A power user end energy supply method and system based on redundant capacity proportion calculation

Abstract

The application provides a power user end energy supply method based on redundancy capacity proportion calculation, and belongs to the technical field of energy scheduling and power distribution safety; the method comprises the following steps: mapping the load output proportion through a preset mapping table to determine the dynamic scheduling weight of each user; and generating a corresponding real-time energy supply priority sequence according to the real-time energy supply priority sequence and a preset scheduling strategy; determining the current redundancy capacity of a target transformer according to the target transformer operation parameters obtained in real time; when the current redundancy capacity is lower than the current total load demand, performing a load scheduling operation according to the real-time energy supply priority sequence and the preset scheduling strategy until the current redundancy capacity returns to the preset safe operation range. The application can effectively identify the power consumption demand difference of users in the same level, prevent the mismatch between power supply resource allocation and actual demand, and realize the dynamic and accurate power supply scheduling decision.

Description

Technical Field

[0001] This invention belongs to the field of energy dispatch and power distribution security technology, and in particular relates to a power user-end energy supply method and system based on redundancy capacity ratio calculation. Background Technology

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

[0003] With the deepening of electricity marketization and the rapid development of distributed energy, the energy management needs of electricity users are becoming increasingly complex.

[0004] In existing technologies, monitoring devices installed on transformers can collect real-time operating parameters such as voltage, current, and power. These parameters are then combined with historical electricity consumption data to establish a load prediction model. When a potential transformer overload risk is predicted, the system will reduce or interrupt user loads according to a preset fixed priority order. Specifically, the system divides all connected user loads into several fixed levels based on their importance. When the transformer load approaches its rated capacity, the system sequentially cuts off power to users in order from the lowest priority level to the next lowest, until the transformer load is reduced to a safe range.

[0005] However, in real-world power user applications, the electricity consumption characteristics of different users exhibit high time-varying and complex characteristics. Users within the same category may have different actual electricity demand intensities at different times. For example, an industrial user may need to operate at full load during peak production periods, while only needing to maintain basic standby power consumption during equipment maintenance. The fixed-level scheduling mode used in existing technologies struggles to identify and respond to such dynamically changing electricity demand differences, reducing the matching degree between users' actual electricity demand and power supply resource allocation, thus leading to low overall power supply efficiency and resource utilization. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides a power user-side energy supply method and system based on redundancy capacity ratio calculation. By effectively identifying the differences in electricity demand among users of the same level, it can prevent the mismatch between power supply resource allocation and actual demand, thereby realizing dynamic and precise power supply scheduling decisions.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a power user-side energy supply method based on redundancy capacity ratio calculation.

[0008] A power user-side energy supply method based on redundancy capacity ratio calculation includes: Obtain the actual load and rated load of multiple users associated with the target transformer, and calculate the load output ratio of each user; The obtained load output ratio is mapped to a load output ratio coefficient by a preset mapping table, and weighted by a preset attribute weight to obtain the dynamic scheduling weight for each user. Based on the obtained dynamic scheduling weights, a real-time power supply priority sequence corresponding to each user is generated, and the current redundancy capacity of the target transformer is determined according to the real-time obtained operating parameters of the target transformer. When the current redundant capacity is lower than the current total load demand, load scheduling operation is performed on the target transformer according to the real-time power supply priority sequence and preset scheduling strategy until the current redundant capacity is restored to the preset safe operating range.

[0009] Furthermore, the calculation of the load output ratio includes: acquiring historical load data sequences of multiple users within a preset historical period; determining typical load curve clusters corresponding to typical operating conditions through a clustering algorithm based on the distribution characteristics of load values ​​in the historical load data; and determining the preset high percentile values ​​within the obtained typical load curve clusters as the benchmark load values.

[0010] Furthermore, the calculation of the load output ratio also includes: obtaining the current actual load of multiple users, performing similarity matching between the real-time load segments within a preset time window before the current scheduling cycle and the typical load curve cluster, and determining the typical load curve cluster with the highest similarity as the current matching curve cluster; taking the benchmark load value corresponding to the current matching curve cluster as the dynamic rated load, and calculating the ratio between the current actual load and the dynamic rated load to obtain the load output ratio.

[0011] Furthermore, load scheduling operations are performed on the target transformer according to the real-time power supply priority sequence and preset scheduling strategy, including: using the difference between the current total load demand and the current redundant capacity as the target power reduction, and comparing the target power reduction with the preset coarse adjustment power threshold; and performing the corresponding load scheduling operation according to the comparison result.

[0012] Further, the execution of the corresponding load scheduling operation includes: When the target power reduction is greater than or equal to the coarse adjustment power threshold, it is determined to be an emergency dispatch condition, and power outage operations are performed on the user loads in order of weight from low to high according to the real-time power supply priority sequence. When the target power reduction is less than the coarse adjustment power threshold, a preset number of users with the lowest priority who have not performed the power outage operation are determined from the real-time power supply priority sequence as target scheduling users; the operating status of non-production equipment associated with the target scheduling users is detected through the user-side smart gateway and an adjustable load list is determined; based on the load equipment in the adjustable load list, a power reduction operation is performed on the target scheduling users.

[0013] Furthermore, before performing power reduction operations on the target dispatch user based on the load devices in the adjustable load list, the predicted load growth amount obtained based on the preset load prediction model within a preset time period is compared with the total load capacity of the adjustable load list. When the predicted load growth amount is greater than a preset proportion of the total load capacity, supplementary target dispatch users are determined from the real-time power supply priority sequence until the total load capacity meets the predicted load growth amount.

[0014] Furthermore, based on the historical runtime data associated with the load devices in the adjustable load list, the scheduling start-stop frequency of the load devices in a preset past period is calculated; the load devices whose scheduling start-stop frequency does not exceed the preset frequency are identified as the updated adjustable load list.

[0015] A second aspect of the present invention provides a power user-side energy supply system based on redundancy capacity ratio calculation.

[0016] A power user-end energy supply system based on redundancy capacity ratio calculation includes: The load output ratio calculation module is configured to: obtain the actual load and rated load of multiple users associated with the target transformer, and calculate the load output ratio of each user; The weighted calculation module is configured to: map the obtained load output ratio to a load output ratio coefficient through a preset mapping table, and perform weighted calculation in combination with preset attribute weights to obtain the dynamic scheduling weight for each user; The current redundancy capacity calculation module is configured to: generate a real-time power supply priority sequence for each user based on the obtained dynamic scheduling weights, and determine the current redundancy capacity of the target transformer based on the real-time obtained operating parameters of the target transformer. The load scheduling module is configured to: when the current redundant capacity is lower than the current total load demand, perform load scheduling operation on the target transformer according to the real-time power supply priority sequence and the preset scheduling strategy until the current redundant capacity is restored to the preset safe operating range.

[0017] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a power user-end energy supply method based on redundancy capacity ratio calculation as described in the first aspect of the present invention.

[0018] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the power user-end energy supply method based on redundancy capacity ratio calculation as described in the first aspect of the present invention.

[0019] The above one or more technical solutions have the following beneficial effects: This invention effectively solves the technical problems of fixed scheduling priorities and difficulty in reflecting the urgency of users' actual electricity consumption in existing technologies by generating dynamic scheduling weights through weighted calculation of users' real-time load output ratios and static attribute data, and then performing load scheduling based on this weight sequence. This enables dynamic and precise allocation of power grid resources and ensures fairness and efficiency in scheduling decisions. Furthermore, this invention introduces a task value correction factor to further correct the dynamic scheduling weights. This factor integrates the intrinsic value of users' production tasks, stage sensitivity, and actual dependence on the power grid, thereby improving the level of continuity assurance for the core business of high-value users.

[0020] This invention uses clustering algorithms and similarity matching to dynamically determine the user's baseline load, replacing the technical means of calculating the load output ratio using static rated load. This solves the technical problem that rated load cannot accurately reflect the diverse operating conditions of users, leading to inaccurate assessment of electricity intensity. This helps to achieve a more accurate characterization of the user's load status and provides a more reliable data foundation for subsequent refined scheduling.

[0021] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 This is a flowchart of a power user-end energy supply method based on redundancy capacity ratio calculation in Embodiment 1 of the present invention.

[0024] Figure 2 This is a functional calculation flowchart of a power user-end energy supply system based on redundancy capacity ratio calculation in Embodiment 2 of the present invention. Detailed Implementation

[0025] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0026] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0027] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0028] Example 1 This embodiment discloses a power user-side energy supply method based on redundancy capacity ratio calculation.

[0029] like Figure 1 As shown, a power user-side energy supply method based on redundant capacity ratio calculation includes: Step S1: Obtain the actual load and rated load of multiple users associated with the target transformer, and calculate the load output ratio of each user; Step S2: Map the obtained load output ratio to a load output ratio coefficient through a preset mapping table, and perform weighted calculations in combination with preset attribute weights to obtain the dynamic scheduling weight for each user. Step S3: Generate a real-time power supply priority sequence for each user based on the obtained dynamic scheduling weights, and determine the current redundancy capacity of the target transformer based on the real-time obtained operating parameters of the target transformer. Step S4: When the current redundant capacity is lower than the current total load demand, perform load scheduling operation on the target transformer according to the real-time power supply priority sequence and preset scheduling strategy until the current redundant capacity is restored to the preset safe operating range.

[0030] Based on the above process, this invention generates a dynamic priority sequence by integrating real-time user load status and static attributes. This solves the problems of existing scheduling strategies being rigid and unable to reflect the urgency of actual electricity demand, achieving dynamic, precise, and fair load scheduling, thereby improving the allocation efficiency of power grid resources. To facilitate understanding of the technical solution of this invention, the specific implementation methods of this invention will be further explained and described below.

[0031] In step S1, the actual load and rated load of multiple users associated with the target transformer are obtained, and the load output ratio of each user is calculated.

[0032] The system acquires historical load data sequences from multiple users within a preset historical period. Based on the distribution characteristics of load values ​​in the historical load data, it uses a clustering algorithm to determine typical load curve clusters corresponding to typical operating conditions, and then determines the preset high percentile values ​​within the typical load curve clusters as the baseline load values.

[0033] After obtaining the current actual load of multiple users, the real-time load segments within the preset time window before the current scheduling cycle are matched with the typical load curve clusters, and the typical load curve cluster with the highest similarity is determined as the current matching curve cluster; the benchmark load value corresponding to the current matching curve cluster is determined as the dynamic rated load, and the ratio of the current actual load to the dynamic rated load is calculated to obtain the load output ratio.

[0034] By adopting the above technical solution, the historical start-stop frequency of equipment is considered when determining the adjustable load list. By excluding equipment that is scheduled too frequently, the problem that frequent start-stop may damage user equipment and reduce user willingness to participate can be effectively solved, thereby protecting user assets and improving the sustainability of load scheduling and user satisfaction.

[0035] Furthermore, in the specific implementation process, step S1 can be achieved through the following method: When the dispatching equipment initiates a periodic load monitoring task or receives a transformer load alarm signal, it establishes a data connection with the smart metering terminals of each user under the target transformer via a communication interface. Following a preset sampling period (e.g., every 15 seconds, every 1 minute, or every 5 minutes), it reads the current active power measurement value from each user's smart meter as the actual load data. Simultaneously, it retrieves the rated load data for each user from the user profile database stored locally. This rated load data can be derived from the electricity capacity declared by the user during the installation process, the maximum demand value stipulated in the power supply contract, or the peak power calculated from historical electricity consumption data. Subsequently, the dispatching equipment performs a division operation for each user, dividing the user's actual load value by its rated load value to calculate a dimensionless ratio between 0 and 1 (or possibly greater than 1, indicating overload operation). This ratio is the load output ratio. Finally, the dispatching equipment stores the calculated load output ratios for each user in a temporary data cache for subsequent weight calculations and priority ranking.

[0036] As an optional implementation, the actual load and rated load, as well as the load output ratio, can be obtained and calculated in various ways. For example, the dispatching equipment can use an active polling method for data acquisition. The dispatching equipment sends data request commands to the smart meters on each user's side at preset time intervals. Upon receiving the request, the smart meter encapsulates the currently collected power data into a standard message format and returns it to the dispatching equipment. The dispatching equipment parses the received message and extracts the active power field as the actual load value. Simultaneously, it queries the rated load value of the user from a relational database based on the user's unique identifier, and then performs floating-point division to obtain the load output ratio, retaining a specified number of significant digits. Optionally, the dispatching equipment can also use a passive listening method for data acquisition. Each user's smart meter is configured to actively report at set intervals. The smart meter automatically sends measurement data to the dispatching equipment's data receiving interface according to the set reporting cycle. After receiving the data, the dispatching equipment performs timestamp verification and data integrity checks. Once the data is confirmed to be valid, the dispatching equipment writes the actual load value into a memory data structure and calls a pre-loaded rated load configuration table to complete the ratio calculation. It is understandable that other methods can be used to collect load data and calculate output ratios. For example, the initial calculation can be completed on the user side via an edge computing gateway, and only the calculation results can be uploaded. Alternatively, load information can be obtained indirectly through a data interface with the distribution automation system. This is not a limitation here. It should be noted that the rated load can be determined using a static configuration method, i.e., directly using the fixed values ​​recorded in the user's profile, or a dynamic calculation method, i.e., dynamically adjusting the rated load reference value based on the user's recent electricity consumption patterns.

[0037] The dispatching equipment acquires historical load data sequences from multiple users within a preset historical period. This historical period can be set to a time span of three months, six months, or one year to ensure that the data sample is sufficiently representative and covers different operating conditions. Based on the distribution characteristics of load values ​​in the historical load data, the dispatching equipment analyzes and processes the historical load curves using a clustering algorithm. Load curves with similar variation patterns and amplitude characteristics are grouped into the same category, thereby determining typical load curve clusters corresponding to typical operating conditions. For example, for industrial users, different categories may be identified, such as normal production condition curve clusters, maintenance condition curve clusters, and full-load production condition curve clusters. The dispatching equipment determines the preset high percentile value of the load value within each typical load curve cluster as the benchmark load value. This high percentile value can be set to the 95th percentile or the 90th percentile to exclude the influence of occasional extreme values ​​while reflecting the typical high load level under that operating condition.

[0038] After acquiring the current actual load of multiple users, the dispatching equipment performs similarity matching between real-time load segments within a preset time window prior to the current dispatching cycle and an established cluster of typical load curves. This time window can be set to a length of 15 minutes, 30 minutes, or 1 hour, and similarity calculation can employ methods such as Euclidean distance, dynamic time warping distance, or Pearson correlation coefficient. The dispatching equipment identifies the cluster of typical load curves with the highest similarity as the current matching cluster and determines the benchmark load value corresponding to this current matching cluster as the dynamic rated load. Finally, the dispatching equipment calculates the ratio between the current actual load and the dynamic rated load to obtain a load output ratio that accurately reflects the user's electricity consumption intensity under current operating conditions. This method, compared to the calculation method using a fixed rated load, better adapts to the dynamic changes in user electricity consumption behavior.

[0039] In step S2, the obtained load output ratio is mapped to a load output ratio coefficient through a preset mapping table, and weighted calculation is performed in combination with preset attribute weights to obtain the dynamic scheduling weight for each user.

[0040] This step is executed after the scheduling equipment has completed the calculation of the load output ratio for all users. It loads a preset mapping table from the configuration file or parameter database. This mapping table defines the mapping relationship between the load output ratio range and the corresponding coefficient value. For example, the output ratio from 0 to 0.3 is mapped to a coefficient of 0.2, which is used to represent a low load state with a large adjustment range; the output ratio from 0.3 to 0.6 is mapped to a coefficient of 0.5, which is used to represent a medium load state; the output ratio from 0.6 to 0.9 is mapped to a coefficient of 0.8, which is used to represent a high load state with sensitive adjustment; and the output ratio above 0.9 is mapped to a coefficient of 1.0, which is used to represent a full load or overload state with a high dependence on power supply.

[0041] The dispatching equipment queries a mapping table based on the load output ratio calculated for each user to determine the corresponding load output ratio coefficient. Then, the dispatching equipment retrieves the static attribute data for each user from the user profile database and converts this data into numerical form according to preset quantification rules. For example, the importance levels of Class I, Class II, and Class III users are quantified as values ​​of 1.0, 0.7, and 0.4, respectively. Next, the dispatching equipment performs a weighted calculation based on preset attribute weight configurations. Assuming the weight of the load output ratio coefficient is w1 and the weight of the quantified static attribute value is w2, the dynamic dispatch weight is equal to the product of the load output ratio coefficient and w1, plus the product of the quantified static attribute value and w2. Finally, the dispatching equipment stores the calculated dynamic dispatch weights for each user in the dispatching data structure.

[0042] Furthermore, in the specific implementation process, step S2 can be achieved through the following method: As an optional implementation, the mapping processing and weighted calculation of the preset mapping table can be implemented in various ways. For example, the scheduling device can use a piecewise linear mapping method to convert the load output ratio to a coefficient. That is, the preset mapping table is stored in the form of a piecewise function. The scheduling device first determines the interval to which the load output ratio to be converted belongs, and then calculates the load output ratio coefficient according to the slope and intercept of the linear function corresponding to the interval. After that, the load output ratio coefficient is multiplied by the quantified values ​​of multiple static attributes extracted from the user attribute table, such as the user level value, industry sensitivity coefficient, and historical performance score, by their respective preset weight coefficients, and then summed to obtain the comprehensive dynamic scheduling weight.

[0043] The dispatching equipment can also use a lookup table interpolation method to achieve mapping transformation. A preset mapping table stores several typical output ratio points and their corresponding coefficient values ​​in discrete key-value pairs. For any input load output ratio value, the dispatching equipment looks up two adjacent typical points, and then calculates the corresponding load output ratio coefficient using linear interpolation or spline interpolation. This coefficient is then weighted and fused with the static attribute comprehensive score value according to a preset ratio coefficient. The static attribute comprehensive score value is obtained by weighting multiple dimensions such as user type weight, power supply reliability requirement weight, and electricity consumption scale weight. It is understandable that other methods can be used to achieve mapping and weighted calculation, such as using a neural network model for nonlinear mapping of the output ratio or using fuzzy logic methods to handle the dynamic adjustment of attribute weights; this is not limited here. It should be noted that the specific values ​​of the preset attribute weights can be adjusted according to the actual application scenario and dispatching strategy objectives. For example, in industrial park scenarios emphasizing production continuity, the weight of the load output ratio coefficient can be appropriately increased, while in residential area scenarios emphasizing user fairness, the user level factor in the static attribute weights can be appropriately increased.

[0044] In step S3, a real-time power supply priority sequence corresponding to each user is generated based on the obtained dynamic scheduling weights, and the current redundant capacity of the target transformer is determined according to the real-time obtained operating parameters of the target transformer.

[0045] By acquiring the task progress data of load production tasks associated with target users and the self-generated electricity of target users, the difference between the power required for load production tasks and the self-generated electricity is calculated to obtain the grid dependence index. The basic criticality score and stage sensitivity multiplier corresponding to the task progress data are retrieved from a pre-set task profile database. This database pre-stores task progress data, including task completion rate, task urgency indicator, and expected completion timestamp, indicating the impact level of different task types on business continuity at different production stages. The basic criticality score and stage sensitivity multiplier are multiplied to obtain the task intrinsic value score. This intrinsic value score is then weighted based on the grid dependence index to obtain the task value correction factor. The dynamic scheduling weights are multiplied by the task value correction factor to obtain the updated dynamic scheduling weights corresponding to multiple users.

[0046] By adopting the above technical solution, using clustering algorithms and similarity matching to dynamically determine the user's baseline load, the problem that static rated load is difficult to reflect the user's actual operating conditions and leads to inaccurate load output ratio can be solved. This enables accurate assessment of the user's electricity intensity and provides a data foundation for the accurate calculation of subsequent dynamic scheduling weights.

[0047] Furthermore, in the specific implementation process, step S3 can be achieved through the following method: Step S3 is executed after the scheduling device has completed the calculation of the dynamic scheduling weights of all users. The dynamic scheduling weight data of all users is loaded into a memory array or list structure, and then the array is sorted according to a preset sorting method. For example, when using the descending sorting method, the user with the largest dynamic scheduling weight value is placed at the beginning of the sequence, indicating that the power supply guarantee priority of this user is the highest, and it should be considered last when performing load reduction scheduling.

[0048] After sorting, the scheduling equipment encapsulates the sorting results into a real-time power supply priority sequence data structure. This structure includes information such as user identifier, dynamic scheduling weight value, and sequence position. Simultaneously, the scheduling equipment acquires the transformer's operating parameter data in real time through the communication interface with the target transformer monitoring terminal. It extracts the transformer's current output active power as the current actual load and reads the transformer's rated capacity from the transformer configuration parameters or dynamically calculates the upper limit of safe operating capacity based on factors such as current ambient temperature and heat dissipation conditions. Next, the scheduling equipment subtracts the current actual load from the upper limit of safe operating capacity to calculate the current redundancy capacity. This value reflects the additional power space that the transformer can supply without exceeding the safe operating limits. Finally, the scheduling equipment stores the generated real-time power supply priority sequence and the calculated current redundancy capacity in the scheduling status data area for subsequent judgment and scheduling operation steps.

[0049] As an optional implementation, priority sequences and redundancy capacity determination can be achieved in various ways. For example, the scheduling device can use a comparison-based quicksort algorithm to generate the priority sequence. Specifically, the scheduling device selects an element from the dynamic scheduling weight array as a benchmark value, compares other elements in the array with the benchmark value, and divides them into two subarrays: one greater than the benchmark and the other less than the benchmark. The same operation is then recursively performed on the two subarrays until the arrays are completely ordered. After sorting, priority numbers are assigned sequentially from largest to smallest to generate a real-time power supply priority sequence. Simultaneously, the scheduling device sends a data request command to the transformer monitoring terminal to obtain the current output power value and reads the transformer's rated capacity value from the local configuration file, obtaining the current redundancy capacity through subtraction.

[0050] The scheduling equipment can also use a priority queue data structure to achieve dynamic sorting and sequence generation. Specifically, the scheduling equipment maintains a priority queue with a max-heap structure. Whenever a user's dynamic scheduling weight is updated, the user's information is inserted into the priority queue, and the heap structure is automatically adjusted to ensure that the head element is always the user with the highest weight. When a priority sequence needs to be generated, elements are popped from the queue sequentially to obtain an ordered sequence. Simultaneously, the scheduling equipment obtains operating parameter data by subscribing to a real-time push service for transformer monitoring data. Based on the current transformer oil temperature and ambient temperature, it queries the temperature-capacity correction curve to determine the dynamic safety capacity upper limit, and then subtracts the current load power to obtain the current redundancy capacity considering the temperature effect; this is not limited here. It should be noted that the calculation of the current redundancy capacity can also consider the transformer's short-term overload capacity, i.e., allowing the transformer to operate at a power exceeding its rated capacity for a certain period in an emergency. In this case, the calculation benchmark for the redundancy capacity can be adjusted accordingly.

[0051] In step S4, when the current redundant capacity is lower than the current total load demand, load scheduling operation is performed on the target transformer according to the real-time power supply priority sequence and the preset scheduling strategy until the current redundant capacity is restored to the preset safe operating range.

[0052] The difference between the current total load demand and the current redundancy capacity is used as the target power reduction, and the target power reduction is compared with the preset coarse adjustment power threshold.

[0053] If the target power reduction is greater than or equal to the preset coarse adjustment power threshold, it is determined to be an emergency dispatch condition. Power outages are performed on user loads in order of weight from low to high according to the real-time power supply priority sequence, so that the remaining target power reduction is less than the preset coarse adjustment power threshold, and the cumulative power reduction is obtained.

[0054] If the remaining target power reduction is determined to be less than the preset coarse adjustment power threshold, a preset number of users with the lowest priority who have not performed power outage operations are identified from the real-time power supply priority sequence as target scheduling users. The operating status of preset non-production equipment associated with the target scheduling users is detected by the user-side smart gateway, and an adjustable load list is determined. This adjustable load list is determined by excluding backup load equipment in emergency operation status from the preset non-production equipment after detecting the operating status of the preset non-production equipment. The backup load equipment includes fire-fighting equipment and security equipment. Based on the load equipment in the adjustable load list, power reduction operations are performed on the target scheduling users until the current redundant capacity is within the preset safe operating range.

[0055] By adopting the above technical solutions and introducing a two-level dispatching strategy that combines coarse and fine adjustments, power can be quickly reduced by first cutting off power, and then non-production equipment can be finely adjusted. This can solve the problem of a single dispatching method being too crude or slow to respond, thereby achieving flexible dispatching that minimizes the impact on users while ensuring the safety of the power grid.

[0056] Furthermore, before performing power reduction operations on the target scheduled users based on the load devices in the adjustable load list, the predicted load growth amount obtained based on the preset load prediction model within a preset time period is compared with the total load capacity of the adjustable load list. When the predicted load growth amount is greater than a preset proportion of the total load capacity, supplementary target scheduled users are determined from the real-time power supply priority sequence until the total load capacity meets the predicted load growth amount.

[0057] By adopting the above technical solutions, a forward-looking dispatch mechanism based on load forecasting is introduced. By predicting future load growth and expanding the adjustable load resource pool in advance, the problem of insufficient adjustable capacity in passive dispatch when dealing with load surges can be solved, thereby realizing the transformation from passive response to active defense and enhancing the stability of power grid operation.

[0058] After determining the target power reduction by the difference between the current total load demand and the current redundant capacity, users with the lowest dynamic scheduling weight are selected in a preset order based on the real-time power supply priority sequence until the sum of the rated loads of the selected users is greater than or equal to the target power reduction, resulting in a set of users to be reduced. The user-side equipment in this set of users to be reduced is equipped with power regulation components. After determining that all users in the set of users to be reduced are simultaneously de-energized as a centralized reduction scheme, if the load change rate of the centralized reduction scheme exceeds a preset safe change rate threshold, partial power reduction is carried out according to the ratio of the rated load of the users in the set of users to be reduced to the total rated load of the set of users to be reduced, resulting in a distributed reduction scheme. Based on the distributed reduction scheme, corresponding load control commands are generated, and load scheduling operations are performed on the users in the set of users to be reduced until the current redundant capacity is within the preset safe operating range.

[0059] By adopting the above technical solutions, an execution strategy combining centralized and decentralized load reduction is introduced. By evaluating the load change rate to select the execution method, the problem of secondary impact on the power grid and endangering the safe and stable operation of the power grid that may be caused by centralized power outages can be solved. In this way, the smoothness and safety of the load reduction process can be ensured while responding quickly.

[0060] Furthermore, in the specific implementation process, step S4 can be achieved through the following method: 1) Current total load demand refers to the sum of the actual load demands of all users under the target transformer at the current moment, used to characterize the total power that the transformer needs to supply. Preset scheduling strategy represents the set of load scheduling execution rules pre-configured in the scheduling equipment, including scheduling trigger conditions, scheduling object selection rules, scheduling operation types, and scheduling execution order—strategy parameters that control load scheduling behavior.

[0061] 2) Load dispatching operation refers to the actions performed by dispatching equipment to control the power supply status of users, including but not limited to cutting off power supply to users, limiting power consumption of users, switching users to backup power sources, and restoring normal power supply to users.

[0062] 3) The preset safe operating range represents the reasonable range of transformer redundancy capacity pre-configured in the dispatching equipment. It is used to determine whether the transformer operating status is in a safe and controllable state. This range is usually set to a redundancy capacity greater than a certain minimum safety margin value.

[0063] After the dispatching equipment completes the real-time power supply priority sequence generation and current redundant capacity calculation, it compares the current redundant capacity with the current total load demand. The current total load demand can be obtained by summing the actual loads of all users. If the current redundant capacity is positive and greater than the lower limit of the preset safe operating range, the transformer is determined to be operating normally and no load dispatching operation is required. If the current redundant capacity is lower than the current total load demand (i.e., the redundant capacity is negative) or, although positive, is lower than the safety margin requirement, the transformer is determined to be at risk of overload or already overloaded, and the load dispatching process needs to be initiated. The dispatching equipment determines the target value of the load power to be reduced according to the preset dispatching strategy. This target value is usually set as the power reduction amount that will restore the redundant capacity to the preset safe operating range after dispatching. Next, the dispatching equipment selects users sequentially according to the real-time power supply priority sequence, starting with the lowest priority user, to perform load dispatching operations. The specific dispatching operation type is determined according to the preset dispatching strategy; it can be either completely cutting off the user's power supply or limiting the user's maximum power consumption through user-side control equipment. After each dispatching operation, the dispatching equipment recalculates the current redundant capacity to determine whether it has reached the preset safe operating range. If it has, the dispatching operation stops; otherwise, it continues to perform dispatching operations on the next priority user. Finally, the dispatching equipment stores the execution record of this load dispatching in the dispatching log database and continuously monitors the transformer operating status. When load demand decreases or other users reduce power consumption, causing redundant capacity to recover, the normal power supply status of the dispatched users is restored sequentially in reverse priority order.

[0064] As an optional implementation, load dispatching operations based on preset dispatching strategies and real-time power supply priority sequences can be implemented in various ways. For example, the dispatching equipment can use a step-by-step power outage dispatching method to achieve load reduction. That is, the dispatching equipment sends a tripping command to the power supply control switch corresponding to the user with the lowest priority according to the real-time power supply priority sequence, completely cutting off the power supply to that user. Then, after waiting for a preset stabilization time interval, the transformer operating parameters are collected again and the current redundant capacity is calculated. If the redundant capacity is still lower than the preset safe operating range lower limit, the tripping command is sent to the next lowest priority user. This cycle continues until the redundant capacity is restored to the safe range. The dispatching equipment also records the power outage time and the estimated recovery time of each disconnected user for subsequent power supply restoration dispatching.

[0065] The dispatching equipment can also employ a tiered power limiting dispatching method to achieve load reduction. This involves selecting several lower-priority users as dispatch targets based on a real-time power supply priority sequence. Power limiting commands are then issued to each dispatch target via the user-side smart gateway, limiting the user's maximum available power to a preset percentage of their rated load. Upon receiving the commands, the power control equipment on each user's side implements power limiting by disconnecting some load circuits or adjusting the operating parameters of the frequency converter. The dispatching equipment continuously monitors changes in transformer redundancy capacity. If the power reduction effect of the limiting measures is insufficient, the power limiting percentage is further reduced or the number of dispatch targets is increased. It is understood that other methods can also be used to achieve load dispatching operations, such as initiating user-side backup power switching or triggering user-owned generator sets to connect to the grid to reduce power demand on transformers; these are not limited here.

[0066] As an optional implementation, load dispatching operations can employ a phased dispatching strategy combining coarse and fine adjustments to achieve more precise power reduction control. Specifically, the dispatching equipment first uses the difference between the current total load demand and the current redundant capacity as the target power reduction, which represents the amount of power that needs to be reduced from the current load to restore the transformer to a safe operating state. The dispatching equipment compares the target power reduction with a preset coarse adjustment power threshold, which is used to distinguish between boundary conditions requiring rapid high-power reduction and fine low-power adjustment. When the target power reduction is greater than or equal to the preset coarse adjustment power threshold, the dispatching equipment enters the coarse adjustment phase, sequentially performing power outage operations on user loads according to the real-time power supply priority sequence from low to high weight. After each power outage operation, the cumulative power reduction is updated and the remaining target power reduction is calculated, continuously executing power outage operations until... Once the remaining target power reduction is less than the preset coarse adjustment power threshold, the coarse adjustment phase is completed, and the cumulative power reduction is obtained. When it is determined that the remaining target power reduction is less than the preset coarse adjustment power threshold, the dispatching equipment enters the fine adjustment phase. It determines a preset number of users with the lowest priority who have not performed a power outage operation from the real-time power supply priority sequence as target dispatching users. This preset number can be dynamically determined based on the magnitude of the remaining target power reduction and the average adjustable power of the users. The dispatching equipment detects the operating status of preset non-production equipment associated with the target dispatching users through the user-side smart gateway. Preset non-production equipment refers to auxiliary electrical equipment in the user's premises that is not directly used for core production or business activities, including lighting equipment, air conditioning equipment, elevator equipment, and office equipment.

[0067] The dispatching equipment determines an adjustable load list based on the detected operating status of pre-set non-production equipment. When determining the adjustable load list, it is necessary to exclude backup load equipment in emergency operation mode. Backup load equipment includes critical safety equipment such as fire-fighting equipment and security equipment, whose power supply should not be interrupted under any circumstances. Finally, the dispatching equipment performs power reduction operations on the target dispatching users based on the load equipment in the adjustable load list. This selective shutdown or reduction of the operating power of non-production equipment achieves refined power reduction, continuing until the current redundancy capacity is within the pre-set safe operating range.

[0068] As an optional embodiment, before performing power reduction operations on target dispatch users based on load devices in the adjustable load list, the dispatching equipment can also perform a load forecast assessment to ensure that the adjustable load capacity can meet the dispatching needs of future periods. Specifically, the dispatching equipment compares the predicted load growth amount obtained based on a preset load forecasting model within a preset time period with the total load capacity of the adjustable load list. This preset load forecasting model can employ algorithms such as time series analysis, machine learning regression, or neural network prediction to predict load changes in the near future based on historical load data and current load change trends. The preset time period can be set to a duration of 15 minutes, 30 minutes, or 1 hour. The dispatching equipment calculates the sum of the rated power of all load devices in the adjustable load list as the total load capacity, which represents the maximum power reduction space that the current adjustable load list can provide. When the predicted load growth amount is greater than a preset proportion of the total load capacity, for example, when the predicted load growth amount exceeds 80% of the total load capacity, it indicates that the adjustment margin of the current adjustable load list is insufficient to cope with possible future load growth, and the dispatching equipment needs to expand the adjustable range. The dispatching equipment determines the supplementary target dispatching users from the real-time power supply priority sequence, selects users that have not yet been included in the target dispatching user range in order of priority from low to high, adds their non-production equipment to the adjustable load list, and continues to expand until the updated total load capacity meets the adjustment needs of the predicted load growth, thereby ensuring that the dispatching equipment still has sufficient load adjustment capacity to cope with possible load fluctuations in the future period.

[0069] As an optional embodiment, before performing power reduction operations on target dispatch users based on load devices in the adjustable load list, the dispatching device can also perform a device dispatching frequency check to protect load devices from damage caused by frequent start-ups and shutdowns. Specifically, based on historical runtime data associated with load devices in the adjustable load list, the dispatching device retrieves dispatching records for each load device within a preset past period from the dispatching log database. This preset past period can be set to a time span such as the past 24 hours, the past week, or the past month. The dispatching device calculates the dispatching start-up and shutdown frequency of each load device within the preset past period. This dispatching start-up and shutdown frequency represents the number of times the load device performs switching actions due to dispatching commands, including the cumulative number of times power is cut off and restored. The dispatching device compares the dispatching start-up and shutdown frequency of each load device with a preset frequency, which is determined based on the device type and device tolerance. The dispatching equipment retains load devices whose dispatch start-stop frequency does not exceed the preset frequency in the adjustable load list, and removes load devices whose dispatch start-stop frequency has exceeded the preset frequency from the adjustable load list, resulting in an updated adjustable load list. This updated adjustable load list only includes load devices that still have dispatch margin and will not exceed the equipment start-stop limits due to this dispatch, thereby extending the service life of the load devices while ensuring the dispatching effect.

[0070] As an optional implementation, load dispatching operations can employ a combination of centralized and decentralized load reduction to minimize the impact on the power grid. Specifically, after determining the target power reduction power as the difference between the current total load demand and the current redundant capacity, the dispatching equipment selects users in a preset order, starting from the user with the lowest dynamic dispatch weight, based on the real-time power supply priority sequence. The rated load of the selected users is accumulated until the sum of the rated loads of the selected users is greater than or equal to the target power reduction power. The selected set of users is then determined as the set of users to be reduced. The user-side equipment in the set of users to be reduced is equipped with power regulation components, enabling it to receive power control commands.

[0071] The dispatching equipment first evaluates a centralized load reduction scheme that simultaneously disconnects all users in the target user set. It calculates the load change rate after implementing this scheme, which represents the magnitude of load power change per unit time. If the load change rate of the centralized scheme exceeds a preset safe change rate threshold, it indicates that simultaneous power outages could cause a significant power surge to the grid and potentially affect grid stability. The dispatching equipment then switches to a distributed load reduction scheme. It calculates the power reduction amount each user should bear based on the proportion of their rated load to the total rated load of the target user set. For example, if a user's rated load accounts for 30% of the total rated load, that user should reduce their target power reduction by 30%, resulting in the distributed load reduction scheme. Based on the distributed load reduction scheme, the dispatching equipment generates corresponding load control commands and issues differentiated power limit commands to each user in the target user set through the user-side smart gateway. Each user reduces their power consumption to the specified level according to the commands, thus achieving the target power reduction across multiple users. This achieves the power reduction target while avoiding the impact of centralized power outages on the grid. The dispatching equipment continuously monitors the dispatching effect until the current redundant capacity is within the preset safe operating range.

[0072] Example 2 This embodiment discloses a power user-side energy supply system based on redundancy capacity ratio calculation.

[0073] A power user-end energy supply system based on redundancy capacity ratio calculation includes: The load output ratio calculation module is configured to: obtain the actual load and rated load of multiple users associated with the target transformer, and calculate the load output ratio of each user; The weighted calculation module is configured to: map the obtained load output ratio to a load output ratio coefficient through a preset mapping table, and perform weighted calculation in combination with preset attribute weights to obtain the dynamic scheduling weight for each user; The current redundancy capacity calculation module is configured to: generate a real-time power supply priority sequence for each user based on the obtained dynamic scheduling weights, and determine the current redundancy capacity of the target transformer based on the real-time obtained operating parameters of the target transformer. The load scheduling module is configured to: when the current redundant capacity is lower than the current total load demand, perform load scheduling operation on the target transformer according to the real-time power supply priority sequence and the preset scheduling strategy until the current redundant capacity is restored to the preset safe operating range.

[0074] As another alternative implementation method, such as Figure 2 As shown, this modular system can also be implemented using the following methods: The first step is to obtain the actual load and rated load of multiple users associated with the target transformer, and then calculate the ratio of the actual load to the rated load to obtain the load output ratio.

[0075] The second step involves mapping the load output ratio using a preset mapping table to obtain the load output ratio coefficient. Then, based on preset attribute weights, the load output ratio coefficient and static attribute data are weighted and calculated to obtain the dynamic scheduling weight for each user.

[0076] The third step involves obtaining the grid dependence index by calculating the difference between the power required for the load production task and the self-generated electricity of the target user, based on the task progress data of the load production task associated with the target user and the self-generated electricity of the target user.

[0077] The target user refers to the specific electricity consumer targeted in the weighting adjustment calculation, typically referring to industrial or large commercial users with internal production management systems (such as MES or ERP). The load production task refers to the production or business activity being performed within the target user, with a clearly defined process flow and electricity consumption characteristics, such as a welding process on an automobile production line, a data backup operation in a data center, or a batch reaction process in a chemical plant. Task progress data refers to a dynamic set of information describing the real-time status of the load production task, including but not limited to the specific stage of the task, the real-time power consumption of the current stage, the estimated remaining completion time, and power demand forecasts for subsequent stages. This data typically originates from the user's Manufacturing Execution System (MES) or Building Automation System (BMS). Self-generated electricity represents the electrical power that the target user can generate at the current moment through its own distributed energy facilities, sources of which include the discharge capacity of photovoltaic power generation systems, energy storage systems (such as batteries), and the available output power of self-provided generator sets.

[0078] This method is executed after the scheduling equipment calculates the user's initial dynamic scheduling weight. Its application scenarios primarily target users whose production activities are highly sensitive to power supply continuity and who possess a certain degree of self-generation or energy storage capacity. Specifically, the scheduling equipment initiates a data request to the target user's internal production management system (such as MES) through a pre-defined data interface to obtain detailed progress data of the currently executing critical load production task. This request may include a user identifier and timestamp, and the user's system returns a structured data packet containing fields such as task ID, current stage, and real-time power consumption. Simultaneously, the scheduling equipment queries the target user's energy management system (EMS) or directly from equipment terminals such as photovoltaic inverters, energy storage converters (PCS), and generator controllers through another data interface to obtain the target user's current real-time available output power. It then aggregates these self-generated power sources from different sources to obtain the target user's total self-generated power at the current moment. Subsequently, the scheduling equipment parses the power required for the load production task from the acquired task progress data and performs a subtraction operation, subtracting the total self-generated power from the required power. The calculated result is the grid dependence index. A positive result indicates that the user's self-generated electricity is insufficient to support the current production task, and the user must obtain power equal to the difference from the grid. A zero or negative result indicates that the user's self-generated electricity is sufficient or exceeds the power demand of the current production task, and the user's dependence on the grid is low. Finally, the dispatching equipment associates the calculated grid dependence index with the user ID and temporarily stores it in memory for subsequent steps to perform weighted calculations of task value.

[0079] It should be noted that the calculation of self-generated electricity needs to take into account the dynamic characteristics of various energy sources. For example, for energy storage systems, their discharge power depends not only on the maximum discharge rate but also on the current state of charge (SOC). The dispatching equipment should take these constraints into account when calculating to obtain a true and effective value of self-generated electricity.

[0080] The fourth step is to query the basic criticality score and stage sensitivity multiplier corresponding to the task progress data from the preset task profile database.

[0081] Among them, the preset task profile database refers to a structured information database that is pre-built and stored in the scheduling device. It establishes standardized feature files or "profiles" for different production tasks of different types of users.

[0082] This step is executed after the scheduling device obtains the task progress data of the target user. After retrieving the task progress data containing task type identifiers and current stage information from the user's MES or other systems, the scheduling device uses this identifier information as query keywords. The scheduling device accesses its local or network-connected preset task profile database, which can be a relational data table or a key-value store. The scheduling device uses the task type ID (e.g., "PCB-Etching-Process") as the primary key to directly retrieve a record associated with that task type. This record contains a basic criticality score, pre-declared and verified by industry experts or the user themselves. For example, for critical lithography tasks in the semiconductor industry, the score may be as high as 95 (out of 100), while for general assembly tasks, the score may only be 60. Additionally, if the task is phased, the record will also contain a substructure (such as a JSON object or nested table) with the stage name (e.g., "Heating-Phase", "Cooling-Phase") as the key and the "stage sensitivity multiplier" as the value. The scheduling device retrieves the corresponding multiplier value from the substructure based on the current stage name reported in the task progress data. For example, the multiplier for "Heating-Phase" might be 1.5, indicating that this stage is extremely sensitive to disconnection, while the multiplier for "Standby-Phase" might be 0.8, indicating lower sensitivity. Finally, the scheduling device extracts the base criticality score and the stage sensitivity multiplier, and passes them to the next step for calculation.

[0083] The fifth step is to multiply the basic criticality score by the stage sensitivity multiplier to obtain the task intrinsic value score, and then weight the task intrinsic value score based on the power grid dependence index to obtain the task value correction factor.

[0084] This step is executed after the scheduling equipment obtains the task's basic criticality score, stage sensitivity multiplier, and grid dependence index. The scheduling equipment first performs a multiplication operation, multiplying the obtained basic criticality score (e.g., 90) by the stage sensitivity multiplier (e.g., 1.5) to obtain the task's intrinsic value score (90 * 1.5 = 135). The higher this score, the more valuable it is to ensure the task's power supply continuity at this moment. Next, the scheduling equipment needs to link this intrinsic value with the task's actual demand on the grid. The scheduling equipment uses a weighting function, taking the grid dependence index as input, to adjust the task's intrinsic value score. For example, the weighting function can be a normalized logic function (Sigmoid function) or a piecewise function. Assuming the grid dependence index is D and the task's intrinsic value score is V_internal, the task value correction factor F_correction can be expressed as F_correction = 1 + (V_internal / V_max) * f(D), where V_max is the theoretical maximum intrinsic value score (used for normalization), and f(D) is a gain function with respect to dependence. When D is negative or zero (not dependent on the power grid), f(D) is 0, and the correction factor is 1, which has no effect on the original weights. When D is positive and increases, the value of f(D) increases from 0 to 1, allowing tasks with higher power grid dependence to have their intrinsic value translated into a greater increase in scheduling weights. The final calculated task value correction factor (e.g., 1.25) will be used to further correct the user's dynamic scheduling weights.

[0085] The sixth step is to multiply the dynamic scheduling weights with the task value correction factor to obtain the updated dynamic scheduling weights corresponding to multiple users.

[0086] After the scheduling device calculates the task value correction factor for each target user, it iterates through the list of users calculated in steps one through five. For each user in the list, the scheduling device reads from memory its dynamic scheduling weight calculated in step two (e.g., 0.85 for an industrial user) and the task value correction factor calculated in step five that corresponds to the user's current production task (e.g., 1.25). Then, the scheduling device performs a multiplication operation on these two values. This product is the user's updated dynamic scheduling weight. If a user's task value correction factor is greater than 1, it means that their current production task has high value and grid dependence, and their final weight will be increased, thus placing them higher in the priority ranking. Conversely, if a user has no critical production task, or their task is not dependent on the grid, their task value correction factor is 1, and their dynamic scheduling weight remains unchanged. For users without production tasks or who are not subject to this correction process (such as ordinary residential users), their task value correction factor defaults to 1. After the scheduling device completes this multiplication operation for all users, it overwrites the original old weight value with the calculated new weight value.

[0087] The seventh step is to sort the dynamic scheduling weights of each user according to the preset sorting method, generate a real-time power supply priority sequence for multiple users, and determine the current redundant capacity of the target transformer based on the real-time obtained operating parameter data of the target transformer.

[0088] This step is executed after the dispatching equipment completes the calculation of the final dynamic dispatch weights for all users. Its application scenarios include the beginning of each dispatch cycle (e.g., every 5 minutes) or when an abnormal transformer load is detected, generating a power supply guarantee order for users and simultaneously accurately assessing the grid's supply capacity. Specifically, the dispatching equipment first loads the updated dynamic dispatch weight set for all users into memory. Then, the dispatching equipment calls a sorting algorithm (such as quicksort, merge sort, etc.) to sort this weight set according to a preset descending order rule. After sorting, each user has a ranking from 1 to N. The dispatching equipment solidifies this ordered list containing user IDs and their rankings into a "real-time power supply priority sequence." For example, user A with a weight of 1.0625 ranks first, user B with a weight of 0.98 ranks second, and user Z with a weight of 0.45 ranks last. Simultaneously, the dispatching equipment sends instructions to the intelligent monitoring terminal connected to the target transformer through its data acquisition interface, or receives data packets actively reported by the terminal, thereby obtaining the latest operating parameters of the transformer. The dispatching equipment extracts the current total output active power P_load from the parameters. Next, the dispatching equipment, based on the transformer's nameplate rated capacity S_rated and combined with the real-time acquired winding temperature T_winding and ambient temperature T_ambient, queries the built-in "Transformer Dynamic Capacity Increase Model" or "Load Capacity Curve" to calculate the upper limit of the dynamic safe operating capacity S_safe under the current conditions. This S_safe may be slightly higher than S_rated due to good heat dissipation conditions. Finally, the dispatching equipment performs a subtraction operation, subtracting the current total output active power P_load from the upper limit of the dynamic safe operating capacity under the current conditions S_safe to obtain the current redundancy capacity. The real-time power supply priority sequence and the current redundancy capacity are stored together in the dispatching equipment's real-time status database.

[0089] Step 8: If the current redundant capacity is determined to be lower than the current total load demand, load scheduling operation is performed on the target transformer based on the preset scheduling strategy and real-time power supply priority sequence until the current redundant capacity is within the preset safe operating range.

[0090] In this embodiment, by employing the technical feature of integrating task process data with grid dependence to calculate task value correction factors, scheduling decisions can be based on the intrinsic value, stage sensitivity, and actual dependence of the current production task on the grid. This achieves a comprehensive technical effect of ensuring the continuity of core businesses of high-value users, improving the level of refined grid management, and enhancing economic benefits.

[0091] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0092] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in a power user-end energy supply method based on redundancy capacity ratio calculation as described in Embodiment 1 of this disclosure.

[0093] Example 4 The purpose of this embodiment is to provide an electronic device.

[0094] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in a power user-end energy supply method based on redundancy capacity ratio calculation as described in Embodiment 1 of this disclosure.

[0095] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0096] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0097] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A power user-end energy supply method based on redundancy capacity ratio calculation, characterized in that, include: Obtain the actual load and rated load of multiple users associated with the target transformer, and calculate the load output ratio of each user; The obtained load output ratio is mapped to a load output ratio coefficient by a preset mapping table, and weighted by a preset attribute weight to obtain the dynamic scheduling weight for each user. Based on the obtained dynamic scheduling weights, a real-time power supply priority sequence corresponding to each user is generated, and the current redundancy capacity of the target transformer is determined according to the real-time obtained operating parameters of the target transformer. When the current redundant capacity is lower than the current total load demand, load scheduling operation is performed on the target transformer according to the real-time power supply priority sequence and preset scheduling strategy until the current redundant capacity is restored to the preset safe operating range.

2. The power user-end energy supply method based on redundancy capacity ratio calculation as described in claim 1, characterized in that, The calculation of the load output ratio includes: acquiring historical load data sequences of multiple users within a preset historical period; determining typical load curve clusters corresponding to typical operating conditions based on the distribution characteristics of load values ​​in the historical load data using a clustering algorithm; and determining the preset high percentile values ​​within the obtained typical load curve clusters as the benchmark load values.

3. The power user-end energy supply method based on redundancy capacity ratio calculation as described in claim 1, characterized in that, The calculation of the load output ratio further includes: obtaining the current actual load of multiple users, matching the real-time load segments within a preset time window before the current scheduling cycle with the typical load curve cluster, and determining the typical load curve cluster with the highest similarity as the current matching curve cluster; taking the benchmark load value corresponding to the current matching curve cluster as the dynamic rated load, and calculating the ratio between the current actual load and the dynamic rated load to obtain the load output ratio.

4. The power user-end energy supply method based on redundancy capacity ratio calculation as described in claim 1, characterized in that, The load scheduling operation is performed on the target transformer according to the real-time power supply priority sequence and the preset scheduling strategy, including: using the difference between the current total load demand and the current redundant capacity as the target power reduction, and comparing the target power reduction with the preset coarse adjustment power threshold; and performing the corresponding load scheduling operation according to the comparison result.

5. A power user-end energy supply method based on redundancy capacity ratio calculation as described in claim 4, characterized in that, The execution of the corresponding load scheduling operation includes: When the target power reduction is greater than or equal to the coarse adjustment power threshold, it is determined to be an emergency dispatch condition, and power outage operations are performed on the user loads in order of weight from low to high according to the real-time power supply priority sequence. When the target power reduction is less than the coarse adjustment power threshold, a preset number of users with the lowest priority who have not performed the power outage operation are determined from the real-time power supply priority sequence as target scheduling users; the operating status of non-production equipment associated with the target scheduling users is detected through the user-side smart gateway and an adjustable load list is determined; based on the load equipment in the adjustable load list, a power reduction operation is performed on the target scheduling users.

6. The power user-end energy supply method based on redundancy capacity ratio calculation as described in claim 5, characterized in that, Before performing power reduction operations on the target dispatch user based on the load devices in the adjustable load list, the predicted load growth amount obtained based on the preset load prediction model within a preset time period is compared with the total load capacity of the adjustable load list. When the predicted load growth amount is greater than a preset proportion of the total load capacity, a supplementary target dispatch user is determined from the real-time power supply priority sequence until the total load capacity meets the predicted load growth amount.

7. A power user-end energy supply method based on redundancy capacity ratio calculation as described in claim 6, characterized in that, Based on the historical runtime data associated with the load devices in the adjustable load list, calculate the scheduling start-stop frequency of the load devices in a preset past period; The load equipment whose scheduled start-stop frequency does not exceed the preset frequency will be identified as the updated adjustable load list.

8. A power user-end energy supply system based on redundancy capacity ratio calculation, characterized in that, include: The load output ratio calculation module is configured to: obtain the actual load and rated load of multiple users associated with the target transformer, and calculate the load output ratio of each user; The weighted calculation module is configured to: map the obtained load output ratio to a load output ratio coefficient through a preset mapping table, and perform weighted calculation in combination with preset attribute weights to obtain the dynamic scheduling weight for each user; The current redundancy capacity calculation module is configured to: generate a real-time power supply priority sequence for each user based on the obtained dynamic scheduling weights, and determine the current redundancy capacity of the target transformer based on the real-time obtained operating parameters of the target transformer. The load scheduling module is configured to: when the current redundant capacity is lower than the current total load demand, perform load scheduling operation on the target transformer according to the real-time power supply priority sequence and the preset scheduling strategy until the current redundant capacity is restored to the preset safe operating range.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the power user-end power supply method based on redundancy capacity ratio calculation as described in any one of claims 1-7.

10. An electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the power user-end power supply method based on redundancy capacity ratio calculation as described in any one of claims 1-7.

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