Electrical load space-time distribution modeling and adaptive optimization regulation and control system

Abstract

The invention relates to the technical field of electrical load management, and discloses an electrical load spatial-temporal distribution modeling and adaptive optimization regulation and control system. The system comprises a load monitoring module, an optimization decision-making module, an exception handling module, a communication coordination module and a data tracing module. The load monitoring module collects load data by using a space-time correlation analysis technology; the optimization decision-making module adopts a dynamic clustering algorithm to generate a regulation and control strategy; the exception handling module corrects load exception in a graded manner based on a multi-scale coordination mechanism; the communication coordination module realizes instruction interaction through a hierarchical routing protocol; and the data tracing module generates a topology storage index by using a multi-dimensional feature coding algorithm. The system can accurately collect load data, reasonably distribute power, effectively process load abnormity, guarantee communication stability, facilitate data tracing, improve the operation efficiency and reliability of an electric power system, and meet the requirement of a modern electric power system for electrical load management.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical load management, and in particular to an electrical load spatiotemporal distribution modeling and adaptive optimization control system. Background Art

[0002] In modern power systems, the management of electrical loads faces many complex and severe challenges. With the rapid development of social economy, the number of various types of electrical equipment has increased dramatically, and the power usage scenarios have become more diverse, which makes the distribution of electrical loads in time and space highly complex and uncertain.

[0003] From the perspective of time, the demand for electricity varies greatly in different seasons and at different times. For example, in summer, the widespread use of refrigeration equipment such as air conditioners causes the electricity load to rise sharply during high temperature periods; in winter, heating equipment becomes the main driving force for peak electricity consumption. In addition, the electricity consumption patterns on weekdays and weekends are also very different. Commercial and industrial electricity consumption on weekdays is more concentrated, while residential electricity consumption on weekends is relatively prominent. This kind of temporal fluctuation makes it difficult for the power system to accurately predict load demand, and it is easy to have insufficient or excessive power supply. Insufficient power supply will lead to power outages, affecting corporate production and residents' lives; excessive power supply will cause energy waste and increase power generation costs.

[0004] In terms of spatial dimension, different areas of the city have different functional positioning and electricity consumption characteristics. The commercial area is concentrated with a large number of shopping malls, office buildings, etc., and its electricity load intensity is large and relatively concentrated during business hours; the production equipment of various industrial enterprises in the industrial area consumes a lot of electricity and has high requirements for power supply stability; while the electricity load in residential areas is mainly concentrated in the morning and evening hours, and is greatly affected by the living habits of residents. This uneven spatial distribution makes it difficult for the power grid to allocate power resources, which can easily cause overload of the power grid in some areas and low utilization of the power grid in some areas. Grid overload will not only affect the quality of power supply and shorten the service life of equipment, but may also cause safety accidents; low grid utilization will cause waste of grid investment and reduce the economic benefits of the power system.

[0005] The traditional electrical load management method has been difficult to meet the needs of modern power systems. The traditional method often relies on manual experience or simple statistical models for load forecasting and regulation, lacking accurate analysis of the spatio-temporal changes of the load and the ability of dynamic response. In the face of complex and changeable load conditions, the traditional method cannot adjust the power distribution strategy in time, resulting in low operating efficiency and reduced reliability of the power system. Moreover, the communication ability of the traditional regulation system is limited, making it difficult to achieve efficient cross-regional data transmission and command interaction, and unable to meet the collaborative regulation needs of large-scale power systems. At the same time, in terms of data management, the traditional method lacks an effective data traceability and indexing mechanism, making it difficult to make full use of historical data and unable to provide strong support for load analysis and optimization of regulation strategies. Therefore, it is urgent to develop a system that can accurately model the spatio-temporal distribution of electrical loads and achieve adaptive optimization regulation. Summary of the Invention

[0006] The purpose of the present invention is to provide a spatio-temporal distribution modeling and adaptive optimization regulation system for electrical loads to solve the problems raised in the above background technology.

[0007] To achieve the above purpose, the present invention provides the following technical solutions: A spatio-temporal distribution modeling and adaptive optimization regulation system for electrical loads, the system includes:

[0008] Load monitoring module: Real-time collection of electrical load data in the target area through spatio-temporal correlation analysis technology, the data includes load intensity, load fluctuation frequency and spatial distribution density;

[0009] Optimization decision module: Using a dynamic clustering algorithm to generate a load regulation strategy, the strategy includes power distribution ratio, equipment start-stop sequence and regulation response threshold;

[0010] Abnormal handling module: Hierarchical correction of load abnormalities based on a multi-scale coordination mechanism, the first scale deals with regional load imbalance, and the second scale suppresses local node overload;

[0011] Communication coordination module: Realize command interaction with the main control platform and distributed regulation terminals through a hierarchical routing protocol, and support cross-regional data aggregation and link redundancy switching;

[0012] Data traceability module: Using a multi-dimensional feature coding algorithm to generate a topological storage index for load data according to geographical grid identification, time period, equipment type and regulation mode.

[0013] Preferably, the dynamic clustering algorithm includes:

[0014] Construct an association matrix of load characteristics and regulation parameters, and the matrix contains clustering center templates under different scenarios;

[0015] Match the corresponding template according to the spatially distributed density collected in real time, and dynamically adjust the calculation step size of the power distribution ratio through the gradient descent mechanism;

[0016] Use the grey relational model to prioritize the device start-stop sequences, and optimize the triggering conditions of the regulation response threshold based on the grid capacity margin.

[0017] Preferably, the multi-scale coordination mechanism includes:

[0018] In the treatment of regional load imbalance, set the dynamic balance threshold range, and calculate the node adjustment amount boundary according to the historical load distribution curve;

[0019] In the suppression of local node overload, load the grid topology feature library, which includes line impedance parameters, equipment thermal stability limits, and load transfer paths;

[0020] Analyze the periodicity of the load fluctuation sequence through the trend prediction model, and trigger the hierarchical correction instruction if abnormal harmonic components are detected.

[0021] Preferably, the hierarchical routing protocol includes:

[0022] Divide the communication instruction stream into protocol frames of variable length, and append a check code and a routing level identifier to each frame;

[0023] Use the event-driven algorithm to synchronize the instruction cycle of the master control platform and the execution cycle of the regulation terminal, and compensate for the data delay caused by network congestion through the retransmission queue mechanism.

[0024] Preferably, the multi-dimensional feature coding algorithm includes:

[0025] Encode the geographical grid identifier and the device type into a composite digital tag, and generate a unique topology identifier through feature mapping;

[0026] Segmentally encode the time period to distinguish the seasonal pattern and the minute-level time granularity;

[0027] Build a tree-like index structure based on the regulation mode type to support data association queries according to the peak shaving and valley filling, balanced scheduling, and emergency response modes.

[0028] Preferably, the grey relational model includes:

[0029] Design a three-input and two-output controller, with the input variables being the load intensity deviation, the device health index, and the environmental temperature change;

[0030] Construct a triangular membership function to divide the association interval of the input variables, and the output variable is the device start-stop priority coefficient;

[0031] Solve the association through the weighted average method to generate the execution sequence of the regulation strategy.

[0032] Preferably, the trend prediction model includes:

[0033] Construct an ARIMA time series model to predict the benchmark trend of load fluctuations;

[0034] Extract the harmonic component features of the actual load sequence, including amplitude fluctuation, phase shift, and spectral energy distribution;

[0035] Calculate the deviation degree of the harmonic component through the sliding standard deviation. If it exceeds the preset tolerance, activate the node overload suppression program.

[0036] Preferably, the event-driven algorithm includes: dividing the communication process into non-equal-length event windows, allocating independent transmission channels for different routing level instructions, adopting a dynamic buffering mechanism to handle the preemption requests of high-priority instructions, and calibrating the state synchronization between the master control platform and the control terminal through the heartbeat detection protocol.

[0037] Preferably, the tree-like index structure includes: encoding the control mode into multi-level branch nodes, associating each node with the corresponding load data set, constructing a skip list to achieve fast location query of cross-mode data, and marking the physical address of the data shard through a hash index.

[0038] Preferably, the triangular membership function includes:

[0039] Define three associated intervals for the load intensity deviation: low deviation interval, medium deviation interval, and high deviation interval;

[0040] Define four associated intervals for the equipment health index: healthy state, sub-healthy state, warning state, and failure state;

[0041] Generate twelve control strategy combinations through multi-dimensional matching rules.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] In terms of load monitoring and precise data acquisition, through the spatio-temporal correlation analysis technology, the electrical load data of the target area is collected in real time, covering key information such as load intensity, load fluctuation frequency, and spatial distribution density. This enables the system to comprehensively and accurately grasp the spatio-temporal changes of the load, providing a solid data foundation for subsequent control decisions. Compared with traditional monitoring methods, this comprehensive data collection can capture the dynamic changes of the load more timely and accurately. For example, when the power consumption load in a certain area suddenly increases due to the holding of a large-scale event, the system can quickly sense the changes in load intensity and spatial distribution density, providing a basis for adjusting the power supply in advance.

[0044] The optimization decision-making module uses a dynamic clustering algorithm to generate load regulation strategies, including power distribution ratios, device start-stop sequences, and regulation response thresholds. By constructing an association matrix between load characteristics and regulation parameters, it can match corresponding templates according to the spatially distributed density collected in real time and use a gradient descent mechanism to dynamically adjust the calculation step size of the power distribution ratio, making the power distribution more reasonable. It uses a grey correlation model to prioritize the device start-stop sequences and optimizes the trigger conditions of the regulation response thresholds in combination with the grid capacity margin to ensure the safe and stable operation of the grid while meeting the power demand. During peak electricity consumption periods, the system can quickly determine which devices can be temporarily stopped and how to reasonably allocate the remaining power resources according to the dynamic clustering algorithm, avoiding grid failures caused by overload.

[0045] The anomaly handling module performs hierarchical correction of load anomalies based on a multi-scale coordination mechanism. In the treatment of regional load imbalance, it sets a dynamic balance threshold range and calculates the boundary of node adjustment amount according to the historical load distribution curve, which can effectively maintain the power supply-demand balance within the region. In the suppression of local node overload, it loads a grid topology feature library, including line impedance parameters, device thermal stability limits, and load transfer paths. At the same time, it analyzes the periodicity of the load fluctuation sequence through a trend prediction model, triggers a hierarchical correction instruction when detecting abnormal harmonic components, and timely handles local node overload problems to improve the reliability of grid operation. When there is an overload risk at a certain local node, the system can quickly find a suitable load transfer path according to the grid topology feature library and transfer part of the load to other nodes to avoid damage to this node due to overload.

[0046] The communication coordination module realizes instruction interaction with the master control platform and distributed regulation terminals through a hierarchical routing protocol, supporting cross-regional data aggregation and link redundancy switching. It divides the communication instruction stream into protocol frames of variable length, appends a check code and a routing level identifier to each frame, uses an event-driven algorithm to synchronize the instruction cycle of the master control platform and the execution cycle of the regulation terminal, and compensates for data delays caused by network congestion through a retransmission queue mechanism to ensure communication stability and data transmission accuracy. This enables the system to achieve efficient coordinated regulation in a large-scale power system. Even in a complex network environment with congestion, it can ensure that instructions are timely and accurately conveyed to each regulation terminal, improving the timeliness and effectiveness of regulation.

[0047] The data traceability module uses a multi-dimensional feature encoding algorithm to generate a topological storage index for the load data according to the geographical grid identifier, time period, device type, and regulation mode. The geographical grid identifier and device type are encoded into a composite digital tag, the time period is segmented and encoded, and a tree-like index structure is constructed based on the regulation mode type to support data association queries according to the peak shaving and valley filling, balanced scheduling, and emergency response modes. This data management method facilitates users to quickly and accurately query and analyze historical data, providing strong support for load forecasting, regulation strategy optimization, and fault troubleshooting. When analyzing the electricity consumption pattern in a specific region during a specific season, relevant load data can be quickly screened through the multi-dimensional feature encoding algorithm to provide a reference for formulating targeted regulation strategies. Brief Description of the Drawings

[0048] Figure 1 is the working principle diagram of the electrical load spatio-temporal distribution modeling and adaptive optimization regulation system described in the present invention;

[0049] Figure 2 is the working flow chart of the hierarchical routing protocol;

[0050] Figure 3 is the working flow chart of the grey correlation model;

[0051] Figure 4 is the working flow chart of the event-driven algorithm. Detailed Embodiment

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] Please refer to Figures 1 - 4 , the present invention provides an electrical load spatio-temporal distribution modeling and adaptive optimization regulation system, and its detailed embodiment will be elaborated below.

[0054] The load monitoring module collects electrical load data of the target area in real time through spatio-temporal correlation analysis technology. In actual application scenarios, such as a large industrial park with many different types of factories and enterprises, the electricity consumption situation is complex and changeable. The module deploys monitoring devices at various key locations in the park, such as substations and main transmission line nodes. These devices can accurately measure the load intensity, that is, the magnitude of power consumption per unit time, obtained through real-time monitoring and calculation of current and voltage; at the same time, monitor the load fluctuation frequency, which reflects the frequency of load changes and can be obtained through analysis of the power change curve; and can also obtain the spatial distribution density to determine the electricity-intensive degree of different regions, achieved with the help of Geographic Information System (GIS) technology and the power data acquisition network. In this way, the real-time state of the electrical load in the target area can be comprehensively and accurately grasped, providing a reliable data basis for subsequent analysis and regulation.

[0055] The optimization decision-making module uses a dynamic clustering algorithm to generate load regulation strategies. This module collects real-time data transmitted by the load monitoring module and generates strategies including power allocation ratios, equipment start-stop sequences, and regulation response thresholds in combination with preset situations in different scenarios. Taking the power grid of a city as an example, during the busy periods of industrial production and commercial activities during the day, and the peak periods of residential electricity consumption at night, the electricity consumption situations are very different. The optimization decision-making module will, according to these different scenarios, through the dynamic clustering algorithm, reasonably allocate power resources, determine which equipment needs to be started or shut down first, and under what load change conditions to trigger regulation actions, ensuring the stable operation of the power grid and improving the power utilization efficiency.

[0056] The anomaly handling module performs hierarchical correction of load anomalies based on a multi-scale coordination mechanism. In the actual operation of the power grid, there may be regional load imbalances, such as a large number of new electrical equipment suddenly added in a certain area, resulting in the power demand in this area far exceeding the supply capacity; or there may be local node overloads, such as a transmission line being overloaded due to too many connected devices exceeding its carrying capacity. For these situations, the anomaly handling module, at the first scale, handles regional load imbalances, adjusts power distribution through specific calculation and analysis methods to balance the load within the region; at the second scale, suppresses local node overloads, takes corresponding measures, such as adjusting the equipment operation state and transferring part of the load, to ensure the safe operation of the nodes.

[0057] The communication coordination module realizes command interaction with the master control platform and distributed control terminals through a hierarchical routing protocol. In a large-scale power system, the master control platform needs to effectively control and manage a large number of control terminals distributed in different regions. The communication coordination module reasonably encodes and transmits commands to ensure that the commands can be accurately and timely delivered to each control terminal. At the same time, it supports cross-regional data aggregation to summarize and analyze power data from different regions, as well as link redundancy switching. When a communication link fails, it can automatically switch to the backup link to ensure the continuity and stability of communication.

[0058] The data traceability module uses a multi-dimensional feature coding algorithm to generate a topological storage index for load data according to geographical grid identification, time period, equipment type, and control mode. In this way, when querying and analyzing historical load data, the required data can be quickly and accurately located. For example, when studying the electricity consumption of specific equipment in a certain period in a specific area, or analyzing the load change trend under a certain control mode, through the index mechanism of the data traceability module, relevant data can be efficiently obtained, providing strong support for the optimal operation and fault troubleshooting of the power system.

[0059] The following further features and specific implementation manners of the present invention will be described in detail through 6 embodiments.

[0060] Embodiment 1:

[0061] In the optimization decision module, the specific implementation process of the dynamic clustering algorithm is as follows: First, construct an association matrix of load characteristics and control parameters. Assuming that the electricity consumption of different regions of a certain urban power grid is taken as the research object, the electricity consumption patterns in different regions in different seasons and different time periods can be regarded as different scenarios. Each row in the matrix represents a scenario, and each column represents a load characteristic or control parameter, such as load intensity, spatial distribution density, power distribution ratio, etc. In the matrix, clustering center templates under different scenarios are preset in advance, and these templates are parameter combinations corresponding to typical electricity consumption patterns determined according to historical data and expert experience.

[0062] Next, match the corresponding template according to the spatially distributed density collected in real time. Suppose the currently collected spatially distributed density of a certain area is , and compare with the spatially distributed density values of each template in the association matrix to find the closest template. Then, dynamically adjust the calculation step size of the power distribution ratio through the gradient descent mechanism. Suppose the power distribution ratio is , and the step size is . In the gradient descent algorithm, the update formula of is , where is the current power distribution ratio value, and is the objective function Regarding the gradient, by continuously iterating and updating the value, making it approach the optimal power allocation ratio.

[0063] Adopt the grey correlation model to prioritize the device start-stop sequences. Design a three-input and two-output controller, where the input variables are the load intensity deviation , the device health index and the environmental temperature change . Construct triangular membership functions to divide the correlation intervals of the input variables. For the load intensity deviation , define the low deviation interval , the medium deviation interval and the high deviation interval ( , are thresholds set according to the actual situation, ); for the device health index , define the healthy state , the sub-healthy state , the warning state and the failure state ( ). Generate different control strategy combinations through multi-dimensional matching rules, and the output variable is the device start-stop priority coefficient . Finally, optimize the triggering condition of the control response threshold based on the grid capacity margin . Let the control response threshold be , the optimization formula of can be expressed as ( is the initial control response threshold,

[0064] Example 2:

[0065] When the abnormal handling module handles regional load imbalance, set the dynamic balance threshold range. Assume a large commercial area as an example, whose electricity load varies significantly with business hours. According to the historical load distribution curve, analyze the load change rules in different time periods. Let the maximum historical load in this area be , the minimum value be , set the lower limit of the dynamic balance threshold range to , and the upper limit to ( , are coefficients set according to the actual situation, , ). When the load within the region exceeds this range, it is considered that regional load imbalance occurs. Then, calculate the boundary of the node adjustment amount according to the historical load distribution curve. Assume that the historical load data of a certain node at a certain moment is . By statistically analyzing the historical data over a period of time (such as the same time period in the past week), calculate the mean value and the standard deviation of the load of this node. The lower limit of the node adjustment amount boundary is , and the upper limit is ( , are coefficients set according to the requirements of grid stability). When adjusting the node load, ensure that the adjustment amount is within this boundary range to avoid adverse effects on the power grid caused by excessive adjustment.

[0066] In the suppression of local node overload, load the power grid topology feature library. Take the node where a certain transmission line connects multiple factories as an example. The power grid topology feature library includes line impedance parameters , the thermal stability limit of the equipment and the load transfer path. When it is detected that the current of a certain node exceeds the current value corresponding to its equipment thermal stability limit ( is related to and can be calculated according to the equipment thermal characteristics formula), it is determined that the node is overloaded. At this time, according to the load transfer path in the power grid topology feature library, transfer part of the load to other lines or nodes with remaining capacity. For example, if node is overloaded and its load transfer path is from node to node , then by adjusting the operating state of the relevant equipment, guide a part of the current from node to node .

[0067] Analyze the periodicity of the load fluctuation sequence through the trend prediction model. Construct an ARIMA time series model to predict the benchmark trend of the load fluctuation. Assume that the load fluctuation sequence is , and the ARIMA model is expressed as , where is the backward shift operator, is the autoregressive polynomial, is the moving average polynomial, is the order of differencing, is the white noise sequence. Extract the harmonic component characteristics of the actual load sequence, including amplitude fluctuation , phase shift and spectral energy distribution . Calculate the deviation degree of the harmonic component through the sliding standard deviation. Let the size of the sliding window be , at time , the sliding standard deviation of the harmonic component is calculated as , where is the value of the harmonic component within the sliding window, and is the mean value of the harmonic components within the sliding window. If exceeds the preset tolerance , the node overload suppression program is activated to process the overloaded node in a timely manner to ensure the safe and stable operation of the power grid.

[0068] Embodiment 3:

[0069] In the communication coordination module, the working process of the hierarchical routing protocol is as follows: Divide the communication instruction stream into protocol frames of variable length. Let the communication instruction stream be , and divide it into protocol frames of different lengths according to the content and type of the instruction ( ). Each protocol frame is appended with a check code and a routing level identifier . The check code is used to detect whether an error occurs in the protocol frame during transmission and can be generated by algorithms such as CRC (Cyclic Redundancy Check). The routing level identifier indicates the target level to which the protocol frame needs to be transmitted. For example, the main control platform is the highest level, and the control terminals are divided into different levels according to their jurisdiction and functions.

[0070] Use the event-driven algorithm to synchronize the instruction cycle of the main control platform and the execution cycle of the control terminal. Divide the communication process into event windows of unequal length. Let the length of the event window be , which will change dynamically according to different communication tasks and data volumes. Allocate independent transmission channels for instructions at different routing levels. For example, for high-priority emergency control instructions, allocate channels with larger bandwidth and smaller transmission delay; for low-priority regular data query instructions, allocate channels with relatively smaller bandwidth. Use the dynamic buffer mechanism to handle the preemption requests of high-priority instructions. When a high-priority instruction arrives, if the current channel is transmitting a low-priority instruction and the buffer space is sufficient, the low-priority instruction is temporarily stored in the buffer, and the high-priority instruction is transmitted first. Calibrate the status synchronization between the main control platform and the control terminal through the heartbeat detection protocol. Let the heartbeat detection period be , and the main control platform and the control terminal send heartbeat signals to each other every time. If the heartbeat signal of the other party is not received within the specified time, it is considered that the communication link has a fault, and the link redundancy switching mechanism is triggered.

[0071] Compensate for data latency caused by network congestion through the retransmission queue mechanism. When network congestion occurs during communication, the sender puts the unsuccessfully transmitted protocol frames into the retransmission queue . Let the number of retransmissions be , and the initial retransmission interval time be . Each retransmission interval time increases according to a certain rule, such as ( ), to avoid aggravating network congestion due to frequent retransmissions. When the number of retransmissions reaches a certain upper limit , if the protocol frame still fails to be successfully transmitted, an error report is sent to the main control platform to take further measures to ensure the reliability and stability of communication.

[0072] Example 4:

[0073] In the data traceability module, the specific implementation of the multi-dimensional feature coding algorithm is as follows: encode the geographical grid identifier and device type into a composite digital label. Assume that the geographical grid is divided by longitude and latitude. Taking a certain city as an example, the city is divided into multiple grids, and each grid is assigned a unique geographical grid identifier , such as . The device type is encoded as , such as represents industrial equipment, represents residential electrical equipment, etc. Combine and into a composite digital label , and generate a unique topological identifier through feature mapping, which can be implemented by means of a hash function, etc., such as , to ensure that each combination of geographical grid and device type has a unique topological identifier, facilitating subsequent data storage and query.

[0074] Segment the time period for encoding to distinguish the seasonal pattern and minute-level time granularity. Divide a year into four seasons, which are encoded as (spring), (summer), (autumn), (winter). For the minute-level time granularity, a day is divided into minute time periods, and each period is assigned a unique number ( ). The time period encoding ( is the season encoding, with a value of ), so that different seasons and different minutes of time points can be accurately identified, facilitating querying of load data by time dimension.

[0075] Construct a tree - like index structure based on the regulation mode type. Encode the regulation modes as multi - level branch nodes. For example, the peak - shaving and valley - filling mode is encoded as , the balanced scheduling mode is encoded as , and the emergency response mode is encoded as . Each node is associated with the corresponding load data set. For example, in the peak - shaving and valley - filling mode, the associated load data set contains the load adjustment records in different time periods and different regions under this mode. Construct a skip list to achieve fast location query of cross - mode data. By establishing skip links between the nodes of the tree - like structure, when querying relevant data under different regulation modes, it is possible to quickly jump to the target node. Mark the physical address of the data shard through a hash index. Suppose the data shard is , its physical address is , and the hash index function is . In this way, when querying data, the actual location of the data in the storage medium can be quickly found according to the characteristics of the data, improving the efficiency of data traceability.

[0076] Example 5:

[0077] In the optimization decision - making module, the grey relational model is used for the priority sorting of equipment start - up and shutdown. First, design a three - input and two - output controller. The input variables are the load intensity deviation , the equipment health index , and the environmental temperature change .

[0078] Construct a triangular membership function to divide the association interval of the input variables. For the load intensity deviation , assume that according to historical data and power grid operation experience, the low - deviation interval is determined to be (unit: kW), the medium - deviation interval is , and the high - deviation interval is . When the actually measured load intensity deviation falls within the low - deviation interval, it indicates that the load change is relatively stable and has little impact on equipment start - up and shutdown; when it falls within the medium - deviation interval, the equipment start - up and shutdown need to be considered comprehensively according to other factors; when it falls within the high - deviation interval, it may be necessary to quickly adjust the equipment operation status to balance the load.

[0079] For the equipment health index , set the healthy state interval as , the sub - healthy state interval as , the warning state interval as , and the failure state interval as 。The equipment health index is obtained by monitoring and analyzing various operating parameters of the equipment, such as temperature, vibration, current, etc. Equipment in a healthy state can operate normally. Equipment in a sub-healthy state requires close attention. Equipment in a warning state may need to arrange maintenance in advance or adjust the operation time. Equipment in a failure state should stop operating as soon as possible.

[0080] Twelve combinations of regulation strategies are generated through multi-dimensional matching rules. For example, when is in the low deviation range, is in a healthy state, and is within the normal range, the equipment start-stop priority coefficient is set to a lower value, indicating that this equipment does not need to be given priority for start-stop operations; when is in the high deviation range, is in a warning state, and exceeds the normal range, it is set to a higher value, indicating that this equipment needs to be given priority to consider stopping operation or adjusting its operating power.

[0081] The execution sequence of the regulation strategy is generated by solving the association through the weighted average method. Let the load intensity deviation , the equipment health index , and the environmental temperature change corresponding weights be , , , and . The determination of the weights is based on the analysis of a large amount of historical data and the evaluation of the influence degree of different factors in the actual power grid operation. For example, during the high-temperature period in summer, the environmental temperature change has a greater impact on the power load, and may be appropriately increased; while in areas where the equipment is aging more seriously, the influence of the equipment health index is more critical, and will be increased accordingly.

[0082] The calculation formula for the equipment start-stop priority coefficient is: . Among them, , , are the quantization values of the corresponding input variables in different association intervals obtained according to the triangular membership function. Taking as an example, when is in the low deviation range , may take the value of ; when in the medium deviation range , takes the value of ; in the high deviation range When the value is . Similarly, according to the equipment health index and the change in environmental temperature in the associated range where they are located, determine and values.

[0083] Based on the calculated equipment start-stop priority coefficient , sort all equipment to form a control strategy execution sequence. The larger the coefficient value of the equipment, the higher its priority in the control strategy, that is, the start-stop operation is preferred. For example, at a certain moment, after calculation, the value of equipment A is , and the value of equipment B is . Then, when formulating the control strategy, priority will be given to operating equipment A. For example, when the load is too high, stop the operation of equipment A first to effectively regulate the grid load and ensure the stable operation of the power grid.

[0084] Example 6:

[0085] In the exception handling module, the trend prediction model plays an important role. First, construct an ARIMA time series model to predict the baseline trend of load fluctuations. Taking the residential electricity load in a certain area as an example, collect the hourly load data over a past period (such as one year) as sample data , ( is the number of samples). Through data analysis and trial calculations, determine the parameters of the ARIMA model, such as the autoregressive order , the differencing order and the moving average order . Assume that after calculation, the ARIMA model for the residential electricity load in this area is determined to be , where ( , are autoregressive coefficients), ( is the moving average coefficient), .

[0086] Use this model to predict the load for a future period to obtain the baseline trend prediction value . For example, predict the load situation for the next 24 hours to provide a reference basis for power dispatching. If it is predicted that the load will continue to rise and approach or exceed the grid's carrying capacity, take corresponding control measures in advance, such as adjusting the power generation plan and notifying large electricity-consuming enterprises to adjust their production times, etc.

[0087] Extract the harmonic component characteristics of the actual load sequence, including amplitude fluctuations , phase shifts and spectral energy distribution . Process the actual load sequence through mathematical methods such as Fourier transform to decompose it into harmonic components of different frequencies. For example, for a harmonic component with a frequency of , its amplitude fluctuations reflect the intensity changes of this harmonic at different times; the phase shift represents the phase difference between this harmonic and the fundamental wave; the spectral energy distribution reflects the energy proportion of this harmonic in the entire load spectrum.

[0088] Calculate the deviation degree of the harmonic component through the moving standard deviation. Let the moving window size be (which can be adjusted according to the actual situation). At time , the moving standard deviation of the harmonic component is calculated by the formula , where are the harmonic component values within the moving window, and is the mean value of the harmonic components within the moving window. Assume a preset tolerance (determined according to the stable operation requirements of the power grid and historical data). When exceeds , it indicates that the harmonic component has abnormal fluctuations, which may lead to overload of local nodes.

[0089] If it exceeds the preset tolerance, activate the node overload suppression program. When it is detected that the deviation degree of the harmonic component of a certain node exceeds the preset tolerance, first judge the load situation of this node. If the node load has approached or exceeded its carrying capacity, immediately take measures to suppress the overload. For example, by adjusting the operating state of the equipment connected to this node, such as reducing the power of some equipment and stopping the operation of some non-critical equipment; or according to the load transfer path in the power grid topology feature library, transfer some loads to other nodes with remaining capacity. At the same time, record relevant data, including the time of the anomaly, the node location, the harmonic component characteristics, etc., for subsequent analysis and summary of the power grid anomalies, continuously optimize the anomaly handling strategy, and ensure the safe and stable operation of the power grid.

[0090] It should be noted that in this text, relational terms such as first and second are only used 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 term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0091] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An electrical load spatiotemporal distribution modeling and adaptive optimization control system, characterized in that: include: Load monitoring module: collects electrical load data of the target area in real time through time-space correlation analysis technology, including load intensity, load fluctuation frequency and spatial distribution density; Optimization decision module: uses dynamic clustering algorithm to generate load control strategy, which includes power allocation ratio, equipment start and stop sequence and control response threshold; Abnormal processing module: Based on the multi-scale coordination mechanism, load abnormalities are corrected in a graded manner. The first scale handles regional load imbalances, and the second scale suppresses local node overloads. Communication coordination module: realizes command interaction with the main control platform and distributed control terminals through a hierarchical routing protocol, and supports cross-regional data aggregation and link redundancy switching; Data tracing module: A multi-dimensional feature encoding algorithm is used to generate a topological storage index for load data according to geographic grid identification, time period, equipment type and control mode.

2. The electrical load spatiotemporal distribution modeling and adaptive optimization control system according to claim 1 is characterized in that: The dynamic clustering algorithm includes: Construct a correlation matrix between load characteristics and control parameters, which contains cluster center templates under different scenarios; According to the spatial distribution density collected in real time, the corresponding template is matched, and the calculation step size of the power allocation ratio is dynamically adjusted through the gradient descent mechanism; The grey correlation model is used to prioritize the equipment start and stop sequences, and the triggering conditions of the control response threshold are optimized based on the grid capacity margin.

3. The electrical load spatiotemporal distribution modeling and adaptive optimization control system according to claim 1 is characterized in that: The multi-scale coordination mechanism includes: In the regional load imbalance processing, the dynamic balance threshold range is set, and the node adjustment amount boundary is calculated according to the historical load distribution curve; In local node overload suppression, a power grid topology feature library is loaded, wherein the feature library includes line impedance parameters, equipment thermal stability limits, and load transfer paths; The periodicity of the load fluctuation sequence is analyzed through the trend prediction model, and if abnormal harmonic components are detected, the graded correction instructions are triggered.

4. The electrical load spatiotemporal distribution modeling and adaptive optimization control system according to claim 1 is characterized in that: The hierarchical routing protocol includes: Divide the communication instruction stream into protocol frames of variable length, and attach a checksum and routing level identifier to each frame; An event-driven algorithm is used to synchronize the command cycle of the main control platform and the execution cycle of the control terminal, and a retransmission queue mechanism is used to compensate for data delays caused by network congestion.

5. The electrical load spatiotemporal distribution modeling and adaptive optimization control system according to claim 1 is characterized in that: The multi-dimensional feature encoding algorithm includes: Encode the geographic grid identifier and the device type into a composite digital label and generate a unique topological identifier through feature mapping; Segment coding of time periods to distinguish seasonal patterns from minute-level time granularity; A tree index structure is constructed based on the control mode type to support data association queries based on peak shaving, valley filling, balanced scheduling, and emergency response modes.

6. The electrical load spatiotemporal distribution modeling and adaptive optimization control system according to claim 2 is characterized in that: The grey relational model includes: Design a three-input dual-output controller, with the input variables being load intensity deviation, equipment health index, and ambient temperature change; Construct a triangular membership function to divide the associated interval of the input variable, and the output variable is the equipment start-stop priority coefficient; The control strategy execution sequence is generated by deallocation through weighted average method.

7. The electrical load spatiotemporal distribution modeling and adaptive optimization control system according to claim 3 is characterized in that: The trend prediction model includes: Construct an ARIMA time series model to predict the baseline trend of load fluctuations; Extract the harmonic component characteristics of the actual load sequence, including amplitude fluctuation, phase shift and spectrum energy distribution; The degree of deviation of the harmonic components is calculated by the sliding standard deviation, and if it exceeds the preset tolerance, the node overload suppression program is activated.

8. The electrical load spatiotemporal distribution modeling and adaptive optimization control system according to claim 4 is characterized in that: The event-driven algorithm includes: dividing the communication process into unequal length event windows, allocating independent transmission channels for instructions at different routing levels, using a dynamic buffer mechanism to handle preemption requests for high-priority instructions, and calibrating the state synchronization between the main control platform and the control terminal through a heartbeat detection protocol.

9. The electrical load spatiotemporal distribution modeling and adaptive optimization control system according to claim 5, characterized in that: The tree-like index structure includes: encoding the control mode into multi-level branch nodes, each node is associated with a corresponding load data set, a jump linked list is constructed to realize fast positioning query of cross-mode data, and the physical address of the data segment is marked by a hash index.

10. The electrical load spatiotemporal distribution modeling and adaptive optimization control system according to claim 6, characterized in that: The triangular membership function includes: Define three associated intervals of load intensity deviation: low deviation interval, medium deviation interval and high deviation interval; Define four associated intervals of the equipment health index: healthy state, sub-healthy state, warning state and fault state; Twelve control strategy combinations are generated through multi-dimensional matching rules.

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