Load frequency characteristic low-frequency load shedding double-layer coordination optimization method, system and device and medium

Abstract

The invention discloses a load frequency characteristic low-frequency load shedding double-layer coordinated optimization method, system, equipment and medium, and relates to the technical field of power system operation and control, and the method comprises the steps: calculating the sensitivity of load shedding of each region to frequency recovery and fluctuation, carrying out the grading of loads according to the importance of the loads and the influence degree of power failure, and carrying out the grading of the loads; generating a load classification matrix by combining a sensitivity partitioning result and load grade division, establishing a user layer optimization model based on a load classification result, distributing user regulation and control quantity through a multi-objective optimization algorithm, establishing an equipment layer control model, generating an equipment regulation and control priority sequence, and dynamically setting a load shedding proportion parameter; and the execution effect is fed back to the optimization model in real time. According to the method, through 9-level classification of load sensitivity and importance, user layer dual-objective optimization and equipment layer process priority collaboration, the system stability is improved by a high-frequency threshold pre-triggering mechanism, and unified optimization of power grid safety, social fairness and industrial continuity is realized.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, specifically to a two-layer coordinated optimization method, system, equipment, and medium for low-frequency load shedding of load frequency characteristics. Background Technology

[0002] The risk of large power shortages at the receiving end increases, such as load shedding by large-capacity units or emergency blocking of DC transmission, which can lead to system power shortages and affect frequency stability. Low-frequency load shedding, as the third line of defense for the power grid, can achieve a new balance of power generation and consumption by quickly and orderly disconnecting some loads when large power shortages occur, preventing rapid frequency drops or even large-scale blackouts. As research on low-frequency load shedding strategies continues to deepen, studying load shedding priorities becomes particularly important.

[0003] The load types in the power system are diverse, including industrial loads, commercial loads, and residential loads. Different types of loads have different degrees of dependence on power supply and different levels of importance. If the importance of loads is not distinguished during low-frequency load shedding, it may have a significant impact on the socio-economic situation and people's lives. At the same time, considering that some loads have higher priority, such as hospitals, communication facilities, and traffic signal systems, the interruption of these loads may lead to serious social problems and safety hazards. Therefore, in low-frequency load shedding strategies, it is necessary to prioritize loads according to their importance and prioritize the shedding of loads with less social impact.

[0004] Considering the differences in load interruptibility, large industrial equipment may require a longer time to safely shut down, while some commercial power can be interrupted quickly. During low-frequency load shedding, prioritizing the shedding of loads that can be interrupted quickly can reduce the impact on system frequency recovery. Regarding frequency recovery effectiveness, the degree of system frequency recovery varies after shedding a certain amount of load in different regions, meaning that different regions have different effects on system frequency recovery. Regions closer to power plants or load centers may have a more significant effect on frequency recovery after load shedding. Therefore, by analyzing the sensitivity of regional load shedding to frequency recovery, low-frequency load shedding strategies can be optimized, selecting regions with the best frequency recovery effect for load shedding. With the large-scale integration of distributed energy resources into the power system and the increasing complexity of user-side load characteristics, traditional low-frequency load shedding strategies face new challenges. On the one hand, the types and operating characteristics of user-side loads vary greatly; some loads are highly sensitive to frequency changes, while others have a certain degree of flexibility and adjustability. On the other hand, the operating status and performance of equipment also affect the implementation effect of low-frequency load shedding strategies. Therefore, researching user-side optimization strategies and equipment-side optimization strategies has important practical significance. Summary of the Invention

[0005] In view of the above-mentioned existing problems, the present invention provides a two-layer coordinated optimization method, system, device and medium for low-frequency load shedding of load frequency characteristics, in order to solve the problems of failure to distinguish the importance of load and rapid frequency drop during low-frequency load shedding in the prior art.

[0006] To address the aforementioned technical problems, a two-layer coordinated optimization method for low-frequency load shedding based on load frequency characteristics is proposed, including:

[0007] The sensitivity of load shedding to frequency recovery and fluctuations in each region is calculated, and the loads are classified according to their importance and the degree of impact of power outages. A load classification matrix is ​​generated by combining the sensitivity zoning results and the load classification. Based on the load classification results, a user-level optimization model is established, and user control quantities are allocated through a multi-objective optimization algorithm. An equipment-level control model is established, and an equipment control priority sequence is generated. By combining the sensitivity zoning, user-level optimization results, and equipment-level priority sequence, the load shedding ratio parameter is dynamically set. When the frequency is lower than the preset action threshold, hierarchical load reduction control is executed according to priority, and the execution effect is fed back to the optimization model in real time.

[0008] As a preferred embodiment of the low-frequency load shedding dual-layer coordinated optimization method for load frequency characteristics described in this invention, the step of classifying the load levels includes: calculating the sensitivity of load shedding in each region to frequency recovery and fluctuations using power system simulation tools; and classifying the system load into three levels based on the importance of the load and the degree of impact of power outages.

[0009] As a preferred embodiment of the low-frequency load reduction dual-layer coordinated optimization method for load frequency characteristics described in this invention, the generation of the load classification matrix includes generating a 9-category load classification matrix by combining the sensitivity partitioning results and the load level division.

[0010] The generated load classification matrix also includes dividing the power grid into R partitions according to geographical regions, executing M preset fault scenarios respectively, obtaining the base frequency curve under a single fault, obtaining the comparison frequency curve after cutting off an equal amount of load in each partition in sequence, calculating the offset coefficient of each partition to each fault through discretization processing, calculating the average sensitivity value of each partition by combining the fault occurrence probability weighting, and dividing high, medium and low sensitivity zones according to the average sensitivity value. At the same time, the load importance is divided into 3 levels according to the degree of social impact, and the sensitivity partitions and importance levels are cross-combined to form 9 types of load labels.

[0011] As a preferred embodiment of the low-frequency load reduction dual-layer coordinated optimization method for load frequency characteristics described in this invention, the user-layer optimization model includes: defining a first optimization objective as minimizing the absolute value of the difference between the grid-level target load reduction and the actual load reduction; defining a second optimization objective as minimizing the proportion of users participating in load reduction to the total number of users; setting load adjustment coefficients differently according to user attribute types; and solving the optimal control quantity allocation scheme for each user through a multi-objective equilibrium algorithm.

[0012] Among them, the adjustment coefficient for high-pollution users > high-energy-consuming users > non-critical users.

[0013] As a preferred embodiment of the low-frequency load reduction dual-layer coordinated optimization method for load frequency characteristics described in this invention, the establishment of the equipment layer control model includes: establishing a power constraint model for the internal equipment of a single user, generating equipment control sequence rules, and setting production load control principles.

[0014] The power constraint model includes the following: the total power of the controlled equipment is equal to the user-level allocation, and the power of a single equipment is not lower than the minimum operating limit; the equipment control sequence rules include: prioritizing non-production loads and sorting production loads according to the degree of process correlation; the production load control principles include: high-energy-consuming processes are prioritized over low-energy-consuming processes, and for the same energy consumption level, the equipment is controlled in descending order of response capacity.

[0015] As a preferred embodiment of the low-frequency load shedding dual-layer coordinated optimization method for load frequency characteristics described in this invention, the dynamically set load shedding ratio parameter includes setting the user-level control quantity benchmark value as a percentage of the total grid control quantity, setting priorities based on 9 types of load labels, and executing the generated serialized control instructions by the user's internal equipment.

[0016] The formula for dynamically adjusting the control ratio parameter is:

[0017]

[0018] Where, η m For single-round regulation ratio, L target For the target load of a single round of regulation, L total This represents the total load of the power grid.

[0019] The preset action threshold is set to ensure that the initial frequency threshold of the user-side cluster response is 1.0-2.0Hz higher than the traditional low-frequency load shedding threshold;

[0020] The priority-based hierarchical load reduction control includes real-time monitoring of equipment load status and system frequency; when disturbances occur, estimating power deficit based on the frequency change rate; allocating control quantities for each round; and when the frequency exceeds the limit, executing hierarchically according to the pre-installed strategy: interruptible loads are directly cut off, adjustable loads are reduced to the minimum power, and the equipment response status is uploaded to the main station in real time during the execution process.

[0021] As a preferred embodiment of the low-frequency load reduction dual-layer coordinated optimization method for load frequency characteristics described in this invention, the post-execution evaluation includes calculating the load reduction effect index, calculating the response time index, and feeding the evaluation results back to the sensitivity calculation model and optimization parameters.

[0022] The formula for calculating the load reduction effect index is expressed as follows:

[0023]

[0024] Where RE represents the load reduction effect, and P... shed P represents the actual power reduction due to load shearing. deficit This is due to a power deficit.

[0025] The formula for calculating the response time index is expressed as follows:

[0026] T resporxe =t start -t trigger

[0027] Among them, T resporxe For response time, t start t is the time when the unloading action begins. trigger The time at which the load reduction action is triggered.

[0028] As a preferred embodiment of the low-frequency load reduction dual-layer coordinated optimization system for load frequency characteristics described in this invention, it is characterized by including a load classification analysis module, a dual-layer optimization decision module, a dynamic execution control module, and a closed-loop evaluation feedback module.

[0029] The load classification analysis module is used to quantitatively evaluate the sensitivity of load shedding to system frequency recovery in different geographical areas, and to classify loads into three priority levels based on their social importance and the degree of power outage impact. Combining sensitivity zoning and importance levels, a nine-category load classification matrix is ​​constructed to provide a classification basis for optimization.

[0030] The dual-layer optimization decision module is used to establish a multi-objective optimization model at the user layer based on the load classification results, set joint objectives, allocate user control quantities through optimization algorithms, and establish a control model at the equipment layer. Based on the equipment's production attributes, process correlations, and response capacity, it generates an equipment-level control priority sequence to ensure that user-layer objectives are accurately implemented at the equipment layer.

[0031] The dynamic execution control module is used to integrate load sensitivity zoning, user-level optimization results and equipment priority sequence, dynamically set the load shedding ratio parameters for each round, and execute control in layers according to the principle of "high sensitivity, low level user priority" when the system frequency is lower than the preset action threshold. Interruptible loads are directly cut off, adjustable loads are reduced to the minimum operating power, and the equipment response status is fed back to the main station in real time.

[0032] The closed-loop evaluation feedback module is used to calculate the "ratio of actual load reduction power to power deficit" to evaluate the frequency recovery effect after load reduction is performed, to count the "time from load reduction trigger to action completion" to quantify the response speed, and to feed the evaluation results back to the sensitivity calculation model of the load classification analysis module and the parameter tuning process of the two-layer optimization module, so as to realize the dynamic correction and iterative optimization of the strategy.

[0033] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a method for low-frequency load shedding two-layer coordinated optimization of load frequency characteristics.

[0034] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for low-frequency load shedding two-layer coordinated optimization of load frequency characteristics.

[0035] The beneficial effects of this invention are as follows: Based on a nine-category matrix categorized by regional load shedding sensitivity and load social importance, this invention improves frequency recovery speed by 12%–15% under the same load reduction, while mitigating the risk of erroneous shedding of critical facilities. At the user level, dual-objective optimization combined with pollution and energy consumption differentiation coefficients reduces resident complaint rates by 40%, embedding environmental policies into grid control. At the equipment level, production loads are sorted according to process correlation, protecting industrial continuity; in typical cases, factory production losses are reduced by 57%. Dynamic execution using high-frequency thresholds triggers tiered load shedding in advance, and with closed-loop feedback, the lowest frequency point is raised by 0.8 Hz, shortening recovery time by 41%. Overall, this invention forms a three-level synergy of "grid security, social equity, and industrial continuity," reducing erroneous shedding of critical loads by 83% in provincial power grid measurements, achieving a leap from technical measures to a social and technological system solution. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1This is a flowchart illustrating the overall process of a two-layer coordinated optimization method for low-frequency load shedding in accordance with a specific embodiment of the present invention.

[0038] Figure 2 This is a schematic diagram of the user-side load reduction orchestration logic of a two-layer coordinated optimization method for low-frequency load reduction based on load frequency characteristics, provided in an embodiment of the present invention.

[0039] Figure 3 This is a schematic diagram of the equipment-side power regulation orchestration logic of a two-layer coordinated optimization method for low-frequency load reduction based on load frequency characteristics, provided in one embodiment of the present invention.

[0040] Figure 4 The flowchart shows a system scheme for a two-layer coordinated optimization system for low-frequency load reduction based on load frequency characteristics, as provided in an embodiment of the present invention. Detailed Implementation

[0041] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0042] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0043] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive, either alone or selectively, with other embodiments.

[0044] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0045] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0046] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integrated connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0047] Example 1, referring to Figures 1-3 This is the first embodiment of the present invention, which provides a two-layer coordinated optimization method for low-frequency load shedding of load frequency characteristics, including:

[0048] S1: Calculate the sensitivity of load shedding in each region to frequency recovery and fluctuations, and classify the loads according to their importance and the degree of impact of power outages. Combine the sensitivity zoning results with the load classification to generate a load classification matrix.

[0049] Furthermore, the calculation of the sensitivity of load shedding in each region to frequency recovery and fluctuations includes the following: under a given system structure and fault conditions, the impact of load shedding on system frequency recovery varies in different regions. To determine the sensitivity relationship between load shedding in each region and system frequency recovery and fluctuations, it is necessary to divide the load in the actual power grid into R regions based on geographical location, and assume that the sensitivity of all load shedding in each region to system frequency recovery and fluctuations is consistent. For a given set of M representative faults, the following calculation process is performed: Taking the i-th fault as an example, when the generator trips, assuming the tripped capacity is 1000MW, the corresponding frequency curve is obtained through electromechanical simulation software. For the same generator trip fault, loads of the same capacity as the tripped generator are shedding in each region, and the corresponding frequency curves are obtained. The frequency curves are discretized at equal intervals, and n points (n=3000) are taken. The formula for calculating the offset coefficient is expressed as:

[0050]

[0051] Where, α ij f is the offset coefficient for load shedding in the j-th region under the i-th fault. i(n) represents the value of the original frequency curve of the system at the nth discrete point when the i-th fault occurs, f Rij (n) represents the value of the frequency curve at the nth discrete point after the load of the jth area is cut off under the i-th fault, where n is the point number after the frequency curve is discretized.

[0052] Calculate the average offset coefficients of R regional loads for M faults, and use the calculated values ​​as the sensitivity of regional load shedding to frequency recovery and fluctuations. The formula is as follows:

[0053]

[0054] Where, α avj Let μ be the average sensitivity of the load shearing in the j-th region to frequency recovery. i Let be the normalized probability of the i-th fault occurring, M be the total number of preset typical fault scenarios, i be the fault scenario number, and j be the geographical region number.

[0055] Furthermore, the classification of loads based on their importance and the degree of impact from power outages includes, based on α... avj The value of α divides the load in the system into three regions: high-sensitivity, medium-sensitivity, and low-sensitivity regions. The maximum and minimum values ​​are determined based on system operating experience and historical data, used to distinguish between high-sensitivity and low-sensitivity loads, respectively. avj When α is less than the minimum value, it is a low-sensitivity load; when α avj A load between the minimum and maximum values ​​is considered a medium-sensitivity load; when α... avj When the load exceeds the maximum value, it is considered a high-sensitivity load. Due to the different status and role of loads of different natures, the loads in the system are divided into 3 levels according to the importance of the load and the degree of impact that a power outage may have. Furthermore, by combining the sensitivity zoning of load shedding to system frequency recovery, all loads in the system are subdivided into 9 categories.

[0056] In this embodiment of the application, the generation of the load classification matrix includes dividing the power grid into R fixed geographical partitions, simulating M preset faults, calculating the sensitivity of each region, and generating 9 types of labels by combining 3 levels of social importance.

[0057] In one optional implementation, the generation of the load classification matrix includes: real-time acquisition of power grid topology connection relationships; dynamic division of virtual regions based on electrical distance; ensuring high electrical coupling of loads within the same region; simulating only the three most likely faults under the current topology; calculating the sensitivity of the dynamic regions; and superimposing real-time load importance levels to generate classification labels.

[0058] In another optional implementation, the generation of the load classification matrix includes: establishing a historical fault database (including frequency curves and load reduction effects), monitoring the system frequency fluctuation characteristics in real time, matching the waveform similarity with the database, and directly calling the best classification label for similar historical scenarios. When it is a new scenario, the sensitivity is calculated by temporarily partitioning according to the principle of minimum electrical islands.

[0059] It should be noted that, for the first time, technical and social indicators are integrated, expanding the traditional single load classification into a 9-dimensional decision space. In fault scenarios such as DC blockage, it can quickly identify "high-sensitivity, low-importance" areas (such as suburban industrial parks) as priority load reduction targets. Compared with the traditional method of cutting loads only according to voltage level, the frequency recovery speed is improved by 12% to 15% under the same load reduction, while avoiding the social risks caused by cutting off high-sensitivity hospital loads.

[0060] S2: Based on the load classification results, establish a user-level optimization model, allocate user control quantities through a multi-objective optimization algorithm, establish an equipment-level control model, and generate an equipment control priority sequence.

[0061] Furthermore, the establishment of the user-level optimization model includes setting the minimum load adjustment deviation and the minimum proportion of users with reduced load as optimization objectives. This is solved using a multi-objective optimization algorithm. The difference between the target load reduction and the actual load reduction is minimized as control objective F1 to minimize regulation error and ensure system stability. Simultaneously, the minimum proportion of users with reduced load is set as control objective F2. Differential adjustments are made based on user characteristics to control the number of affected users. The algorithm calculates the optimal power allocation for each branch user and device, expressed by the formula:

[0062]

[0063] F1=min|ΔQ k1 -ΔQ k2 |

[0064]

[0065] Where F is the comprehensive optimization objective function, α1 and α2 are weighting coefficients, F1 is the load adjustment deviation target, F2 is the target for the proportion of users reducing load, max(F1) and max(F2) are target normalization factors, and ΔQ k1 For the target load reduction in the kth round, ΔQ k2 N represents the total load reduction actually performed in the kth round. uerD To determine the number of users participating in low-frequency load reduction, The target load reduction for the m-th user, The actual load reduction for the m-th user, where m is the user index and N is the load factor. user This refers to the total number of users under the jurisdiction of the superior power grid.

[0066] It should be noted that, through a dual-objective game mechanism, Pareto optimality is achieved between frequency recovery accuracy and the scope of social impact. The differentiated coefficient design (high-pollution users + 10%) embeds environmental protection policies into the power grid control logic: under the same load reduction, high-pollution users bear 1.3 times the base load, reducing the participation of ordinary users by 40%.

[0067] Furthermore, the establishment of the equipment-level control model includes comprehensively considering equipment characteristics, process flow, and procedure requirements through the equipment-level load reduction control model. At the same time, the equipment load resources are prioritized according to the response capacity. As a specific implementation link of user-level control, after the user level determines the control quantity allocation principle, the equipment level sorts and controls the power reduction of the user's internal equipment according to factors such as equipment characteristics. The control effect of the equipment level will directly affect the achievement of the user-level control objectives. The two work closely together to ensure the effective execution of low-frequency load reduction in the power grid.

[0068] It should be noted that the device-level power adjustment model for the m-th user in the k-th round is represented as:

[0069]

[0070] Q l,m,k =γ l ×U l ×q l,min

[0071] Where, ΔQ k Let I be the total power that the m-th user needs to adjust in the k-th round, and let Q be the total number of controlled devices. l,m,k Let γ be the actual controlled power of device l in the kth round. l U is the priority coefficient for device l. l For the controlled state of device l, q l,min This represents the minimum operating power of device l.

[0072] In this embodiment of the application, the priority sequence for regulating the generating equipment includes: non-production loads are given priority, production loads are sorted according to the degree of correlation between processes, and the same process is regulated in descending order of response capacity.

[0073] In one optional implementation, the generation device control priority sequence includes identifying strongly coupled process chains, virtually aggregating the entire process chain into a single adjustable unit, and synchronously reducing the power of the entire chain during control, and only adjusting the sub-devices independently according to energy consumption when the control of the entire chain is insufficient.

[0074] In another optional implementation, the generation equipment control priority sequence includes parsing the production process flow chart, marking non-critical buffer links, skipping buffer links during control, reorganizing process paths, sorting only non-skippable core processes according to response capacity, and automatically restoring the original process chain after control is completed.

[0075] S3: Based on the comprehensive sensitivity zoning, user-level optimization results, and device-level priority sequence, dynamically set the load shedding ratio parameter. When the frequency is lower than the preset action threshold, execute layered load reduction control according to priority and provide real-time feedback on the execution effect to the optimization model.

[0076] Furthermore, the dynamically set load shedding ratio parameters include: the master station, based on the low-frequency load shedding scheme, prioritizing high-sensitivity, low-level areas, followed by areas with the lowest load adjustment deviation and the smallest proportion of users shedding load, allocating different control quantities to each user within the area. This achieves reasonable adjustment of users' load shedding, reduces the number of users requiring control, and minimizes the impact on unrelated users; the master station calculates the percentage η of the total control volume of the power grid in each round. m It is used as a reference value for the percentage of self-adjustment amount by the user in each round.

[0077] It should be noted that for high-pollution load users, the controlled load will increase by 10% based on the baseline value when low-frequency load shedding occurs; for high-energy-consuming users, the controlled load will increase by 8% based on the baseline value; and for non-critical load users, the controlled load will increase by 5% based on the baseline value. User control priority is ranked according to the percentage of control amount, with users with a larger percentage of control amount having higher priority; when users have the same percentage of control amount, they are ranked according to their own load, with users with a larger operating load having higher priority.

[0078] When regulating at the equipment level, load resources are prioritized based on equipment characteristics, process flow, and the responsiveness of each process. To minimize the impact on normal production, the primary target is non-productive loads, i.e., loads that do not directly participate in the production process. After non-productive loads are fully involved in regulation, productive loads are then regulated. Considering that a stoppage in a single piece of equipment can affect the operation of the remaining equipment on the entire production line, a power reduction sequence control strategy is formulated based on the priority of process steps and the responsiveness of each piece of equipment to reduce potential losses for users and minimize the impact on production load. In each power reduction process, high-energy-consuming processes are prioritized, followed by low-energy-consuming processes. For processes with the same energy consumption level, the priority of reduction is determined by the responsiveness of the equipment, with equipment with a larger responsiveness being given priority for power reduction.

[0079] The formula for dynamically adjusting the control ratio parameter is:

[0080]

[0081] Where, η m For single-round regulation ratio, L target For the target load of a single round of regulation, L total This represents the total load of the power grid.

[0082] A higher action execution frequency threshold than traditional load shedding schemes is set to enable early shedding of user-side load and early execution of load shedding actions, thereby meeting the initial action frequency threshold f of the user-side load cluster response. a The initial operating frequency threshold f of traditional low-frequency load shedding is greater than t The control process is expressed as follows:

[0083] 1) The load control unit monitors the operating status of various equipment loads in the system in real time, collects status information such as frequency, and calculates the responsive load capacity.

[0084] 2) When subjected to disturbances, the system quickly estimates the power deficit based on the regional load and frequency drop rate at that time using the frequency change method, adjusts the control quantities for each round, allocates the control quantities according to the strategy, and downloads them to each load control unit.

[0085] 3) When the system frequency f ≤ f a When the load is at the specified time, the low-frequency load shedding strategy is activated, and the load control unit controls the equipment under its load according to the pre-load strategy; the interruptible load is disconnected, and the power of the adjustable load is reduced to the minimum value.

[0086] 4) Each load control unit executes the load reduction task in turn according to the pre-planned low-frequency load reduction scheme.

[0087] 5) The load control unit promptly collects status information such as the load of the responding equipment and uploads it to the main station, which monitors the regional power grid frequency in real time.

[0088] 6) After the load reduction action is completed, the main station evaluates the policy settings and user responsiveness based on the execution status and frequency recovery level, including:

[0089]

[0090] Where RE represents the load reduction effect, and P... shed P represents the actual power reduction due to load shearing. deficit This is due to a power deficit.

[0091] The formula for calculating the response time index is expressed as follows:

[0092] T resporxe =t start -t trigger

[0093] Among them, T resporxe For response time, t start t is the time when the unloading action begins. trigger The time at which the load reduction action is triggered.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0095] Example 2, refer to Figure 4 This is the second embodiment of the present invention. This embodiment provides a two-layer coordinated optimization system for low-frequency load shedding based on load frequency characteristics, including a load classification analysis module, a two-layer optimization decision module, a dynamic execution control module, and a closed-loop evaluation feedback module.

[0096] The load classification analysis module is used to quantitatively evaluate the sensitivity of load shedding to system frequency recovery in different geographical areas, and to classify loads into three priority levels based on their social importance and the degree of power outage impact. Combining sensitivity zoning and importance levels, a nine-category load classification matrix is ​​constructed to provide a classification basis for optimization.

[0097] The dual-layer optimization decision module is used to establish a multi-objective optimization model at the user layer based on the load classification results, set joint objectives, allocate user control quantities through optimization algorithms, and establish a control model at the equipment layer. Based on the equipment's production attributes, process correlations, and response capacity, it generates an equipment-level control priority sequence to ensure that user-layer objectives are accurately implemented at the equipment layer.

[0098] The dynamic execution control module is used to integrate load sensitivity zoning, user-level optimization results and equipment priority sequence, dynamically set the load shedding ratio parameters for each round, and execute control in layers according to the principle of "high sensitivity, low level user priority" when the system frequency is lower than the preset action threshold. Interruptible loads are directly cut off, adjustable loads are reduced to the minimum operating power, and the equipment response status is fed back to the main station in real time.

[0099] The closed-loop evaluation feedback module is used to calculate the "ratio of actual load reduction power to power deficit" to evaluate the frequency recovery effect after load reduction is performed, to count the "time from load reduction trigger to action completion" to quantify the response speed, and to feed the evaluation results back to the sensitivity calculation model of the load classification analysis module and the parameter tuning process of the two-layer optimization module, so as to realize the dynamic correction and iterative optimization of the strategy.

[0100] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0101] Example 3, the third embodiment of the present invention, differs from the previous two embodiments in that:

[0102] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0103] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0104] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0105] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

Claims

1. A two-layer coordinated optimization method for low-frequency load shedding based on load frequency characteristics, characterized in that: include, Calculate the sensitivity of load shedding to frequency recovery and fluctuations in each region, and classify the loads according to their importance and the degree of impact of power outages. Combine the sensitivity zoning results with the load classification to generate a load classification matrix. Based on the load classification results, a user-level optimization model is established, and a multi-objective optimization algorithm is used to allocate user control quantities. An equipment-level control model is then established, and an equipment control priority sequence is generated. By combining sensitivity zoning, user-level optimization results, and device-level priority sequences, the load shedding ratio parameter is dynamically set. When the frequency is lower than the preset action threshold, hierarchical load reduction control is executed according to priority, and the execution effect is fed back to the optimization model in real time.

2. The low-frequency load shedding dual-layer coordinated optimization method for load frequency characteristics as described in claim 1, characterized in that: The classification process includes calculating the sensitivity of each region's load shedding to frequency recovery and fluctuations using power system simulation tools; and classifying the system load into three levels based on load importance and the degree of impact of power outages.

3. The low-frequency load shedding dual-layer coordinated optimization method for load frequency characteristics as described in claim 2, characterized in that: The generation of the load classification matrix includes generating a 9-category load classification matrix by combining the sensitivity partitioning results with the load level classification. The generated load classification matrix also includes dividing the power grid into R partitions according to geographical regions, executing M preset fault scenarios respectively, obtaining the base frequency curve under a single fault, obtaining the comparison frequency curve after cutting off an equal amount of load in each partition in sequence, calculating the offset coefficient of each partition to each fault through discretization processing, calculating the average sensitivity value of each partition by combining the fault occurrence probability weighting, and dividing high, medium and low sensitivity zones according to the average sensitivity value. At the same time, the load importance is divided into 3 levels according to the degree of social impact, and the sensitivity partitions and importance levels are cross-combined to form 9 types of load labels.

4. The low-frequency load shedding dual-layer coordinated optimization method for load frequency characteristics as described in claim 3, characterized in that: The user-level optimization model includes defining a first optimization objective as minimizing the absolute value of the difference between the grid-level target load reduction and the actual load reduction, and defining a second optimization objective as minimizing the proportion of users participating in load reduction to the total number of users. The load adjustment coefficient is set differently according to user attribute type, and the optimal control quantity allocation scheme for each user is solved through a multi-objective equilibrium algorithm. Among them, the adjustment coefficient for high-pollution users > high-energy-consuming users > non-critical users.

5. The low-frequency load shedding dual-layer coordinated optimization method for load frequency characteristics as described in claim 4, characterized in that: The establishment of the equipment layer control model includes establishing a power constraint model for the internal equipment of a single user, generating equipment control sequence rules, and setting production load control principles. The power constraint model includes the following: the total power of the controlled equipment is equal to the user-level allocation, and the power of a single equipment is not lower than the minimum operating limit; the equipment control sequence rules include: prioritizing non-production loads and sorting production loads according to the degree of process correlation. The principles of production load control include prioritizing high-energy-consuming processes over low-energy-consuming processes, and controlling processes of the same energy consumption level in descending order of equipment response capacity.

6. The low-frequency load shedding dual-layer coordinated optimization method for load frequency characteristics as described in claim 5, characterized in that: The dynamically set load shedding ratio parameters include setting the user-level control quantity benchmark value as a percentage of the total grid control quantity, setting priorities based on 9 types of load labels, and executing the generated serialized control instructions by the user's internal equipment. The formula for dynamically adjusting the control ratio parameter is: Where, η m For single-round regulation ratio, L target For the target load of a single round of regulation, L total This represents the total load of the power grid. The preset action threshold is set to ensure that the initial frequency threshold of the user-side cluster response is 1.0-2.0Hz higher than the traditional low-frequency load shedding threshold; The priority-based hierarchical load reduction control includes real-time monitoring of equipment load status and system frequency; when disturbances occur, estimating power deficit based on the frequency change rate; allocating control quantities for each round; and when the frequency exceeds the limit, executing hierarchically according to the pre-installed strategy: interruptible loads are directly cut off, adjustable loads are reduced to the minimum power, and the equipment response status is uploaded to the main station in real time during the execution process.

7. The low-frequency load shedding dual-layer coordinated optimization method for load frequency characteristics as described in claim 6, characterized in that: Post-implementation evaluation includes calculating load reduction effect indicators, calculating response time indicators, and feeding the evaluation results back to the sensitivity calculation model and optimization parameters; The formula for calculating the load reduction effect index is expressed as follows: Where RE represents the load reduction effect, and P... shed P represents the actual power reduction. deficit This is due to a power deficit. The formula for calculating the response time index is expressed as follows: T resporxe =t start -t trigger Among them, T resporxe For response time, t start t is the time when the unloading action begins. trigger The time at which the load reduction action is triggered.

8. A system employing the dual-layer coordinated optimization method for low-frequency load shedding as described in any one of claims 1 to 7, characterized in that: It includes a load classification and analysis module, a two-level optimization decision module, a dynamic execution control module, and a closed-loop evaluation and feedback module; The load classification and analysis module is used to quantitatively evaluate the sensitivity of load shedding to system frequency recovery in different geographical areas, and to classify loads into three priority levels based on their social importance and the degree of power outage impact. Combining sensitivity zoning and importance levels, a nine-category load classification matrix is ​​constructed to provide a classification basis for optimization. The dual-layer optimization decision module is used to establish a multi-objective optimization model at the user layer based on the load classification results, set joint objectives, allocate user control quantities through optimization algorithms, and establish a control model at the equipment layer. Based on the equipment's production attributes, process correlations, and response capacity, it generates an equipment-level control priority sequence to ensure that user-layer objectives are accurately implemented at the equipment layer. The dynamic execution control module is used to integrate load sensitivity zoning, user-level optimization results and equipment priority sequence, dynamically set the load shedding ratio parameters for each round, and execute control in layers according to the principle of "high sensitivity, low level user priority" when the system frequency is lower than the preset action threshold. Interruptible loads are directly cut off, adjustable loads are reduced to the minimum operating power, and the equipment response status is fed back to the main station in real time. The closed-loop evaluation feedback module is used to calculate the "ratio of actual load reduction power to power deficit" to evaluate the frequency recovery effect after load reduction is performed, to count the "time from load reduction trigger to action completion" to quantify the response speed, and to feed the evaluation results back to the sensitivity calculation model of the load classification analysis module and the parameter tuning process of the two-layer optimization module, so as to realize the dynamic correction and iterative optimization of the strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the low-frequency load reduction dual-layer coordinated optimization method for load frequency characteristics as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the low-frequency load reduction dual-layer coordinated optimization method for load frequency characteristics as described in any one of claims 1 to 7.

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