linearized power flow model and active power compensation model for the power grid; S2. Based on the linearized power flow model and active power compensation model, establish optimization models and constraints for active and reactive power output to ensure that voltage and frequency active power compensation errors are within the set range. S3. Solve the optimization model to obtain the active and reactive power output strategies; thereby coordinating the active and reactive power output of the power grid, effectively compensating for frequency fluctuations and regulating the voltage of power grid nodes; In step S3, the optimization model is solved by constructing the Lagrange function and KKT conditions for the constrained optimization problem. The active power output and reactive power output strategies include Lagrange multipliers. Step S3 includes: designing event triggering conditions so that each power grid node can transmit Lagrange multipliers aperiodically while ensuring convergence, thereby effectively saving the communication resources of the entire network; The specific design of the event triggering conditions includes: designing event triggering conditions for the Lagrange multipliers. When the deviation of the Lagrange multipliers of the grid node meets the event triggering conditions, that is, when the designed triggering threshold is reached, the grid node transmits its local variables to the neighboring grid nodes, that is, updates its event sampled value to the local value; otherwise, it does not transmit its real-time local value, and keeps its sampled value as the local value of the previous moment when the event triggering conditions were met. The bias of the Lagrange multipliers includes the bias of local harmonizing variables and the bias of harmonizing variables based on event sampling; The deviation of the local coordination variable is expressed by the following formula: ， in, and Let i represent the voltage-constrained Lagrange multipliers at the local level and the event-triggered level at grid node i, respectively. Sampling time; The bias of the event-sampling-based harmonized variable is expressed by the following formula: ， in, and Let represent the compensation error constraint Lagrange multipliers at the local and event-triggered nodes i in the power grid, respectively.

2. The method as described in claim 1, characterized in that, In step S2, the optimization model is expressed by the following formula: ； in, , These represent the demand costs for active power and reactive power, respectively. , These represent the vectors of active power and reactive power at each power grid node, respectively. This represents the set of allowable output ranges, including both active and reactive power.

3. The method as described in claim 2, characterized in that, The constraints of the optimization model are expressed by the following formula: in, Indicates voltage constraint. This indicates the active power compensation error constraint. and These represent the lower and upper bounds of the voltage constraint, respectively. These represent the lower and upper bounds of the frequency active power compensation error constraint, respectively.

4. The method as described in claim 1, characterized in that, A weighted ball-based acceleration control algorithm is used to iterate the Lagrange multipliers to improve the convergence speed and smooth the active and reactive power output.

5. A computer-readable medium having a computer program stored thereon, characterized in that, The When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-4.

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