Grid dynamic coordination optimization method, system and equipment based on power grid and medium

Abstract

The invention relates to the technical field of power system automation control, in particular to a grid dynamic coordination optimization method, system and device based on a power grid and a medium. Voltage fluctuation characteristics and load change gradients of nodes of the power grid are collected in real time, and instability early warning coefficients of the nodes of the power grid are calculated through chaotic evolution analysis; when the instability early warning coefficient exceeds a dynamic stability threshold value, extracting a grid region to which the node belongs, and constructing a multi-grid game utility function; the power adjustment range and the voltage constraint condition of each grid are input into a biological metabolism dynamics engine, and the power distribution weight of each grid is calculated through an ATP-ADP energy circulation mechanism; iteratively calculating an optimal power distribution strategy through an evolutionary game equilibrium solver based on the power distribution weight; generating a dynamic boundary impedance adjusting instruction according to the optimal power distribution strategy, and packaging the dynamic boundary impedance adjusting instruction and the interconnection switch operation sequence into a control message; and analyzing the control message into a three-dimensional operation guide animation through the augmented reality interaction platform, and executing power grid coordination optimization after the three-dimensional operation guide animation is confirmed by a gesture of an operator.

Description

Technical Field

[0001] This invention relates to the field of power system automation control technology, and in particular to a grid-based dynamic coordination optimization method, system, equipment and medium based on power grids. Background Technology

[0002] With the large-scale grid connection of new energy sources and the diversification of power loads, modern power grid operation exhibits complex characteristics of strong nonlinearity, multi-time-scale coupling, and superposition of random disturbances on both the source and load sides. The stable operation of the power grid system faces unprecedented challenges, and traditional power grid control technologies are no longer sufficient to meet the complex operational needs of modern power grids.

[0003] Traditional power grid optimization control techniques mainly rely on model predictive control and linear programming methods to achieve power allocation and voltage regulation by constructing deterministic models. These methods have significant limitations in handling the nonlinear dynamic characteristics of the power grid, failing to accurately characterize the chaotic properties of voltage fluctuations. This results in a lack of sensitivity to initial conditions in instability early warning mechanisms, leading to a high false alarm rate. Furthermore, existing linear modeling methods cannot effectively capture sudden instability trends in power grid operation, resulting in a delayed response from early warning mechanisms.

[0004] In recent years, dynamic optimization methods based on machine learning have gradually emerged, such as using deep reinforcement learning to construct policy networks to cope with complex operating conditions. However, existing game-theoretic optimization frameworks mostly rely on the static Nash equilibrium assumption and do not consider dynamic impedance matching and load priority differences between multiple grids, resulting in policy convergence speeds that cannot meet real-time scheduling requirements. In addition, traditional power grid control execution mainly relies on manual operation, which is highly complex, prone to errors, and lacks intuitive visualization and interaction methods. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention is proposed.

[0006] Therefore, the problem to be solved by this invention is how to establish a dynamic coordination optimization method for power grids that can accurately identify power grid instability risks, achieve multi-grid collaborative optimization, and provide intuitive and visual operation guidance, so as to improve the stability, reliability and operational safety of power grid operation and meet the dynamic coordination control requirements in modern complex power grid environments.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide a grid-based dynamic coordination optimization method for power grids, which includes real-time acquisition of voltage fluctuation characteristics and load change gradients of each node in the power grid;

[0009] The voltage fluctuation characteristics and load change gradients are time-stamped and the instability early warning coefficient of each power grid node is calculated through chaotic evolution analysis.

[0010] When the instability warning coefficient is greater than the preset dynamic stability threshold, the grid region to which the node belongs is extracted and a multi-grid game utility function is constructed;

[0011] The power adjustment range and voltage constraints of each grid are input into the biological metabolic dynamics engine, and the power allocation weight of each grid is calculated through the ATP-ADP energy cycle mechanism.

[0012] Based on the power allocation weights, the optimal power distribution strategy is iteratively calculated using an evolutionary game equilibrium solver.

[0013] Based on the optimal power distribution strategy, dynamic boundary impedance adjustment instructions and handshake switch operation sequences are generated and encapsulated into control messages.

[0014] The control message is parsed into a 3D operation guidance animation through an augmented reality interactive platform, and the power grid coordination optimization is performed after the operator confirms it with gestures.

[0015] As a preferred embodiment of the grid-based dynamic coordination optimization method for power grids described in this invention, the step of calculating the instability early warning coefficient of each power grid node through chaotic evolution analysis includes:

[0016] The instantaneous values ​​of the voltage waveform at each node are sampled, and the spectral characteristics of the voltage signal are extracted through frequency domain transformation.

[0017] The voltage spectrum characteristics and load change gradients are aligned with the data by timestamps to form a dynamic voltage load dataset;

[0018] Phase space reconstruction is performed based on the voltage load dynamic dataset, and the maximum Lyapunov exponent of each power grid node is calculated as the instability early warning coefficient.

[0019] As a preferred embodiment of the grid-based dynamic coordination optimization method for power grids described in this invention, the construction of the multi-grid game utility function includes:

[0020] The identifiers and maximum Lyapunov exponent values ​​of the out-of-limit nodes are parsed to determine the grid regions corresponding to each node and generate a grid node mapping table.

[0021] Calculate the average value of the maximum Lyapunov exponent of each node in each grid, and determine the instability risk weight of each grid based on the ratio of this average value to the dynamic stability threshold.

[0022] The incremental power transmission efficiency is used as the power adjustment benefit for each grid, and the product of the instability risk weight and the power adjustment magnitude is used as the risk penalty term to construct a multi-grid game utility function.

[0023] As a preferred embodiment of the grid-based dynamic coordination optimization method for power grids described in this invention, the step of calculating the power allocation weights of each grid through the ATP-ADP energy cycle mechanism includes:

[0024] The upper limit of the power adjustment range was converted into an ATP synthesis rate parameter, and the lower limit of the power adjustment range was converted into an ADP concentration inhibitory factor.

[0025] The initial ATP concentration of each grid is set as the energy reserve rate, and the initial ADP concentration is set as the load demand rate.

[0026] The Michaelis-Menten equation was used to calculate the ATP-ADP cycle process in stages until metabolic homeostasis was reached.

[0027] Extract the steady-state ATP concentration and calculate the proportion of ATP concentration in each grid to the total concentration as the power allocation weight.

[0028] As a preferred embodiment of the grid-based dynamic coordination optimization method for power grids described in this invention, the step of iteratively calculating the optimal power distribution strategy through an evolutionary game equilibrium solver includes:

[0029] The power allocation weights of each grid are used as the initial strategy parameters for the evolutionary game.

[0030] The game payoff function for each grid is defined based on voltage constraints and load data;

[0031] The policy parameters of each grid are updated using the replicator dynamic equation. The adjustment of the policy parameters is the product of the current policy parameter and the difference between the grid's payoff function value and the average payoff function value.

[0032] When the change in policy parameters in all adjacent iterations of all grids is less than the convergence threshold, the final policy parameters are taken as the optimal power distribution policy.

[0033] As a preferred embodiment of the grid-based dynamic coordination optimization method for power grids described in this invention, the generation of dynamic boundary impedance adjustment commands and tie switch operation sequences includes:

[0034] The power allocation values ​​of each grid are extracted from the optimal power distribution strategy, and the impedance adjustment values ​​of each grid boundary node are calculated based on the power impedance mapping relationship.

[0035] Based on the optimal power distribution strategy and the real-time grid topology, the switch number and operation type to be operated are determined according to the tie switch operation priority rules.

[0036] The impedance adjustment commands and switching operation sequences are arranged in a timing order and encapsulated into a standard format control message.

[0037] As a preferred embodiment of the grid-based dynamic coordination optimization method for power grids described in this invention, the step of parsing control messages into three-dimensional operation guidance animations via an augmented reality interactive platform includes:

[0038] The control message is transmitted to the augmented reality interaction platform for timestamp calibration and data parsing.

[0039] The impedance adjustment command is converted into a dynamic color gradient arrow, the switch operation command is converted into a device highlight flashing icon, and a 3D animation with a time-space synchronization icon is generated.

[0040] The operator's confirmation signal is obtained through gesture recognition, which triggers the physical execution of impedance adjustment commands and switching operation sequences.

[0041] Secondly, embodiments of the present invention provide a grid-based dynamic coordination and optimization system based on a power grid, which includes a data acquisition module for real-time acquisition of voltage fluctuation characteristics and load change gradients of each node in the power grid.

[0042] The chaos analysis module timestamps the voltage fluctuation characteristics and load change gradients, and calculates the instability early warning coefficient of each power grid node through chaotic evolution analysis.

[0043] The game construction module extracts the grid region to which the node belongs and constructs a multi-grid game utility function when the instability warning coefficient is greater than the dynamic stability threshold.

[0044] The weight calculation module inputs the power adjustment range and voltage constraint conditions of each grid into the bio-metabolic dynamics engine, and calculates the power allocation weight of each grid through the ATP-ADP energy cycle mechanism.

[0045] The strategy optimization module, based on power allocation weights, iteratively calculates the optimal power distribution strategy through an evolutionary game equilibrium solver.

[0046] The instruction generation module generates dynamic boundary impedance adjustment instructions and handshake switch operation sequences based on the optimal power distribution strategy, and encapsulates them into control messages.

[0047] The interactive execution module parses control messages into 3D operation guidance animations through an augmented reality interactive platform, and then executes power grid coordination optimization after the operator confirms the gestures.

[0048] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the grid-based dynamic coordination optimization method for power grids as described in the first aspect of the present invention.

[0049] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the grid-based dynamic coordination optimization method for power grids as described in the first aspect of the present invention.

[0050] The beneficial effects of this invention are as follows: This invention calculates the instability early warning coefficient of each power grid node using chaotic evolution analysis technology, breaking through the limitations of traditional linear early warning methods and accurately identifying nonlinear dynamic characteristics and chaotic phenomena in power grid operation. By using the maximum Lyapunov exponent as the instability early warning coefficient, it effectively solves the problem of the traditional early warning mechanism's lack of sensitivity to initial conditions, significantly reducing the false alarm rate and missed alarm rate, and providing a more reliable early warning guarantee for the safe operation of the power grid.

[0051] The multi-grid coordination optimization method achieved significant results. The multi-grid game utility function constructed in this invention realizes intelligent coordination and interest balancing among grids, effectively solving the problem of difficulty in balancing interest conflicts and resource competition among grids in traditional methods. By using the power transmission efficiency increment as the gain term and the instability risk weight as the penalty term, the method ensures that the overall system efficiency is improved while minimizing security risks, achieving a globally optimal coordination control effect.

[0052] This invention is the first to introduce biological metabolic dynamics into the field of power grid allocation, providing a novel biomimetic theoretical basis for power allocation by simulating the ATP-ADP energy cycle mechanism. This innovative modeling method can naturally handle the relationship between energy storage and consumption in the power grid, and realizes a power allocation strategy that is more in line with natural laws through enzyme-catalyzed reaction kinetics described by the Michaelis-Menten equation, opening up a new technical path for power grid optimization control.

[0053] The evolutionary game equilibrium solver used in this invention is based on the replicator dynamic equation, which can quickly converge to an evolutionarily stable strategy, effectively overcoming the slow convergence speed of traditional static Nash equilibrium methods. The evolutionarily stable strategy has good anti-interference ability and can automatically return to the equilibrium state even under small perturbations, ensuring the robustness and practicality of the power distribution strategy.

[0054] This invention transforms abstract control commands into intuitive 3D operation guidance animations through an augmented reality interactive platform, significantly improving the intuitiveness and safety of operation. Dynamic color-gradient arrows and highlighted flashing device indicators enable operators to clearly understand each operation step, effectively reducing the complexity and risk of misoperation associated with traditional manual operation.

[0055] The invention significantly improves the intelligence level of human-computer interaction. The gesture recognition system integrated in this invention, based on deep learning algorithms, can accurately recognize the operator's confirmation gestures, achieving a natural and intuitive human-computer interaction method. The dual confirmation mechanism and multi-person collaborative confirmation function further enhance the safety and reliability of operations, providing power grid operators with advanced intelligent operating tools.

[0056] The complete technical solution constructed by this invention forms a complete closed-loop control system from data acquisition, risk assessment, intelligent optimization to visualized execution. Each link works closely together in a coordinated manner. The entire system has good scalability and adaptability, and can be flexibly applied to power grid systems of different scales and types, providing a comprehensive solution for dynamic coordinated control in modern complex power grid environments. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 The flowchart shows a grid-based dynamic coordination optimization method for power grids.

[0059] Figure 2 A computer equipment diagram for a grid-based dynamic coordination optimization method for power grids;

[0060] Figure 3 This is a schematic diagram of a biological metabolic dynamics model based on a grid-based dynamic coordination optimization method for power grids.

[0061] Figure 4 The flowchart of the evolutionary game theory of the grid-based dynamic coordination optimization method based on power grids;

[0062] Figure 5 This is a distribution diagram of power grid impedance regulation equipment based on a grid-based dynamic coordination optimization method for power grids.

[0063] Figure 6 This is a schematic diagram of the augmented reality interactive interface for a grid-based dynamic coordination optimization method for power grids.

[0064] Figure 7This is a schematic diagram of the gesture recognition process for a grid-based dynamic coordination optimization method for power grids.

[0065] Figure 8 This is another schematic diagram of the gesture recognition process for a grid-based dynamic coordination optimization method for power grids. Detailed Implementation

[0066] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0067] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0068] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0069] Example 1

[0070] Reference Figures 1-2 This is the first embodiment of the present invention, which provides a grid-based dynamic coordination optimization method for power grids, including:

[0071] S100: Real-time acquisition of voltage fluctuation characteristics and load change gradients at various nodes of the power grid;

[0072] S200: Time-stamp aligns voltage fluctuation characteristics with load change gradients, and calculates instability early warning coefficients for each power grid node through chaotic evolution analysis;

[0073] S300: When the instability warning coefficient is greater than the preset dynamic stability threshold, extract the grid region to which the node belongs and construct a multi-grid game utility function;

[0074] S400: Input the power adjustment range and voltage constraint conditions of each grid into the bio-metabolic dynamics engine, and calculate the power allocation weight of each grid through the ATP-ADP energy cycle mechanism;

[0075] S500: Based on power allocation weights, the optimal power distribution strategy is iteratively calculated through an evolutionary game equilibrium solver;

[0076] S600: Generates dynamic boundary impedance adjustment commands and handshake switch operation sequences based on the optimal power distribution strategy, and encapsulates them into control messages;

[0077] S700: The control message is parsed into a 3D operation guidance animation through the augmented reality interactive platform, and the power grid coordination optimization is performed after the operator confirms it with gestures.

[0078] In the modern power grid operation environment, traditional power grid control methods face multiple technical challenges. The S100-S700 technical solutions primarily address the following specific issues: First, voltage fluctuations and load changes at power grid nodes exhibit strong randomness and nonlinearity, making it difficult for traditional linear prediction methods to accurately capture these complex dynamic characteristics, leading to inaccurate system state assessments. Second, existing instability early warning mechanisms are mostly based on static threshold judgments, failing to effectively identify chaotic characteristics and initial value sensitivity in power grid operation, easily resulting in false alarms or missed alarms. Third, power coordination optimization among multiple grids lacks an effective game theory mechanism, making it difficult to balance conflicts of interest and resource competition among grids, resulting in poor global optimization performance. Finally, traditional power grid operation relies on human experience, is complex and prone to errors, and lacks intuitive visualization guidance and real-time feedback mechanisms.

[0079] Claim 1 of this invention constitutes a complete closed-loop control system for dynamic coordination and optimization of the power grid through seven key steps. This technical solution can independently solve the aforementioned technical problems: by real-time acquisition of voltage fluctuation characteristics and load change gradients at each node of the power grid, a comprehensive perception foundation for the system state is established; chaotic evolutionary analysis is used to calculate the instability early warning coefficient, overcoming the limitations of traditional linear early warning methods and accurately identifying the nonlinear dynamic characteristics of the power grid; a multi-grid game utility function is constructed, realizing intelligent coordination and benefit balancing among grids; a biological metabolic dynamics engine is introduced to simulate the ATP-ADP energy cycle mechanism, providing a biomimetic theoretical basis for power allocation; iterative calculation through an evolutionary game equilibrium solver ensures the global optimality and rapid convergence of the optimization strategy; the generated control message integrates impedance adjustment and switching operation commands, realizing unified execution of coordinated control; and finally, a three-dimensional visual operation guide is provided through an augmented reality interactive platform, significantly improving the safety and accuracy of operation. The entire technical solution forms a complete closed-loop control system from risk perception and intelligent decision-making to safe execution.

[0080] The main English terms involved in this technical solution include: ATP (adenosine triphosphate) is the basic unit of cellular energy metabolism, used in this invention to simulate the energy reserve state in a power grid; ADP (adenosine diphosphate) is the product of ATP hydrolysis, representing the energy consumption state in the system; the Lyapunov exponent is an important parameter in chaos theory for measuring system stability, with a positive value indicating chaotic characteristics; PMU (phasor measurement unit) is a device in a power grid used to synchronously measure voltage and current phasors; SCADA (Supervisory Control and Data Acquisition) is an important component of industrial control systems; IEC standards are standards for electrical equipment and systems developed by the International Electrotechnical Commission. A precise understanding of these terms is crucial for the implementation of this technical solution.

[0081] Example 2

[0082] Reference Figures 2-8 This is the second embodiment of the present invention.

[0083] In this embodiment of the application, step S100 involves real-time acquisition of voltage fluctuation characteristics and load change gradients at each node of the power grid, including the following steps A1-A2:

[0084] A1: Multi-dimensional power grid data is collected through a distributed sensor network. Specifically, high-precision voltage and current sensors are deployed at each power grid node to achieve real-time monitoring of node voltage amplitude, phase, and frequency. High-speed sampling technology is used to acquire voltage fluctuation characteristics, with a sampling frequency of no less than 10kHz, ensuring the capture of high-frequency components and transient changes in the voltage signal. Simultaneously, power sensors monitor the changes in active and reactive power at each node in real time, calculating the load change gradient. The data acquisition system uses the IEEE 1588 precise time protocol for time synchronization, ensuring that the timestamp accuracy of data from each node reaches the microsecond level.

[0085] In an optional embodiment, voltage fluctuation feature extraction can also be combined with wavelet transform technology to decompose the voltage signal into different frequency components and analyze the energy distribution characteristics within each frequency band. This method can better identify transient disturbances and harmonic components in the voltage signal, improving the system's sensitivity to abnormal grid conditions.

[0086] It should be noted that the deployment of sensor networks needs to take into account the topology of the power grid and the importance of key nodes. High-precision sensors should be deployed in key locations such as substations, generator outlets, and important load centers to ensure the comprehensiveness and representativeness of data collection.

[0087] A2: Establish a data preprocessing and quality control mechanism to filter, denoise, and detect anomalies in the collected raw data. A Kalman filter algorithm is used to smooth voltage and load data, removing measurement noise and random disturbances. Simultaneously, a data quality assessment system is established, using methods such as data consistency checks, rate of change analysis, and correlation analysis to identify and remove outlier data points. The load change gradient is calculated using the finite difference method, quantifying the severity of load fluctuations by analyzing the rate of change of load data at adjacent time points.

[0088] In an optional embodiment, machine learning algorithms can also be introduced to intelligently assess data quality. By training a neural network model to learn the distribution characteristics of normal data, suspicious abnormal data can be automatically identified and marked, thereby improving the automation level of data quality control.

[0089] It should be noted that the parameter settings for data preprocessing need to be adjusted according to the specific characteristics of the power grid, including the cutoff frequency of the filter and the threshold setting for anomaly detection, in order to ensure that useful system dynamic information is retained while removing noise.

[0090] In this embodiment of the application, step S200 aligns the voltage fluctuation characteristics with the load change gradient using timestamps, and calculates the instability early warning coefficient for each grid node through chaotic evolution analysis, including the following steps B1-B2:

[0091] B1: Calculate the instability warning coefficients for each power grid node using chaotic evolution analysis, including:

[0092] The instantaneous values ​​of the voltage waveform at each node are sampled, and the spectral characteristics of the voltage signal are extracted through frequency domain transformation.

[0093] The voltage spectrum characteristics and load change gradients are aligned with the data by timestamps to form a dynamic voltage load dataset;

[0094] Phase space reconstruction is performed based on voltage load dynamic dataset, and the maximum Lyapunov exponent of each power grid node is calculated as the instability early warning coefficient.

[0095] Precise time synchronization and data alignment are implemented, employing a GPS time synchronization system to provide a unified time reference for the entire power grid monitoring network. Each acquisition node maintains synchronization with the master clock server via the NTP network time protocol, with time accuracy controlled within 1 millisecond. During data alignment, the voltage fluctuation characteristics and load change gradient data acquired by each node are first sorted according to their timestamps. Then, interpolation algorithms are used to address time misalignment issues caused by differences in sampling frequency or transmission delays. Cubic spline interpolation is employed to compensate for missing data points, ensuring complete correspondence of data from all nodes on the time axis.

[0096] Specifically, the timestamp alignment algorithm can be expressed as: Let the voltage data of node i at time t be V. i (t), load data is P i (t), for any target time t k The aligned data V is obtained through interpolation calculation. i (t k ) and P i (t k ).

[0097] In an optional embodiment, the Dynamic Time Warping (DTW) algorithm can also be used to handle the time delay differences that may exist between different nodes, especially in the case of long-distance transmission or network congestion, to ensure that the time correspondence of data is accurate and reliable.

[0098] It should be noted that, given the power grid topology, the time synchronization system needs to cover all monitoring nodes of the entire power grid to form a unified time reference network, ensuring the time consistency of distributed data collection.

[0099] B2: Chaotic evolution analysis based on phase space reconstruction theory is performed first. The embedding dimension and delay time parameters of the time series are determined. The optimal delay time τ and embedding dimension m are determined using the mutual information method and the pseudo-nearest neighbor method, respectively, to construct a voltage-load joint phase space. In the reconstructed phase space, the Wolf algorithm is used to calculate the maximum Lyapunov exponent for each node. The chaotic characteristics of the system are quantified by tracking the separation velocity of adjacent orbits in the phase space. During the calculation, appropriate evolution time steps and neighborhood radii are selected to ensure the accuracy and stability of the Lyapunov exponent calculation.

[0100] The specific phase space reconstruction process is as follows: for the voltage time series v of node i... i (t), construct the embedding vector:

[0101] V i (t)=[v i (t),v i (t+τ),…,V i (t+(m-1) τ ]

[0102] Where τ is the delay time and m is the embedding dimension.

[0103] In an optional embodiment, the recursive graph analysis method can be combined to further quantify the dynamic characteristics of the system through recursive quantification analysis (RQA) indicators such as recursion rate and determinism, providing richer characteristic parameters for instability early warning.

[0104] It should be noted that the selection of parameters for chaotic evolution analysis needs to take into account the time scale characteristics of the power grid system. Typically, the fast dynamic processes of the power grid are on the order of milliseconds to seconds, while the slow processes are on the order of minutes. Therefore, it is necessary to select an appropriate analysis window length to capture the dynamic behavior at different time scales.

[0105] In this embodiment of the application, when the instability warning coefficient is greater than the preset dynamic stability threshold in step S300, the grid region to which the node belongs is extracted and a multi-grid game utility function is constructed, including the following steps C1-C2:

[0106] C1: Constructing the utility function for multi-grid games, including:

[0107] The identifiers and maximum Lyapunov exponent values ​​of the out-of-limit nodes are parsed to determine the grid regions corresponding to each node and generate a grid node mapping table.

[0108] Calculate the average value of the maximum Lyapunov exponent of each node in each grid, and determine the instability risk weight of each grid based on the ratio of this average value to the dynamic stability threshold.

[0109] The incremental power transmission efficiency is used as the power adjustment benefit for each grid, and the product of the instability risk weight and the power adjustment magnitude is used as the risk penalty term to construct a multi-grid game utility function.

[0110] A dynamic threshold judgment and grid division mechanism is established. First, the dynamic stability threshold of each node is determined based on the historical operation data and stability analysis results of the power grid. A hierarchical approach is adopted for threshold setting, with differentiated threshold standards set for different voltage levels and node importance. When the maximum Lyapunov exponent of a node exceeds its corresponding threshold, an instability warning for that node is triggered, and the system automatically extracts the node's identifier, geographical location information, and electrical connection relationships. Grid division is based on the physical topology and electrical distances of the power grid. A spectral clustering algorithm is used to divide the power grid nodes into several relatively independent grid regions, with nodes within each grid exhibiting strong electrical coupling relationships.

[0111] In the specific grid partitioning process, the adjacency matrix A of the power grid is constructed, where A_ij represents the electrical connection strength between nodes i and j. The optimal number of clusters and the clustering results are determined by the eigenvalue decomposition of the Laplace matrix.

[0112] In an optional embodiment, mesh partitioning can also take into account the similarity of load characteristics, grouping nodes with similar load variation patterns into the same mesh to improve the coordination and optimization effect within the mesh.

[0113] It should be noted that the entire power grid is divided into multiple relatively independent grid regions, each with a clear boundary definition and internal node composition, providing a foundation for subsequent game theory optimization.

[0114] C2: The optimal power distribution strategy is calculated iteratively using an evolutionary game equilibrium solver, including:

[0115] The power allocation weights of each grid are used as the initial strategy parameters for the evolutionary game.

[0116] The game payoff function for each grid is defined based on voltage constraints and load data;

[0117] The policy parameters of each grid are updated using the replicator dynamic equation. The adjustment of the policy parameters is the product of the current policy parameter and the difference between the grid's payoff function value and the average payoff function value.

[0118] When the change in policy parameters in all adjacent iterations of all grids is less than the convergence threshold, the final policy parameters are taken as the optimal power distribution policy.

[0119] A multi-grid game utility function is constructed, treating each grid as an independent player, with each grid's strategy choice being its power adjustment scheme. First, the instability risk weights of nodes within each grid are analyzed, and the weighted average of the maximum Lyapunov exponents of all nodes within the grid is calculated. The weighting coefficients are determined based on the importance and load capacity of the nodes. Then, the payoff function for each grid is defined, including the positive payoff from improved power transmission efficiency and the negative payoff from increased instability risk. The utility function is constructed using a multi-objective optimization approach, aiming to minimize security risks while improving overall system efficiency.

[0120] The game utility function can be expressed as:

[0121] U i =α×R i -β×C i ×P i

[0122] Among them U i R is the utility value of grid i. i For power adjustment benefits, C i As the instability risk weight, P i The power adjustment range is represented by α and β, which are trade-off coefficients.

[0123] In an optional embodiment, a collaborative benefit term between grids can also be introduced, which provides additional benefits when adjacent grids adopt a coordinated adjustment strategy to promote collaborative behavior between grids.

[0124] It should be noted that the parameter settings of the utility function need to be determined through training with historical data and simulation verification to ensure that the function can accurately reflect the real benefits of each grid and the consistency of the overall system objectives.

[0125] In this embodiment of the application, step S400 inputs the power adjustment range and voltage constraint conditions of each grid into the bio-metabolic kinetics engine, and calculates the power allocation weight of each grid through the ATP-ADP energy cycle mechanism, including the following steps D1-D2:

[0126] D1: Calculate the power allocation weights for each grid using the ATP-ADP energy cycle mechanism, including:

[0127] The upper limit of the power adjustment range was converted into an ATP synthesis rate parameter, and the lower limit of the power adjustment range was converted into an ADP concentration inhibitory factor.

[0128] The initial ATP concentration of each grid is set as the energy reserve rate, and the initial ADP concentration is set as the load demand rate.

[0129] The Michaelis-Menten equation was used to calculate the ATP-ADP cycle process in stages until metabolic homeostasis was reached.

[0130] Extract the steady-state ATP concentration and calculate the proportion of ATP concentration in each grid to the total concentration as the power allocation weight.

[0131] A parameter mapping mechanism for the bio-metabolic kinetic model is established to convert the physical parameters of the power grid into corresponding parameters of the bio-metabolic model. The upper limit of the power adjustment range corresponds to the maximum ATP synthesis rate, reflecting the grid's maximum power generation or regulation capacity; the lower limit of the power adjustment range corresponds to the ADP concentration inhibition effect, representing the grid's minimum power demand constraint. Voltage constraints are converted into enzyme activity regulators; when the voltage deviates from the normal range, the corresponding regulators will inhibit or promote the metabolic reaction. The time-step execution requirement is converted into the number of metabolic reaction cycles, with each cycle representing a power adjustment time window.

[0132] The specific parameter conversion relationship is as follows: ATP synthesis rate V max =k1×P max

[0133] Where k1 is the conversion coefficient, P max Maximum power adjustment capability of the grid;

[0134] ADP inhibition constant Where k2 is the conversion coefficient, P min This represents the minimum power requirement for the grid.

[0135] In an optional embodiment, a temperature effect can also be introduced to simulate the influence of the power grid operating environment. When the power grid load is heavy or the ambient temperature is high, the temperature parameters of the metabolic reaction are increased accordingly, affecting the rate of the ATP-ADP cycle.

[0136] It should be noted that, referring to Figure 3 The diagram shows a biological metabolic kinetic model. The entire model includes multiple stages such as ATP synthesis, ADP hydrolysis, and energy transfer, and each stage has a corresponding electrical grid physical process.

[0137] D2: Perform the ATP-ADP cycle calculation to simulate the energy conversion mechanism in cellular metabolism. The initial ATP concentration in each grid cell is set as the current energy reserve ratio, and the initial ADP concentration is set as the real-time load demand ratio. The Michaelis-Menten equation is used to describe the enzyme reaction kinetics, calculating the ATP generation and consumption rates at each time step. The cycle includes ATP hydrolysis releasing energy to meet load demand, and ADP rephosphorylation generating ATP to store excess energy. Iterative calculations continue until the system reaches metabolic equilibrium, at which point the ATP concentration distribution in each grid cell reflects the optimal energy allocation scheme of the system.

[0138] The specific form of the Michaelis equation is:

[0139]

[0140] Where v is the reaction rate, V max [S] represents the maximum reaction rate, [S] represents the substrate concentration, and k represents the maximum reaction rate. m is the Michaelis constant.

[0141] In an optional embodiment, competitive inhibition effects of various enzymatic reactions can also be considered. When multiple grids require power support at the same time, priority is automatically allocated through competitive inhibition mechanism to avoid resource conflicts.

[0142] It should be noted that the criterion for judging metabolic balance is that the rate of change of ATP concentration in adjacent iterations is less than a preset threshold, usually set to 0.1%, to ensure that the system reaches a stable energy distribution state.

[0143] In this embodiment of the application, step S500, based on the power allocation weights, iteratively calculates the optimal power distribution strategy through an evolutionary game equilibrium solver, including the following steps E1-E2:

[0144] E1: Initialize the evolutionary game parameters and policy space. Use the power allocation weights of each grid as components of the initial policy vector to construct the policy space matrix. The dimension of the policy space equals the number of grids, and each dimension corresponds to the power adjustment policy of one grid. Define the policy update rule, using a replicator dynamic equation to describe the evolution process of each grid's policy. Set a payoff matrix to reflect the payoff of each grid under different policy combinations. The game model considers the mutual influence between grids; when a grid adjusts its power, it affects the voltage level and transmission loss of adjacent grids, thus affecting its payoff.

[0145] The basic form of the reproducer dynamic equation is:

[0146]

[0147] Where x i f is the policy ratio for grid i. i (x) represents the expected return of grid i under the strategy combination x. This represents the average return across all grids.

[0148] In an optional embodiment, mutation and selection mechanisms can also be introduced to simulate random mutation and natural selection in the process of biological evolution, enhance the algorithm's ability to escape local optima, and improve global search performance.

[0149] It should be noted that, referring to Figure 4 The evolutionary game flowchart shown illustrates the entire solution process, which includes multiple stages such as strategy initialization, payoff calculation, strategy update, and convergence judgment, forming a complete iterative solution loop.

[0150] E2: Perform iterative solution and convergence check, solving the replicator dynamic equations using numerical integration methods to calculate the change in policy for each grid at each time step. A fourth-order Runge-Kutta method is used for numerical integration to ensure solution accuracy and stability. In each iteration, the expected reward for each grid under the current policy combination is first calculated, and then the policy proportions are updated according to the replicator dynamic equations. A convergence criterion is set: when the policy changes for all grids are less than a preset convergence threshold, the system is considered to have reached an evolutionarily stable policy (ESS). The convergence threshold needs to balance computational accuracy and efficiency, and is typically set to 0.01.

[0151] In an optional embodiment, an adaptive step size adjustment mechanism can be adopted to accelerate the convergence process. The iteration step size is dynamically adjusted according to the severity of policy changes. When the policy changes are large, a smaller step size is used to ensure stability, and a larger step size is used to improve efficiency when convergence is near.

[0152] It should be noted that the evolutionary stabilization strategy is resistant to disturbances. Even if subjected to small disturbances, the system will automatically return to the equilibrium state, which ensures the robustness and practicality of the power distribution strategy.

[0153] In this embodiment of the application, step S600 generates a dynamic boundary impedance adjustment command and a handshake switch operation sequence according to the optimal power distribution strategy, and encapsulates them into a control message, including the following steps F1-F2:

[0154] F1: Generates dynamic boundary impedance adjustment commands and handover switch operation sequences, including:

[0155] The power allocation values ​​of each grid are extracted from the optimal power distribution strategy, and the impedance adjustment values ​​of each grid boundary node are calculated based on the power impedance mapping relationship.

[0156] Based on the optimal power distribution strategy and the real-time grid topology, the switch number and operation type to be operated are determined according to the tie switch operation priority rules.

[0157] The impedance adjustment commands and switching operation sequences are arranged in a timing order and encapsulated into a standard format control message.

[0158] The impedance regulation requirements for each grid boundary are calculated based on the optimal power distribution strategy. First, the target power allocation value for each grid is extracted and compared with the current actual power to calculate the power difference requiring regulation. Based on the equivalent circuit model of the power grid, the boundary impedance regulation required to achieve the target power distribution is calculated using the power-impedance sensitivity matrix. Impedance regulation is mainly achieved through flexible transmission equipment such as Controllable Series Compensators (TCSCs) and Static Synchronous Compensators (STATCOMs). Regulation commands include equipment identification, target impedance value, and regulation time information to ensure the completeness and executability of the commands.

[0159] The impedance adjustment amount can be calculated as follows:

[0160] ΔX=J -1 ×ΔP

[0161] Where ΔX is the impedance adjustment vector, J is the power-impedance Jacobian matrix, and ΔP is the power adjustment demand vector.

[0162] In an optional embodiment, the dynamic response characteristics of impedance regulation can also be considered. The regulation timing can be reasonably arranged according to the response time and regulation accuracy of different devices to avoid mutual interference during the regulation process.

[0163] It should be noted that, referring to Figure 5 The diagram shows the distribution of power grid impedance regulation equipment. Various flexible transmission equipment are distributed on the critical lines at the grid boundary, and overall power flow optimization is achieved through coordinated control.

[0164] F2: Determine the operation sequence and priority rules for tie switches, analyze the requirements of the optimal power distribution strategy on the power grid topology, and identify tie lines that need to be put into or disconnected. Tie switch operations follow a safety-first principle, first checking system stability before and after the operation to ensure it will not cause system instability or equipment overload. Establish an operation priority ranking, with higher priority given to important tie lines, while also considering time constraints and equipment status. Generate standardized operation sequences, including detailed information such as the operation object, operation type (putting into / disconnecting), operation time, and preconditions.

[0165] In an optional embodiment, an operational risk assessment mechanism can also be established to assess the risk level of each switch operation, with high-risk operations requiring additional confirmation procedures and safety measures.

[0166] It should be noted that the generation of the tie switch operation sequence needs to take into account the N-1 safety principle of the power grid to ensure that the failure of any single device will not lead to overall system instability or large-scale power outage.

[0167] In this embodiment of the application, step S700 involves parsing the control message into a three-dimensional operation guidance animation via an augmented reality interactive platform. After confirmation by the operator's gestures, power grid coordination optimization is performed, including the following steps G1-G2:

[0168] G1: Parses control messages into 3D operation guidance animations via an augmented reality interactive platform, including:

[0169] The control message is transmitted to the augmented reality interaction platform for timestamp calibration and data parsing.

[0170] The impedance adjustment command is converted into a dynamic color gradient arrow, the switch operation command is converted into a device highlight flashing icon, and a 3D animation with a time-space synchronization icon is generated.

[0171] The operator's confirmation signal is obtained through gesture recognition, which triggers the physical execution of impedance adjustment commands and switching operation sequences.

[0172] To establish an augmented reality visualization system, abstract control commands in control messages are first converted into intuitive 3D graphical elements. Impedance adjustment commands are transformed into dynamic colored arrows, with the arrow color indicating the adjustment direction (red for increasing, blue for decreasing) and the arrow thickness indicating the adjustment magnitude. Handling switch operation commands are converted into a highlighting flashing effect on the equipment, with flashing frequency and color used to distinguish different operation types. A virtual-real mapping mechanism is established to overlay virtual operation instructions onto the real power grid equipment monitoring screen, forming an augmented reality operating environment. Spatiotemporal synchronization markers ensure a real-time correspondence between virtual elements and the actual equipment status.

[0173] In an alternative embodiment, voice prompts and haptic feedback can also be introduced to provide the operator with multimodal information perception, especially in visually limited environments, where key operational information can be prompted by voice and vibration.

[0174] It should be noted that, referring to Figure 6 The augmented reality interface shown allows operators to see virtual operation instructions superimposed on the real device through a head-mounted display or handheld terminal, enabling intuitive human-computer interaction.

[0175] G2: Implements gesture recognition and operation confirmation mechanisms, deploying a computer vision-based gesture recognition system capable of accurately recognizing operator confirmation gestures (such as nodding, waving, pointing, etc.). Gesture recognition employs deep learning algorithms, training a convolutional neural network model to learn the characteristics of different gestures, achieving an accuracy rate of over 95%. A dual confirmation mechanism is established, requiring the operator to make two consecutive confirmation gestures to trigger actual device operation, preventing accidental operation. During operation, the system monitors device response and system status changes in real time, promptly feeding back the operation results to the operator. If an anomaly is detected, the system automatically pauses the operation and issues a warning.

[0176] In an optional embodiment, a multi-person collaborative confirmation mechanism can also be established, requiring multiple operators to confirm important operations simultaneously, thereby improving the security and reliability of the operation.

[0177] It should be noted that, referring to Figure 7 and Figure 8 The gesture recognition process shown involves the system capturing the operator's movements through a camera, and then performing steps such as image preprocessing, feature extraction, and pattern recognition to ultimately identify the operator's intentions and execute corresponding control commands.

[0178] This embodiment constructs a power grid dynamic coordination and optimization system integrating data acquisition, risk assessment, intelligent optimization, and visualized operation through a complete technical solution from S100 to S700. This system effectively solves complex control problems in modern power grid operation, improving the power grid's safety, economy, and operability. Through innovative applications of biological metabolic dynamics modeling and evolutionary game theory, it provides a new theoretical foundation and technical means for power grid optimization control. The application of augmented reality interaction technology significantly enhances the intuitiveness and safety of operation, providing power grid operators with advanced human-computer interaction tools. The entire technical solution has good scalability and adaptability, and can be applied to power grid systems of different scales and types.

[0179] Example 3

[0180] The above is a schematic scheme of a grid-based dynamic coordination optimization method based on power grids. It should be noted that the technical solution of this grid-based dynamic coordination optimization system and the technical solution of the aforementioned grid-based dynamic coordination optimization method belong to the same concept. Details not described in detail in the technical solution of the grid-based dynamic coordination optimization system in this embodiment can be found in the description of the technical solution of the aforementioned grid-based dynamic coordination optimization method.

[0181] This embodiment also provides a grid-based dynamic coordination and optimization system based on power grids, including:

[0182] The data acquisition module collects the voltage fluctuation characteristics and load change gradients of each node in the power grid in real time.

[0183] The chaos analysis module timestamps the voltage fluctuation characteristics and load change gradients, and calculates the instability early warning coefficient of each power grid node through chaotic evolution analysis.

[0184] The game construction module extracts the grid region to which the node belongs and constructs a multi-grid game utility function when the instability warning coefficient is greater than the dynamic stability threshold.

[0185] The weight calculation module inputs the power adjustment range and voltage constraint conditions of each grid into the bio-metabolic dynamics engine, and calculates the power allocation weight of each grid through the ATP-ADP energy cycle mechanism.

[0186] The strategy optimization module, based on power allocation weights, iteratively calculates the optimal power distribution strategy through an evolutionary game equilibrium solver.

[0187] The instruction generation module generates dynamic boundary impedance adjustment instructions and handshake switch operation sequences based on the optimal power distribution strategy, and encapsulates them into control messages.

[0188] The interactive execution module parses control messages into 3D operation guidance animations through an augmented reality interactive platform, and then executes power grid coordination optimization after the operator confirms the gestures.

[0189] This embodiment also provides an electronic device suitable for grid-based dynamic coordination optimization based on power grids, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the grid-based dynamic coordination optimization method based on power grids as proposed in the above embodiment.

[0190] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the grid-based dynamic coordination optimization method for power grids as proposed in the above embodiments.

[0191] The storage medium proposed in this embodiment and the grid-based dynamic coordination optimization method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0192] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0193] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A grid-based dynamic coordination optimization method for power grids, characterized in that: This includes real-time acquisition of voltage fluctuation characteristics and load change gradients at various nodes of the power grid; The voltage fluctuation characteristics and load change gradients are time-stamped and the instability early warning coefficient of each power grid node is calculated through chaotic evolution analysis. When the instability warning coefficient is greater than the preset dynamic stability threshold, the grid region to which the node belongs is extracted and a multi-grid game utility function is constructed; The power adjustment range and voltage constraints of each grid are input into the biological metabolic dynamics engine, and the power allocation weight of each grid is calculated through the ATP-ADP energy cycle mechanism. Based on the power allocation weights, the optimal power distribution strategy is iteratively calculated using an evolutionary game equilibrium solver. Based on the optimal power distribution strategy, dynamic boundary impedance adjustment instructions and handshake switch operation sequences are generated and encapsulated into control messages. The control message is parsed into a 3D operation guidance animation through an augmented reality interactive platform, and the power grid coordination optimization is performed after the operator confirms it with gestures.

2. The grid-based dynamic coordination optimization method for power grids as described in claim 1, characterized in that: The calculation of instability early warning coefficients for each power grid node through chaotic evolution analysis includes: The instantaneous values ​​of the voltage waveform at each node are sampled, and the spectral characteristics of the voltage signal are extracted through frequency domain transformation. The voltage spectrum characteristics and load change gradients are aligned with the data by timestamps to form a dynamic voltage load dataset; Phase space reconstruction is performed based on the voltage load dynamic dataset, and the maximum Lyapunov exponent of each power grid node is calculated as the instability early warning coefficient.

3. The grid-based dynamic coordination optimization method for power grids as described in claim 2, characterized in that: The construction of the multi-grid game utility function includes: The identifiers and maximum Lyapunov exponent values ​​of the out-of-limit nodes are parsed to determine the grid regions corresponding to each node and generate a grid node mapping table. Calculate the average value of the maximum Lyapunov exponent of each node in each grid, and determine the instability risk weight of each grid based on the ratio of this average value to the dynamic stability threshold. The incremental power transmission efficiency is used as the power adjustment benefit for each grid, and the product of the instability risk weight and the power adjustment magnitude is used as the risk penalty term to construct a multi-grid game utility function.

4. The grid-based dynamic coordination optimization method for power grids as described in claim 3, characterized in that: The calculation of power allocation weights for each grid using the ATP-ADP energy cycle mechanism includes: The upper limit of the power adjustment range was converted into an ATP synthesis rate parameter, and the lower limit of the power adjustment range was converted into an ADP concentration inhibitory factor. The initial ATP concentration of each grid is set as the energy reserve rate, and the initial ADP concentration is set as the load demand rate. The Michaelis-Menten equation was used to calculate the ATP-ADP cycle process in stages until metabolic homeostasis was reached. Extract the steady-state ATP concentration and calculate the proportion of ATP concentration in each grid to the total concentration as the power allocation weight.

5. The grid-based dynamic coordination optimization method for power grids as described in claim 4, characterized in that: The strategy for iteratively calculating the optimal power distribution using an evolutionary game equilibrium solver includes: The power allocation weights of each grid are used as the initial strategy parameters for the evolutionary game. The game payoff function for each grid is defined based on voltage constraints and load data; The policy parameters of each grid are updated using the replicator dynamic equation. The adjustment of the policy parameters is the product of the current policy parameter and the difference between the grid's payoff function value and the average payoff function value. When the change in policy parameters in all adjacent iterations of all grids is less than the convergence threshold, the final policy parameters are taken as the optimal power distribution policy.

6. The grid-based dynamic coordination optimization method for power grids as described in claim 5, characterized in that: The generation of dynamic boundary impedance adjustment commands and handover switch operation sequences includes: The power allocation values ​​of each grid are extracted from the optimal power distribution strategy, and the impedance adjustment values ​​of each grid boundary node are calculated based on the power impedance mapping relationship. Based on the optimal power distribution strategy and the real-time grid topology, the switch number and operation type to be operated are determined according to the tie switch operation priority rules. The impedance adjustment commands and switching operation sequences are arranged in a timing order and encapsulated into a standard format control message.

7. A grid-based dynamic coordination optimization system based on a power grid, wherein the grid-based dynamic coordination optimization method based on any one of claims 1 to 6 is characterized in that: It also includes a data acquisition module, which collects the voltage fluctuation characteristics and load change gradients of each node in the power grid in real time; The chaos analysis module timestamps the voltage fluctuation characteristics and load change gradients, and calculates the instability early warning coefficient of each power grid node through chaotic evolution analysis. The game construction module extracts the grid region to which the node belongs and constructs a multi-grid game utility function when the instability warning coefficient is greater than the dynamic stability threshold. The weight calculation module inputs the power adjustment range and voltage constraint conditions of each grid into the bio-metabolic dynamics engine, and calculates the power allocation weight of each grid through the ATP-ADP energy cycle mechanism. The strategy optimization module, based on power allocation weights, iteratively calculates the optimal power distribution strategy through an evolutionary game equilibrium solver. The instruction generation module generates dynamic boundary impedance adjustment instructions and handshake switch operation sequences based on the optimal power distribution strategy, and encapsulates them into control messages. The interactive execution module parses control messages into 3D operation guidance animations through an augmented reality interactive platform, and then executes power grid coordination optimization after the operator confirms the gestures.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the grid-based dynamic coordination optimization method based on any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the grid-based dynamic coordination optimization method based on any one of claims 1 to 6.

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