A distributed collaborative load optimization scheduling method and system for power distribution cabinets

Abstract

The embodiment of the application discloses a distributed cooperative load optimization scheduling method and system for power distribution cabinets. The technical scheme provided by the embodiment of the application accurately calculates the target power value and the corresponding maximum sustainable time of the second power distribution cabinet target load which can be safely reduced according to the adjustable type of the second power distribution cabinet target load and the set comfort constraint boundary, and executes scheduling control, realizes accurate quantization of adjustable capacity and dynamic matching of time dimension, improves the accuracy and execution success rate of cooperative response between distributed power distribution cabinets, and effectively realizes dynamic balance and optimization of regional load on the premise of ensuring equipment safety and user experience.

Description

Technical Field

[0001] This application relates to the field of power transmission and distribution technology, and in particular to a distributed collaborative load optimization scheduling method and system for distribution cabinets. Background Technology

[0002] Currently, in regional power distribution systems (such as industrial areas and hotels), load coordination and scheduling strategies between distribution cabinets are commonly used to achieve load balancing and energy efficiency optimization. This is to avoid the impact of peak-hour load surges on line safety and reduce peak electricity costs. The load coordination and scheduling method between distribution cabinets typically involves collecting aggregated data such as total power from the local controller of each cabinet and exchanging information with adjacent distribution cabinets based on preset thresholds and priority rules. For example, when distribution cabinet A detects that its load exceeds its load threshold, it sends a request to the adjacent distribution cabinet B to reduce its load. Distribution cabinet B responds to the request based on its adjustable load capacity and the excess load capacity of distribution cabinet A, assisting the overloaded distribution cabinet in reducing the total load in the area by shutting down some secondary loads or limiting the load output power, thereby maintaining power balance throughout the entire power distribution area.

[0003] However, existing distributed distribution cabinet load collaborative scheduling methods only perform coarse power adjustments based on aggregated data at the distribution cabinet level or preset fixed parameters when adjusting load. For the dispatched distribution cabinet, its load is usually scheduled according to preset priorities, resulting in a large deviation between the actual executable capacity reduction and the scheduled capacity, thus affecting the reliability of collaborative scheduling and user experience. Summary of the Invention

[0004] This application provides a distributed collaborative load optimization scheduling method and system for power distribution cabinets, which can realize the accurate quantification of the adjustable capacity of distributed power distribution cabinets and dynamic matching in the time dimension, improve the accuracy of collaborative response and execution success rate between distributed power distribution cabinets, and solve the technical problems of large deviation between actual schedulable capacity and theoretical value and poor collaborative scheduling reliability caused by ignoring individual load constraints in existing power distribution cabinet load scheduling schemes.

[0005] In a first aspect, embodiments of this application provide a distributed collaborative load optimization scheduling method for power distribution cabinets, comprising: Receive a load scheduling request from the first power distribution cabinet. The load scheduling request includes the power reduction value and expected duration of the load scheduling required by the first power distribution cabinet. The power reduction value is determined based on the difference between the current total power of the first power distribution cabinet and the preset target threshold. The expected duration is set according to the predicted overload duration of the first power distribution cabinet and the minimum adjustment time constraint of the internal adjustable load. In response to a load dispatch request, the target load is selected from each adjustable load based on the current operating status, minimum continuous operating time and minimum continuous shutdown time of each adjustable load in the second distribution cabinet. The target power value that can be safely reduced at the current moment and the corresponding maximum duration are calculated based on the adjustable type of the target load and the set comfort constraint boundary. Based on the target power value and maximum duration, it is matched with the power reduction value and expected duration, and the target load is scheduled and controlled according to the matching result.

[0006] Furthermore, based on the current operating status, minimum continuous operating time, and minimum continuous shutdown time of each adjustable load in the second distribution cabinet, target loads are selected from each adjustable load, including: If the current adjustable load is in the on state and has been running for less than the minimum continuous running time, or if the current adjustable load is in the off state and has been shut down for less than the minimum continuous shutdown time, the current adjustable load is filtered out, and the remaining adjustable loads are selected as the target loads.

[0007] Furthermore, based on the adjustable type of the target load and the set comfort constraint boundaries, the target power value that can be safely reduced at the current moment and the corresponding maximum duration are calculated, including: When the adjustable type of the target load is continuously adjustable, determine the safe lower limit power corresponding to the comfort constraint boundary set for the target load, calculate the power difference between the current operating power of the target load and the safe lower limit power, and calculate the target power value based on the power difference. Predict the maximum duration for which the target load can maintain its regulated state based on the rate of change of environmental parameters within the comfort constraint boundary.

[0008] Furthermore, based on the adjustable type of the target load and the set comfort constraint boundaries, the target power value that can be safely reduced at the current moment and the corresponding maximum duration are calculated, including: When the adjustable type of the target load is a gear switching type, the target power value is calculated based on the power difference between the current operating gear of the target load and the lowest gear allowed by the set comfort constraint boundary. Predict the maximum duration for which the target load can maintain its regulated state based on the rate of change of environmental parameters within the comfort constraint boundary.

[0009] Furthermore, the method also includes: When the adjustable type of the target load is on / off control, the target power value is determined based on the rated power of the target load; The maximum possible duration of maintaining the shutdown state is calculated based on the difference between the minimum continuous shutdown time of the target load and the already shut-down time.

[0010] Furthermore, based on the target power value and maximum usable duration, a matching process is performed with the power reduction value and desired duration. The target load is then scheduled and controlled according to the matching results, including: The maximum duration of each target load is compared with the expected duration, and the target loads whose maximum duration is greater than or equal to the expected duration are selected as candidate loads. The target power values ​​of the candidate loads are summed to obtain the total power that can be supported; Based on the comparison between the total available power and the power reduction value, some or all of the candidate loads are selected as execution loads, scheduling instructions are generated and sent to the execution loads for execution.

[0011] Furthermore, the method also includes: If the total available power is less than the reduced power value, the total available power will be returned to the first distribution cabinet so that the first distribution cabinet can initiate new dispatch requests to other distribution cabinets.

[0012] In a second aspect, embodiments of this application provide a distributed collaborative load optimization scheduling system for power distribution cabinets, comprising: The receiving module is used to receive the load scheduling request of the first power distribution cabinet. The load scheduling request includes the power reduction value and expected duration of the load scheduling required by the first power distribution cabinet. The power reduction value is determined based on the difference between the current total power of the first power distribution cabinet and the preset target threshold. The expected duration is set according to the predicted overload duration of the first power distribution cabinet and the minimum adjustment time constraint of the internal adjustable load. The response module is used to respond to load scheduling requests. Based on the current operating status, minimum continuous operating time and minimum continuous shutdown time of each adjustable load in the second distribution cabinet, it selects the target load from each adjustable load and calculates the target power value that can be safely reduced at the current moment and the corresponding maximum duration based on the adjustable type of the target load and the set comfort constraint boundary. The execution module is used to match the target power value and maximum duration with the power reduction value and expected duration, and to perform scheduling control of the target load based on the matching result.

[0013] In a third aspect, embodiments of this application provide an electronic device, including: Memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the distributed collaborative load optimization scheduling method for power distribution cabinets as described in the first aspect.

[0014] In a fourth aspect, embodiments of this application provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the distributed collaborative load optimization scheduling method for a power distribution cabinet as described in the first aspect.

[0015] This embodiment of the application receives a load scheduling request from a first distribution cabinet. The load scheduling request includes the power reduction value and expected duration of the load to be scheduled by the first distribution cabinet. The power reduction value is determined based on the difference between the current total power of the first distribution cabinet and a preset target threshold. The expected duration is set based on the predicted overload duration of the first distribution cabinet and the minimum adjustment time constraint of the internal adjustable load. In response to the load scheduling request, a target load is selected from each adjustable load according to the current operating status, minimum continuous operating time and minimum continuous shutdown time of each adjustable load in the second distribution cabinet. The target power value and the corresponding maximum duration that the target load can be safely reduced at the current moment are calculated according to the adjustable type of the target load and the set comfort constraint boundary. Based on the target power value and the maximum duration, the power reduction value and the expected duration are matched, and the scheduling control of the target load is performed according to the matching result. By employing the aforementioned technical means, and based on the adjustable type of the target load of the second distribution cabinet and the set comfort constraint boundary, the target power value that can be safely reduced and the corresponding maximum duration are accurately calculated, and scheduling control is executed. This achieves precise quantification of adjustable capacity and dynamic matching in the time dimension, improves the accuracy of coordinated response and execution success rate between distributed distribution cabinets, and effectively realizes dynamic balance and optimization of regional load while ensuring equipment safety and user experience. Attached Figure Description

[0016] Figure 1 This is a flowchart of a distributed collaborative load optimization scheduling method for a power distribution cabinet provided in Embodiment 1 of this application; Figure 2 This is an interactive schematic diagram of the power distribution cabinet in Embodiment 1 of this application; Figure 3 This is one of the scheduling calculation flowcharts for the target load in Embodiment 1 of this application; Figure 4 This is the second flowchart of the scheduling calculation of the target load in Embodiment 1 of this application; Figure 5 This is the third flowchart of the target load scheduling calculation in Embodiment 1 of this application; Figure 6This is a flowchart of the load scheduling execution process in Embodiment 1 of this application; Figure 7 This is a schematic diagram of a distributed collaborative load optimization scheduling system for a power distribution cabinet provided in Embodiment 2 of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0018] Example 1: Figure 1 A flowchart of a distributed collaborative load optimization scheduling method for a power distribution cabinet, as provided in Embodiment 1 of this application, is given. This method can be executed by a distributed collaborative load optimization scheduling device for a power distribution cabinet. This device can be implemented through software and / or hardware. It can consist of two or more physical entities, or it can consist of a single physical entity. Generally, this device can be a regional power distribution management server, a power distribution cabinet controller, or other processing equipment.

[0019] The following description uses the distribution cabinet controller as the main entity executing the distributed collaborative load optimization scheduling method for the distribution cabinet as an example. (Refer to...) Figure 1 The distributed collaborative load optimization scheduling method for power distribution cabinets specifically includes: S110. Receive a load scheduling request from the first power distribution cabinet. The load scheduling request includes the power reduction value and expected duration of the load scheduling required by the first power distribution cabinet. The power reduction value is determined based on the difference between the current total power of the first power distribution cabinet and the preset target threshold. The expected duration is set according to the predicted overload duration of the first power distribution cabinet and the minimum adjustment time constraint of the internal adjustable load.

[0020] This application achieves distributed collaborative load optimization scheduling among distribution cabinets by enabling collaborative interaction between them. The distribution cabinet requiring load optimization scheduling in the current area is defined as the first distribution cabinet. When the local controller of the first distribution cabinet detects that its total power exceeds a preset target threshold, and preliminary adjustments by the internal adjustable loads fail to reduce the power to a reasonable range, the controller initiates the collaborative scheduling process. First, the controller calculates the difference between the total power of all currently running loads in the distribution cabinet and the preset target threshold, and adds a preset safety margin to this difference to determine the required power reduction. The safety margin is introduced to address uncertainties caused by load fluctuations and communication delays, ensuring that the regional power load achieves dynamic balance after scheduling execution.

[0021] It should be noted that in the distributed distribution cabinet collaborative load optimization scheduling method, for multiple distributed distribution cabinets located in the same power distribution management area (such as distribution cabinets on different floors of a shopping mall), load scheduling does not refer to directly cutting off or adjusting the load of the first distribution cabinet itself. Instead, when the first distribution cabinet in a certain area experiences excessive power consumption, causing its total power to exceed a preset operating threshold, the first distribution cabinet initiates a load scheduling request to the second distribution cabinet in the same area. The request includes a power reduction value calculated based on its own overload level and the expected duration. Upon receiving the request, the second distribution cabinet evaluates and selectively adjusts the adjustable loads connected to it (such as shutting down some secondary equipment, reducing operating power, or switching to a low-power mode), thereby reducing its share of the total power supply capacity in the area and indirectly releasing the area's power supply capacity for the first distribution cabinet to use, thus keeping the total power of the entire area within a safe range. In other words, the essence of this scheduling method is to coordinate the load distribution among multiple distribution cabinets in the same area, using the power reduction of the second distribution cabinet to compensate for the power shortfall of the first distribution cabinet, thereby achieving overall load balance and optimization in the area. In this context, the first distribution cabinet refers to the one that, during distributed collaborative scheduling, initiates a load scheduling request because its total power exceeds a preset operating threshold and its local adjustability is insufficient to reduce the power to a safe range; thus, it is the scheduling requester. The second distribution cabinet refers to the adjacent distribution cabinet that receives the load scheduling request from the first distribution cabinet and evaluates and responds based on its own adjustability; thus, it is the scheduling responder. Adjustable load refers to electrical equipment connected to the outgoing circuit of the distribution cabinet, possessing controllable adjustment capabilities, and whose operating power can be changed by commands issued by the local controller. Its adjustment methods include continuous adjustment, level switching, or on / off control. The power reduction value refers to the amount of load power that the first distribution cabinet needs to reduce to restore its total power to below the preset target threshold, calculated by adding a safety margin to the difference between the current total power and the target threshold. The expected duration refers to the length of time the first distribution cabinet requests assistance from neighboring distribution cabinets to maintain the load reduction state; it is set comprehensively based on the predicted overload duration of the first distribution cabinet and the minimum adjustment time constraint of its internal adjustable loads.

[0022] Specifically, the controller of the first distribution cabinet predicts the expected duration of the current overload state based on historical load data, and combines this with the minimum adjustment time constraints of each adjustable load within the cabinet to set a desired duration. This desired duration is used to coordinate the time matching between the overload demand of the first distribution cabinet and the adjustment capability of the second distribution cabinet. Its specific value can be determined by comprehensively considering the overload characteristics of the first distribution cabinet itself and the physical constraints of its internal adjustable loads. Specifically, based on the historical load data of the first distribution cabinet, the possible duration of the current overload state is estimated. For example, by analyzing the load curves of similar workdays, a predicted overload duration value is obtained, which reflects the lower limit of the time required for external support from the first distribution cabinet. Furthermore, it is necessary to iterate through the minimum adjustment time constraints of all adjustable loads in the first distribution cabinet. The minimum adjustment time constraint can be the response time required for the load to stabilize its power after receiving a command, or the lockout time required for certain devices to undergo a fixed period before participating in adjustment again after completing one adjustment. The maximum value among these constraints is taken as the minimum adjustment time constraint, which reflects the inherent time-dimension limitation of the first distribution cabinet's own adjustment capability. Based on the above-mentioned overload duration prediction value and minimum adjustment time constraint, the controller of the first distribution cabinet sets the expected duration to be no less than the larger of the above-mentioned overload duration prediction value and minimum adjustment time constraint, so as to ensure that the second distribution cabinet can stably maintain the downward adjustment state during the overload of the first distribution cabinet. At the same time, the adjustable load of the first distribution cabinet itself also has a time margin for relay adjustment when needed, thereby avoiding repeated overloads due to the time setting being too short or waste of adjustment resources due to the setting being too long.

[0023] Furthermore, referring to Figure 2 The first distribution cabinet 11 is connected to the second distribution cabinet via a communication gateway 13. The first distribution cabinet 11 sends a load scheduling request, including the power reduction value and the expected duration, to the second distribution cabinet 12 in the same power distribution management area to request its support. Based on the received load scheduling request, the second distribution cabinet 12 can optimize the scheduling of the loads it manages, thereby ensuring that the load in the same power distribution area is maintained within a safe range.

[0024] S120. In response to the load dispatch request, based on the current operating status, minimum continuous operating time and minimum continuous shutdown time of each adjustable load in the second distribution cabinet, select the target load from each adjustable load, and calculate the target power value that the target load can be safely reduced at the current moment and the corresponding maximum duration based on the adjustable type of the target load and the set comfort constraint boundary.

[0025] Furthermore, after receiving a load dispatch request from the first distribution cabinet, the local controller of the second distribution cabinet first performs a detailed evaluation of each adjustable load within the cabinet. Specifically, the controller reads the locally maintained load characteristic database to obtain the current operating status, minimum continuous operating time, and minimum continuous shutdown time for each adjustable load. Based on these physical constraint parameters, the controller performs initial load screening. An adjustable load refers to an electrical device connected to the outgoing circuit of the first distribution cabinet, possessing controllable adjustment capabilities, and whose operating power can be changed by commands issued by the local controller. Its adjustment methods include continuous adjustment, speed switching, or on / off control. The current operating status refers to the operating mode and parameters of the adjustable load at the real-time monitoring moment, including whether the load is on or off, its current operating power, the time it has been continuously running or off, and real-time values ​​of environmental parameters related to comfort constraints (such as temperature and illuminance). The minimum continuous operating time refers to the shortest duration that the adjustable load must maintain its operating state from the moment of startup to ensure equipment safety, lifespan, or process requirements; during this period, shutdown or power reduction operations are not permitted. Minimum continuous shutdown time refers to the shortest duration during which an adjustable load must remain in a shutdown state to ensure equipment safety, lifespan, or process requirements, starting or increasing power during this period.

[0026] Specifically, based on the current operating status, minimum continuous operating time, and minimum continuous shutdown time of each adjustable load in the second distribution cabinet, target loads are selected from each adjustable load, including: If the current adjustable load is in the on state and has been running for less than the minimum continuous running time, or if the current adjustable load is in the off state and has been shut down for less than the minimum continuous shutdown time, the current adjustable load is filtered out, and the remaining adjustable loads are selected as the target loads.

[0027] For loads that are currently on but have not yet reached the minimum continuous operating time, shutting them down could shorten the equipment's lifespan or cause startup failures. Therefore, these loads are deemed temporarily unadjustable. Similarly, for loads that are currently off but have not yet reached the minimum continuous off time, forcing them to start or participate in adjustment could violate the equipment's physical characteristics and is therefore also eliminated. After screening based on the equipment's physical characteristics, the remaining adjustable loads are identified as the target loads.

[0028] By introducing minimum continuous running time and minimum continuous shutdown time as screening criteria, the compatibility between coordinated scheduling instructions and the physical laws of equipment can be ensured. This avoids equipment damage or ineffective adjustment caused by forcibly adjusting loads within the protection period, and provides a reliable foundation for subsequent accurate quantification of adjustability.

[0029] Subsequently, for each target load, considering its adjustable type (e.g., continuous adjustment, level switching, or on / off control) and preset comfort constraints (e.g., minimum illuminance, maximum room temperature), the target power value that can be safely reduced at the current moment and the maximum duration that can be maintained in this reduced state are calculated. The adjustable type refers to the classification of adjustable load adjustment methods, including continuous adjustment (continuous adjustment with continuously adjustable power), level switching (switching between preset levels), and on / off control (only on / off control). The preset comfort constraints refer to the allowable range of physical quantities preset to ensure user experience, such as the minimum illuminance for lighting loads and the maximum or minimum temperature for temperature control loads, serving as boundary conditions that load adjustment cannot exceed. The safely reduced target power value refers to the power value that the second distribution cabinet can stably reduce after adjusting a target load, provided that the load's physical constraints and comfort constraints are met. The corresponding maximum duration refers to the longest time that the second distribution cabinet can maintain the reduced power value of a target load without triggering the recovery condition within the physical and comfort constraints of that load after reducing the power value to the target value.

[0030] For example, for continuously adjustable loads, the target power value is determined based on the difference between the current operating power and the lower safety limit power allowed by the comfort constraint boundary, while the maximum duration is predicted based on the rate of change of relevant environmental parameters within the comfort constraint boundary. Through this process, the adjustable load capacity of the second distribution cabinet can be accurately quantified, converting the adjustable capacity into specific power values ​​and duration information.

[0031] Optional, refer to Figure 3 Based on the adjustable type of the target load and the set comfort constraint boundaries, calculate the target power value that the target load can be safely reduced at the current moment and the corresponding maximum duration, including: S1201. When the adjustable type of the target load is continuously adjustable, determine the lower limit power corresponding to the comfort constraint boundary set for the target load, calculate the power difference between the current operating power of the target load and the lower limit power, and calculate the target power value based on the power difference. S1202. Predict the maximum duration for which the target load can maintain its regulated state based on the rate of change of environmental parameters within the comfort constraint boundary.

[0032] For target loads with continuously adjustable characteristics, the calculation of their target power value and maximum duration can be combined with the threshold information in the comfort constraint boundary and the dynamic changes in environmental parameters. When calculating the target power value, the controller first reads the safe lower limit power corresponding to the comfort constraint boundary set for that load from the local load characteristic database. The safe lower limit power refers to the minimum power value at which the load can operate stably under the premise of meeting the allowable range of physical quantities (such as minimum illuminance or maximum temperature) corresponding to the preset comfort constraint boundary of the target load. For example, for lighting loads, the safe lower limit power refers to the power value required to maintain the minimum allowable illuminance of the work surface; for temperature control loads, the safe lower limit power refers to the minimum power value that the cooling or heating equipment can operate under the premise of maintaining the maximum or minimum allowable room temperature. Subsequently, the controller collects the current real-time operating power of the load and calculates the difference between it and the safe lower limit power. This difference is the maximum power reduction that the load can safely achieve at the current moment. Based on this, the controller determines the target power value for this adjustment, such as setting the target power value as the current operating power minus this difference.

[0033] When predicting the maximum allowable duration, the controller extrapolates based on the rate of change of relevant environmental parameters within the comfort constraint boundary. Taking fan coil units in cooling mode as an example, the controller uses an empirical formula fitted from historical data, combined with the current room temperature, outdoor temperature, and room insulation performance, to predict the time required for the room temperature to rise from its current value to the maximum allowable temperature set by the comfort constraint boundary after the fan speed is reduced to the target level. For lighting loads, it predicts the time it takes for illuminance to decay to the minimum allowable value by combining historical data from light sensors. By considering the continuous changing trends of environmental parameters, the calculated maximum allowable duration is dynamically adaptable, ensuring that user comfort remains within an acceptable range during the adjustment process, significantly improving the accuracy and reliability of distributed collaborative scheduling.

[0034] Optionally, refer to Figure 4 Based on the adjustable type of the target load and the set comfort constraint boundaries, calculate the target power value that the target load can be safely reduced at the current moment and the corresponding maximum duration, including: S1203. When the adjustable type of the target load is a gear switching type, calculate the target power value based on the power difference between the current operating gear of the target load and the lowest gear allowed by the set comfort constraint boundary. S1204. Predict the maximum duration for which the target load can maintain its regulated state based on the rate of change of environmental parameters within the comfort constraint boundary.

[0035] When the adjustable type of the target load is a speed-switching type, its safe target power reduction value and corresponding maximum duration can be quantified by combining the discrete adjustment characteristics of the load and comfort constraint boundaries. First, when calculating the target power value, the controller reads the current operating speed of the load and its corresponding real-time operating power from the local load characteristic database. At the same time, it determines the lowest speed that the load is allowed to operate at under the current conditions based on the preset comfort constraint boundaries. For example, for multi-speed fan coil units, the comfort constraint boundary is usually reflected in the room temperature not being allowed to exceed a certain upper limit. The controller judges whether to allow a speed reduction to medium or low speed based on the closeness of the current room temperature to the upper limit and according to the set rules. For multi-level lighting equipment, the lowest switchable brightness level is determined based on the constraint that the illuminance of the working surface is not lower than the minimum allowable value. Subsequently, the controller calculates the difference between the current operating speed power and the minimum allowable speed power. This difference is the maximum power reduction that the load can safely reduce at the current moment. The controller determines the target power value for this adjustment based on this difference, such as setting it to the current power minus this difference.

[0036] When predicting the maximum duration, the controller also extrapolates based on the rate of change of environmental parameters within the comfort constraint boundary. By performing joint quantization of gear-switching loads based on gear difference and environmental change rate, accurate assessment of discrete adjustment capability is achieved, ensuring that user comfort remains within limits during adjustment and further improving the precision of distributed collaborative scheduling.

[0037] Furthermore, referring to Figure 5 The method also includes: S1205. When the adjustable type of the target load is on / off control type, determine the target power value based on the rated power of the target load. S1206. Calculate the maximum possible duration of maintaining the shutdown state based on the difference between the minimum continuous shutdown time of the target load and the already shut-down time.

[0038] When the adjustable type of the target load is on / off control, its safely adjustable target power value and corresponding maximum duration are quantified based on the load's rated power and time constraints. First, in calculating the target power value, the on / off control type load can only achieve two states: on or off. It lacks continuous adjustment or level switching capabilities. Therefore, its safely adjustable target power value is the load's rated power. The controller reads the load's rated power parameter from the local load characteristic database. This parameter is directly used as the power reduction value that can be achieved by performing a shutdown operation on the load, i.e., the target power value.

[0039] The maximum possible duration is calculated by combining the minimum continuous shutdown time constraint of the load with the current shutdown time. On / off control loads (such as water dispensers, water heaters, and storage-type electric water heaters) typically have heat storage or insulation characteristics. After shutdown, they can maintain basic functions for a certain period. However, due to the physical laws of the equipment, the shutdown state must be maintained for a preset minimum continuous shutdown time after each shutdown to ensure compressor pressure balance, heating element cooling, or to avoid lifespan damage caused by frequent start-stop cycles. The controller reads the minimum continuous shutdown time parameter of the load and records the shutdown time from the last shutdown time to the current time. It calculates the difference between the minimum continuous shutdown time and the current shutdown time. This difference is the maximum possible duration for which the load can maintain its shutdown state without triggering the equipment protection mechanism after a shutdown operation is performed at the current time. For example, if the minimum continuous shutdown time for a certain load is 30 minutes, and 20 minutes have passed since the last shutdown, then if it is shut down again now, its maximum duration is 10 minutes. After this time, the equipment will be allowed to restart because it has reached the minimum continuous shutdown time limit. However, if it is necessary to maintain shutdown in actual operation, other constraints need to be considered.

[0040] By quantifying on / off control loads based on the difference between rated power and time, accurate assessment of discrete regulation resources is achieved, further improving the adaptability and reliability of distributed collaborative scheduling in diverse load scenarios.

[0041] S130. Based on the target power value and maximum duration, match it with the power reduction value and expected duration, and perform scheduling control of the target load according to the matching result.

[0042] After calculating the adjustability of each target load, the local controller of the second distribution cabinet enters the matching and execution of load scheduling. By selecting target loads whose maximum duration matches the expected duration, partial and full scheduling control is performed on these loads, thereby reducing the power share of the second distribution cabinet from the total regional power supply capacity. This indirectly releases regional power supply capacity for use by the first distribution cabinet, keeping the total power of the entire region within a safe range and achieving overall load balancing and optimization in the region.

[0043] Specifically, refer to Figure 6 Based on the target power value and maximum sustainable duration, it is matched with the power reduction value and expected duration, and the target load is scheduled and controlled according to the matching result, including: S1301. Compare the maximum duration of each target load with the expected duration, and select the target loads whose maximum duration is greater than or equal to the expected duration as candidate loads. S1302. The target power values ​​of the candidate loads are summed to obtain the total power that can be supported; S1303. Based on the comparison between the total available power and the power reduction value, select some or all of the candidate loads as execution loads, generate scheduling instructions and issue them to the execution loads for execution.

[0044] The controller compares the maximum duration of each target load with the expected duration requested by the first distribution cabinet, selecting loads with a maximum duration not less than the expected duration as candidate loads. This ensures that all loads participating in the support can maintain a stable reduced state throughout the entire request period, avoiding situations where support fails due to premature recovery caused by the load's own constraints.

[0045] Subsequently, the controller accumulates the target power values ​​of the candidate loads to obtain the total supportable power that the second distribution cabinet can provide to the first distribution cabinet at the current moment. This total supportable power is compared with the requested power reduction value. If the total supportable power is greater than or equal to the power reduction value, it indicates that the second distribution cabinet has the capability to independently meet the scheduling request. At this point, the controller selects some or all of the candidate loads as execution loads based on their priority or other preset rules, generates specific scheduling instructions, and sends them to the corresponding intelligent execution module via the local bus to complete the power reduction operation for the target load.

[0046] Optionally, the method further includes: If the total available power is less than the reduced power value, the total available power will be returned to the first distribution cabinet so that the first distribution cabinet can initiate new dispatch requests to other distribution cabinets.

[0047] If the total available power is less than the reduced power value, it indicates that the second distribution cabinet cannot fully meet the request. In this case, the controller encapsulates the total available power value into a response message and returns it to the first distribution cabinet. The first distribution cabinet then initiates a supplementary scheduling request to other neighboring distribution cabinets or activates its local secondary adjustment mechanism. Through this process, the physical characteristics and comfort constraints of each load can be accurately quantified and dynamically matched, thereby achieving accurate and reliable distributed collaborative load optimization scheduling of distribution cabinets and improving the accuracy and success rate of collaborative responses between distributed distribution cabinets.

[0048] Optionally, during actual operation, parameters such as the minimum continuous operating time, minimum continuous shutdown time, and the rate of change of environmental parameters within the comfort constraint boundary of the adjustable load can be adaptively changed according to factors such as seasonal changes, equipment aging, and changes in usage habits. To this end, the local controller of the second distribution cabinet continuously collects long-term operating data for each adjustable load, including the time of each start-up and shutdown, the distribution of continuous operating time, and the trajectory of changes in environmental parameters (such as room temperature and illuminance) after adjustment. Machine learning algorithms (such as sliding window averaging) are then used to train on this historical data to extract statistical patterns of load characteristics under different time periods and operating conditions. For example, by fitting temperature change data under fan coil cooling conditions over the past week, the model of room temperature rise rate at different fan speeds is updated; by analyzing the daily start-up and shutdown patterns of the water dispenser, the recommended value of its minimum continuous shutdown time is dynamically adjusted. When a load scheduling request is received, the controller calls the updated characteristic parameters to calculate the adjustability, making the evaluation results more consistent with the actual physical state of the current equipment.

[0049] Furthermore, when the load scheduling request of the first distribution cabinet is sent to multiple neighboring distribution cabinets in the same distribution area (such as the second and third distribution cabinets), and the sum of the total supportable power returned by each distribution cabinet is greater than or equal to the required power reduction value, this application achieves optimal collaborative scheduling by planning the optimal allocation rule for the collaborative support tasks of multiple neighboring distribution cabinets. After collecting the response messages returned by all neighboring distribution cabinets, the controller of the first distribution cabinet obtains the total supportable power and corresponding support duration provided by each neighboring distribution cabinet. Simultaneously, it combines auxiliary information such as the communication delay between each neighboring distribution cabinet and the first distribution cabinet, historical collaborative success rate, and the load stress level of each neighboring distribution cabinet to construct a multi-objective optimization model. The multi-objective optimization model aims to minimize the interference of support tasks on neighboring distribution cabinets (e.g., prioritizing distribution cabinets with sufficient downscalable capacity and low load rate), maximize the reliability of support execution (e.g., prioritizing distribution cabinets with stable communication quality and high historical success rate), and meet the expected duration constraint, thereby solving for the optimal support task allocation scheme. The solution clearly specifies the target power value that each neighboring distribution cabinet needs to actually execute, and the first distribution cabinet sends a confirmation command to the corresponding distribution cabinet. This achieves optimized allocation of multi-source support resources, meeting the needs of the first distribution cabinet while minimizing the impact and risk of coordinated scheduling on the overall power distribution system.

[0050] As described above, by receiving a load scheduling request from the first distribution cabinet, the load scheduling request includes the power reduction value and expected duration of the load scheduling required by the first distribution cabinet. The power reduction value is determined based on the difference between the current total power of the first distribution cabinet and a preset target threshold. The expected duration is set based on the predicted overload duration of the first distribution cabinet and the minimum adjustment time constraint of the internal adjustable load. In response to the load scheduling request, based on the current operating status, minimum continuous operating time and minimum continuous shutdown time of each adjustable load in the second distribution cabinet, a target load is selected from each adjustable load. Based on the adjustable type of the target load and the set comfort constraint boundary, the target power value that can be safely reduced at the current moment and the corresponding maximum duration are calculated. Based on the target power value and the maximum duration, the power reduction value and the expected duration are matched, and the scheduling control of the target load is performed according to the matching result. By employing the aforementioned technical means, and based on the adjustable type of the target load of the second distribution cabinet and the set comfort constraint boundary, the target power value that can be safely reduced and the corresponding maximum duration are accurately calculated, and scheduling control is executed. This achieves precise quantification of adjustable capacity and dynamic matching in the time dimension, improves the accuracy of coordinated response and execution success rate between distributed distribution cabinets, and effectively realizes dynamic balance and optimization of regional load while ensuring equipment safety and user experience.

[0051] Example 2: Based on the above embodiments, Figure... is a structural schematic diagram of a distributed collaborative load optimization scheduling system for a power distribution cabinet provided in Embodiment 2 of this application. (Reference) Figure 7 The distributed collaborative load optimization scheduling system for power distribution cabinets provided in this embodiment specifically includes: The receiving module 21 is used to receive the load scheduling request of the first power distribution cabinet. The load scheduling request includes the power reduction value and expected duration of the load scheduling required by the first power distribution cabinet. The power reduction value is determined based on the difference between the current total power of the first power distribution cabinet and the preset target threshold. The expected duration is set according to the predicted overload duration of the first power distribution cabinet and the minimum adjustment time constraint of the internal adjustable load. The response module 22 is used to respond to the load scheduling request, select the target load from each adjustable load according to the current operating status, minimum continuous operating time and minimum continuous shutdown time of each adjustable load in the second distribution cabinet, and calculate the target power value that the target load can be safely reduced at the current moment and the corresponding maximum duration according to the adjustable type of the target load and the set comfort constraint boundary. The execution module 23 is used to match the target power value and the maximum duration with the power reduction value and the expected duration, and to perform scheduling control of the target load according to the matching result.

[0052] Furthermore, based on the current operating status, minimum continuous operating time, and minimum continuous shutdown time of each adjustable load in the second distribution cabinet, target loads are selected from each adjustable load, including: If the current adjustable load is in the on state and has been running for less than the minimum continuous running time, or if the current adjustable load is in the off state and has been shut down for less than the minimum continuous shutdown time, the current adjustable load is filtered out, and the remaining adjustable loads are selected as the target loads.

[0053] Specifically, based on the adjustable type of the target load and the set comfort constraint boundaries, the target power value that can be safely reduced at the current moment and the corresponding maximum duration are calculated, including: When the adjustable type of the target load is continuously adjustable, determine the safe lower limit power corresponding to the comfort constraint boundary set for the target load, calculate the power difference between the current operating power of the target load and the safe lower limit power, and calculate the target power value based on the power difference. Predict the maximum duration for which the target load can maintain its regulated state based on the rate of change of environmental parameters within the comfort constraint boundary.

[0054] Specifically, based on the adjustable type of the target load and the set comfort constraint boundaries, the target power value that can be safely reduced at the current moment and the corresponding maximum duration are calculated, including: When the adjustable type of the target load is a gear switching type, the target power value is calculated based on the power difference between the current operating gear of the target load and the lowest gear allowed by the set comfort constraint boundary. Predict the maximum duration for which the target load can maintain its regulated state based on the rate of change of environmental parameters within the comfort constraint boundary.

[0055] Specifically, the method also includes: When the adjustable type of the target load is on / off control, the target power value is determined based on the rated power of the target load; The maximum possible duration of maintaining the shutdown state is calculated based on the difference between the minimum continuous shutdown time of the target load and the already shut-down time.

[0056] Specifically, based on the target power value and maximum sustainable duration, a matching process is performed with the power reduction value and expected duration. The target load is then scheduled and controlled according to the matching results, including: The maximum duration of each target load is compared with the expected duration, and the target loads whose maximum duration is greater than or equal to the expected duration are selected as candidate loads. The target power values ​​of the candidate loads are summed to obtain the total power that can be supported; Based on the comparison between the total available power and the power reduction value, some or all of the candidate loads are selected as execution loads, scheduling instructions are generated and sent to the execution loads for execution.

[0057] Specifically, the method also includes: If the total available power is less than the reduced power value, the total available power will be returned to the first distribution cabinet so that the first distribution cabinet can initiate new dispatch requests to other distribution cabinets.

[0058] As described above, by receiving a load scheduling request from the first distribution cabinet, the load scheduling request includes the power reduction value and expected duration of the load scheduling required by the first distribution cabinet. The power reduction value is determined based on the difference between the current total power of the first distribution cabinet and a preset target threshold. The expected duration is set based on the predicted overload duration of the first distribution cabinet and the minimum adjustment time constraint of the internal adjustable load. In response to the load scheduling request, based on the current operating status, minimum continuous operating time and minimum continuous shutdown time of each adjustable load in the second distribution cabinet, a target load is selected from each adjustable load. Based on the adjustable type of the target load and the set comfort constraint boundary, the target power value that can be safely reduced at the current moment and the corresponding maximum duration are calculated. Based on the target power value and the maximum duration, the power reduction value and the expected duration are matched, and the scheduling control of the target load is performed according to the matching result. By employing the aforementioned technical means, and based on the adjustable type of the target load of the second distribution cabinet and the set comfort constraint boundary, the target power value that can be safely reduced and the corresponding maximum duration are accurately calculated, and scheduling control is executed. This achieves precise quantification of adjustable capacity and dynamic matching in the time dimension, improves the accuracy of coordinated response and execution success rate between distributed distribution cabinets, and effectively realizes dynamic balance and optimization of regional load while ensuring equipment safety and user experience.

[0059] The distributed collaborative load optimization scheduling system for power distribution cabinets provided in Embodiment 2 of this application can be used to execute the distributed collaborative load optimization scheduling method for power distribution cabinets provided in Embodiment 1 above, and has the corresponding functions and beneficial effects.

[0060] Example 3: This application provides an electronic device in embodiment three, referring to... Figure 8 The electronic device includes a processor 31, a memory 32, a communication module 33, an input device 34, and an output device 35. The electronic device may have one or more processors and one or more memories. The processor, memory, communication module, input device, and output device of the electronic device can be connected via a bus or other means.

[0061] Memory, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the distributed collaborative load optimization scheduling method for power distribution cabinets described in any embodiment of this application (e.g., receiving module, response module, and execution module in a distributed collaborative load optimization scheduling system for power distribution cabinets). Memory may primarily include a program storage area and a data storage area, wherein the program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the device, etc. Furthermore, memory may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0062] The communication module is used for data transmission.

[0063] The processor executes various functional applications and data processing of the device by running software programs, instructions, and modules stored in memory, thereby realizing the aforementioned distributed collaborative load optimization scheduling method for power distribution cabinets.

[0064] Input devices can be used to receive input numerical or character information, and to generate key signal inputs related to user settings and function control of the device. Output devices may include display devices such as displays.

[0065] The electronic equipment provided above can be used to execute the distributed collaborative load optimization scheduling method for power distribution cabinets provided in Embodiment 1 above, and has corresponding functions and beneficial effects.

[0066] Example 4: This application embodiment also provides a storage medium containing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to execute a distributed collaborative load optimization scheduling method for a power distribution cabinet. The distributed collaborative load optimization scheduling method for a power distribution cabinet includes: receiving a load scheduling request from a first power distribution cabinet, the load scheduling request including a reduction power value and a desired duration for the load scheduling required by the first power distribution cabinet, the reduction power value being determined based on the difference between the current total power of the first power distribution cabinet and a preset target threshold, and the desired duration being set based on the predicted overload duration of the first power distribution cabinet and the minimum adjustment time constraint of the internal adjustable load; responding to the load scheduling request, selecting a target load from each adjustable load according to the current operating state, minimum continuous operating time, and minimum continuous shutdown time of each adjustable load in a second power distribution cabinet, and calculating the target power value and the corresponding maximum duration that the target load can be safely reduced at the current moment according to the adjustable type of the target load and the set comfort constraint boundary; matching the target power value and the maximum duration with the reduction power value and the desired duration, and performing scheduling control of the target load according to the matching result.

[0067] Storage medium – any type of memory device or storage device. The term “storage medium” is intended to include: mounting media, such as CD-ROM, floppy disk, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. Storage medium may also include other types of memory or combinations thereof. Furthermore, storage medium may reside in a first computer system in which the program is executed, or it may reside in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term “storage medium” can include two or more storage media residing in different locations (e.g., in different computer systems connected via a network). Storage medium may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0068] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the distributed collaborative load optimization scheduling method for power distribution cabinets as described above, but can also execute related operations in the distributed collaborative load optimization scheduling method for power distribution cabinets provided in any embodiment of this application.

[0069] The distributed collaborative load optimization scheduling system, storage medium, and electronic equipment for power distribution cabinets provided in the above embodiments can execute the distributed collaborative load optimization scheduling method for power distribution cabinets provided in any embodiment of this application. For technical details not described in detail in the above embodiments, please refer to the distributed collaborative load optimization scheduling method for power distribution cabinets provided in any embodiment of this application.

[0070] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A distributed collaborative load optimization scheduling method for power distribution cabinets, characterized in that, include: Receive a load scheduling request from the first power distribution cabinet. The load scheduling request includes the power reduction value and expected duration of the load scheduling required by the first power distribution cabinet. The power reduction value is determined based on the difference between the current total power of the first power distribution cabinet and a preset target threshold. The expected duration is set according to the predicted overload duration of the first power distribution cabinet and the minimum adjustment time constraint of the internal adjustable load. In response to the load scheduling request, based on the current operating status, minimum continuous operating time and minimum continuous shutdown time of each adjustable load in the second distribution cabinet, a target load is selected from each adjustable load, and the target power value that can be safely reduced at the current moment and the corresponding maximum duration are calculated based on the adjustable type of the target load and the set comfort constraint boundary. Based on the target power value and the maximum possible duration, the load is matched with the power reduction value and the expected duration, and the target load is scheduled and controlled according to the matching result.

2. The distributed collaborative load optimization scheduling method for power distribution cabinets according to claim 1, characterized in that, The step of selecting a target load from each adjustable load in the second distribution cabinet based on the current operating status, minimum continuous operating time, and minimum continuous shutdown time of each adjustable load includes: If the adjustable load is currently in the on state and has been running for less than the minimum continuous running time, or if the adjustable load is currently in the off state and has been off for less than the minimum continuous off time, the current adjustable load is filtered out, and the remaining adjustable loads are selected as the target load.

3. The distributed collaborative load optimization scheduling method for power distribution cabinets according to claim 1, characterized in that, The step of calculating the target power value that can be safely reduced at the current moment and the corresponding maximum duration based on the adjustable type of the target load and the set comfort constraint boundary includes: When the adjustable type of the target load is continuously adjustable, determine the lower safety limit power corresponding to the comfort constraint boundary set by the target load, calculate the power difference between the current operating power of the target load and the lower safety limit power, and calculate the target power value based on the power difference; The maximum duration for which the target load can maintain its regulated state is predicted based on the rate of change of environmental parameters within the comfort constraint boundary.

4. The distributed collaborative load optimization scheduling method for power distribution cabinets according to claim 1, characterized in that, The step of calculating the target power value that can be safely reduced at the current moment and the corresponding maximum duration based on the adjustable type of the target load and the set comfort constraint boundary includes: When the adjustable type of the target load is a gear switching type, the target power value is calculated based on the power difference between the current operating gear of the target load and the lowest gear allowed by the set comfort constraint boundary. The maximum duration for which the target load can maintain its regulated state is predicted based on the rate of change of environmental parameters within the comfort constraint boundary.

5. The distributed collaborative load optimization scheduling method for power distribution cabinets according to claim 1, characterized in that, The method further includes: When the adjustable type of the target load is on / off control type, the target power value is determined according to the rated power of the target load; The maximum possible duration of maintaining the shutdown state is calculated based on the difference between the minimum continuous shutdown time and the shutdown time of the target load.

6. The distributed collaborative load optimization scheduling method for power distribution cabinets according to claim 1, characterized in that, The step of matching the target power value and the maximum sustainable duration with the power reduction value and the expected duration, and performing scheduling control of the target load based on the matching result, includes: The maximum possible duration of each target load is compared with the expected duration, and the target loads whose maximum possible duration is greater than or equal to the expected duration are selected as candidate loads. The target power values ​​of the candidate loads are summed to obtain the total supportable power; Based on the comparison between the total supportable power and the power reduction value, some or all of the candidate loads are selected as execution loads, scheduling instructions are generated and sent to the execution loads for execution.

7. The distributed collaborative load optimization scheduling method for power distribution cabinets according to claim 6, characterized in that, The method further includes: If the total available power is less than the reduced power value, the total available power is returned to the first distribution cabinet so that the first distribution cabinet can initiate new scheduling requests to other distribution cabinets.

8. A distributed collaborative load optimization scheduling system for power distribution cabinets, characterized in that, include: The receiving module is used to receive a load scheduling request from the first power distribution cabinet. The load scheduling request includes the power reduction value and expected duration of the load scheduling required by the first power distribution cabinet. The power reduction value is determined based on the difference between the current total power of the first power distribution cabinet and a preset target threshold. The expected duration is set according to the predicted overload duration of the first power distribution cabinet and the minimum adjustment time constraint of the internal adjustable load. The response module is used to respond to the load scheduling request, select the target load from each adjustable load according to the current operating status, minimum continuous operating time and minimum continuous shutdown time of each adjustable load in the second power distribution cabinet, and calculate the target power value that the target load can be safely reduced at the current moment and the corresponding maximum duration according to the adjustable type of the target load and the set comfort constraint boundary. An execution module is used to match the target power value and the maximum duration with the power reduction value and the expected duration, and to perform scheduling control of the target load according to the matching result.

9. An electronic device, characterized in that, include: Memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the distributed collaborative load optimization scheduling method for power distribution cabinets as described in any one of claims 1-7.

10. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the distributed collaborative load optimization scheduling method for power distribution cabinets as described in any one of claims 1-7.

Images