A power grid emergency control resource selection method and system based on dynamic coupling

Abstract

The application provides a power grid emergency control resource selection method and system based on dynamic coupling, and belongs to the technical field of power system operation control; the method comprises the following steps: for each expected fault scene in the preset key expected fault set in power grid operation, the near response efficiency and influence efficiency of each control resource are evaluated based on the electrical distance and transient sensitivity, and a dynamic comprehensive index is constructed; each control resource is sorted based on the dynamic comprehensive index, and a priority list is generated; when an actual power grid fault is detected, the emergency control is realized by matching with the priority list and combining the available regulation capacity of each control resource. The application can realize real-time and accurate optimization of emergency control resources, and further improve the rapidity and reliability of power grid emergency control.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and control technology, and particularly relates to a method and system for selecting emergency control resources for power grids based on dynamic coupling. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With a high proportion of new energy sources being integrated into the power grid, the operation of modern power systems exhibits significant high uncertainty and strong nonlinearity, posing a severe challenge to the safe and stable operation of the power grid. When the power grid faces severe disturbances and experiences instability in power angle, frequency, voltage, and other conditions, the rapid and precise implementation of emergency control measures (such as generator tripping, load shedding, and DC modulation) is crucial to ensuring power grid security.

[0004] However, existing methods generally suffer from the following technical drawbacks:

[0005] (1) Existing methods struggle to accurately assess the true control value of different control resources under different emergency conditions when faced with challenges such as grid operation uncertainty and enhanced dynamic characteristics brought about by high proportion of renewable energy access. This leads to the selection results not matching actual needs and may even exacerbate system instability. Specifically, existing methods often use static or single indicators for evaluation, failing to capture the dynamic changes and trade-offs between the efficiency and effectiveness of control resources under grid emergency conditions, resulting in insufficient robustness and adaptability of control decisions.

[0006] (2) In emergency control scenarios, the response speed and control effect of control resources are extremely important. However, existing methods are difficult to achieve rapid and accurate evaluation and sorting of massive candidate resources while ensuring computational efficiency, thus failing to meet the needs of near real-time or online control of the power grid. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, the present invention provides a method and system for selecting emergency control resources for power grids based on dynamic coupling, which can optimize emergency control resources in real time and accurately, thereby improving the speed and reliability of emergency control of power grids.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0009] The first aspect of this invention provides a method for selecting power grid emergency control resources based on dynamic coupling.

[0010] A method for selecting power grid emergency control resources based on dynamic coupling includes:

[0011] Acquire power grid operation status data and fault information, and pre-set a set of key anticipated faults;

[0012] For each anticipated fault scenario in the key anticipated fault set, the proximity response efficiency of each control resource is evaluated based on electrical distance, and the impact effectiveness of each control resource on the transient stability of the system is evaluated through transient sensitivity.

[0013] The local response efficiency and impact effectiveness are normalized to obtain efficiency factor and effectiveness factor; the fusion weight of efficiency factor and effectiveness factor is dynamically adjusted according to the real-time urgency of the system to construct a dynamic comprehensive index.

[0014] Based on the aforementioned dynamic comprehensive indicators, each control resource is sorted to generate a dynamic control resource priority list corresponding to the current operating state and fault scenario. When an actual power grid fault is detected, the fault information is matched with the priority list, and an emergency control strategy is generated and executed in combination with the available regulation capacity of each control resource.

[0015] Furthermore, the power grid operating status data and fault information include: PMU measurement data, SCADA data, power grid topology data, generator parameters, load data, fault type, fault location, and fault clearing time.

[0016] Furthermore, the electrical distance is calculated based on the power sensitivity method to reflect the degree of electrical coupling between the control resource and the fault point; the transient sensitivity is calculated based on the participation factor of the extended equal area criterion.

[0017] Furthermore, the construction of the dynamic comprehensive index includes: using a weighted summation model and combining it with a dynamic weight adjustment strategy to fuse the efficiency factor and the effectiveness factor; wherein the dynamic weight adjustment strategy is implemented under the dominant instability mode.

[0018] Furthermore, in the process of generating the emergency control strategy, a dual guarantee mechanism of over-cut priority and under-cut blocking is adopted to ensure that the cumulative control quantity under any working condition is not lower than the target value.

[0019] Furthermore, before matching fault information with the priority list, the controllable capacity of various control resources is quantified based on the determined priority list; during the quantification process, a three-tiered backup resource pool is constructed simultaneously, reserving an emergency capacity of no less than a preset percentage of the target control quantity.

[0020] Furthermore, the generation of the emergency control strategy includes: combining control resources under the undercut protection mechanism; specifically, accumulating the adjustment amount of control resources according to the priority order in the priority list until the target value is met; if all available control resources are exhausted before the combination is completed and the target value is still not reached, the backup control resources are activated to forcibly supplement the resources.

[0021] A second aspect of the present invention provides a power grid emergency control resource selection system based on dynamic coupling.

[0022] A power grid emergency control resource selection system based on dynamic coupling includes:

[0023] The data acquisition module is configured to: acquire power grid operating status data and fault information, and preset a set of key anticipated faults;

[0024] The data analysis module is configured to: for each anticipated fault scenario in the set of key anticipated faults, evaluate the proximity response efficiency of each control resource based on electrical distance, and evaluate the impact effectiveness of each control resource on the transient stability of the system through transient sensitivity.

[0025] The dynamic comprehensive index construction module is configured to: normalize the nearest response efficiency and impact effectiveness to obtain efficiency factor and effectiveness factor; and dynamically adjust the fusion weight of efficiency factor and effectiveness factor according to the real-time urgency of the system to construct dynamic comprehensive index.

[0026] The priority sorting module is configured to sort each control resource based on the dynamic comprehensive index and generate a dynamic control resource priority list corresponding to the current operating state and fault scenario.

[0027] The emergency control resource selection module is configured to: when an actual power grid fault is detected, match the fault information with the priority list, and generate and execute an emergency control strategy based on the available regulation capacity of each control resource.

[0028] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps of a power grid emergency control resource selection method based on dynamic coupling as described in the first aspect of the present invention.

[0029] The fourth aspect of the present invention provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a power grid emergency control resource selection method based on dynamic coupling as described in the first aspect of the present invention.

[0030] The above one or more technical solutions have the following beneficial effects:

[0031] (1) This invention addresses each anticipated fault scenario within a pre-defined critical fault set during power grid operation. Based on electrical distance and transient sensitivity, it assesses the proximity response efficiency and impact effectiveness of each control resource and constructs a dynamic comprehensive index. This dynamic comprehensive index allows for real-time capture of power grid operation changes and adaptive adjustment of control resource priorities. This index integrates proximity response efficiency and the influence of critical nodes, enabling control resources to act more precisely on fault points compared to existing technologies, maximizing system stability. It effectively solves the control strategy mismatch problem caused by the complex and ever-changing power grid operation modes under high-proportion renewable energy access, ensuring the system can still respond quickly and accurately under various complex disturbance scenarios.

[0032] (2) When an actual power grid fault is detected, this invention matches the fault information with the priority list and, in conjunction with the available regulating capacity of each control resource, generates and executes an emergency control strategy. By uniformly evaluating heterogeneous emergency control resources such as generators, energy storage, and SVG, and quantifying their relative value using dynamic comprehensive indicators, collaborative optimization configuration can be achieved, avoiding resource waste. At the same time, the dynamic weight adjustment mechanism allows the system to flexibly prioritize control efficiency or effectiveness. This dynamic balance significantly enhances the robustness of the emergency control strategy, reduces the probability of false activation or failure to activate, and thus fully leverages the advantages of various resources to improve overall control effectiveness.

[0033] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0034] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0035] Figure 1 This is a flowchart of a power grid emergency control resource selection method based on dynamic coupling in Embodiment 1 of the present invention.

[0036] Figure 2 This is a schematic diagram of the dynamic weight adjustment curve in Embodiment 1 of the present invention.

[0037] Figure 3 This is a flowchart illustrating the dynamic optimization of control resources in Embodiment 1 of the present invention. Detailed Implementation

[0038] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0039] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0040] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0041] The overall approach of this invention is as follows: This invention provides a dynamic coupling-based method for selecting emergency control resources in power grids. This method creatively integrates the "proximity response efficiency" and "critical node influence" of control resources, constructing a comprehensive optimization index that can dynamically respond to changes in system state. This dynamic comprehensive index is used to rank all controllable resources in real time, generating a dynamic "control resource priority list," thus providing a core basis for generating emergency control strategies and significantly improving the adaptability, speed, and reliability of power grid emergency control. This not only overcomes the limitations of single-method approaches in existing technologies but also provides interpretable and highly generalizable physical mechanism guidance for next-generation intelligent emergency control decision models, possessing significant theoretical and practical application value.

[0042] Example 1

[0043] This embodiment discloses a method for selecting power grid emergency control resources based on dynamic coupling.

[0044] like Figure 1 As shown, a power grid emergency control resource selection method based on dynamic coupling includes:

[0045] Step S1: Obtain power grid operation status data and fault information, and preset a set of key anticipated faults;

[0046] Step S2: For each anticipated fault scenario in the set of key anticipated faults, evaluate the proximity response efficiency of each control resource based on electrical distance, and evaluate the impact performance of each control resource on the transient stability of the system through transient sensitivity.

[0047] Step S3: Normalize the nearest response efficiency and impact effectiveness to obtain efficiency factor and effectiveness factor; dynamically adjust the fusion weight of efficiency factor and effectiveness factor according to the real-time urgency of the system to construct a dynamic comprehensive index.

[0048] Step S4: Sort each control resource based on the dynamic comprehensive index to generate a dynamic control resource priority list corresponding to the current operating state and fault scenario; when an actual power grid fault is detected, match the fault information with the priority list, and generate and execute an emergency control strategy based on the available adjustment capacity of each control resource.

[0049] Based on the above process, the present invention can optimize emergency control resources in real time and accurately, thereby improving the rapidity and reliability of power grid emergency control. For the convenience of understanding the technical solution of the present invention, the following further explains and illustrates the specific implementation methods in the technical solution of the present invention.

[0050] The evaluation objects of the present invention cover two categories of global control resources, and their specific characteristics are as follows, which are used to provide a clear basis for subsequent quantitative evaluation:

[0051] 1) Traditional emergency control resources, including: generator tripping, load shedding, DC modulation, traditional thermal power peak shaving resources, static var compensator. Its core characteristics are large regulation capacity and strong stability, but the response speed is relatively slow and the regulation granularity is relatively coarse.

[0052] 2) New energy emergency control resources, including:

[0053] Wind power resources: covering centralized wind farms and distributed clusters, supporting power curtailment and additional power generation, with a medium response speed. The main constraints include real-time output, minimum technical output, and maximum allowable power curtailment.

[0054] Photovoltaic resources: covering centralized photovoltaic power stations and distributed clusters, supporting short-term power curtailment. The main constraints include irradiance, photovoltaic panel temperature, and output prediction error.

[0055] Energy storage system: having a fast response ability from milliseconds to seconds, supporting bidirectional regulation. The main constraints include remaining battery capacity, charge-discharge efficiency, and maximum charge-discharge power.

[0056] Virtual power plant (VPP): as a virtual entity aggregating wind power, photovoltaic, energy storage, EV and other resources, presenting a unified regulation ability externally. The main constraints include aggregated capacity, reserve capacity, and coordinated response time.

[0057] Static var generator (SVG): as a reactive power compensation device supporting new energy power stations, having a millisecond-level response ability. The main constraints include rated reactive power capacity and voltage regulation range.

[0058] In step S1, obtain the power grid operation status data and fault information, and preset the key contingency set.

[0059] This step provides basic data input for data analysis. The collected power grid operation status data and fault information include, but are not limited to, PMU measurement data, SCADA data, power grid topology data, generator parameters, load data, fault type, fault location, and fault clearing time, etc. This module also needs to clearly obtain the key contingency set preset by system operation personnel in advance. Among them, the exclusive measurement and status data of new energy control resources are as follows:

[0060] 1) Wind power: Real-time output Minimum technical output Maximum permissible power reduction and predicted fluctuation range .

[0061] 2) Photovoltaics: Real-time power output Minimum technical output Maximum permissible power reduction Real-time irradiance and output prediction error .

[0062] 3) Energy storage: Remaining electricity Charge and discharge efficiency Maximum charge and discharge power and charge / discharge state .

[0063] 4) VPP: Internal resource composition list, adjustable resource capacity set, aggregated standby capacity and collaborative response time .

[0064] 5) SVG: Real-time reactive power output Rated reactive power capacity and response time .

[0065] The aforementioned real-time status and parameter data of new energy sources form the basis for calculating their specific correction factors. The collected data is then preprocessed, including data cleaning, correction, and format standardization, to provide accurate and reliable input for subsequent calculations.

[0066] In step S2, for each anticipated fault scenario in the set of critical anticipated faults, the proximity response efficiency of each control resource is evaluated based on electrical distance, and the impact effectiveness of each control resource on the transient stability of the system is evaluated through transient sensitivity. This can be achieved specifically through the following methods:

[0067] Step S2-1: Electrical distance calculation method based on real-time power sensitivity.

[0068] Calculate the electrical distance of each controllable resource relative to the fault point. The electrical distance characterizes the degree of electrical coupling between the control resource and the fault point; the smaller the value, the higher the response efficiency of the control resource to the fault point.

[0069] For nodes and key nodes in specific failure scenarios One method for calculating electrical distance is based on the nodal impedance matrix, as shown in equation (1):

[0070] (1)

[0071] in, Represents a node With nodes Electrical distance between them, in per unit value ; and They are nodes and Self-impedance; For nodes and The mutual impedance between them.

[0072] Another method for calculating electrical distance is based on power sensitivity. In power flow calculations, the Jacobian matrix is ​​satisfied, as shown in equation (2):

[0073] (2)

[0074] in, and These are the change vectors of active power and reactive power injected into the nodes, respectively. The Jacobian matrix for calculating system power flow; and These are the vectors representing the changes in the phase angle and magnitude of the node voltage, respectively. It is the partial derivative matrix of active power with respect to voltage phase angle, used to represent the sensitivity of active power to changes in voltage phase angle; It is the partial derivative matrix of active power with respect to voltage amplitude, used to represent the sensitivity of active power to changes in voltage amplitude; It is the partial derivative matrix of reactive power with respect to voltage phase angle, used to represent the sensitivity of reactive power to changes in voltage phase angle; It is the partial derivative matrix of reactive power with respect to voltage amplitude, used to represent the sensitivity of reactive power to changes in voltage amplitude.

[0075] Multiply both sides of the formula by the left side. The power sensitivity matrix can be obtained, as shown in equation (3):

[0076] (3)

[0077] (4)

[0078] in, It is the inverse of the Jacobian matrix, i.e., the sensitivity matrix; It is an active-phase sensitivity matrix, used to represent the change in the node voltage phase angle when the injected active power changes; It is a reactive power-phase angle sensitivity matrix, used to represent the change in the node voltage phase angle when the reactive power injected into the node changes; It is an active power-amplitude sensitivity matrix, used to represent the change in node voltage amplitude when the injected active power changes; It is a reactive power-amplitude sensitivity matrix, used to represent the change in node voltage amplitude when the reactive power injected into the node changes; It is a node With nodes Electrical distance between them; It is a sensitivity matrix The first in OK Column elements are used to represent nodes. The impact of reactive power changes on nodes Sensitivity to the effect of voltage amplitude. It is a sensitivity matrix The first in OK Column elements are used to represent nodes. The impact of reactive power changes on nodes Sensitivity to the effect of voltage amplitude. Furthermore, and The relevant explanation is shown in equation (5):

[0079] (5)

[0080] in, Represents a node voltage amplitude, Represents a node Injected reactive power; Represents a node The voltage amplitude; Represents a node Injected reactive power.

[0081] To address the power output fluctuations of wind and solar power, the electrical distance is modified to reflect the impact of fluctuations on response efficiency, as shown in equation (6):

[0082] (6)

[0083] in, Nodes corresponding to new energy resources Corrected electrical distance; The original value calculated by equation (4); For nodes Real-time output of new energy sources; For nodes Forecasted output of new energy sources; For nodes Rated power of new energy sources; This represents the fluctuation impact coefficient. The greater the fluctuation, the larger the corrected electrical distance, indicating a lower response efficiency.

[0084] (7)

[0085] in, The fluctuation impact coefficient is typically 0.1-0.3 for wind power and 0.05-0.2 for photovoltaic power. The standard deviation of the short-term output forecast error for this resource; Its rated power; It is a dimensionless scaling constant. Equation (7) ensures that the fluctuation impact coefficient is positively correlated with the actual uncertainty of the resources.

[0086] In emergency control decision-making in power systems, the dynamism, real-time performance, and control effectiveness of the method are core requirements. Specifically, the power sensitivity-based method constructs a model through the inverse Jacobian matrix to quantify the dynamic correlation and causal relationship between control behavior and system stability indicators, adapting to the dynamic characteristics of power grids with a high proportion of renewable energy. Relying on real-time online updates of measurement data can enhance robustness and enable rapid response to fluctuations in operating conditions.

[0087] In contrast, the node impedance matrix is ​​constrained by the system topology and parameters, and can only characterize static electrical connections. It cannot represent transient coupling during faults, and the matrix reconstruction is complex and lacks real-time performance when the system changes rapidly. Therefore, it is not suitable for the solution scenario of this invention.

[0088] In summary, in order to better meet the emergency control needs of complex power grids, this invention selects a power sensitivity-based method to calculate electrical distance.

[0089] Step S2-2: Adaptive multi-mode transient stability participation factor fusion calculation.

[0090] Transient stability participation factor or sensitivity It is a key indicator for measuring the impact of specific control resources in a power grid on the transient stability of the system. It is also used in subsequent calculations of the normalized efficiency factor. The core underlying indicator is to quantify the degree of change in the system's transient stability margin or a key transient stability indicator when a control variable undergoes a small change. A larger value indicates a higher effectiveness of the resource in suppressing system instability.

[0091] Participation factor calculation based on the extended equal area criterion (EEAC): The extended equal area criterion is an effective method to simplify a multi-machine system into a single-machine infinite bus system (OMIB), thereby allowing for an intuitive assessment of the system's transient stability margin. Based on this, the transient stability sensitivity is defined as the partial derivative of the system's transient stability margin with respect to the control resource power injection, as shown in equation (8):

[0092] (8)

[0093] in, For resources based on EEAC theory Transient stability sensitivity, typically measured in units of or The larger its absolute value, the more significant the improvement in the system's stability margin by this method; This represents the transient stability margin of the system, typically expressed in per-unit values ​​or radians.

[0094] To address the rapid response characteristics of resources such as energy storage and SVG, the EEAC sensitivity is modified as shown in equation (9):

[0095] (9)

[0096] in, For new energy resources EEAC sensitivity correction value; This represents the average response time for traditional resources. For new energy resources Response time; This refers to the response speed weight. A faster response speed results in higher corrected sensitivity, highlighting the advantage of fast-response resources in suppressing system instability. The response speed weight can be further expressed as:

[0097] (10)

[0098] in, This represents the critical stability time window of the system under the current fault condition, which can be obtained through online anticipated fault analysis. The shorter the response time, the closer this coefficient is to 1, and the more significant the amplification effect on sensitivity, thus mathematically quantifying its "speed advantage".

[0099] In EEAC theory, The deceleration area can be calculated and acceleration area The difference is obtained, that is .when When the system is stable; when At that time, the system became unstable; For resources Initial power injection; For resources A tiny perturbation is applied by power injection. Apply a small perturbation By recalculating the transient stability margin of the system, we can obtain... Estimate For new energy systems, the compatibility with new energy sources can be further estimated. .

[0100] Sensitivity calculation based on linearized state-space model: For more complex system dynamic characteristic analysis, a linearized state-space model can be used to calculate the sensitivity of control resources to system oscillation modes or participation factors. The linearized differential-algebraic equation of the system is usually expressed as:

[0101] (11)

[0102] in, For state variable deviation vectors (such as generator rotor angle, angular velocity, voltage amplitude, etc.); For the control variable deviation vector (such as excitation voltage, speed governor setpoint, etc.); This is the system matrix, reflecting the dynamic characteristics within the system; This is the control matrix, which reflects the influence of control inputs on state variables.

[0103] System Matrix eigenvalues Characterized the first The stability of each oscillation mode, among which The damping factor, Indicates instability. This refers to the oscillation frequency. (Resource) The participation factor for the critical oscillation mode is calculated using the participation vector. The comprehensive participation factor formula is as follows:

[0104] (12)

[0105] (13)

[0106] in, and Right eigenvectors and left eigenvector The middle corresponds to the state variable The amount; For eigenvalue analysis-based resources The higher the overall participation factor value, the more resources are available. The deeper the involvement in the dominant oscillation mode of the system, the greater the potential for the regulation effect to improve damping; The set of critical or weakly damped oscillation modes that need to be suppressed; participation factor Reflects state variables For pattern The greater the absolute value of the state variable's participation, the greater its influence on the mode. By mapping changes in control resources to changes in state variables, the sensitivity of control resources to specific oscillation modes can be further derived.

[0107] Based on the focus of system analysis, namely, transient stability under large disturbances or stability under small disturbances, the above two types of indicators can be selected or combined as the final sensitivity value. For specific anticipated failure scenarios, The effectiveness in suppressing system instability under this fault scenario was quantified. Specifically:

[0108] (14)

[0109] in, For resources The transient stability participation factor / sensitivity is the core original indicator for subsequent performance factor calculation; The fusion weighting coefficient for large disturbance sensitivity and small disturbance sensitivity is [value missing]. These are structural parameters that can be set according to actual needs.

[0110] Taking into account the regulation potential and response characteristics of new energy resources, a weighted adjustment is made to the final fusion sensitivity:

[0111] (15)

[0112] in, For new energy resources The corrected final transient stability sensitivity. The sensitivity based on eigenvalue analysis is calculated by equation (13); For new energy resources The characteristic adjustment coefficient is dimensionless. This formula uses the coefficient... The inherent regulatory advantages of different new energy resource types are quantified so that they are correctly reflected in the final sensitivity assessment.

[0113] Based on EEAC and Focusing on transient stability under large disturbances, based on eigenvalue analysis Focusing on stability under small disturbances, the choice or fusion of these two approaches can adapt to the analysis needs of different power grid operation scenarios, and the calculated results... and This will be used directly in the calculation of the subsequent equation (17).

[0114] In step S3, the nearest response efficiency and impact effectiveness are normalized to obtain an efficiency factor and an effectiveness factor. The fusion weights of the efficiency factor and effectiveness factor are dynamically adjusted according to the real-time urgency of the system to construct a dynamic comprehensive index. This can be achieved through the following methods:

[0115] Step S3-1: Unbiased equivalent mapping normalization of the multidimensional value space.

[0116] To ensure the effective integration and comparison of indices with different physical dimensions, normalization is an essential step. This invention employs a maximum value normalization method to uniformly map the efficiency factor and performance factor to the dimensionless interval [0,1].

[0117] Efficiency factor normalization: due to electrical distance A smaller value indicates higher efficiency; therefore, this invention uses its reciprocal. To represent efficiency. The normalized efficiency factor. As shown in equation (16):

[0118] (16)

[0119] in, Indicating traditional resources / new energy resources The maximum value, This represents the set of all traditional and new energy resources. After this processing, the most efficient resource is... The value is 1, while the values ​​for other resources are between 0 and 1.

[0120] Normalization of performance factors: performance factors This represents the transient stability participation factor or sensitivity; a higher value indicates higher efficiency. Normalized efficiency factor. As shown in equation (17):

[0121] (17)

[0122] in, Indicating traditional resources / new energy resources The maximum value. Similarly, the most efficient resource's The value is 1, while the values ​​for other resources are between 0 and 1.

[0123] In equations (16) and (17), the parameters above the... This is to distinguish between traditional resources and new energy resources. If it is a traditional resource, then... , Similarly, if it is a new energy resource, then , .

[0124] Step S3-2, Dynamic Weight Fusion Model.

[0125] The dynamic weighted fusion model is one of the core innovations of this invention, and its fundamental purpose is to resolve the inherent contradiction between "rapid response" and precise "effectiveness" in emergency control. This model adaptively adjusts the weights to organically combine normalized efficiency and effectiveness factors, forming a comprehensive and optimized index that can reflect the real-time operating status and urgency of the power grid.

[0126] The physical mechanism of dynamic coupling: The urgency of the power grid determines the time scale preference of the optimal control strategy. In extreme emergencies, the system's stabilization window is extremely short, and the response speed of control measures is the primary factor in suppressing instability and preventing the escalation of accidents. At this time, the locational proximity of resources is more critical than their deep dynamic impact and should be given higher weight. Conversely, when system disturbances are minor or have stabilized, there is more time for fine-tuning, and the ability of control measures to deeply influence the system's dominant instability mode becomes key to improving stability; their weight should be increased accordingly. The dynamic weighting mechanism of this model is precisely designed to achieve this time-scale-based adaptive trade-off.

[0127] Weighted summation model: Based on the above mechanism, the resources are calculated. Normalized efficiency factor under current operating conditions and specific fault scenarios and normalized efficiency factor Subsequently, this invention employs a weighted summation model to integrate the efficiency factor and the effectiveness factor, as shown in equation (18):

[0128] (18)

[0129] in, For resources At any moment The dynamic comprehensive index score is a dimensionless quantity. The higher the score, the greater the comprehensive control value of the resource under the current system state, and the higher its priority. and They are time points Efficiency weights and effectiveness weights are assigned. These two weights are dynamically adjusted, reflecting the degree of emphasis placed on efficiency and effectiveness under different emergency situations.

[0130] Weight and The following constraints must be met: Therefore, it is only necessary to determine and The percentage of any one weight can be used to calculate the percentage of the other weight.

[0131] Dynamic weight adjustment strategy: weight coefficient and Dynamic adjustment is key to achieving the above mechanism. This adjustment is based on a comprehensive state index that characterizes the real-time urgency level of the system. This indicator is calculated from real-time measurement data and its design takes into account different instability modes to ensure universality.

[0132] (19)

[0133] in, This refers to the system frequency deviation. The rate of change of frequency; For critical node voltage deviation, The transient stability margin estimate can be obtained using a fast evaluation algorithm; to These are weighting coefficients pre-set based on grid characteristics, and can be adaptively selected according to the dominant instability mode. For example, if the problem is determined to be a power angle stability issue, the weighting coefficients are increased. Weighting, voltage stability issues are improved Weights. Function and Will Mapped to weight values.

[0134] (20)

[0135] (twenty one)

[0136] Different emergency characteristics require different approaches. To determine the specific The values ​​are shown in Table 1. Selection principles for different emergency characteristics. Because The weights were determined. This allows you to determine another weight. This will not be elaborated upon here.

[0137] Table 1 Examples of Three Typical Function Selection Scenarios

[0138]

[0139] This section demonstrates a commonly used S-shaped function based on the rate of change of frequency, as shown in the following equation:

[0140] (twenty two)

[0141] (twenty three)

[0142] in, For a moment The rate of change of system frequency; The steepness parameter of the function curve is a preset positive real number parameter that determines the sensitivity of the weight to changes in the rate of change of frequency. The center point or threshold parameter of the function is a

[0143] The frequency change rate reference value is preset according to the system characteristics. When hour, That is, efficiency and effectiveness have equal weights.

[0144] like Figure 2 As shown, when the absolute value of the rate of change of frequency Much larger than the threshold At that time, the system was in a state of extreme emergency. Approaching 1, the system places extreme emphasis on control efficiency in order to rapidly suppress instability. When the absolute value of the rate of frequency change... Much smaller than the threshold At this point, the system tends to reach a stable state. As the value approaches zero, the system places greater emphasis on control efficiency in order to perform precise calibration. The larger the value, the faster the weight switching; The smaller the value, the smoother the weight switching.

[0145] The purpose of this step is to generate a dynamic comprehensive index score ranking list for all candidate control resources under the current operating state for each anticipated failure scenario. The core calculation process of the dynamic comprehensive index is as follows: Figure 3 As shown, specifically, firstly, the system status is perceived in real time and a preset fault set is traversed based on a hybrid event- and time-driven mechanism; then, considering the heterogeneous characteristics of traditional units and new energy sources, standard sensitivity paths and modified sensitivity paths are adopted respectively to accurately quantify the electrical distance and stability support capabilities of various resources for the system; finally, through multi-dimensional index normalization and dynamic weight fusion algorithms, a comprehensive performance ranking of various resources is generated, providing a quantitative decision-making basis for the precise control of the system under complex fault scenarios. In the specific implementation process, this process is executed independently and cyclically for each anticipated fault scenario, ultimately generating a resource priority list for that fault scenario under the current operating state.

[0146] In step S4, each control resource is sorted based on dynamic comprehensive indicators to generate a dynamic control resource priority list corresponding to the current operating state and fault scenario. When an actual power grid fault is detected, the fault information is matched with the priority list, and an emergency control strategy is generated and executed based on the available regulation capacity of each control resource.

[0147] The regulation capacity characteristic calculation and overshoot correction mechanism described in this step are the core algorithms used by the online emergency control execution module to quickly synthesize specific control strategies after obtaining the priority list.

[0148] This step employs a dual safeguard mechanism of "overcut priority - undercut prevention" to ensure the cumulative control quantity under any operating condition. Not lower than the target value First, based on the resource priority sequence determined in step S2, the controllable capacity of various resources is quantified, namely: for traditional units, the difference between the current output and the upper limit is calculated as the adjustable margin, as shown in equation (24); for energy storage systems, the real-time discharge capacity is calculated by combining the remaining power and discharge efficiency, as shown in equation (25); for new energy power plants, the minimum value between the preset maximum allowable power reduction and the difference between the current output and the upper limit is directly used as the available capacity, as shown in equations (26) and (27); for virtual power plants, the aggregated capacity is calculated as shown in equation (28). In this process, a three-tiered backup resource pool is constructed simultaneously, reserving an emergency capacity of no less than 15% of the target control amount, including multi-level protection resources such as cross-regional DC support, spinning standby units, and load rotation plans.

[0149] (twenty four)

[0150] (25)

[0151] (26)

[0152] (27)

[0153] (28)

[0154] in, To adjust the available capacity of traditional resources, The rated maximum power for safe operation of the equipment. This represents the current real-time operating power of the equipment. The capacity can be adjusted for energy storage and discharge. To store the current remaining electricity; For energy storage discharge efficiency; The longest discharge duration that must be maintained for energy storage; To contribute the most technological expertise to wind farms and photovoltaic power plants; This represents the maximum allowable power reduction for wind farms / solar power plants; Reserved capacity to be retained for VPP.

[0155] The resource combination employs a strict "undercut protection" mechanism: resource adjustments are accumulated in priority order until the target value is met. If all available resources are exhausted before the combination is completed, but are still insufficient to reach the target value, backup resources are immediately activated to forcibly supplement the resources, ensuring that the final control amount accurately covers the minimum safety threshold.

[0156] When the cumulative amount reaches the target value, that is Implement "overshoot safety control", specifically: if the overshoot rate Maintain the current combination scheme and mark the overcut; if the overshoot... Then, a granular optimization algorithm is executed, automatically selecting the resource with the largest coarse adjustment step size and replacing it with a fine-grained adjustment resource. Through iterative optimization, the overshoot is converged to a smaller value. Within safe limits.

[0157] Based on the above methods, the present invention achieves the following significant technological advancements:

[0158] 1) This invention constructs a dynamic comprehensive index based on a clear physical mechanism. The calculation process and results have good interpretability, avoiding the shortcomings of "black box" models. Its dynamism and universality endow this method with strong generalization ability, which can adapt to different power grid structures, fault types and operating conditions, reduce dependence on data of specific scenarios and reduce maintenance complexity, and improve reliability under unknown or extreme conditions.

[0159] 2) This invention creatively encapsulates the process of controlling resource value assessment and priority generation into a combination of periodic dynamic optimization modules. The calculation results are pre-stored as core knowledge, which can be queried and invoked by the online real-time emergency control module based on the matching fault scenario in milliseconds. This effectively resolves the contradiction between the complexity of resource optimization and the real-time nature of emergency control, ensuring both the dynamic adaptability of the results and optimizing the online decision-making time to meet the requirements of engineering practice.

[0160] Example 2

[0161] This embodiment discloses a power grid emergency control resource selection system based on dynamic coupling.

[0162] A power grid emergency control resource selection system based on dynamic coupling includes:

[0163] The data acquisition module is configured to: acquire power grid operating status data and fault information, and preset a set of key anticipated faults;

[0164] The data analysis module is configured to: for each anticipated fault scenario in the set of key anticipated faults, evaluate the proximity response efficiency of each control resource based on electrical distance, and evaluate the impact effectiveness of each control resource on the transient stability of the system through transient sensitivity.

[0165] The dynamic comprehensive index construction module is configured to: normalize the nearest response efficiency and impact effectiveness to obtain efficiency factor and effectiveness factor; and dynamically adjust the fusion weight of efficiency factor and effectiveness factor according to the real-time urgency of the system to construct dynamic comprehensive index.

[0166] The priority sorting module is configured to sort each control resource based on the dynamic comprehensive index and generate a dynamic control resource priority list corresponding to the current operating state and fault scenario.

[0167] The emergency control resource selection module is configured to: when an actual power grid fault is detected, match the fault information with the priority list, and generate and execute an emergency control strategy based on the available regulation capacity of each control resource.

[0168] Example 3

[0169] The purpose of this embodiment is to provide a computer-readable storage medium.

[0170] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a power grid emergency control resource selection method based on dynamic coupling as described in Embodiment 1 of this disclosure.

[0171] Example 4

[0172] The purpose of this embodiment is to provide an electronic device.

[0173] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in a power grid emergency control resource selection method based on dynamic coupling as described in Embodiment 1 of this disclosure.

[0174] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0175] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0176] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for selecting emergency control resources for power grids based on dynamic coupling, characterized in that, include: Acquire power grid operation status data and fault information, and pre-set a set of key anticipated faults; For each anticipated fault scenario in the key anticipated fault set, the proximity response efficiency of each control resource is evaluated based on electrical distance, and the impact effectiveness of each control resource on the transient stability of the system is evaluated through transient sensitivity. The local response efficiency and impact effectiveness are normalized to obtain the efficiency factor and effectiveness factor. The fusion weights of efficiency factors and effectiveness factors are dynamically adjusted based on the real-time urgency of the system to construct a dynamic comprehensive index. Based on the aforementioned dynamic comprehensive indicators, each control resource is sorted to generate a dynamic control resource priority list corresponding to the current operating state and fault scenario. When an actual power grid fault is detected, the fault information is matched with the priority list, and an emergency control strategy is generated and executed in combination with the available regulation capacity of each control resource.

2. The power grid emergency control resource selection method based on dynamic coupling as described in claim 1, characterized in that, The power grid operating status data and fault information include: PMU measurement data, SCADA data, power grid topology data, generator parameters, load data, fault type, fault location, and fault clearing time.

3. The power grid emergency control resource selection method based on dynamic coupling as described in claim 1, characterized in that, The electrical distance is calculated based on the power sensitivity method and is used to reflect the degree of electrical coupling between the control resource and the fault point; the transient sensitivity is calculated based on the participation factor of the extended equal area criterion.

4. The power grid emergency control resource selection method based on dynamic coupling as described in claim 1, characterized in that, The construction of the dynamic comprehensive index includes: using a weighted summation model and combining it with a dynamic weight adjustment strategy to fuse the efficiency factor and the effectiveness factor; wherein the dynamic weight adjustment strategy is implemented under the dominant instability mode.

5. The power grid emergency control resource selection method based on dynamic coupling as described in claim 1, characterized in that, During the generation of the emergency control strategy, a dual guarantee mechanism of over-cut priority and under-cut blocking is adopted to ensure that the cumulative control quantity under any working condition is not lower than the target value.

6. The power grid emergency control resource selection method based on dynamic coupling as described in claim 1, characterized in that, Before matching fault information with the priority list, the controllable capacity of various control resources is quantified based on the determined priority list. During the quantification process, a three-tiered backup resource pool is constructed simultaneously to reserve an emergency capacity of no less than a preset percentage of the target control quantity.

7. The power grid emergency control resource selection method based on dynamic coupling as described in claim 1, characterized in that, The generation of the emergency control strategy includes: combining control resources under the undercut protection mechanism; specifically, accumulating the adjustment amount of control resources according to the priority order in the priority list until the target value is met; if all available control resources are exhausted before the combination is completed and the target value is still not reached, the backup control resources are activated to forcibly supplement the resources.

8. A power grid emergency control resource selection system based on dynamic coupling, characterized in that, include: The data acquisition module is configured to: acquire power grid operating status data and fault information, and preset a set of key anticipated faults; The data analysis module is configured to: for each anticipated fault scenario in the set of key anticipated faults, evaluate the proximity response efficiency of each control resource based on electrical distance, and evaluate the impact effectiveness of each control resource on the transient stability of the system through transient sensitivity. The dynamic comprehensive index construction module is configured to: normalize the nearest response efficiency and impact effectiveness to obtain efficiency factor and effectiveness factor; and dynamically adjust the fusion weight of efficiency factor and effectiveness factor according to the real-time urgency of the system to construct dynamic comprehensive index. The priority sorting module is configured to sort each control resource based on the dynamic comprehensive index and generate a dynamic control resource priority list corresponding to the current operating state and fault scenario. The emergency control resource selection module is configured to: when an actual power grid fault is detected, match the fault information with the priority list, and generate and execute an emergency control strategy based on the available regulation capacity of each control resource.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the power grid emergency control resource selection method based on dynamic coupling as described in any one of claims 1-7.

10. An electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the power grid emergency control resource selection method based on dynamic coupling as described in any one of claims 1-7.

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