Bidirectional communication load node control method and system for power demand response

Abstract

The invention discloses a bidirectional communication load node control method and system for power demand response, and relates to the technical field of load control. The bidirectional communication load node control method and system oriented to the power demand response comprises the following steps: S1, acquiring load control communication data, establishing a load control communication database, and preprocessing the load control communication data; s2, evaluating the impact risk of the load breaking behavior of each node on the electrical system, and constructing a load breaking candidate node list and a node number index table; s3, constructing a scheduling priority sequence and executing a load breaking operation, evaluating the disturbance risk of each round of load breaking behavior on the stability of the power system, adjusting a load breaking interval in combination with an evaluation result, and updating the node number index table and the load breaking execution list; and S4, analyzing unit power impact sensitivity characteristics, identifying strategy sensitive nodes, and updating a threshold parameter table. The problems of loop current abrupt change and protection maloperation caused by neglecting node power difference in load breaking control are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of load control, and in particular to a two-way communication load node control method and system for power demand response. Background Art

[0002] As power systems evolve toward efficient and refined regulation, load control plays an increasingly important role in ensuring safe and stable power system operation. To improve load response efficiency and communication coordination, existing technologies have explored load sharing strategies and communication data management.

[0003] For example, the invention with announcement number CN105634975B provides a load sharing method for a shortwave communication network, including: when a service session needs to be initiated, the upstream device selects a specific downstream device according to a load balancing algorithm; after the shortwave service controller in the first load sharing domain and the regional proxy controller in the second load sharing domain are selected, the calling device initiates a session request to the called device via the shortwave communication network and establishes a session connection. In the present invention, the shortwave service controller and the regional proxy controller adopt a full load sharing method, and the failure of any device does not affect the operation of network services; when a service session is completed, each device in the load sharing domain can provide complete control functions, and the specific devices participating in the shared session are determined by the upstream device through a load balancing algorithm when the session is initiated, and the control service relationship remains unchanged until the session ends. In this way, the load on each device is basically the same, improving network utilization efficiency.

[0004] For example, the invention with announcement number: CN113923171B relates to a communication management method based on a load identification and detection platform, which belongs to the field of communication technology. It includes the following steps: A: sending of communication data messages; A1: classifying the sent messages and adding the messages in each sending message queue to the total sending queue according to the priority from high to low; A2: detecting whether the total sending queue is empty. If the total sending queue is not empty, a sending packet is dequeued for sending. If the sending queue is empty, it is not processed temporarily and is sent after there is a message in the sending queue. B: reply of communication data messages; B1: classifying the reply messages; B2: replying according to different reply message types. This invention solves the problem that the MCU that the upper computer communicates with can only maintain one long link and has limited performance, thereby improving the communication efficiency of the load identification and detection platform.

[0005] However, although the above technologies provide beneficial solutions in communication load scheduling and communication data processing, there has been no in-depth research on the dynamic correlation mechanism between communication behavior and node load off risk in load control scenarios. In particular, there is a lack of load off scheduling optimization methods under the combined effects of multiple factors such as communication performance, electrical status, and response characteristics.

[0006] Therefore, in response to the above problems, there is an urgent need for a two-way communication load node control method and system for power demand response. Summary of the Invention

[0007] Technical problems solved In response to the shortcomings of the existing technology, the present invention provides a two-way communication load node control method and system for power demand response, which solves the problem that load shedding control ignores node power differences, leading to sudden changes in loop current and protection malfunction.

[0008] Technical Solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a two-way communication load node control method for power demand response, comprising: S1, collecting load control communication data throughout the entire load response process, establishing a load control communication database, and preprocessing the load control communication data; S2, based on the preprocessed load control communication data, evaluating the impact risk of each node's load shedding behavior on the electrical system, eliminating impact risk nodes and screening out unstable nodes based on the evaluation results, and constructing a list of candidate load shedding nodes and a node number index table; S3, receiving information on candidate load shedding nodes, constructing a scheduling priority sequence and executing load shedding operations based on communication delay and load shedding impact risk assessment values, evaluating the disturbance risk of each round of load shedding behavior on the stability of the power system, adjusting the load shedding interval based on the evaluation results, and updating the node number index table and the load shedding execution list; S4, based on the load shedding execution list and real-time load control communication data, analyzing the unit power impact sensitivity characteristics, constructing a load shedding impact feedback curve and an adjustment deviation sequence, identifying strategy sensitive nodes, and updating the threshold parameter table to complete the adaptive correction of the load shedding strategy.

[0009] Furthermore, the specific steps of collecting load control communication data in the whole process of load response, establishing a load control communication database, and preprocessing the load control communication data are as follows: multi-dimensional synchronous sampling of the electrical operating status, power regulation behavior, communication link performance, equipment physical characteristics, environmental interference factors and node control response capabilities involved in the whole process of load response is carried out to obtain load control communication data in real time. The load control communication data includes operating power, node current, loop current, node voltage, current change rate, node communication delay, grid frequency, node temperature and ambient temperature; the load control communication data is stored and a load control communication database is established. The communication database is built and the historical response success rate of the nodes is obtained from the load control communication database; the time series of the load control communication data is synchronized by a sliding window multi-scale resampling algorithm based on multi-channel data alignment, and the sampling offset is corrected; the load control communication data is cleaned of anomalies by a transient mutation identification and abnormal boundary elimination algorithm to eliminate false signals introduced by fluctuating loads; the response validity of the load control communication data is determined by a state and behavior two-state consistency verification model to identify short-term response failures and miscontrol records; the load control communication data is normalized by a unified scale transformation using a cross-dimensional indicator normalization mapping and a variation factor compression algorithm.

[0010] Furthermore, based on the pre-processed load control communication data, the specific steps for evaluating the impact risk caused by the load-off behavior of each node on the electrical system are as follows: based on the pre-processed load control communication data, the operating power, node voltage, loop current, node current, current change rate, grid frequency and node historical response success rate are extracted, and a field correspondence is established according to the node to evaluate the impact risk caused by the node load-off behavior on the electrical system: calculate the operating power divided by the product of the node voltage and the loop current, and then multiply it by the ratio of the node current to the loop current to obtain the power-current composite proportion item; add the current change rate and the inverse of the grid frequency to form the electrical state sensitivity item; calculate one minus the node historical response success rate to obtain the response uncertainty item; multiply the power-current composite proportion item, the electrical state sensitivity item and the response uncertainty item to obtain the load-off impact risk assessment value.

[0011] Furthermore, based on the evaluation results, impact risk nodes are eliminated and unstable nodes are screened out, and the specific steps for constructing a list of candidate load-shedding nodes and a node number index table are as follows: sort the load-shedding impact risk assessment values ​​corresponding to all nodes in descending order to construct a load-shedding impact sorted list; traverse each node in the sorted list to screen out nodes with load-shedding impact risk assessment values ​​higher than the load-shedding impact risk threshold, mark them as impact risk nodes, and add them to the load-shedding shielding queue; the remaining nodes are retained in the load-shedding candidate queue to form a preliminary response object set; conditional filtering is performed on the nodes in the load-shedding candidate queue to eliminate nodes with operating power lower than the power response threshold; at the same time, the node historical response success rate field is screened, and nodes with consecutive response failures exceeding the response failure threshold are marked as unstable nodes, and are removed from this round of scheduling; the remaining nodes in the load-shedding candidate queue are arranged in order from low to high according to the load-shedding impact risk assessment value, and a list of candidate load-shedding nodes and a corresponding node number index table are output.

[0012] Furthermore, the specific steps of receiving the information of the candidate nodes for load shedding, constructing a scheduling priority sequence based on the communication delay and the load shedding impact risk assessment value, and executing the load shedding operation are as follows: receiving a list of candidate nodes for load shedding and an index table of corresponding node numbers, performing batch reading of the node communication delay field, and calculating the average communication response time by node; sorting all candidate nodes for load shedding from small to large according to the average communication response time, and when the average communication response times are the same, scheduling the node with the smallest load shedding impact risk assessment value first, and constructing a scheduling priority queue with a double-factor joint sorting; executing the load shedding control instructions in sequence according to the scheduling priority queue, recording the node numbers of the node that has triggered the load shedding instructions, establishing a load shedding execution list, and forming a load shedding scheduling queue with the remaining nodes to be executed.

[0013] Furthermore, the specific steps for evaluating the disturbance risk of each round of load shedding behavior on the stability of the power system are as follows: After each batch of load shedding operations is completed, the instantaneous change of the current loop current is read, and the disturbance risk of this round of load shedding behavior on the stability of the power system is comprehensively evaluated: the instantaneous change of the loop current is calculated and divided by the sum of the current change rate and the minimum term to obtain the unit current change impact intensity term; the load shedding impact risk assessment value is divided by the result of the node communication delay plus one to obtain the risk delay adjustment term; the absolute value of the difference between the node's own temperature and the ambient temperature is calculated, and then divided by the ambient temperature, and the result is added by one to obtain the thermal imbalance impact factor; the unit current change impact intensity term, the risk delay adjustment term and the thermal imbalance impact factor are multiplied to obtain the load shedding execution risk assessment value.

[0014] Furthermore, the specific steps for adjusting the load off interval based on the evaluation results and updating the node number index table and the load off execution list are as follows: compare the load off execution risk assessment value and the load off execution risk threshold in real time. If the load off execution risk assessment value is less than or equal to the load off execution risk threshold, continue to execute the next batch of load off operations; if the load off execution risk assessment value is greater than the load off execution risk threshold, trigger the dynamic interval adjustment mechanism: extract the node own temperature and ambient temperature fields of the corresponding node, calculate the temperature difference ratio, and combine the loop current change generated by the previous batch to determine the time interval for the next load off operation through the mapping relationship; for the nodes that failed to respond in each batch of load off operations, read the node communication delay and the node historical response success rate, and count the number of consecutive response failures; if the number of consecutive response failures exceeds the response failure threshold, and the node communication delay is higher than the communication delay threshold, the node is marked as a failed node, removed from the load off scheduling queue, and the node number index table and the load off execution list are updated synchronously.

[0015] Furthermore, based on the load shedding execution list and real-time load control communication data, the specific steps for analyzing the unit power impact sensitivity characteristics are as follows: receive the updated load shedding execution list and node number index table, extract the operating power corresponding to the load shedding node, and calculate the average operating power of the load shedding nodes in this batch; calculate the instantaneous change of the loop current and divide it by the average operating power of the load shedding node to obtain the unit power current impact term; divide the current change rate by the grid frequency and add one to form an electrical dynamic adjustment term; calculate the absolute value of the difference between the node's own temperature and the ambient temperature, divide it by the ambient temperature, and add one to the result to obtain the thermal imbalance impact factor; add the thermal imbalance impact factor to the electrical dynamic adjustment term to obtain a composite environmental adjustment factor; multiply the unit power current impact term, the composite environmental adjustment factor and the load shedding execution risk assessment value to obtain a unit power impact sensitivity value.

[0016] Furthermore, the specific steps of constructing the load shedding impact feedback curve and the adjustment deviation sequence, identifying the strategy sensitive nodes, and updating the threshold parameter table to complete the adaptive correction of the load shedding strategy are as follows: summarize the unit power impact sensitivity value and the load shedding impact risk assessment value in the current round of load shedding execution, and construct the load shedding impact feedback curve; read the operating power of each node in the previous round of load shedding scheduling and the instantaneous change of the loop current read in this round, and calculate the difference between the node load shedding power target value and the actual current response value to form an adjustment deviation sequence; judge each node: when the adjustment deviation exceeds the response deviation tolerance threshold, or the unit power impact sensitivity value exceeds the impact sensitivity threshold When any of the conditions is met, the node is marked as a strategy-sensitive node and the load shedding strategy parameter adaptive correction process is triggered; in the adaptive correction process, the threshold parameters are dynamically adjusted according to the slope of the impact feedback curve and the deviation direction: the load shedding impact risk threshold is adjusted to the average value of the load shedding impact risk assessment values ​​of all current sensitive nodes; the response failure threshold is adjusted in a weighted decreasing manner based on the most frequent failure interval in the historical response failure records of the node; the peak sequence of the load shedding execution risk assessment values ​​of all nodes in the current batch is taken, and the load shedding execution risk threshold is updated according to its local mean sliding fitting; the updated threshold parameters are written into the threshold parameter update table for loading in the next round of load shedding process.

[0017] The second aspect of the present invention provides a two-way communication load node control system for power demand response, including: a load control communication data acquisition and preprocessing module, a load off impact risk assessment and response screening module, a batch load off and dynamic interval joint scheduling module and a load off feedback perception and threshold parameter correction module, wherein: the load control communication data acquisition and preprocessing module is used to collect load control communication data in the entire load response process, establish a load control communication database, and preprocess the load control communication data; the load off impact risk assessment and response screening module is used to evaluate the impact risk caused by the load off behavior of each node on the electrical system based on the preprocessed load control communication data, and eliminate the impact risk nodes and screen them based on the evaluation results. In addition to unstable nodes, a list of candidate load shedding nodes and a node number index table are constructed; a batch load shedding and dynamic interval joint scheduling module is used to receive information on candidate load shedding nodes, build a scheduling priority sequence and execute load shedding operations based on communication delay and load shedding impact risk assessment values, evaluate the disturbance risk of each round of load shedding behavior on the stability of the power system, adjust the load shedding interval based on the assessment results, and update the node number index table and load shedding execution list; a load shedding feedback perception and threshold parameter correction module is used to analyze the unit power impact sensitivity characteristics based on the load shedding execution list and real-time load control communication data, construct a load shedding impact feedback curve and an adjustment deviation sequence, identify strategy-sensitive nodes, and update the threshold parameter table to complete the adaptive correction of the load shedding strategy.

[0018] Beneficial effects The present invention has the following beneficial effects: (1) This two-way communication load node control method and system for power demand response evaluates the impact risk of each node on the electrical system during load shedding operation by constructing a power and current composite proportion item, an electrical state sensitivity item, and a response uncertainty item, thereby forming a load shedding impact risk assessment value, achieving effective identification and elimination of high-risk nodes, laying the foundation for the subsequent construction of an unloading candidate node queue, and improving the accuracy of node screening and the pertinence of the strategy.

[0019] (2) This two-way communication load node control method and system for power demand response comprehensively considers the average communication response time of the node and the load shedding impact risk assessment value, and adopts a dual-factor joint sorting mechanism to prioritize the scheduling of nodes with fast communication response and low load shedding risk. Under the condition of similar communication delay, nodes with low impact risk are preferentially selected to execute load shedding instructions, effectively avoiding execution failures caused by communication bottlenecks and system disturbances caused by high-risk nodes, and ensuring the real-time and safety of load shedding scheduling.

[0020] (3) This two-way communication load node control method and system for power demand response determines whether the load shedding interval needs to be adjusted by comparing the risk assessment results after each batch of operations with the load shedding execution risk threshold. When the risk is too high, it further extracts indicators such as the node temperature difference ratio and the instantaneous change in current, and dynamically sets the time interval for the next round of load shedding based on the mapping relationship, thereby achieving real-time response and regulation of the system disturbance level and improving the adaptability of the load shedding scheduling strategy to changes in system stability.

[0021] (4) This two-way communication load node control method and system for power demand response analyzes the unit power impact sensitivity value of each batch of load shedding nodes and combines the deviation between the operating power of the nodes in the previous and next rounds and the actual current response to form a load shedding impact feedback curve and an adjustment deviation sequence. This process supports retrospective quantitative analysis of node execution effects, helps identify strategy-sensitive nodes and potential abnormal points, and forms a data basis for load shedding strategy correction, thereby improving the strategy's dynamic correction capability and the level of closed-loop control. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of a two-way communication load node control method for power demand response; Figure 2 This is a structural diagram of a two-way communication load node control system for power demand response; Figure 3 Execute risk assessment value distribution and dynamic interval trigger decision diagram for load shedding; Figure 4 This is the unit power shock sensitivity distribution diagram. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] See also Figure 1-Figure 4 , an embodiment of the present invention provides a technical solution: a two-way communication load node control method for power demand response, comprising: S1, collecting load control communication data throughout the entire load response process, establishing a load control communication database, and preprocessing the load control communication data; S2, based on the preprocessed load control communication data, evaluating the impact risk of load shedding behavior of each node on the electrical system, eliminating impact risk nodes and screening out unstable nodes based on the evaluation results, and constructing a list of candidate load shedding nodes and a node number index table; S3, receiving information on candidate load shedding nodes, constructing a scheduling priority sequence and executing load shedding operations based on communication delay and load shedding impact risk assessment values, evaluating the disturbance risk of each round of load shedding behavior on the stability of the power system, adjusting the load shedding interval based on the evaluation results, and updating the node number index table and the load shedding execution list; S4, based on the load shedding execution list and real-time load control communication data, analyzing the unit power impact sensitivity characteristics, constructing a load shedding impact feedback curve and an adjustment deviation sequence, identifying strategy-sensitive nodes, and updating the threshold parameter table to complete the adaptive correction of the load shedding strategy.

[0025] Specifically, the specific steps of collecting load control communication data in the whole process of load response, establishing a load control communication database, and preprocessing the load control communication data are as follows: multi-dimensional synchronous sampling of the electrical operating status, power regulation behavior, communication link performance, equipment physical characteristics, environmental interference factors and node control response capability involved in the whole process of load response, and real-time acquisition of load control communication data, wherein the electrical operating status includes node voltage, node current and loop current, the power regulation behavior includes the dynamic change process of operating power, the communication link performance includes node communication delay and data packet arrival time accuracy, the equipment physical characteristics include the node's own temperature and historical load capacity, the environmental interference factors include ambient temperature and local frequency fluctuation amplitude, and the node control response capability includes node response continuity and node response accuracy; the load control communication data includes operating power, node current, loop current, node voltage, current change rate, node communication delay, grid frequency, node's own temperature and ambient temperature, and each data is bound with millisecond-level timestamps within the sampling period to ensure the real-time and continuity of the collection process; the load control communication data is stored and a support A structured load control communication database with high-frequency access and historical trajectory tracking is used to obtain the historical node response success rate from the load control communication database and organize it by node number index. A sliding window multi-scale resampling algorithm based on multi-channel data alignment is used to synchronize the load control communication data, correcting time offset errors caused by inconsistent sensor sampling intervals. Anomalies in the load control communication data are cleaned using a transient mutation identification and abnormal boundary elimination algorithm. Burst-type false signals are identified based on the extreme values ​​of the change rate, eliminating uncontrolled disturbance data introduced by fluctuating loads. A state-behavior two-state consistency verification model is used to determine the response validity of the load control communication data. Short-term response failures and miscontrol records are identified by combining node voltage change behavior with the regulation command feedback state, eliminating invalid response data from incomplete load shedding processes. A cross-dimensional indicator normalization mapping and variation factor compression algorithm are used to uniformly transform the load control communication data. Independent normalization models are constructed for node current, node communication delay, current change rate, and node temperature to achieve comparability of different dimensional features within a unified feature space, ultimately completing the normalization processing of all load control communication data.

[0026] In this implementation, the integrity and accuracy of the load control communication database are improved by performing refined preprocessing on the load control communication data collected throughout the entire load response process. The sliding window multi-scale resampling algorithm is used to correct sampling time offsets to ensure data time series synchronization. The transient mutation identification and abnormal boundary elimination algorithms are used to eliminate false signals and improve data reliability. A state and behavior dual-state consistency verification model is used to identify response failure records and enhance the ability to judge response effectiveness. A cross-dimensional indicator normalization mapping and variation factor compression algorithm are used to achieve unified scale conversion of different feature fields, ensuring the robustness and decision consistency of subsequent load shedding impact risk assessment and dispatch control modules in a high-dimensional heterogeneous data environment, significantly enhancing the robustness and accuracy of node control responses.

[0027] Specifically, based on the pre-processed load control communication data, the specific steps for evaluating the impact risk caused by the load-off behavior of each node on the electrical system are as follows: First, from the load control communication database that has completed unified scale transformation and abnormality cleaning, extract the operating power, node voltage, loop current, node current, current change rate, grid frequency and node historical response success rate of each node in a specific time period, establish a field correspondence according to the node number and perform structured mapping to ensure that each data indicator is associated with the corresponding node one by one; secondly, according to the energy flow characteristics of the electrical system, calculate the operating power divided by the product of the node voltage and loop current to obtain the power proportion under unit voltage and current conditions, and then multiply it. The ratio of node current to loop current is used to form a power-current composite proportion term, which is used to characterize the coupling strength of node power to the local current channel. Subsequently, the current change rate is added to the inverse of the grid frequency to construct an electrical state sensitivity term that reflects the stability of the node's transient behavior and captures the degree of trend of electrical fluctuations caused by node response. Furthermore, the response uncertainty term is calculated by subtracting the node's historical response success rate from one to quantify the fluctuation amplitude of the node control reliability. Finally, the power-current composite proportion term, the electrical state sensitivity term, and the response uncertainty term are multiplied in sequence to obtain a comprehensive load-off impact risk assessment value, providing a highly reliable risk quantification basis for subsequent node screening and batch scheduling.

[0028] The specific calculation formula for the load-off shock risk assessment value is: ; Where D represents the load-off shock risk assessment value, Indicates operating power, represents the node voltage, represents the node current, represents the loop current, v represents the current change rate, f represents the grid frequency, and R represents the node's historical response success rate.

[0029] In this embodiment, the present invention realizes the accurate analysis of multi-source load control communication data at the node granularity by constructing a load shedding impact risk assessment mechanism with operating power, node voltage, loop current, node current, current change rate, grid frequency and node historical response success rate as core fields, and enhances the quantitative expression capability of the disturbance caused by node load shedding behavior. By introducing the power current composite proportion term to strengthen the analysis of the impact of node power distribution in the local current path, using the electrical state sensitivity term to reflect the triggering effect of node response behavior on transient fluctuations, and combining with the response uncertainty term to capture the node control stability deviation, the accuracy of the load shedding impact risk assessment value is comprehensively improved.

[0030] Specifically, based on the evaluation results, impact risk nodes are eliminated and unstable nodes are screened out, and the specific steps for constructing a list of candidate load-shedding nodes and a node number index table are as follows: sort the load-shedding impact risk assessment values ​​corresponding to all nodes in descending order, construct a load-shedding impact sorting list, and record the correspondence between the node number and its load-shedding impact risk assessment value in real time during the sorting process to ensure the consistency of the sorting result and the number index; traverse each node in the sorting list, screen out nodes whose load-shedding impact risk assessment value is higher than the load-shedding impact risk threshold, and mark them as impact risk nodes one by one in combination with the risk level identification standard set in the threshold parameter table, add them to the load-shedding shielding queue and update the node status label synchronously; retain the remaining nodes in the load-shedding candidate queue to form a preliminary response object set, and assign them a dispatch standby identification; The nodes in the process are filtered conditionally to eliminate nodes whose operating power is lower than the power response threshold. The operating power field is obtained through the operating power sampling value of the latest period in the load control communication data, and the upper and lower limit parameters of the dynamic power response are referred to in the judgment. At the same time, the node historical response success rate field is screened, and the number of consecutive response failures is calculated in combination with the historical response status records. Nodes whose consecutive response failures exceed the response failure threshold are marked as unstable nodes and eliminated from this round of scheduling. Their scheduling freeze marks are recorded in the node status table. The remaining nodes in the load off candidate queue are arranged in order from low to high according to the load off impact risk assessment value. The mapping relationship between the node number and the assessment value is retained during the sorting process. Finally, a list of load off candidate nodes and the corresponding node number index table are output for subsequent scheduling control process loading.

[0031] In this implementation plan, by constructing a load shedding impact sorting list, impact risk nodes whose load shedding impact risk assessment values ​​are higher than the load shedding impact risk threshold are screened out, and conditional filtering is performed in combination with the operating power field and the node historical response success rate field to ensure that nodes whose operating power is not lower than the power response threshold and whose consecutive response failure times do not exceed the response failure threshold are included in the load shedding candidate queue, effectively ensuring the stability and security of the load shedding candidate node list, and through the synchronous update of the node number index table and the node status table, providing high-accuracy and high-reliability basic data support for the subsequent scheduling control process.

[0032] Specifically, the specific steps of receiving information on candidate load shedding nodes, building a scheduling priority sequence based on communication delay and load shedding impact risk assessment values, and executing load shedding operations are as follows: receiving a list of candidate load shedding nodes and a corresponding node number index table, extracting the communication delay field and node number field in the candidate load shedding nodes, performing batch reading operations, establishing a matching relationship between the communication delay field and the number field for each node, calculating the average communication response time of each node, and performing sliding mean smoothing on the response time series within the sampling time interval to eliminate transient deviations; sorting all candidate load shedding nodes from small to large according to the average communication response time, and when the average communication response times are the same, giving priority to scheduling the node with the smallest load shedding impact risk assessment value, and building a scheduling priority queue with a dual-factor joint sorting; issuing load shedding control instructions to each node in turn according to the arrangement order of the scheduling priority queue, recording the node number information triggered by each instruction in real time, and establishing a load shedding execution list. At the same time, the nodes whose instructions have not been triggered in the current batch are classified into the set of nodes to be executed, generating this round of load shedding scheduling queue for sequential reading and status management of subsequent scheduling control processes.

[0033] In this implementation plan, by introducing a dual-factor joint sorting mechanism of average communication response time and load off impact risk assessment value, the priority division of load off candidate nodes is refined, and the accuracy of scheduling priority queue construction is effectively improved; combining batch reading and node number field mapping operations, it ensures that the execution order of scheduling control instructions is highly consistent with the node response capability; by synchronously establishing the load off execution list and the load off scheduling queue, dynamic management and continuous tracking of the load off status are realized, providing stable data support for subsequent load off execution risk assessment and scheduling strategy adjustment, and improving the response stability and control timeliness under multi-node collaborative control.

[0034] Specifically, the steps for assessing the disturbance risk of each round of load shedding on the power system stability are as follows: After each batch of load shedding operations is completed, the instantaneous change in the current loop current is read and, combined with the node number index table and the load shedding execution list, data is located to comprehensively assess the disturbance risk of this round of load shedding on the power system stability. The amplitude change trend of the electrical disturbance at different nodes is determined by calculating the instantaneous change in the loop current and dividing it by the sum of the node current change rate and the minimum term, forming a unit current change impact intensity term. The load shedding impact risk assessment value of each node is divided by the result of the node's communication delay plus one to form a risk delay adjustment term, which reflects the impact of communication performance on the propagation time of the load shedding risk. The node's own temperature and ambient temperature fields are read, the absolute value of the difference between the two is calculated, and the difference is divided by the ambient temperature and added to one to obtain a thermal imbalance impact factor, which is used to characterize the interference effect of temperature gradients on the node's response stability. Finally, the unit current change impact intensity term, the risk delay adjustment term, and the thermal imbalance impact factor are multiplied to calculate the load shedding execution risk assessment value, providing a quantitative basis for the load shedding interval control strategy and node stability judgment.

[0035] The specific calculation formula for the load shedding execution risk assessment value is: ; Where Q represents the load shedding execution risk assessment value, Indicates the instantaneous change of the loop current, v indicates the current change rate, represents the minimum term, D represents the load-off impact risk assessment value, T represents the node communication delay, represents the node's own temperature, Indicates the ambient temperature.

[0036] In this embodiment, Table 1 is a load off execution risk assessment value data table, which lists in detail the key indicators of the five communication load nodes in the load off behavior assessment process. The key indicators include the instantaneous change of loop current, the current change rate, the load off impact risk assessment value, the node communication delay, the node's own temperature, the ambient temperature and the finally calculated load off execution risk assessment value. Among them: the instantaneous change of the loop current of node N1 is 11.63, the current change rate is 1.09, the load off impact risk assessment value is 0.0470, the communication delay is 0.1763, the node temperature is 52.3, the ambient temperature is 28.3, and the final calculated load off execution risk assessment value is 0.78; the instantaneous change of the loop current of node N2 is 8.32, the current change rate is 0.96, the load off impact risk assessment value is 0.0375, the communication delay is 0.0562, the node temperature is 28.2, the ambient temperature is 20.8, and the load off execution risk assessment value is 0.85; the instantaneous change of the loop current of node N3 is 9.30, the current change rate is 1.43, and the load off impact risk assessment value is 0 .0699, the communication delay is 0.0757, the node temperature is 41.6, the ambient temperature is 25.3, and the load off execution risk assessment value is 0.69; the instantaneous change of the loop current of node N4 is 9.17, the current change rate is 1.10, the load off impact risk assessment value is 0.0641, the communication delay is 0.0802, the node temperature is 41.5, the ambient temperature is 25.1, and the load off execution risk assessment value is 0.81; the instantaneous change of the loop current of node N5 is 8.13, the current change rate is 1.09, the load off impact risk assessment value is 0.0687, the communication delay is 0.0868, the node temperature is 44.1, the ambient temperature is 22.3, and the load off execution risk assessment value is 0.92.

[0037] Table 1 Load shedding execution risk assessment data table

[0038] like Figure 3 The figure shows the distribution of load shedding execution risk assessment values ​​and the dynamic interval triggering judgment diagram, which shows the load shedding execution risk assessment values ​​corresponding to nodes N1 to N5 in the current round of load shedding operations, which are used to determine whether the dynamic interval adjustment mechanism is triggered. The blue dotted line is the load shedding execution risk threshold. The risk assessment value of each node is connected by a broken line, and the color is used at the origin to distinguish whether the dynamic interval mechanism is triggered: the red dot indicates that the dynamic interval adjustment mechanism has been triggered, and the green dot indicates that the dynamic interval adjustment mechanism has not been triggered. It can be seen from the figure that only the risk assessment value of node N5 exceeds the threshold and triggers the interval adjustment process. The assessment values ​​of the remaining nodes are lower than the threshold and the mechanism is not triggered, indicating that the overall disturbance risk control of this round of load shedding control is in a relatively safe range.

[0039] In this implementation plan, by introducing three indicators, namely the unit current change impact intensity item, the risk delay adjustment item and the thermal imbalance impact factor, multi-dimensional quantitative modeling of the electrical disturbance degree, communication response characteristics and thermal stability factors during the load shedding execution process is achieved, thereby forming a load shedding execution risk assessment value, effectively improving the ability to make a refined assessment of the disturbance degree caused by each round of load shedding operation, providing a basis for judging the continuity, stability and accuracy of the subsequent load shedding interval adjustment mechanism and the node dynamic screening strategy, and enhancing the dynamic adaptability of the load response control strategy to operational risks.

[0040] Specifically, the specific steps of adjusting the load shedding interval in combination with the evaluation results and updating the node number index table and the load shedding execution list are as follows: compare the load shedding execution risk assessment value calculated after the load shedding operation of the current batch has been completed with the load shedding execution risk threshold in real time, and continuously read the instantaneous change of the loop current, the current change rate, the node communication delay, the node's own temperature and the ambient temperature of the nodes in the load shedding execution list during the evaluation process to construct a disturbance characteristic data set of the load shedding process; if the current load shedding execution risk assessment value is less than or equal to the load shedding execution risk threshold, it indicates that this round of load shedding behavior does not cause a significant disturbance to the stability of the power system, and then directly enter the next round of load shedding batch scheduling process; if the current load shedding execution risk assessment value is greater than the load shedding execution risk threshold, it is determined that the current load control behavior has a risk of exceeding the disturbance standard, and the dynamic interval adjustment mechanism is immediately triggered, in which the node's own temperature and the ambient temperature of the executed node are extracted The ambient temperature field is used to calculate the temperature difference ratio, which is used to quantify the thermal stability state deviation. At the same time, a disturbance thermal sensitive mapping model is constructed in combination with the loop current change generated by the previous batch of load-shedding operations. The optimal execution time interval of the next load-shedding operation is determined by matching the mapping model. For the response failure nodes that fail to complete the load-shedding instruction in each batch of load-shedding operations, their node communication delay fields and node historical response success rate fields are read one by one, and a response behavior tracking sequence is constructed according to the node, and the number of consecutive response failures of each node is counted. If the number of consecutive response failures of a node exceeds the response failure threshold, and its current communication delay value is higher than the communication delay threshold, the node is marked as a failed node according to the stability judgment rule and removed from the current load-shedding scheduling queue. At the same time, the index mapping relationship in the node number index table is synchronously updated to ensure the accuracy of the execution status and the efficiency of node scheduling in the next round of scheduling.

[0041] In this implementation scheme, by comparing the load shedding execution risk assessment value with the load shedding execution risk threshold, dynamically adjusting the load shedding operation interval, and updating the node number index table and the load shedding execution list in real time, it can effectively avoid the disturbance risk caused by load control operations exceeding the power system stability limit. While ensuring operational safety, a mapping model is constructed by combining the temperature difference ratio between the node's own temperature and the ambient temperature with the loop current change, making the load shedding interval more accurate and reasonable; at the same time, a response behavior tracking sequence is constructed based on the node communication delay and the node's historical response success rate, identifying and eliminating failed nodes whose consecutive response failures exceed the threshold and whose communication delay is higher than the threshold, thereby improving the robustness and execution efficiency of the load shedding scheduling process and ensuring that the node number index table and the load shedding execution list maintain state synchronization and information consistency during the scheduling process.

[0042] Specifically, based on the load shedding execution list and real-time load control communication data, the specific steps for analyzing the unit power impact sensitivity characteristics are as follows: receive the updated load shedding execution list and node number index table, extract the operating power field corresponding to the load shedding node, complete field binding based on the node number matching relationship, calculate the average operating power of the load shedding nodes in this batch, and ensure that the power data has time consistency within the statistical period; synchronously read the instantaneous change of the loop current, calculate the instantaneous change of the loop current divided by the average operating power of the load shedding node, construct the unit power current impact term, and accurately quantify the load disturbance amplitude corresponding to the unit power; obtain the current change rate field and the grid frequency field, calculate the current change rate divided by the grid frequency, and add one to the result. , construct an electrical dynamic adjustment item to reflect the dynamic adaptability of the node under changes in electrical operating conditions; call the node's own temperature and ambient temperature fields, calculate the absolute value of the difference between the two, divide it by the ambient temperature, add one to the result, and generate a thermal imbalance impact factor to quantify the impact of the node's thermal state fluctuation on the load-off response process; numerically superimpose the electrical dynamic adjustment item and the thermal imbalance impact factor in sequence to obtain a composite environmental adjustment factor to reflect the coupled influence of the node operating environment on the impact response; finally, multiply the unit power current impact item, the composite environmental adjustment factor, and the load-off execution risk assessment value item by item to output the unit power impact sensitivity value, which provides an accurate quantitative basis for the subsequent strategy sensitive node identification and adaptive correction mechanism.

[0043] The specific calculation formula for the unit power shock sensitivity value is: ; In the formula, S represents the unit power shock sensitivity value, Indicates the instantaneous change of loop current, v indicates the current change rate, f indicates the grid frequency, Indicates the average operating power of the off-load node, represents the node's own temperature, represents the ambient temperature, and Q represents the load-off execution risk assessment value.

[0044] In this embodiment, Table 2 is a unit power shock sensitivity value data table, which lists in detail the instantaneous change in loop current, average operating power, current change rate, grid frequency, node temperature, ambient temperature, load-off execution risk assessment value, and final unit power shock sensitivity value corresponding to the five load-off nodes during the load-off operation. Among them: the instantaneous change of the loop current of node J1 is 12.5, the average operating power of the load-off node is 30.2, the current change rate is 0.75, the grid frequency is 50, the node temperature is 43, the ambient temperature is 35, the load-off execution risk assessment value is 0.78, and the final calculated unit power impact sensitivity value is 0.40; the instantaneous change of the loop current of node J2 is 14.8, the average operating power is 28.5, the current change rate is 0.88, the grid frequency is 50, the node temperature is 46, the ambient temperature is 26, the load-off execution risk assessment value is 0.85, and the unit power impact sensitivity value is 0.57; the instantaneous change of the loop current of node J3 is 13.2, the average operating power is 32.1, and the current change rate is 0.81 , the grid frequency is 50, the node temperature is 41, the ambient temperature is 34, the load off execution risk assessment value is 0.69, and the unit power impact sensitivity value is 0.35; the instantaneous change of the loop current of node J4 is 15.0, the average operating power is 31.4, the current change rate is 0.91, the grid frequency is 50, the node temperature is 44, the ambient temperature is 36, the load off execution risk assessment value is 0.81, and the unit power impact sensitivity value is 0.48; the instantaneous change of the loop current of node J5 is 16.3, the average operating power is 29.7, the current change rate is 1.02, the grid frequency is 50, the node temperature is 47, the ambient temperature is 37, the load off execution risk assessment value is 0.92, and the unit power impact sensitivity value is 0.65.

[0045] Table 2 Unit power shock sensitivity value data table

[0046] like Figure 4The figure shows the unit power shock sensitivity distribution diagram, which displays the unit power shock sensitivity values ​​of the five load shedding nodes, reflecting the comprehensive sensitivity of each node to the current disturbance caused by the unit power during the load shedding operation. The color of the column indicates whether the adaptive correction process is triggered: red indicates that the unit power shock sensitivity value of the node exceeds the shock sensitivity threshold and needs to be included in the strategy adjustment; green indicates that the sensitivity is lower than or equal to the shock sensitivity threshold and no adjustment is required. The blue dotted line is the shock sensitivity threshold line, which serves as an important basis for determining whether to trigger the strategy correction. As can be seen in the figure, the unit power shock sensitivity values ​​of nodes J2 and J5 are 0.57 and 0.65 respectively, which have exceeded the shock sensitivity threshold and triggered the adaptive correction process. The remaining nodes are within the limit and the response is relatively stable. This figure provides visual support for the refined management and control of load shedding strategies and the optimization of node differentiation strategies.

[0047] In this implementation plan, by jointly calling the load-shedding execution list and the node number index table, the operating power of the load-shedding node, the instantaneous change of the loop current, the current change rate, the grid frequency, the node's own temperature, the ambient temperature and the load-shedding execution risk assessment value are extracted, and the unit power current impact item, the electrical dynamic adjustment item, the thermal imbalance impact factor and the composite environmental adjustment factor are constructed in turn, and the unit power impact sensitivity value is calculated to achieve a fine quantification of the impact characteristics of the node load-shedding behavior, effectively improving the response adaptability and control accuracy of the load-shedding strategy in a dynamic environment.

[0048] Specifically, the specific steps of constructing a load shedding impact feedback curve and an adjustment deviation sequence, identifying strategy-sensitive nodes, and updating the threshold parameter table to complete the adaptive correction of the load shedding strategy are as follows: Summarize the unit power impact sensitivity value of each node in the current round of load shedding execution list and the corresponding load shedding impact risk assessment value, establish a field correspondence by node number index, and construct a load shedding impact feedback curve to reflect the dynamic impact change trend caused by the load shedding behavior on the power system; read the operating power field recorded by each node in the previous round of load shedding scheduling and the loop current instantaneous change field read in the current batch, calculate the difference between the node load shedding power target value and the actual induced current response value by node, and construct an adjustment deviation sequence to measure the degree of deviation between the load shedding execution behavior and the expected control target; perform adjustment deviation tolerance judgment and impact sensitivity over-limit identification on each node, and judge whether the adjustment deviation exceeds the response deviation tolerance threshold and whether the unit power impact sensitivity value exceeds the target value. If the node is higher than the impact sensitivity threshold and any of the above judgment conditions is met, the node will be marked as a strategy-sensitive node and the load shedding strategy parameter adaptive correction process will be triggered; in the adaptive correction process, based on the joint analysis of the change direction of the slope of the load shedding impact feedback curve and the fluctuation trend of the adjustment deviation, the load shedding impact risk threshold is dynamically adjusted to the average value of the load shedding impact risk assessment values ​​corresponding to all current strategy-sensitive nodes. At the same time, the corresponding response failure records in the node historical response success rate field are extracted, the frequency of the most frequent failure intervals is counted, and a weighted decreasing adjustment operation based on the failure frequency weight is performed on the response failure threshold; further, the peak values ​​of the load shedding execution risk assessment values ​​of all nodes in the current batch are extracted to form a sequence, and the load shedding execution risk threshold is dynamically updated in combination with the local average sliding fitting method. Finally, the updated threshold parameters are written into the threshold parameter update table, and the data structure verification is completed for loading and use in the next round of load shedding process to ensure the continuity and robustness of the threshold parameter update.

[0049] In this implementation scheme, by constructing a load shedding impact feedback curve and an adjustment deviation sequence, strategy-sensitive nodes are identified, and joint dynamic perception of the unit power impact sensitivity value and the load shedding impact risk assessment value is achieved; by judging whether the adjustment deviation exceeds the response deviation tolerance threshold and whether the unit power impact sensitivity value exceeds the impact sensitivity threshold, the load shedding strategy parameter adaptive correction process is accurately triggered; in the correction process, the load shedding impact risk threshold, the response failure threshold and the load shedding execution risk threshold are dynamically adjusted in a targeted manner based on the node's historical response success rate and the peak sequence of the load shedding execution risk assessment value, and the adjustment results are written into the threshold parameter update table to provide an optimized strategy reference for the next round of load shedding process, thereby effectively enhancing the stability, sensitivity and response accuracy of the load shedding strategy.

[0050] like Figure 2As shown, the second aspect of the present invention provides a two-way communication load node control system for power demand response, including: a load control communication data acquisition preprocessing module, a load off impact risk assessment and response screening module, a batch load off and dynamic interval joint scheduling module and a load off feedback perception and threshold parameter correction module, wherein: the load control communication data acquisition preprocessing module is used to collect load control communication data in the entire load response process, establish a load control communication database, and preprocess the load control communication data; the load off impact risk assessment and response screening module is used to evaluate the impact risk caused by the load off behavior of each node on the electrical system based on the preprocessed load control communication data, and eliminate the impact risk nodes based on the evaluation results and Unstable nodes are screened out, and a list of candidate load shedding nodes and a node number index table are constructed; a batch load shedding and dynamic interval joint scheduling module is used to receive information on candidate load shedding nodes, build a scheduling priority sequence and execute load shedding operations based on communication delay and load shedding impact risk assessment values, evaluate the disturbance risk of each round of load shedding behavior on the stability of the power system, adjust the load shedding interval based on the assessment results, and update the node number index table and the load shedding execution list; a load shedding feedback perception and threshold parameter correction module is used to analyze the unit power impact sensitivity characteristics based on the load shedding execution list and real-time load control communication data, construct a load shedding impact feedback curve and an adjustment deviation sequence, identify strategy-sensitive nodes, and update the threshold parameter table to complete the adaptive correction of the load shedding strategy.

[0051] In this implementation plan, by constructing a system architecture consisting of a load control communication data acquisition and preprocessing module, a load shedding impact risk assessment and response screening module, a batch load shedding and dynamic interval joint scheduling module, and a load shedding feedback perception and threshold parameter correction module, a closed-loop control mechanism for the entire process is realized, from multi-dimensional collection of load control communication data and establishment of a load control communication database, to dynamic assessment of load shedding impact risks and elimination of impact risk nodes and unstable nodes; by integrating communication delays and load shedding impact risk assessment values ​​to construct a scheduling priority sequence, dynamically adjust the load shedding interval and update the node number index table and load shedding execution list in real time, and further combine the unit power impact sensitivity characteristics to construct a load shedding impact feedback curve and an adjustment deviation sequence, thereby realizing the identification of strategy-sensitive nodes and the adaptive correction of load shedding strategy threshold parameters, significantly improving the safety, real-time performance and refinement of load shedding control in the power demand response process from a system level.

[0052] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0053] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A two-way communication load node control method for power demand response, characterized in that: The following steps are involved: S1, collects load control communication data during the entire load response process, establishes a load control communication database, and preprocesses the load control communication data; S2, based on the pre-processed load control communication data, evaluates the impact risk of load shedding on the electrical system caused by each node, eliminates impact risk nodes and screens out unstable nodes based on the evaluation results, and constructs a list of candidate load shedding nodes and a node number index table; S3 receives information about candidate load shedding nodes, builds a dispatch priority sequence based on communication delay and load shedding impact risk assessment, and executes load shedding operations. It assesses the risk of disturbance to power system stability caused by each round of load shedding, adjusts the load shedding interval based on the assessment results, and updates the node number index table and load shedding execution list. S4, based on the load shedding execution list and real-time load control communication data, analyzes the unit power shock sensitivity characteristics, constructs the load shedding shock feedback curve and adjustment deviation sequence, identifies the strategy sensitive nodes, and updates the threshold parameter table to complete the adaptive correction of the load shedding strategy.

2. The two-way communication load node control method for power demand response according to claim 1 is characterized in that: The specific steps of collecting the load control communication data during the entire load response process, establishing a load control communication database, and preprocessing the load control communication data are as follows: Perform multi-dimensional synchronous sampling of the electrical operating status, power regulation behavior, communication link performance, equipment physical characteristics, environmental interference factors, and node control response capabilities involved in the entire load response process, and obtain load control communication data in real time. The load control communication data includes operating power, node current, loop current, node voltage, current change rate, node communication delay, grid frequency, node temperature, and ambient temperature. The load control communication data is stored, a load control communication database is established, and the historical node response success rate is obtained from the load control communication database. The load control communication data is synchronized in time series and the sampling offset is corrected through a sliding window multi-scale resampling algorithm based on multi-channel data alignment; the load control communication data is cleaned of anomalies through a transient mutation recognition and abnormal boundary elimination algorithm to eliminate false signals introduced by fluctuating loads; the response validity of the load control communication data is determined through a state and behavior two-state consistency verification model to identify short-term response failures and erroneous control records; the load control communication data is transformed into a unified scale through a cross-dimensional indicator normalization mapping and variation factor compression algorithm to achieve normalization processing.

3. The two-way communication load node control method for power demand response according to claim 1, characterized in that: The specific steps of evaluating the impact risk of load shedding behavior at each node on the electrical system based on the pre-processed load control communication data are as follows: Based on the preprocessed load control communication data, operating power, node voltage, loop current, node current, current change rate, grid frequency and node historical response success rate are extracted, and the field correspondence is established according to the node to evaluate the impact risk caused by the node load shedding behavior on the electrical system: calculate the operating power divided by the product of the node voltage and the loop current, and then multiply it by the ratio of the node current to the loop current to obtain the power-current composite proportion term; add the current change rate and the inverse of the grid frequency to form the electrical state sensitivity term; calculate one minus the node historical response success rate to obtain the response uncertainty term; multiply the power-current composite proportion term, the electrical state sensitivity term and the response uncertainty term to obtain the load shedding impact risk assessment value.

4. The two-way communication load node control method for power demand response according to claim 1, characterized in that: The specific steps of eliminating impact risk nodes and screening out unstable nodes based on the evaluation results, and constructing a list of candidate load-off nodes and a node number index table are as follows: Sort the load shedding impact risk assessment values ​​corresponding to all nodes in descending order to construct a load shedding impact sorted list. Traverse each node in the sorted list and filter out nodes with load shedding impact risk assessment values ​​higher than the load shedding impact risk threshold. Mark them as impact risk nodes and add them to the load shedding shielding queue. The remaining nodes are retained in the load shedding candidate queue to form a preliminary response object set. Conditional filtering is performed on the nodes in the load shedding candidate queue to remove nodes whose operating power is lower than the power response threshold. The node's historical response success rate field is also screened to mark nodes whose consecutive response failures exceed the response failure threshold as unstable nodes and remove them from the current scheduling round. Arrange the remaining nodes in the load outage candidate queue in order from low to high according to the load outage impact risk assessment value, and output the load outage candidate node list and the corresponding node number index table.

5. The two-way communication load node control method for power demand response according to claim 1, characterized in that: The specific steps of receiving the load shedding candidate node information, establishing a scheduling priority sequence based on the communication delay and the load shedding impact risk assessment value, and executing the load shedding operation are as follows: Receive the list of candidate load shedding nodes and the corresponding node number index table, read the node communication delay field in batches, and calculate the average communication response time by node; sort all candidate load shedding nodes from small to large according to the average communication response time. When the average communication response time is the same, prioritize the node with the smallest load shedding impact risk assessment value to build a scheduling priority queue with two factors jointly sorted; execute the load shedding control instructions in sequence according to the scheduling priority queue, record the node numbers that have triggered the load shedding instructions, establish a load shedding execution list, and form a load shedding scheduling queue for the remaining nodes to be executed.

6. The two-way communication load node control method for power demand response according to claim 1, characterized in that: The specific steps for evaluating the disturbance risk of each round of load shedding on the power system stability are as follows: After each batch of load shedding operations is completed, the instantaneous change in the current loop is read to comprehensively assess the risk of disturbance to the power system stability caused by this round of load shedding. The unit current change impact intensity term is calculated by dividing the instantaneous change in the loop current by the sum of the current change rate and the minimum term. The risk delay adjustment term is obtained by dividing the load shedding impact risk assessment value by the node communication delay plus one. Calculate the absolute value of the difference between the node's own temperature and the ambient temperature, divide it by the ambient temperature, and add one to the result to obtain the thermal imbalance impact factor; The load-off execution risk assessment value is obtained by multiplying the unit current change impact intensity item, the risk delay adjustment item, and the thermal imbalance impact factor.

7. The two-way communication load node control method for power demand response according to claim 1, characterized in that: The specific steps of adjusting the load shedding interval based on the evaluation results and updating the node number index table and the load shedding execution list are as follows: The load shedding execution risk assessment value and the load shedding execution risk threshold are compared in real time. If the load shedding execution risk assessment value is less than or equal to the load shedding execution risk threshold, the next batch of load shedding operations will be executed. If the load shedding execution risk assessment value is greater than the load shedding execution risk threshold, the dynamic interval adjustment mechanism will be triggered: the node temperature and ambient temperature fields of the corresponding node are extracted, the temperature difference ratio is calculated, and the loop current change generated in the previous batch is combined with the mapping relationship to determine the time interval for the next load shedding operation. For nodes that fail to respond in each batch of load shedding operations, read the node communication delay and the node's historical response success rate, and count the number of consecutive response failures; If the number of consecutive response failures exceeds the response failure threshold and the node communication delay is higher than the communication delay threshold, the node will be marked as a failed node and removed from the load shedding scheduling queue, and the node number index table and load shedding execution list will be updated synchronously.

8. The two-way communication load node control method for power demand response according to claim 1, characterized in that: The specific steps of analyzing the unit power surge sensitivity characteristics based on the load shedding execution list and real-time load control communication data are as follows: Receive the updated load shedding execution list and node number index table, extract the operating power corresponding to the load shedding nodes, and calculate the average operating power of the load shedding nodes in this batch; Calculate the instantaneous change in loop current and divide it by the average operating power of the load-free node to obtain the unit power current impact term; divide the current change rate by the grid frequency and add 1 to form the electrical dynamic adjustment term; Calculate the absolute value of the difference between the node's own temperature and the ambient temperature, divide it by the ambient temperature, and add one to the result to obtain the thermal imbalance impact factor. Add the thermal imbalance impact factor to the electrical dynamic adjustment item to obtain the composite environmental adjustment factor. Multiply the unit power current impact item, the composite environmental adjustment factor, and the load-off execution risk assessment value to obtain the unit power impact sensitivity value.

9. The two-way communication load node control method for power demand response according to claim 1, characterized in that: The specific steps of constructing the load shedding impact feedback curve and the adjustment deviation sequence, identifying the strategy sensitive nodes, and updating the threshold parameter table to complete the adaptive correction of the load shedding strategy are as follows: The unit power shock sensitivity values ​​and load shedding shock risk assessment values ​​in this round of load shedding are summarized to construct a load shedding shock feedback curve. The operating power of each node in the previous round of load shedding scheduling and the instantaneous change in the loop current read in this round are read, and the difference between the node load shedding power target value and the actual current response value is calculated to form a regulation deviation sequence. Each node is judged: when the regulation deviation exceeds the response deviation tolerance threshold, or the unit power shock sensitivity value exceeds the shock sensitivity threshold, if any of the conditions are met, the node is marked as a policy-sensitive node, and the load shedding strategy parameter adaptive correction process is triggered. In the adaptive correction process, the threshold parameters are dynamically adjusted according to the slope and deviation direction of the impact feedback curve: the load off impact risk threshold is adjusted to the average value of the load off impact risk assessment values ​​of all current sensitive nodes; the response failure threshold is adjusted in a weighted decreasing manner based on the most frequent failure interval in the historical response failure records of the node; the peak sequence of the load off execution risk assessment values ​​of all nodes in the current batch is taken, and the load off execution risk threshold is updated according to the local mean sliding fitting; the updated threshold parameters are written into the threshold parameter update table for loading in the next round of load off process.

10. A two-way communication load node control system for power demand response, characterized by: include: Load control communication data acquisition and preprocessing module, load shedding impact risk assessment and response screening module, batch load shedding and dynamic interval joint scheduling module, and load shedding feedback perception and threshold parameter correction module, including: The load control communication data acquisition and preprocessing module is used to collect the load control communication data during the entire load response process, establish a load control communication database, and preprocess the load control communication data; The load shedding impact risk assessment and response screening module is used to assess the impact risk of load shedding behavior of each node on the electrical system based on the pre-processed load control communication data, eliminate impact risk nodes and screen out unstable nodes based on the assessment results, and construct a list of candidate load shedding nodes and a node number index table; The batch load shedding and dynamic interval joint scheduling module is used to receive information about candidate load shedding nodes, establish a scheduling priority sequence based on communication delay and load shedding impact risk assessment values, and execute load shedding operations. It assesses the disturbance risk of each round of load shedding behavior on the stability of the power system, adjusts the load shedding interval based on the assessment results, and updates the node number index table and load shedding execution list. The load shedding feedback perception and threshold parameter correction module is used to analyze the unit power impact sensitivity characteristics based on the load shedding execution list and real-time load control communication data, construct a load shedding impact feedback curve and adjustment deviation sequence, identify strategy-sensitive nodes, and update the threshold parameter table to complete the adaptive correction of the load shedding strategy.

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