A method and system for improving power grid operation safety margin

Abstract

The present invention discloses a method and system for improving the safety margin of power grid operation. The method includes obtaining power grid data and performing preprocessing; constructing a power grid simulation model based on the preprocessed data to simulate power grid faults; identifying key nodes of the power grid based on the power grid fault simulation results; constructing a power grid load prediction model based on the key nodes of the power grid and predicting the load conditions of the key nodes of the power grid; obtaining the power grid safety margin through the current load and the load demand value predicted by the load prediction model, deploying energy storage control strategies at key nodes in combination with the power grid safety margin; and performing network security encryption. The present invention combines the load prediction results with the power grid energy storage system, and under the guidance of the load prediction results, realizes the dynamic allocation and regulation of electricity, responds to changes in the power grid load in a timely manner, realizes the optimal allocation of power resources, reduces the pressure on the power grid during peak load periods, ensures stable power grid operation, and improves the safety margin and operating efficiency of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of automated processing of power grid operation safety, and in particular to a method and system for improving the safety margin of power grid operation. Background Art

[0002] With the continuous growth of global energy demand, the safety and stability of the power system has become one of the core issues of power grid operation. The power grid safety margin, as a measure of the ability of the power grid to maintain normal operation when subjected to disturbances, is directly related to the reliability and economy of the power grid. With the increasing complexity of modern power grid structure, factors such as the widespread application of distributed energy systems and the increase in load fluctuations, the research on improving the power grid operation safety margin has gradually shifted to dynamic simulation and real-time monitoring technology. By building more refined power grid models and applying advanced prediction and control algorithms, the power grid's response capability to emergencies is enhanced, and the continuity and safety of power grid operation are ensured.

[0003] However, existing technologies still have many shortcomings in grid safety margin management. First, existing grid fault simulation models often simplify the dynamic characteristics of the grid and cannot accurately reflect the actual operating status of the grid under fault conditions, resulting in deviations between the prediction results and the actual situation. In addition, existing grid energy storage system control strategies are mostly based on static optimization and fail to effectively combine real-time prediction data, resulting in limited ability of the grid to respond to emergencies. It is difficult to optimize power quality and reduce operating costs while ensuring grid safety margin. Finally, with the increase in network security threats, grid data faces more and more security risks during transmission and processing. Existing encryption technologies are unable to cope with these complex network attacks, further affecting the safety and reliability of grid operation. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for improving the safety margin of power grid operation to solve the problem that the existing power grid energy storage system control strategy is mostly based on static optimization and fails to effectively combine real-time prediction data, resulting in limited ability of the power grid to respond to emergencies and inability to effectively improve the safety margin of power grid operation.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for improving the safety margin of power grid operation, comprising:

[0008] Acquire power grid data and pre-process it;

[0009] Build a power grid simulation model based on the preprocessed data to simulate power grid faults;

[0010] Identify key nodes in the power grid based on power grid fault simulation results;

[0011] Based on the identified key grid nodes, a grid load prediction model is constructed to predict the load conditions of the key grid nodes;

[0012] The grid safety margin is obtained by comparing the current grid load with the load demand value predicted by the grid load forecasting model. Energy storage control strategies are then deployed at key nodes based on the grid safety margin.

[0013] Perform cybersecurity encryption on grid data after deployment.

[0014] As a preferred solution of the method for improving the safety margin of power grid operation according to the present invention, the step of constructing a power grid simulation model includes:

[0015] Calculate grid simulation and load flow, and build a grid simulation model by combining pre-processed grid data;

[0016] Conduct preliminary simulation runs and compare the simulation results with historical operating data of the actual power grid under similar conditions to optimize the model;

[0017] For nonlinear elements or devices with time-varying characteristics, user-defined functions are introduced.

[0018] As a preferred solution of the method for improving the safety margin of power grid operation according to the present invention, the power grid fault simulation includes: setting a short circuit fault;

[0019] The short-circuit fault simulation includes obtaining nodes with high loads and located on critical transmission lines in the power grid, setting short-circuit faults for the selected nodes in a power grid simulation model, running the power grid simulation model, and calculating the short-circuit current according to Ohm's law:

[0020] Get the maximum short-circuit current of all nodes and record the grid node with the largest short-circuit current value.

[0021] As a preferred solution of the method for improving the safety margin of power grid operation according to the present invention, the power grid fault simulation includes: setting a line break fault;

[0022] The line-break fault simulation includes, for each transmission line in the power grid, removing it from the power grid model one by one and performing fault simulation;

[0023] Record the voltage amplitude of each node in the power grid under normal and line-break fault conditions respectively and ;

[0024] Calculate the voltage amplitude difference at each node under normal and line-break fault conditions ;

[0025] Compare and contrast the voltage amplitude distribution diagrams of all nodes in the power grid under normal and line-break fault conditions;

[0026] Set the voltage amplitude difference based on the voltage amplitude difference under normal conditions a first threshold range of a reference value;

[0027] The risk type of the node is determined based on the first threshold range, and abnormal nodes are marked in the voltage amplitude distribution graph.

[0028] As a preferred solution of the method for improving the safety margin of power grid operation according to the present invention, the power grid fault simulation includes: setting an overload fault;

[0029] The overload fault simulation includes selecting a grid node operating within a preset rated capacity range, increasing the load of the node in a grid simulation model, simulating an overload scenario, running the grid simulation model, and calculating the voltage of the node;

[0030] Calculate the voltage drop between the node voltage under overload fault conditions and the voltage under normal conditions;

[0031] A voltage drop threshold is set and compared with the voltage drop value. If the voltage drop value is greater than the voltage drop threshold, it is determined that the node voltage has dropped significantly, and the node whose voltage has dropped beyond the set threshold is recorded.

[0032] As a preferred solution of the method for improving the safety margin of power grid operation according to the present invention, the identifying key nodes of the power grid based on the power grid fault simulation results includes:

[0033] Based on the short circuit fault, line break fault and overload fault process, monitor and record the voltage, current and power changes of each node in the power grid in each fault simulation;

[0034] Combining the analysis results of short-circuit faults, line break faults and overload faults, key nodes of the power grid are identified, including nodes with the largest short-circuit current values ​​in short-circuit simulations, abnormal nodes after transmission line breakage, and nodes where the voltage drops beyond the set threshold in overload faults.

[0035] As a preferred solution of the method for improving the grid operation safety margin described in the present invention, the grid safety margin is obtained by using the current grid load and the load demand value predicted by the grid load prediction model, and energy storage control strategies are deployed at key nodes in combination with the grid safety margin, including:

[0036] Based on the current load of the power grid The grid load forecast model predicts that the grid is in time Load demand value at , calculate at time Grid security margin at , expressed as:

[0037]

[0038] in, From the present time to the future time Any time between

[0039] like <1, then perform charging operation and calculate the charging capacity ;

[0040] Calculate the power difference M between the real-time generated power and the load demand, expressed as:

[0041]

[0042] in, It's time The energy storage system power generation capacity at 10 ...

[0043] The power difference Convert to current , expressed as:

[0044]

[0045] in, Is the time when the power grid is The voltage at

[0046] Calculate the charge capacity based on the current MI , expressed as:

[0047]

[0048] in, is in The grid security margin when It's time The remaining capacity of the energy storage system, is the maximum capacity of the energy storage system;

[0049] like >1, then perform the discharge operation and calculate the discharge amount , expressed as:

[0050]

[0051] like =1, no operation is performed.

[0052] In a second aspect, the present invention provides a system for improving the safety margin of power grid operation, comprising:

[0053] Acquisition module, used to acquire power grid data and perform preprocessing;

[0054] A simulation module is used to build a power grid simulation model based on the preprocessed data to simulate power grid faults;

[0055] An identification module, used to identify key nodes of the power grid based on power grid fault simulation results;

[0056] A prediction module is used to build a grid load prediction model based on the identified key grid nodes and predict the load conditions of the key grid nodes;

[0057] The deployment module is used to determine the grid safety margin based on the current grid load and the load demand value predicted by the grid load forecasting model. In combination with the grid safety margin, it deploys energy storage control strategies at key nodes.

[0058] The encryption module is used to perform network security encryption on the deployed power grid data.

[0059] In a third aspect, the present invention provides an electronic device, comprising:

[0060] memory and processor;

[0061] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for improving the safety margin of power grid operation are implemented.

[0062] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for improving the safety margin of power grid operation.

[0063] Compared with the existing technology, the present invention has the following beneficial effects: by combining the load forecast results with the power grid energy storage system, the load forecast model can help to arrange power generation and distribution resources more accurately, reduce operating costs, improve efficiency, and respond to changes in power grid load in a timely manner; the safety margin of the power grid is calculated in combination with the current load and forecast demand, and the energy storage strategy is adjusted when necessary to enhance the stability of the power grid, and the power grid is intelligently regulated. Under the guidance of the load forecast results, the dynamic distribution and adjustment of electricity are realized, the optimal configuration of power resources is achieved, the pressure on the power grid during peak load periods is reduced, the stable operation of the power grid is ensured, and the safety margin and operating efficiency of the power grid are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0065] Figure 1 This is a schematic diagram of the overall process of a method for improving the safety margin of power grid operation according to an embodiment of the present invention;

[0066] Figure 2 The present invention is a flowchart of a method for improving the safety margin of power grid operation according to an embodiment of the present invention for simulating and identifying key nodes of power grid faults. DETAILED DESCRIPTION

[0067] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0068] Example 1

[0069] Reference Figure 1 , as an embodiment of the present invention, provides a method for improving the safety margin of power grid operation, comprising:

[0070] S100: Acquire power grid data and perform preprocessing;

[0071] S200: constructing a power grid simulation model based on the preprocessed data to simulate power grid faults;

[0072] S300: Identify key nodes in the power grid based on power grid fault simulation results;

[0073] S400: Based on the identified key grid nodes, a grid load prediction model is constructed to predict the load conditions of the key grid nodes;

[0074] S500: Calculate the grid safety margin based on the current grid load and the load demand value predicted by the grid load forecasting model. Energy storage control strategies are deployed at key nodes based on the grid safety margin.

[0075] S600: Perform network security encryption on grid data after deployment.

[0076] It should be noted that the effectiveness and accuracy of current grid simulation model construction and fault simulation identification algorithms directly impact subsequent steps, such as the deployment of energy storage control strategies. Static identification of key nodes may not be able to adapt to the ever-changing grid structure and operating conditions. As grid load patterns shift, model updates become less effective, leading to untimely safety margin prediction and updates, impacting data transmission efficiency.

[0077] Therefore, in response to some of the above-mentioned problems, the machine learning model in steps S100-S600 is used to simulate and rehearse fault scenarios, set different fault scenarios for different faults, and perform load prediction in combination with key nodes to obtain the required safety margin. When necessary, the energy storage strategy is adjusted to enhance the stability of the power grid and achieve timely calculation updates and encryption.

[0078] Example 2

[0079] Reference Figure 1-Figure 2 , is an embodiment of the present invention. Based on the above embodiment, a method for improving the safety margin of power grid operation is provided.

[0080] In the embodiment of the present application, obtaining the grid data and preprocessing it in step S100 includes the following steps:

[0081] S101: Install a SCADA system at power grid generator nodes, load nodes, substation nodes, power collection nodes, transmission line endpoints, and grid boundary nodes. Use the SCADA system to collect real-time grid data from each node, including grid topology data, historical operating data, and equipment parameters.

[0082] Specifically, the power grid topology data may include: the topological structure of the power grid, including substations, transmission lines, distribution network nodes and their connection relationships;

[0083] Specifically, the historical operation data may include: historical fault data of power grid lines and load data of each node;

[0084] Specifically, the device parameters may include: device parameters of each node and resistance of the transmission line;

[0085] S102: Perform data cleaning and data verification on the collected power grid data.

[0086] It should be noted that by collecting real-time data from grid operations, comprehensive status information on key grid nodes, such as voltage, current, power, and frequency, can be obtained, providing a foundation for subsequent grid operation analysis and simulation. Data preprocessing and cleaning are performed to remove outliers and noise, ensuring data accuracy and consistency. Normalization allows data of different dimensions to be compared and analyzed under the same standards, thus avoiding errors caused by dimensional differences. Furthermore, appropriate interpolation and correction of missing values ​​ensure data integrity and continuity, reducing the risk of error transmission.

[0087] In an optional embodiment, the power grid data acquired in step S100 may also include meteorological data, which includes humidity, temperature, rainfall, etc. The preprocessing may also include feature engineering, for example: encoding different types of variables and converting categorical variables into numerical types so that they can be correctly interpreted by machine learning algorithms.

[0088] In another optional embodiment, the power grid data acquired in step S100 may further include user behavior data, and the preprocessing may further include principal component analysis (PCA) or other dimensionality reduction techniques to remove redundant features and improve subsequent model performance.

[0089] In the embodiment of the present application, the power grid simulation model is constructed in step S200, including the following steps S201-S203:

[0090] S201: Calculate power grid simulation and load flow, and build a power grid simulation model based on pre-processed power grid data;

[0091] Specifically, it is preferred to calculate the power grid simulation and load flow through MATPOWE.

[0092] S202: Perform a preliminary simulation run and compare the simulation results with historical operating data of the actual power grid under similar conditions to optimize the model;

[0093] The optimization model refers to calculating the gradient of the error between the simulation results and the operating data under similar conditions in the historical operating data of the actual power grid, iterating the parameters of the power grid simulation model using the gradient descent method based on the calculated gradient, and setting the mean square error as the loss function. When the loss function converges, the iterative optimization is completed.

[0094] S203: For nonlinear elements or devices with time-varying characteristics, introduce custom functions.

[0095] In an optional embodiment, the grid simulation model is constructed in step S200 through a detailed physics-based electromagnetic transient simulation (EMT), which can accurately simulate the transient behavior of the power system, including nonlinear elements and rapidly changing processes; and is more suitable for modern power grids that contain a large amount of renewable energy access, energy storage systems, or other complex power electronic equipment.

[0096] In another optional implementation, the power grid simulation model is constructed in step S200, and simulation can also be performed using a random forest model or the like.

[0097] It should be noted that for nonlinear elements or devices with time-varying characteristics, functions such as diode nonlinear characteristic functions and time-delay dependent Lyapunov functionals can be introduced, specifically:

[0098] The diode nonlinear characteristic function is used for nonlinear elements, specifically:

[0099]

[0100] in is the reverse saturation current, is the thermal voltage, n is the ideality factor, V and I are the voltage and current of the nonlinear element respectively;

[0101] The time-delay dependent Lyapunov functional is used for devices with time-varying characteristics, specifically:

[0102]

[0103] in is the device voltage at time t, is the time-delay history of the system state, T is the transposition operation, s is the integral variable, is the delay duration, P and Q are positive definite matrices.

[0104] It should be noted that MATPOWE is preferred in this application for calculating grid simulation and load flow, which is suitable for steady-state analysis and long-term planning research; by comparing the simulation results with the historical operating data of the actual power grid, the deviations in the model can be quickly identified and adjusted, reducing the demand for a large amount of computing resources and improving the accuracy of the simulation. The introduction of custom functions to handle nonlinear elements or time-varying devices enables customized modeling for specific problems without relying on complex physical models or large amounts of training data. Compared to using EMT or other deep learning models, methods such as MATPOWE can achieve good performance through existing theoretical knowledge and a small amount of calibration, reducing dependence on large-scale historical data; the cost is lower, and it can also meet the steady-state analysis needs of most power systems.

[0105] In the embodiment of this application, Figure 2 In step S200, a power grid fault simulation is performed, including: setting a short circuit fault, specifically including the following steps A1-A2:

[0106] A1: Short-circuit fault simulation involves identifying nodes in the power grid with high loads and on critical transmission lines. In the power grid simulation model, short-circuit faults are set for the selected nodes. The power grid simulation model is then run to calculate the short-circuit current based on Ohm's law:

[0107] For example, it can be expressed as:

[0108]

[0109] in, is the short-circuit current, is the voltage at the short-circuit node, is the equivalent impedance at fault;

[0110] A2: Get the maximum short-circuit current of all nodes and record the grid node with the maximum short-circuit current value.

[0111] In the embodiment of this application, Figure 2 The power grid fault simulation in step S200 also includes: setting a line break fault, specifically including the following steps B1-B6:

[0112] B1: For each transmission line in the power grid, remove it from the power grid model one by one and perform fault simulation;

[0113] B2: Record the voltage amplitude of each node in the power grid under normal and line-break fault conditions respectively and ;

[0114] B3: Calculate the voltage amplitude difference of each node under normal and line-break fault conditions ;

[0115]

[0116] B4: Compare the voltage amplitude distribution diagrams of all nodes in the power grid under normal and line-break fault conditions;

[0117] Specifically, a distribution map can be drawn using software such as MATLAB.

[0118] B5: Set the voltage amplitude difference based on the voltage amplitude difference under normal conditions a first threshold range of a reference value;

[0119] For example, based on historical experience and industry standards, the voltage amplitude difference ∆Vi reference value is set to ±5% of the voltage amplitude difference under normal conditions;

[0120] If ∆Vi is less than 95% of the voltage amplitude difference under normal conditions, the node is judged to have a low voltage risk, which may cause power supply quality to deteriorate or equipment to malfunction. In this case, reactive power compensation should be adjusted and voltage support should be increased.

[0121] If ∆Vi is greater than 105% of the voltage amplitude difference under normal conditions, the node is judged to have a high voltage risk, which may damage electrical equipment and the power grid protection action should be taken, and the reactive power injection of the node should be reduced;

[0122] B6: Determine the risk type of the node based on the first threshold range, and mark the abnormal node in the voltage amplitude distribution diagram.

[0123] In the embodiment of this application, Figure 2 In step S200, power grid fault simulation is performed, which also includes: setting an overload fault, specifically including the following steps C1-C3:

[0124] C1: Overload fault simulation includes selecting a grid node operating within a preset rated capacity range, increasing the load on the node in the grid simulation model, simulating an overload scenario, running the grid simulation model, and calculating the voltage at the node;

[0125] Specifically, it can be expressed as:

[0126]

[0127] in, is the overload current, is the line impedance;

[0128] C2: Calculate the node voltage under overload fault condition The voltage drop value ∆V compared to the voltage under normal conditions;

[0129] C3: Set the voltage drop threshold ∆Vl and compare it with the voltage drop value. If the voltage drop value is greater than the voltage drop threshold, it is determined that the node voltage has dropped significantly, and the node with the voltage exceeding the set threshold is recorded.

[0130] In an optional embodiment, the power grid fault simulation may also include a circuit breaker trip or a generator failure;

[0131] In another optional implementation, the power grid fault simulation may also include a ground fault or voltage instability condition.

[0132] It should be noted that through power grid fault simulation, the system can identify the vulnerability and potential faults of the power grid, especially provide early warning of hidden dangers that may cause large-scale power outages or serious system instability, which helps to optimize the power grid's emergency plan and provide reliable data support, so that power companies can formulate corresponding risk response strategies before the fault occurs, thereby greatly reducing the possibility of large-scale power outages or system collapses caused by sudden faults, improving the flexibility of the power grid to deal with fault scenarios, and providing a scientific basis for the long-term operation safety of the power grid. Through systematic fault simulation, the reliability and safety of power grid operation have been significantly improved, ensuring that under various possible fault conditions, normal operation can be restored quickly, reducing the impact of power supply interruptions on society and the economy, and further improving the safety margin of power grid operation.

[0133] In the embodiment of the present application, identifying key nodes of the power grid based on the power grid fault simulation results in step S300 includes:

[0134] Based on the short circuit fault, line break fault and overload fault process, monitor and record the voltage, current and power changes of each node in the power grid in each fault simulation;

[0135] Combining the analysis results of short-circuit faults, line break faults and overload faults, key nodes of the power grid are identified, including nodes with the largest short-circuit current values ​​in short-circuit simulations, abnormal nodes after transmission line breakage, and nodes where the voltage drops beyond the set threshold in overload faults.

[0136] In an optional embodiment, based on the grid fault simulation results in step S300, complex network theory can also be used to identify key grid nodes within the power system topology. The grid is considered a graph consisting of nodes (e.g., power plants and substations) and edges (transmission lines). Centrality metrics (e.g., degree centrality, betweenness centrality, and closeness centrality) are then applied to assess the importance of each node in the network.

[0137] In another optional embodiment, in step S300, based on the power grid fault simulation results, risk assessment and vulnerability analysis can be performed to identify key nodes in the power grid, and a risk assessment can be performed on each component in the power grid by combining probability theory and statistical methods, considering the possibility of failure and the degree of impact on the entire system.

[0138] It should be noted that step S300 identifies key nodes and can discover which nodes exhibit high risks and have a significant impact on system stability under various fault scenarios. It can strengthen the monitoring and maintenance of these nodes in a targeted manner, deploy preventive measures in advance, and avoid systemic risks caused by key node failures. At the same time, the identified key nodes also provide key data support for subsequent power grid load forecasting and intelligent control strategies, ensuring the safe operation of these nodes during peak load periods and abnormal conditions, improving the safety of the overall power grid operation, and providing a basis for further optimization and intelligent control.

[0139] In the embodiment of the present application, step S400 constructs a power grid load prediction model based on the identified key power grid nodes and predicts the load conditions of the key power grid nodes, including the following steps S401-S403:

[0140] S401: Build a power grid load forecasting model using a long short-term memory (LSTM) network, including an input layer, a hidden layer, and an output layer, with the input layer set to power grid data.

[0141] S402: Using historical power grid data as a training set, inputting it into a power grid load forecasting model for training, and optimizing the power grid load forecasting model using mean square error and Adam optimizer as a loss function;

[0142] S403: cyclically updating the grid load prediction model in a set time period, inputting the real-time collected grid data into the grid load prediction model to obtain a grid load demand prediction value.

[0143] Specifically, the grid load demand forecast value may include the generated power of the energy storage system, the remaining capacity of the energy storage system, and the voltage.

[0144] In an optional embodiment, the grid load prediction model constructed in step S400 can also be constructed by a hybrid model of a convolutional neural network and a recurrent neural network.

[0145] In another optional implementation, the grid load prediction model constructed in step S400 may also be constructed using a support vector regression (SVR) model.

[0146] It should be noted that by constructing a power grid load forecasting model and combining real-time data with simulation results, the forecasting model can be dynamically adjusted, improving the ability to respond to sudden load changes. It can provide a basis for the subsequent control strategy of the power grid energy storage system, and help optimize the charging and discharging plan of the energy storage system, thereby maximizing the economy and efficiency of power grid operation. In addition, the load forecasting model can also provide early warning for power grid operation and management, identify possible load overload situations in advance, reduce the risk of power supply interruption, better plan power resources, and avoid equipment overload and system instability caused by sudden load surges.

[0147] In the embodiment of the present application, in step S500, a grid safety margin is obtained by using the current grid load and the load demand value predicted by the grid load prediction model. Energy storage control strategies are deployed at key nodes based on the grid safety margin, including:

[0148] Based on the current load of the power grid The grid load forecast model predicts that the grid is in time Load demand value at , calculate at time Grid security margin at , expressed as:

[0149]

[0150] in, From the present time to the future time Any time between

[0151] like <1, then perform charging operation and calculate the charging capacity ;

[0152] Calculate the power difference M between the real-time generated power and the load demand, expressed as:

[0153]

[0154] in, It's time The energy storage system power generation capacity at 10 ...

[0155] The power difference Convert to current , expressed as:

[0156]

[0157] in, Is the time when the power grid is The voltage at

[0158] Calculate the charge capacity based on the current MI , expressed as:

[0159]

[0160] in, is in The grid security margin when It's time The remaining capacity of the energy storage system, is the maximum capacity of the energy storage system;

[0161] It should be noted that It is the integral of the short-term safety margin of the power grid from the initial time to time t. Taking into account the dynamic changes of the power grid safety margin over time, it helps the charging strategy to better respond to the long-term trend of the power grid. The margin at a single moment cannot fully reflect the overall stability of the power grid over a period of time. The integral form can capture the overall change of the power grid safety margin, rather than just the instantaneous state at a certain moment. When dealing with the power grid safety margin, the linear method may not fully reflect its impact on the load of the energy storage system. Especially when the margin changes drastically, through the square operation (1+ 2. The introduction of nonlinear characteristics can more sensitively respond to changes in the grid safety margin, ensuring that when the grid safety margin is low, the charging process can be quickly slowed down to maintain the overall stability of the grid;

[0162] It should also be noted that:

[0163]

[0164] The power grid is prone to danger when it is nearing full charge, and precise control strategies must be implemented to avoid safety issues caused by overcharging. Linear methods cannot maintain efficient operation throughout the entire battery charging process. Therefore, a nonlinear formula is introduced to perform power regulation to provide a smooth charging curve. This ensures that the charging rate is automatically reduced in dangerous conditions to prevent battery overheating or damage. By adjusting the charging rate nonlinearly, charging efficiency is maximized at different charging stages, with rapid charging when the battery is low and a gradual slowing of the charging rate when the battery is high.

[0165] like >1, then perform the discharge operation and calculate the discharge amount , expressed as:

[0166]

[0167] like =1, no operation is performed.

[0168] It should be noted that the logarithmic function is used in this step , in the interval close to 0, the logarithmic function changes rapidly. When smaller, The change of will be more significant, and the logarithmic function has a smooth asymptotic characteristic, which can prevent instability in the discharge control process and maintain the stability of the system response when the margin changes significantly;

[0169] According to the calculated discharge amount, a discharge operation is performed to reduce the load demand of the power grid. Based on the safety margin calculation formula, the operation safety margin of the power grid is improved by reducing the load demand of the power grid.

[0170] By predicting future grid power generation data and energy storage conditions and deploying intelligent energy storage systems to regulate the grid, the system can respond quickly to fluctuations in power demand and ensure stable power supply. Traditional power regulation methods often rely on fixed rules and pre-set thresholds, making them difficult to cope with rapidly changing load demands. By incorporating load forecasting results, energy storage systems can dynamically adjust the storage and release of power under the guidance of load forecasts. These steps not only improve the response speed to sudden load changes but also reduce the operational pressure on the grid. Especially during peak load periods, energy storage systems can effectively smooth out peak demand, balance power demand, and avoid the impact of power overload on the system. In addition, through intelligent regulation, the grid can achieve optimal allocation of power resources, ensuring the proper distribution of power across various time periods and nodes. Energy storage systems can charge during low-demand periods and release power during peak periods, reducing energy waste. At the same time, the rational operation of energy storage systems can also reduce the need for emergency power generation, thereby reducing overall operating costs. Ultimately, combining load forecasting with intelligent regulation of energy storage systems significantly improves operational safety and stability, thereby avoiding power supply interruptions caused by load imbalances.

[0171] In the embodiment of the present application, step S600 performs network security encryption on the deployed power grid data, including the following steps:

[0172] The symmetric encryption algorithm AES is used to encrypt power grid operation data, and data is transmitted through the TLS data encryption channel. Digital certificates and public and private key pairs are configured for power grid equipment. During device communication, a two-way authentication mechanism is used to verify device identity. An intrusion detection system is deployed in the power grid to monitor network processes in real time.

[0173] Regularly perform off-site encrypted backup of the power grid's operating data and system configuration.

[0174] In an optional embodiment, the network security encryption of the deployed power grid data in step S600 can also be performed by using a hash function (such as SHA-256 or SHA-3) to generate a summary for the transmitted data, and combining asymmetric encryption technology to add a digital signature to the data; the receiving end can ensure the authenticity and integrity of the data source by verifying the signature.

[0175] In another optional embodiment, the network security encryption of the deployed power grid data in step S600 can also be performed through a zero-trust architecture, assuming that potential threats exist both inside and outside the network, to perform strict identity authentication and permission control on each access request.

[0176] It should be noted that encrypting grid data effectively prevents data leakage or tampering during transmission, thereby ensuring the confidentiality, integrity, and availability of grid operations. For example, load forecasting results and energy storage system control strategies can be securely transmitted between system components, avoiding security issues caused by data leakage or malicious tampering.

[0177] Example 3

[0178] The above is a schematic scheme of a method for improving the safety margin of power grid operation according to this embodiment. It should be noted that the technical scheme of the system for improving the safety margin of power grid operation and the technical scheme of the method for improving the safety margin of power grid operation described above are based on the same concept. For details not described in detail in the technical scheme of the system for improving the safety margin of power grid operation in this embodiment, please refer to the description of the technical scheme of the method for improving the safety margin of power grid operation described above.

[0179] This embodiment further provides a system for improving a power grid operation safety margin, including:

[0180] Acquisition module, used to acquire power grid data and perform preprocessing;

[0181] A simulation module is used to build a power grid simulation model based on the preprocessed data to simulate power grid faults;

[0182] An identification module, used to identify key nodes of the power grid based on power grid fault simulation results;

[0183] A prediction module is used to build a grid load prediction model based on the identified key grid nodes and predict the load conditions of the key grid nodes;

[0184] The deployment module is used to determine the grid safety margin based on the current grid load and the load demand value predicted by the grid load forecasting model. In combination with the grid safety margin, it deploys energy storage control strategies at key nodes.

[0185] The encryption module is used to perform network security encryption on the deployed power grid data.

[0186] This embodiment also provides an electronic device suitable for improving the safety margin of power grid operation, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method for improving the safety margin of power grid operation proposed in the above embodiment.

[0187] This embodiment further provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the method for improving the safety margin of power grid operation proposed in the above embodiment is implemented.

[0188] The storage medium proposed in this embodiment and the method for improving the safety margin of power grid operation proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0189] From the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product can be stored on a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disk, and includes instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0190] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for improving the safety margin of power grid operation, characterized in that: include: Acquire power grid data and pre-process it; Build a power grid simulation model based on the preprocessed data to simulate power grid faults; The power grid simulation model is established through detailed physics-based electromagnetic transient simulation; The constructing of the power grid simulation model includes: Calculate grid simulation and load flow, and build a grid simulation model by combining pre-processed grid data; Conduct preliminary simulation runs and compare the simulation results with historical operating data of the actual power grid under similar conditions to optimize the model; For nonlinear elements or devices with time-varying characteristics, custom functions are introduced; Identify key nodes in the power grid based on power grid fault simulation results; Based on the identified key grid nodes, a grid load prediction model is constructed to predict the load conditions of the key grid nodes; The grid's safety margin is calculated based on the grid's current load and the load demand value predicted by the grid load forecasting model. Energy storage control strategies are deployed at key nodes based on the grid's safety margin, including: Based on the current load of the power grid The grid load forecast model predicts that the grid is in time Load demand value at , calculate at time Grid security margin at , expressed as: ,in, From the present time to the future time Any time between like <1, then perform charging operation and calculate charging capacity ; Calculate the power difference M between the real-time generated power and the load demand, expressed as: ,in, It's time The energy storage system power generation capacity at 10 ... The power difference Convert to current , expressed as: ,in, Is the time when the power grid is The voltage at Calculate the charge capacity based on the current MI , expressed as: ,in, is in The grid security margin when It's time The remaining capacity of the energy storage system, is the maximum capacity of the energy storage system; like >1, then perform the discharge operation and calculate the discharge amount , expressed as: ,like =1, no operation is performed; The network security encryption of the deployed power grid data includes the following steps: The symmetric encryption algorithm AES is used to encrypt power grid operation data, and data is transmitted through the TLS data encryption channel. Digital certificates and public and private key pairs are configured for power grid equipment. During device communication, a two-way authentication mechanism is used to verify device identity. An intrusion detection system is deployed in the power grid to monitor network processes in real time. Regularly perform off-site encrypted backup of the power grid's operating data and system configuration; The power grid data includes power grid topology data, historical operation data and equipment parameters.

2. The method for improving the safety margin of power grid operation according to claim 1, characterized in that: The power grid fault simulation includes: setting a short circuit fault; The short-circuit fault simulation includes obtaining nodes with high loads and located on critical transmission lines in the power grid, setting short-circuit faults for the selected nodes in a power grid simulation model, running the power grid simulation model, and calculating the short-circuit current according to Ohm's law: Get the maximum short-circuit current of all nodes and record the grid node with the largest short-circuit current value.

3. The method for improving the safety margin of power grid operation according to claim 2, characterized in that: The power grid fault simulation includes: setting a disconnection fault; The line-break fault simulation includes, for each transmission line in the power grid, removing it from the power grid model one by one and performing fault simulation; Record the voltage amplitude of each node in the power grid under normal and line-break fault conditions respectively and ; Calculate the voltage amplitude difference at each node under normal and line-break fault conditions ; Compare and contrast the voltage amplitude distribution diagrams of all nodes in the power grid under normal and line-break fault conditions; Set the voltage amplitude difference based on the voltage amplitude difference under normal conditions a first threshold range of a reference value; The risk type of the node is determined based on the first threshold range, and abnormal nodes are marked in the voltage amplitude distribution graph.

4. The method for improving the safety margin of power grid operation according to claim 3, characterized in that: The power grid fault simulation includes: setting an overload fault; The overload fault simulation includes selecting a grid node operating within a preset rated capacity range, increasing the load of the node in a grid simulation model, simulating an overload scenario, running the grid simulation model, and calculating the voltage of the node; Calculate the voltage drop between the node voltage under overload fault conditions and the voltage under normal conditions; A voltage drop threshold is set and compared with the voltage drop value. If the voltage drop value is greater than the voltage drop threshold, it is determined that the node voltage has dropped significantly, and the node whose voltage has dropped beyond the set threshold is recorded.

5. The method for improving the safety margin of power grid operation according to claim 4, characterized in that: The identifying of key nodes of the power grid based on the power grid fault simulation results includes: Based on the short circuit fault, line break fault and overload fault process, monitor and record the voltage, current and power changes of each node in the power grid in each fault simulation; Combining the analysis results of short-circuit faults, line break faults and overload faults, key nodes of the power grid are identified, including nodes with the largest short-circuit current values ​​in short-circuit simulations, abnormal nodes after transmission line breakage, and nodes where the voltage drops beyond the set threshold in overload faults.

6. A system for the method for improving the power grid operation safety margin according to any one of claims 1 to 5, characterized in that: include: Acquisition module, used to acquire power grid data and perform preprocessing; A simulation module is used to build a power grid simulation model based on the preprocessed data to simulate power grid faults; An identification module, used to identify key nodes of the power grid based on power grid fault simulation results; A prediction module is used to build a grid load prediction model based on the identified key grid nodes and predict the load conditions of the key grid nodes; The deployment module is used to determine the grid safety margin based on the current grid load and the load demand value predicted by the grid load forecasting model. In combination with the grid safety margin, it deploys energy storage control strategies at key nodes. The encryption module is used to perform network security encryption on the deployed power grid data.

7. An electronic device comprising: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, characterized in that: when the computer-executable instructions are executed by the processor, the steps of the method for improving the power grid operation safety margin according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing computer-executable instructions, characterized in that: When the computer executable instructions are executed by a processor, the steps of the method for improving the safety margin of power grid operation as described in any one of claims 1 to 5 are implemented.

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