A method for building a distributed power supply collaborative scheduling platform

Abstract

This invention relates to the field of power dispatching technology, and more particularly to a method for constructing a distributed power source collaborative dispatching platform. The method includes the following steps: acquiring distributed power source operating data; determining the adjustable power range based on the distributed power source operating data; identifying collaboratively dispatchable nodes based on the adjustable power range; generating collaborative dispatching instructions based on the collaboratively dispatchable nodes; performing joint dispatching on the collaboratively dispatchable nodes using the collaborative dispatching instructions, and recording the node response status; plotting output response curves based on the node response status; calculating output tracking deviation based on the output response curves; configuring multi-level dispatching strategies based on the output tracking deviation; and deploying a collaborative communication network based on the multi-level dispatching strategies. This invention achieves joint dispatching of distributed power source nodes based on power dispatching technology, improving the consistency and tracking accuracy of distributed power source output response, reducing dispatching lag and deviation accumulation risks, and improving overall dispatching stability and system operating efficiency.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a method for building a distributed power source collaborative dispatching platform. Background Technology

[0002] Existing distributed power generation operation management and scheduling control technologies typically rely on independent node local control or centralized scheduling based on fixed power thresholds to coordinate distributed power output. For example, scheduling commands are issued based on the available capacity of a single node, empirical power ranges, or preset scheduling rules. This approach generally suffers from the following problems: First, most methods focus on judging the output status or instantaneous power level of a single node, lacking joint modeling of the output response process and adjustment capabilities of multiple nodes. This makes it difficult to accurately assess the overall adjustable power range and easily leads to unreasonable allocation of scheduling commands. Second, scheduling strategies are mostly based on fixed scheduling cycles or static power threshold settings, failing to fully consider node response differences and output tracking deviations, easily causing scheduling lags or reduced coordination efficiency. Third, there is a lack of effective linkage between scheduling execution results and the communication network and scheduling strategy architecture. Node response status cannot be used in reverse to adjust scheduling strategies and allocate communication resources. The overall scheduling process lacks closed-loop optimization capabilities, making it difficult to meet the requirements for security, stability, and refined coordinated scheduling under large-scale distributed power generation access conditions. Summary of the Invention

[0003] Therefore, the present invention needs to provide a method for building a distributed power supply collaborative scheduling platform to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for constructing a distributed power supply collaborative scheduling platform includes the following steps: Step S1: Obtain distributed power source operation data; determine the adjustable power range based on the distributed power source operation data; identify cooperatively schedulable nodes based on the adjustable power range; Step S2: Generate collaborative scheduling instructions based on the collaboratively schedulable nodes; perform joint scheduling on the collaboratively schedulable nodes using the collaborative scheduling instructions, and record the node response status; Step S3: Draw the output response curve based on the node response status; calculate the output tracking deviation based on the output response curve; configure a multi-level scheduling strategy based on the output tracking deviation; Step S4: Deploy a collaborative communication network according to the multi-level scheduling strategy; build a scheduling strategy architecture based on the collaborative communication network; and build a distributed power collaborative scheduling platform based on the scheduling strategy architecture.

[0005] The beneficial effects of this invention are as follows: (1) By acquiring distributed power source operation data and determining the adjustable power range, the adjustment capability of multi-node distributed power sources can be quantitatively identified. Based on the adjustable power range, the nodes that can be coordinated and categorized can be screened and classified. This can accurately characterize the response characteristics and adjustment boundaries of each node, provide a unified and reliable data foundation for the generation of coordinated scheduling instructions, and improve the pertinence and consistency of scheduling decisions.

[0006] (2) During the execution of the collaborative scheduling, the output response status of the nodes is recorded and the output response curve is plotted to continuously represent the output change process of the nodes. The output tracking deviation is calculated in combination with the output response curve, and multi-level scheduling strategies are divided in the form of numerical thresholds. This enables the scheduling strategy to switch dynamically with the node response deviation, avoids the problem of insufficient adaptability of a single scheduling mode under different operating conditions, and enhances the precision of collaborative scheduling.

[0007] (3) By matching and deploying the multi-level scheduling strategy with the collaborative communication network and scheduling strategy architecture, the order of scheduling instructions, communication priority and node control timing correspond to the node response characteristics. In the emergency scheduling scenario, the inverter fault code is combined with the compensation scheduling configuration to form a closed-loop adjustment process in which scheduling execution and status feedback are linked, thereby improving the scheduling stability, response reliability and overall collaborative scheduling efficiency under the condition of large-scale access of distributed power sources. Attached Figure Description

[0008] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the steps of building a distributed power supply collaborative scheduling platform according to the present invention. Figure 2 This is a schematic diagram of the output response curve in this invention; Figure 3 This is a schematic diagram of the execution flow of a method for building a distributed power supply collaborative scheduling platform according to the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0009] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0010] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0011] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0012] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for building a distributed power supply collaborative scheduling platform, the method comprising the following steps: Step S1: Obtain distributed power source operation data; determine the adjustable power range based on the distributed power source operation data; identify cooperatively schedulable nodes based on the adjustable power range; In one embodiment, a dispatching master station collects distributed power generation operation data from photovoltaic power generation units, wind power generation units, energy storage units, and controllable load nodes. This operation data includes real-time active power, rated capacity, current output ratio, grid connection status, and ramp rate limits. Based on this operation data, the maximum and minimum adjustable output of each distributed power generation node are calculated to obtain the corresponding adjustable power range. When the adjustable power range exceeds a preset dispatching threshold, the corresponding distributed power generation node is identified as a collaboratively dispatchable node, and a node identifier set is formed.

[0013] In another embodiment, it is assumed that the scheduling area contains 10 distributed power nodes, including 6 photovoltaic nodes, 2 wind power nodes, and 2 energy storage nodes; the rated capacity of each node is distributed between 50kW and 500kW. Based on real-time operating data, it is calculated that 7 of the nodes have an adjustable power range greater than 20kW (e.g., photovoltaic node P3 is 30kW, and energy storage node E1 is 80kW), while the remaining 3 nodes do not meet the conditions due to limited output or abnormal grid connection status. Thus, 7 nodes that can be coordinated and scheduled are identified for subsequent joint scheduling.

[0014] Step S2: Generate collaborative scheduling instructions based on the collaboratively schedulable nodes; perform joint scheduling on the collaboratively schedulable nodes using the collaborative scheduling instructions, and record the node response status; In one embodiment, for the identified collaboratively schedulable nodes, a collaborative scheduling instruction is generated based on the target output demand and the adjustable power range of the nodes, including the target adjustment power, execution sequence, and effective duration. The collaborative scheduling instruction is issued to each collaboratively schedulable node through the scheduling communication interface, so that each node adjusts its output synchronously or in batches according to the instruction requirements. During the scheduling execution process, the actual output changes, response delays, and execution completion status of each node are collected in real time to form node response status data.

[0015] In another embodiment, assume the system generates a set of coordinated scheduling instructions requiring a total output increase of 150kW within 60 seconds, where nodes P3 increase by 30kW, P5 increase by 20kW, E1 releases 50kW, and E2 releases 50kW. After the scheduling is executed, the response status of each node is recorded: P3's response time is 3 seconds, with a final output deviation of 2kW; E1's response time is 1 second, with output fully tracked; P5's response delay is 8 seconds, with an actual increase of only 15kW. This data is uniformly recorded as node response status for subsequent analysis.

[0016] Step S3: Draw the output response curve based on the node response status; calculate the output tracking deviation based on the output response curve; configure a multi-level scheduling strategy based on the output tracking deviation; In one embodiment, the target output and actual output of each co-schedulable node within the scheduling execution cycle are aligned in time series to plot node-level and system-level output response curves; by comparing the target output curve and the actual output curve, the output tracking deviation of each node at different time points is calculated, and the maximum deviation value, average deviation value, and stabilization time are further statistically analyzed; based on the output tracking deviation results, the nodes are divided into different scheduling levels, and corresponding multi-level scheduling strategy parameters are configured.

[0017] In another embodiment, assuming a scheduling cycle of 120 seconds, force response curves are plotted for 7 nodes, revealing that: the average tracking deviation of 3 nodes is less than 3%, the average deviation of 2 nodes is between 3% and 8%, and the maximum deviation of 2 nodes exceeds 10%. Based on this, nodes with deviations less than 3% are configured as Level 1 scheduling nodes, employing a fast scheduling strategy; nodes with deviations between 3% and 8% are configured as Level 2 scheduling nodes; and nodes with deviations exceeding 10% are configured as Level 3 scheduling nodes, participating only in slow scheduling or standby scheduling.

[0018] Step S4: Deploy a collaborative communication network according to the multi-level scheduling strategy; build a scheduling strategy architecture based on the collaborative communication network; and build a distributed power collaborative scheduling platform based on the scheduling strategy architecture.

[0019] In one embodiment, based on the scheduling level and response characteristics of each node in the multi-level scheduling strategy, corresponding communication link parameters and data interaction methods are configured to form a hierarchical collaborative communication network. On this basis, nodes with different scheduling levels are mapped to the corresponding scheduling control levels to construct a scheduling strategy architecture that includes an instruction generation layer, a node coordination layer, and a status feedback layer. By integrating the scheduling strategy architecture, scheduling instruction issuance, node status feedback, and dynamic policy adjustment are realized, thus completing the construction of a distributed power supply collaborative scheduling platform.

[0020] In another embodiment, it is assumed that the first-level scheduling node uses a low-latency communication link (period 100ms), the second-level scheduling node uses a conventional communication link (period 500ms), and the third-level scheduling node uses a status feedback link (period 2s). In the scheduling strategy architecture, the first-level nodes are connected to the fast scheduling layer, the second-level nodes are connected to the conventional scheduling layer, and the third-level nodes are connected to the auxiliary scheduling layer. The final distributed power supply collaborative scheduling platform can manage more than 20 scheduling nodes simultaneously and achieve orderly collaboration and dynamic switching between different scheduling levels.

[0021] It should be noted that you should refer to [link / reference]. Figure 3 The diagram starts from "Start" and executes step S1: acquiring distributed power source operating data, determining the adjustable power range, and identifying nodes that can be coordinated and scheduled. Then, step S2: generating coordinated scheduling instructions, executing joint scheduling, and recording node response status. Next, step S3: plotting output response curves, calculating output tracking deviation, and configuring multi-level scheduling strategies. Then, step S4: deploying a coordinated communication network, building a scheduling strategy architecture, and building a distributed power source coordinated scheduling platform. Finally, the process ends at "End", clearly presenting the complete steps of distributed power source coordinated scheduling from initial preparation to platform construction.

[0022] Of particular importance is the deployment of a collaborative communication network based on a multi-level scheduling strategy, including: The communication requirement parameters are determined based on the multi-level scheduling strategy; the minimum bandwidth and transmission priority are extracted and calculated based on the communication requirement parameters; and a node communication configuration table is generated based on the minimum bandwidth and transmission priority. In one embodiment, the system first analyzes the communication task volume, priority, and latency requirements of each node according to a multi-level scheduling strategy, and calculates the minimum bandwidth for each node. and transmission priority Subsequently, the communication requirements of each node are compiled to generate a node communication configuration table. This configuration table records parameters such as node number, minimum bandwidth, maximum allowable delay, transmission priority, and redundant communication interfaces. It serves as the basis for subsequent status feedback and dynamic switching, ensuring reasonable bandwidth allocation and meeting real-time requirements during multi-node collaborative communication.

[0023] In another embodiment, assume the system monitors a multi-level scheduling task across 8 nodes, where the minimum bandwidth is calculated. (Unit: Mbps) is [12, 18, 15, 20, 10, 25, 14, 16], transmission priority The node communication configuration table is [1,2,2,1,3,1,4,2] (from 1 high to 5 low). The corresponding records are node numbers 1–8. , The number of redundant interfaces (assuming 2 per node) provides a clear basis for the subsequent allocation of status feedback channels.

[0024] The status feedback channels are divided according to the node communication configuration table; the dynamic switching control logic is determined according to the status feedback channels; and the collaborative communication network is deployed based on the dynamic switching control logic.

[0025] In one embodiment, the system uses a node communication configuration table. Divide the nodes into different status backhaul channels Within each channel, the bandwidth and priority of nodes are balanced as much as possible to ensure that high-priority tasks are not blocked by low-priority tasks. Then, based on the status update rate and latency threshold of the nodes within the return channel, the dynamic switching control logic is determined. This includes primary / backup channel switching rules, load redistribution strategies, and abnormal retransmission mechanisms. Ultimately, a collaborative communication network is deployed at the network layer based on dynamic switching control logic to achieve multi-level, adjustable, and highly reliable communication between nodes.

[0026] In another embodiment, assume that the 8 nodes are divided into 3 status feedback channels: Channel C1: Nodes [1,4,6], average bandwidth 19Mbps, highest priority 1; Channel C2: Nodes [2,3,8], average bandwidth 16.3Mbps, highest priority 2; Channel C3: Nodes [5,7], average bandwidth 12Mbps, highest priority 3; the system calculates the average latency of each channel as [8,12,15]ms, and sets the dynamic switching threshold to 10ms. When the average latency of a certain channel exceeds the threshold, the control logic... This will trigger nodes to perform load redistribution or primary / backup channel switching within the channel. For example, node 7 will automatically switch to channel C2 to maintain the overall network communication stability. This strategy ensures continuous and low-latency node status feedback under multi-level scheduling tasks, while also supporting automatic redundancy switching for abnormal nodes.

[0027] Of particular importance is the establishment of a scheduling strategy architecture based on the cooperative communication network, which includes: The system collects transmission priority information based on the cooperative communication network; generates a node scheduling level sequence based on the transmission priority information; and maps the node scheduling level sequence to the cooperative communication network to establish a node scheduling level mapping relationship. In one embodiment, the system first collects the transmission priority information of each node in the cooperative communication network and sorts the transmission priorities of each node to generate a node scheduling level sequence. This sequence ensures that high-priority nodes are scheduled first, based on the node's task priority, bandwidth requirements, and real-time requirements. Then, the node scheduling level sequence is mapped to the entire cooperative communication network to obtain the node scheduling level mapping relationship, thereby providing a basis for subsequent scheduling decisions and communication optimization.

[0028] In another embodiment, assume the system monitors the communication tasks of 6 nodes and collects the transmission priority data of each node: Node 1: P=1 (highest priority); Node 2: P=2; Node 3: P=4; Node 4: P=3; Node 5: P=2; Node 6: P=5 (lowest priority); The node scheduling level sequence is obtained by sorting: [Node 1, Node 2, Node 4, Node 5, Node 3, Node 6]. This sequence is mapped to the communication network to ensure that nodes with higher priority are scheduled first, thereby improving network efficiency.

[0029] Generate node scheduling process logs based on the node scheduling level mapping relationship; determine the scheduling node identifiers based on the node scheduling process logs; and build the scheduling strategy architecture based on the scheduling node identifiers.

[0030] In one embodiment, the system generates a node scheduling process log based on the node scheduling level mapping relationship. This log records information such as the scheduling order, required bandwidth, and scheduling latency of each node for subsequent analysis and optimization. Based on the scheduling process log, the system further determines the scheduling node identifier, identifying nodes that need to be scheduled with priority. Finally, based on the node scheduling identifier, a corresponding scheduling strategy architecture is built to ensure that the communication tasks of each node in the network can be reasonably scheduled according to priority and latency requirements, thereby achieving efficient resource allocation and communication optimization.

[0031] In another embodiment, assuming that during the scheduling of 6 nodes, the node scheduling process log records the following information: Node 1: Scheduling time = 10ms, bandwidth requirement = 50Mbps; Node 2: Scheduling time = 12ms, bandwidth requirement = 30Mbps; Node 3: Scheduling time = 15ms, bandwidth requirement = 20Mbps; Node 4: Scheduling time = 14ms, bandwidth requirement = 40Mbps; Node 5: Scheduling time = 13ms, bandwidth requirement = 25Mbps; Node 6: Scheduling time = 18ms, bandwidth requirement = 10Mbps; Based on this log, Node 1 (highest priority) and Node 2 (second highest priority) will be scheduled first. The scheduling strategy architecture will allocate the communication tasks of the nodes sequentially according to their priority and determine the bandwidth quota and scheduling time for each node. Ultimately, Node 1 and Node 2 will occupy the majority of the bandwidth, ensuring efficient allocation of network load and real-time response of communication tasks.

[0032] Preferably, step S2, which generates a cooperative scheduling instruction based on the cooperatively schedulable nodes, includes: Extract the adjustable power range of the coordinateable nodes; mark nodes with adjustable power ranges greater than a first preset threshold as fast response nodes; mark nodes with adjustable power ranges between the first preset threshold and a second preset threshold as standard response nodes; mark nodes with adjustable power ranges greater than the second preset threshold as auxiliary response nodes; In one embodiment, the dispatch master station collects real-time power output and rated capacity information from each co-schedulable node and calculates the adjustable power range of each node. Based on the adjustable power range, a first preset threshold of 50kW and a second preset threshold of 20kW are set. Nodes with a power range greater than the first preset threshold are marked as fast response nodes, nodes with a power range between the first and second thresholds are marked as standard response nodes, and nodes with a power range greater than the second threshold are marked as auxiliary response nodes. The node identifiers and corresponding dispatch levels form a dispatch node table, providing a basis for subsequent power allocation and joint dispatch.

[0033] In another embodiment, assuming there are 10 coordinately schedulable nodes in the system, with adjustable power ranges of [60, 45, 35, 25, 15, 12, 8, 5, 3, 2] kW respectively. A first threshold of 50 kW and a second threshold of 20 kW are set. Nodes P1 and P2 are marked as fast response nodes; nodes P3 and P4 are marked as standard response nodes; nodes P5 and P6 are marked as auxiliary response nodes; the remaining nodes do not participate in scheduling because their power range is below the second threshold. Through this marking, a set of fast response nodes {P1, P2}, a set of standard response nodes {P3, P4}, and a set of auxiliary response nodes {P5, P6} can be formed.

[0034] Calculate the total power adjustment amount based on the adjustable power range; allocate the target power value according to the fast response node; if the target power value is less than the total power adjustment amount, mobilize the standard response node to perform the baseline output scheduling and record the node output response value; generate a collaborative scheduling instruction based on the node output response value and the auxiliary response node.

[0035] In one embodiment, the total system power adjustment is obtained by accumulating the adjustable power ranges of the collaboratively schedulable nodes. First, a target power value is allocated based on the power capabilities of the fast response nodes, and a baseline output command is issued. When the power allocated by the fast response nodes is insufficient to cover the total adjustment, the standard response nodes are activated to perform baseline output scheduling, and the actual output response values ​​of each node are recorded, including response delay, output deviation, and the final achieved power value. Subsequently, the actual response results of each node are integrated by combining the adjustable power ranges of the auxiliary response nodes to generate a final collaborative scheduling command, which is used to control the joint output of all collaboratively schedulable nodes.

[0036] In another embodiment, assuming the total system power regulation is 150kW, and fast response nodes P1 and P2 can provide a total of 80kW of power, target power values ​​are first issued: P1 50kW, P2 30kW. Since the target power values ​​are less than the total regulation of 150kW, standard response nodes P3 and P4 are activated to perform baseline output scheduling. The response results are recorded: P3 actually outputs 25kW with a delay of 3s; P4 actually outputs 20kW with a delay of 5s. Combining the power available from auxiliary response nodes P5 and P6 (10kW and 12kW respectively), a coordinated scheduling instruction is generated, instructing all nodes to jointly adjust their output in the following scheduling cycle to ensure that the total power regulation is met.

[0037] Preferably, the standard response node is mobilized to perform baseline output scheduling, and the node output response values ​​are recorded, including: The node's operating status is assessed based on the standard response node; the node's power adjustment range is determined based on the node's operating status and the preset target benchmark output value; and a benchmark output command is generated based on the node's power adjustment range. In one embodiment, the scheduling master station first collects the real-time output, available capacity, and historical scheduling records of the standard response nodes, and calculates the current operating status indicators of each node (such as average output deviation, response delay, and load stability). Based on the comparison between the node's operating status and a preset target benchmark output value (e.g., 70% of the node's rated capacity), the node power adjustment range is calculated. Subsequently, the calculated power adjustment range is converted into benchmark output commands, each command containing a node identifier, target power value, and adjustment timing information, in preparation for subsequent communication.

[0038] In another embodiment, assume there are 5 standard response nodes in the system, with their real-time outputs (in kW) as follows: [35, 40, 28, 30, 32]. The preset target baseline output value is 40kW per node. The power adjustment range is calculated based on the output differences: node 1 needs to increase by 5kW, node 2 by 0kW, node 3 by 12kW, node 4 by 10kW, and node 5 by 8kW. Then, a baseline output command sequence is generated: {P1:40kW, P2:40kW, P3:40kW, P4:40kW, P5:40kW}, containing the target power value for each node and the effective time of the adjustment, in preparation for communication.

[0039] The baseline output command is encapsulated into an output control command and sent to the standard response node via the communication channel, and the node output response value is recorded.

[0040] In one embodiment, the dispatch master station encapsulates the generated baseline output command into a standardized output control command data packet, which includes information such as node identifier, target power value, command type, and timestamp. This packet is sent to each standard response node via a dedicated distributed power source communication channel (such as IEC61850 or MQTT protocol), and the node output feedback is monitored in real time. Upon receiving the command, the node performs power adjustment and simultaneously returns the actual output data to the dispatch master station, recording the output response value, including response delay, actual adjusted power, and deviation, for subsequent dispatch optimization and collaborative dispatch command generation.

[0041] In another embodiment, assuming that after the five standard response nodes receive the power control command, their actual power response values ​​(in kW) are [38, 41, 39, 40, 37], and the corresponding response delays (in seconds) are [2, 1, 3, 2, 4]. This feedback data is recorded in the scheduling log to evaluate the response performance and power deviation of each node, and serves as the basis for adjusting the power allocation and collaborative scheduling strategy in the next round.

[0042] Preferably, step 3, which involves plotting the force response curve based on the nodal response state, includes: Read the power increment and fit the actual output value according to the node response status; establish a coordinate mapping relationship with the power increment as the horizontal axis and the fitted actual output value as the vertical axis. In one embodiment, the scheduling master station first collects real-time power output and historical power increment data of the standard response nodes in the most recent scheduling cycle, and calculates the fitted actual power output value for each node based on the current operating status of the nodes. A mapping relationship is established on the coordinate plane, with the power increment as the horizontal axis and the fitted actual power output value as the vertical axis, to form a power regulation response reference model for each node. This mapping relationship reflects the output change trend of the node under different power increments, providing a basis for subsequent power output curve plotting and scheduling optimization.

[0043] In another embodiment, assuming there are 4 standard response nodes in the system, the collected power increments (in kW) are [0, 5, 10, 15], and the corresponding fitted actual output values ​​(in kW) are [30, 34, 38, 41]. Based on these discrete points, a node power increment-output coordinate mapping relationship is established, forming 4 sets of coordinate points {(0, 30), (5, 34), (10, 38), (15, 41)}, which serve as preliminary reference data for constructing the continuous response curve.

[0044] The output coordinate points in the coordinate mapping relationship are filled in ascending order of power increment to form a continuous data point series; smooth interpolation is performed based on the continuous data point series to draw the preliminary output response curve; In one embodiment, discrete coordinate points are filled in sequentially according to the power increment, from smallest to largest, to generate a continuous data point series. Then, a smoothing interpolation method (such as cubic spline interpolation or local weighted regression) is used to calculate interpolation points on the continuous data point series, and a preliminary output response curve for each node is plotted. This curve can intuitively reflect the mapping relationship between the power increment and the actual output, and provides a basis for subsequent trend analysis and differentiated display.

[0045] In another embodiment, it is assumed that the discrete coordinates of the above four nodes are arranged in ascending order to form a point series [(0,30),(5,34),(10,38),(15,41)]. Power output values ​​corresponding to 10 equally spaced power increment points are generated through cubic spline interpolation: [30,31.8,33.5,34.7,36.0,37.2,38.0,39.2,40.2,41], resulting in a continuous data point series. This allows for the plotting of a preliminary power output response curve, reflecting the continuous trend of node power output change.

[0046] The initial output response curve is subjected to noise filtering to determine the response trend characteristics; based on the response trend characteristics, differentiated line types, colors, and legend labels are assigned to form the output response curve.

[0047] In one embodiment, the generated preliminary output response curve is subjected to noise filtering, such as by using moving average filtering or Gaussian filtering, to remove abnormal fluctuations and retain the overall trend. Based on the processed response trend characteristics, different line types (solid lines, dashed lines), colors (red, blue, green, etc.), and legend labels are assigned to the curves of different nodes to form the final visualized output response curve for scheduling analysis and node performance evaluation.

[0048] In another embodiment, assuming the 10 output points obtained by continuous interpolation are [30, 31.8, 33.5, 34.7, 36.0, 37.2, 38.0, 39.2, 40.2, 41], after processing with a moving average filter (window=3), a smooth curve point [30.0, 31.1, 33.3, 34.7, 35.9, 37.1, 38.1, 39.1, 40.5, 41.0] is obtained. Node A is marked with a solid red line, node B with a dashed blue line, and node C with a dotted green line. The legend also labels each node with its number and unit, enabling visualization and trend analysis.

[0049] It should be noted that you should refer to [link / reference]. Figure 2 This figure is a coordinate mapping diagram with power increment (kW) as the horizontal axis and fitted actual output value (kW) as the vertical axis. It shows the output response curves of three nodes with different response states: First, discrete data points of each node under different power increments are collected and arranged in ascending order of power increment. After smoothing and interpolation, a continuous data point series is generated. After noise filtering to determine the trend, a red solid line is assigned to node A (standard response), a blue dashed line is assigned to node B (slower response), and a green dotted line is assigned to node C (faster response). The final curves intuitively reflect the output change trend of different nodes when the power increment changes, and can be used for scheduling analysis and node performance evaluation.

[0050] Preferably, the calculation of the output tracking deviation based on the output response curve in step S3 includes: The local peak points with deviation values ​​greater than zero are detected based on the output response curve; the peak point with the largest deviation amplitude among the local peak points is selected as the maximum positive deviation point; the output overshoot constraint parameters are determined based on the maximum positive deviation point. In one embodiment, the system first acquires the deviation curve between the node's output response curve and the target output reference. It then detects all local peak points with deviation values ​​greater than zero along the deviation curve and calculates the deviation amplitude for each peak point. Next, it selects the peak point with the largest deviation amplitude from these local peaks, defines it as the maximum positive deviation point, and determines the overshoot constraint parameters of the node's output based on the deviation value at this point and the corresponding power increment, for subsequent output calibration and stability control.

[0051] In another embodiment, it is assumed that the positive deviation local peak point detected in the deviation curve of a node corresponds to a power increment (in kW) of [3, 6, 9, 12] and a deviation value (in kW) of [0.8, 1.5, 1.2, 1.0]. The maximum positive deviation point is a power increment of 6 kW, corresponding to a deviation value of 1.5 kW, based on which the overshoot constraint parameters are determined. It is used for subsequent output constraint correction.

[0052] The output response curve is constrained and calibrated according to the overshoot constraint parameters, and the effective output range is recorded; the output tracking deviation is calculated based on the effective output range.

[0053] In one embodiment, the output response curve is constrained and calibrated based on overshoot constraint parameters: when the curve exceeds the overshoot threshold, it is truncated or adjusted to keep the node output within a safe constraint range. Subsequently, the effective output range, i.e., the range of power increments in which the curve remains continuously within the constraint range, is statistically analyzed, and the output tracking deviation, such as average deviation and maximum deviation, is calculated within this range to provide a basis for node performance evaluation, scheduling optimization, and anomaly alarms.

[0054] In another embodiment, assuming that the output response curve of a node in the power increment range [0, 15kW] is calibrated with constraints, the effective output range is [2, 12kW]. Within this effective range, the output tracking deviation (in kW) is calculated for each power increment point: [0.3, 0.5, 0.4, 0.6, 0.2, 0.1, 0.3, 0.4, 0.2, 0.1, 0.2]. Based on this, the average output deviation is approximately 0.32kW, and the maximum output deviation is approximately 0.6kW, which is used for subsequent node performance evaluation and response curve optimization.

[0055] Preferably, step S3, configuring a multi-level scheduling strategy based on the output tracking deviation, includes: If the absolute value of the output tracking deviation is less than or equal to 5% of the preset rated output, configure the basic scheduling strategy; if the absolute value of the output tracking deviation is greater than 5% of the preset rated output but less than or equal to 15% of the preset rated output, configure the enhanced scheduling strategy; if the absolute value of the output tracking deviation is greater than 15% of the preset rated output, configure the emergency scheduling strategy. In one embodiment, the system first acquires the output tracking deviation data of the nodes and compares it with the preset rated output. The strategy type is then categorized based on the absolute value range of the deviation: when the deviation... At rated output, configure basic scheduling strategies to maintain normal node operation; when At rated output, an enhanced scheduling strategy is configured to appropriately adjust node power and ensure stable output; when At rated output, an emergency dispatch strategy is configured to quickly adjust node output to prevent overshoot or system anomalies, providing input for subsequent dispatch and integration.

[0056] In another embodiment, assuming a node has a rated output of 100kW, its output tracking deviation (in kW) monitored for 10 consecutive minutes is [2,4,6,8,12,16,3,10,18,5]. The corresponding strategy configuration is as follows: points (2,4,3,5) with a deviation ≤5kW (≤5% of rated output) are configured with the basic scheduling strategy for 4 time periods; points (6,8,12,10) with a deviation between 5 and 15kW (>5% and ≤15% of rated output) are configured with the enhanced scheduling strategy for 4 time periods; and points (16,18) with a deviation >15kW (>15% of rated output) are configured with the emergency scheduling strategy for 2 time periods. This division can intuitively guide the node power scheduling priority and response strategy.

[0057] Integrate basic scheduling strategies, enhanced scheduling strategies, and emergency scheduling strategies into a multi-level scheduling strategy.

[0058] In one embodiment, basic scheduling strategies, enhanced scheduling strategies, and emergency scheduling strategies are integrated according to priority to form a multi-level scheduling strategy library. This multi-level scheduling strategy can dynamically select the execution strategy based on the current output deviation of the node, realize adaptive adjustment of the node output, and provide a unified scheduling basis for distributed power sources or multi-node systems, thereby improving the overall output stability and response speed.

[0059] In another embodiment, assuming a 10-node microgrid system, based on deviation allocation, each node is assigned a strategy at a certain time as follows: nodes 1–3 execute the basic strategy, nodes 4–7 execute the enhancement strategy, and nodes 8–10 execute the emergency strategy. The system integrates these strategies into a multi-level scheduling matrix, taking into account the output deviation of each node in real time to achieve dynamic priority scheduling and load balancing. The steady-state deviation control effect under different load fluctuations can be verified through simulation, ensuring that the overall output error is ≤5%.

[0060] Preferably, if the absolute value of the output tracking deviation is less than or equal to 5% of the preset rated output, the basic scheduling strategy includes: If the absolute value of the output tracking deviation is less than or equal to 5% of the preset rated output, maintain the preset instruction issuance cycle; perform instruction scheduling simulation according to the preset instruction issuance cycle, and calculate the node response delay; In one embodiment, the system first obtains the output tracking deviation ΔP of each node and compares it with a preset rated output. When At rated output, the system maintains a preset command issuance cycle (e.g., 1 second / time) and executes command scheduling simulations sequentially within this cycle. During each simulation, the system records the node response delay. This includes the time from receiving the scheduling instruction to the actual output response completion. By statistically analyzing the response delays of consecutive nodes, the response patterns of nodes under normal output deviations can be analyzed, providing basic data for subsequent communication evaluation and scheduling priority allocation.

[0061] In another embodiment, it is assumed that the output tracking deviations (unit: %) of 10 nodes are monitored in a microgrid system: [2.1, 3.5, 5.0, 1.8, 4.2, 0.9, 5.0, 2.7, 3.9, 4.8]. When maintaining the preset instruction issuing period of 1 s / time, scheduling simulation is performed on each node and the response delay (unit: ms) is measured: [105, 112, 118, 101, 109, 98, 120, 107, 110, 115]. These data provide a quantitative basis for subsequent communication performance analysis and scheduling priority division.

[0062] Evaluate the communication performance of nodes according to the node response delay, and determine the communication priority identifier; allocate a scheduling execution queue based on the communication priority identifier, and use the scheduling execution queue as the basic scheduling strategy.

[0063] In one embodiment, according to the obtained node response delay , perform communication performance evaluation on each node. The system takes the response delay as the main index and generates node communication priority identifiers (such as high, medium, and low priorities). Subsequently, each node is allocated to the scheduling execution queue according to the communication priority: high-priority nodes are arranged in the front, medium-priority nodes are arranged in the middle, and low-priority nodes are arranged in the back row. This scheduling execution queue forms the basic scheduling strategy and can be directly used for real-time output control and scheduling execution of distributed nodes.

[0064] In another embodiment, it is assumed that the response delays (unit: ms) of 10 nodes are [105, 112, 118, 101, 109, 98, 120, 107, 110, 115]. The system sets the threshold: T_resp ≤ 105 ms is the high priority, 105 < T_resp ≤ 115 ms is the medium priority, and T_resp > 115 ms is the low priority. Then the allocation results are as follows: high-priority nodes: node 4 (101 ms), node 6 (98 ms), node 1 (105 ms); medium-priority nodes: node 2 (112 ms), node 5 (109 ms), node 8 (107 ms), node 9 (110 ms), node 10 (115 ms); low-priority nodes: node 3 (118 ms), node 7 (120 ms). Generate a scheduling execution queue according to the allocation results and provide it as the basic scheduling strategy to the upper-layer scheduling system to achieve priority control of node output and distributed scheduling management.

[0065] Preferably, if the absolute value of the output tracking deviation is greater than 5% of the preset rated output and less than or equal to 15% of the preset rated output, the configured enhanced scheduling strategy includes: If the absolute value of the output tracking deviation is greater than 5% of the preset rated output but less than 15% of the preset rated output, the preset instruction issuance cycle will be shortened to two-thirds to determine the correction instruction cycle. In one embodiment, the system first obtains the output tracking deviation of each node. And compare it with the preset rated output. When At rated output, the preset command issuance period T0 is shortened to two-thirds, i.e. This process establishes a corrected instruction cycle. Subsequently, the system executes instruction scheduling simulations according to the corrected instruction cycle and records the response status of each node, including execution latency, instantaneous deviation, and other information, providing data support for the subsequent generation of enhanced scheduling strategies.

[0066] In another embodiment, assuming that the output tracking deviation (in %) is [6.2, 7.5, 5.8, 9.0, 12.3, 10.5, 6.8, 8.7, 14.1, 11.6] over 10 consecutive nodes, a preset command issuance cycle is defined. The shortened correction period The system performs scheduling simulations on each node during this period and records the node response latency (in milliseconds): [95, 102, 98, 110, 125, 118, 100, 107, 132, 120]. These data provide a basic quantitative basis for subsequent deviation trend analysis and enhanced scheduling strategies.

[0067] Based on the execution instruction scheduling simulation, the instantaneous deviation value of the node is calculated; the deviation trend coefficient is determined based on the instantaneous deviation value of the node; the deviation trend coefficient is multiplied by the correction instruction cycle to form the feedforward compensation value, which is used as an enhanced scheduling strategy.

[0068] In one embodiment, the instantaneous node deviation is scheduled based on the execution instruction simulation. The system calculates the deviation trend coefficient. This is used to reflect the upward or downward trend of the node output deviation. Then, the deviation trend coefficient... With correction instruction cycle Multiply to obtain the feedforward compensation value. This feedforward compensation value is used to dynamically adjust the control commands issued in the next cycle, thereby enhancing the scheduling strategy, reducing output fluctuations, and improving node response accuracy.

[0069] In another embodiment, it is assumed that the instantaneous deviation values ​​are calculated for the aforementioned 10 nodes. (Unit: %): [6.0, 7.2, 5.9, 8.5, 11.8, 10.2, 6.5, 8.3, 13.9, 11.2]. Let the deviation trend coefficient be... =[1.05,0.98,1.02,1.10,1.15,1.08,1.03,1.07,1.20,1.12]. Then the feedforward compensation value... (Unit: s) ≈ [0.700, 0.653, 0.680, 0.734, 0.767, 0.720, 0.687, 0.714, 0.800, 0.747]. This feedforward compensation value is used to adjust the instructions issued by each node in the next cycle, thereby forming an enhanced scheduling strategy to achieve dynamic response optimization and output stability improvement for nodes with moderate deviations.

[0070] Preferably, if the absolute value of the output tracking deviation is greater than 5% of the preset rated output, the emergency dispatch strategy includes: If the absolute value of the output tracking deviation is greater than 15% of the preset rated output, the preset instruction issuance cycle will be shortened to half, and the node output hit rate will be recorded; scheduling nodes with output hit rates lower than the preset hit rate threshold will be selected based on the node output hit rate. In one embodiment, the system first detects the output tracking deviation of each node. And compare it with the preset rated output. When |ΔP|>15% of the rated output, shorten the preset command issuance period T0 to half, that is... This improves node response speed. Subsequently, the system executes command scheduling according to the shortened cycle and records the node output hit rate. (The degree of matching between the actual output of the node and the target output). Based on a preset hit rate threshold. (e.g., 90%), select the set of scheduling nodes that are below the threshold. This provides target nodes for subsequent emergency dispatch strategies.

[0071] In another embodiment, assuming that the output tracking deviation (in %) is [18.2, 21.5, 16.8, 23.0, 19.7, 17.4, 22.1, 20.5] across 8 consecutive nodes, a preset command issuance cycle is defined. The shortened correction period After executing the scheduling simulation, the node output hit rate (in %) was recorded as follows: [88, 82, 91, 79, 85, 93, 80, 84], among which those below the preset hit rate threshold... The nodes are nodes 1, 2, 4, 5, 7, and 8, a total of 6 nodes, which are included in the low-hit-rate scheduling node set. .

[0072] Collect inverter fault signals from the scheduling nodes; determine inverter fault codes based on the inverter fault signals; configure emergency scheduling strategies based on the inverter fault codes.

[0073] In one embodiment, the system schedules nodes based on the selected low hit rate. Collect inverter fault signals from each node. Inverter fault codes are generated based on fault signal analysis. This is used to identify the current fault type of the node (such as overcurrent, overvoltage, abnormal temperature rise, etc.). Then, based on the fault code... Configure emergency scheduling strategies for nodes with low hit rates, including adjusting power distribution, switching redundant nodes, or limiting load allocation, to achieve rapid response and fault isolation.

[0074] In another embodiment, assume a set of low-hit-rate nodes. The six nodes collected inverter fault signals as follows: [overcurrent, abnormal temperature rise, overvoltage, overcurrent, abnormal temperature rise, overvoltage]. The system parsed the fault codes. Accordingly, an emergency scheduling strategy was configured for each node: power distribution to node C1 was reduced by 15%, the priority for triggering heat dissipation and cooling was increased for node C2, node C3 was switched to a standby node, and the load was redistributed. After implementation, the output matching rate of nodes with low hit rates is expected to increase from the original average of 83% to 92%, ensuring that the system can still maintain stable operation in the event of a failure.

[0075] Preferably, the emergency dispatch strategy configured based on the inverter fault code includes: Parse the inverter fault codes to determine the over-temperature fault type; calculate the power deficit value based on the over-temperature fault type; allocate compensation power shares based on the power deficit value; and send the compensation power shares to the preset backup node through a high-priority communication link. In one embodiment, the system first parses the fault codes fed back by the inverter. Identify over-temperature fault types (such as high-temperature overload, abnormal local temperature rise, etc.). Calculate the corresponding power deficit value based on the fault type. (The difference between the actual output power of the node and the preset rated power). Subsequently, the system calculates the power deficit value. Compensation power share Assigned to a pre-defined set of backup nodes After allocation, the high-priority communication link will be used to... The data is distributed to each backup node to ensure that the overall power output remains stable during an over-temperature fault.

[0076] In another embodiment, assuming the system detects over-temperature faults at 5 inverter nodes, corresponding to a power deficit value... (Unit: kW) is [12, 18, 15, 20, 10]. The compensation power share will be determined based on node weights and spare node capacities. The allocation (unit: kW) is [6, 9, 7.5, 10, 5], and is distributed to the three backup nodes via high-priority communication links. The system records the reception status and execution status of each node during the distribution process for subsequent strategy adjustments.

[0077] If the power deviation of the standby node is lower than the preset power deviation for five consecutive cycles, a 20% arithmetic decrease in the compensation power share will be used until the power deviation is less than 5%, at which point the cluster compensation will be terminated to complete the emergency scheduling strategy.

[0078] In one embodiment, the system receives compensated power. The backup nodes are monitored. If the power deviation of a backup node is... The power deviation was below the preset threshold for five consecutive cycles. (e.g., 5% of rated power) then a progressively decreasing compensation strategy is applied to this node: decreasing in 20% increments. until At the same time, the node is removed from the cluster compensation strategy, emergency scheduling adjustments are completed, and power balance is restored.

[0079] In another embodiment, it is assumed that the power deviation of the three backup nodes is monitored. The values ​​(unit: kW) over five consecutive periods are as follows: Node 1: [3.2, 3.5, 3.1, 2.9, 3.0]; Node 2: [4.8, 4.5, 4.7, 4.6, 4.9]; Node 3: [2.1, 2.3, 2.5, 2.2, 2.0]; Since all are below... The system will then implement compensation according to a 20% arithmetic progression: Node 1 initial The power distribution of the standby nodes gradually decreases from 6kW to 4.8kW, then to 3.84kW, 3.07kW, and finally to 2.46kW, before the nodes eventually leave the cluster. Nodes 2 and 3 similarly decrease to 3.84kW and 1.64kW respectively before leaving the cluster. This strategy ensures a smooth power allocation and gradual exit of the standby nodes from the cluster during over-temperature fault recovery, avoiding system fluctuations caused by sudden power changes.

[0080] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0081] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for constructing a distributed power supply collaborative scheduling platform, characterized in that, Includes the following steps: Step S1: Obtain distributed power source operation data; determine the adjustable power range based on the distributed power source operation data; identify cooperatively schedulable nodes based on the adjustable power range; Step S2: Generate collaborative scheduling instructions based on the collaboratively schedulable nodes; perform joint scheduling on the collaboratively schedulable nodes using the collaborative scheduling instructions, and record the node response status; Step S3: Plot the output response curve based on the node response status; calculate the output tracking deviation based on the output response curve; configure a multi-level scheduling strategy based on the output tracking deviation; Step S4: Deploy a collaborative communication network according to the multi-level scheduling strategy; build a scheduling strategy architecture based on the collaborative communication network; and build a distributed power supply collaborative scheduling platform based on the scheduling strategy architecture.

2. The method for constructing a distributed power supply collaborative scheduling platform according to claim 1, characterized in that, Step S2, which generates cooperative scheduling instructions based on the cooperatively schedulable nodes, includes: Extract the adjustable power range of the coordinateable nodes; mark nodes with adjustable power ranges greater than a first preset threshold as fast response nodes; mark nodes with adjustable power ranges between the first preset threshold and a second preset threshold as standard response nodes; mark nodes with adjustable power ranges greater than the second preset threshold as auxiliary response nodes; Calculate the total power adjustment amount based on the adjustable power range; allocate the target power value according to the fast response node; if the target power value is less than the total power adjustment amount, mobilize the standard response node to perform the baseline output scheduling and record the node output response value; generate a collaborative scheduling instruction based on the node output response value and the auxiliary response node.

3. The method for constructing a distributed power supply collaborative scheduling platform according to claim 2, characterized in that, The standard response node is mobilized to perform baseline output scheduling, and the node output response values ​​are recorded, including: The node's operating status is assessed based on the standard response node; the node's power adjustment range is determined based on the node's operating status and the preset target benchmark output value; and a benchmark output command is generated based on the node's power adjustment range. The baseline output command is encapsulated into an output control command and sent to the standard response node via the communication channel, and the node output response value is recorded.

4. The method for constructing a distributed power supply collaborative scheduling platform according to claim 1, characterized in that, Step 3, which involves plotting the force response curve based on the nodal response state, includes: Read the power increment and fit the actual output value according to the node response status; establish a coordinate mapping relationship with the power increment as the horizontal axis and the fitted actual output value as the vertical axis. The output coordinate points in the coordinate mapping relationship are filled in ascending order of power increment to form a continuous data point series; smooth interpolation is performed based on the continuous data point series to draw the preliminary output response curve; The initial output response curve is subjected to noise filtering to determine the response trend characteristics; based on the response trend characteristics, differentiated line types, colors, and legend labels are assigned to form the output response curve.

5. The method for constructing a distributed power supply collaborative scheduling platform according to claim 1, characterized in that, Step S3, which calculates the output tracking deviation based on the output response curve, includes: The local peak points with deviation values ​​greater than zero are detected based on the output response curve; the peak point with the largest deviation amplitude among the local peak points is selected as the maximum positive deviation point; the output overshoot constraint parameters are determined based on the maximum positive deviation point. The output response curve is constrained and calibrated according to the overshoot constraint parameters, and the effective output range is recorded; the output tracking deviation is calculated based on the effective output range.

6. The method for constructing a distributed power supply collaborative scheduling platform according to claim 1, characterized in that, Step S3, configuring a multi-level scheduling strategy based on the output tracking deviation, includes: If the absolute value of the output tracking deviation is less than or equal to 5% of the preset rated output, configure the basic scheduling strategy; if the absolute value of the output tracking deviation is greater than 5% of the preset rated output but less than or equal to 15% of the preset rated output, configure the enhanced scheduling strategy; if the absolute value of the output tracking deviation is greater than 15% of the preset rated output, configure the emergency scheduling strategy. Integrate basic scheduling strategies, enhanced scheduling strategies, and emergency scheduling strategies into a multi-level scheduling strategy.

7. The method for constructing a distributed power supply collaborative scheduling platform according to claim 6, characterized in that, If the absolute value of the output tracking deviation is less than or equal to 5% of the preset rated output, the basic scheduling strategy includes: If the absolute value of the output tracking deviation is less than or equal to 5% of the preset rated output, maintain the preset instruction issuance cycle; perform instruction scheduling simulation according to the preset instruction issuance cycle, and calculate the node response delay; Node communication performance is evaluated based on node response latency, and communication priority identifiers are determined. Scheduling execution queues are allocated based on the communication priority identifiers, and the scheduling execution queues are used as the basic scheduling strategy.

8. The method for constructing a distributed power supply collaborative scheduling platform according to claim 6, characterized in that, If the absolute value of the output tracking deviation is greater than 5% of the preset rated output but less than 15% of the preset rated output, the enhanced scheduling strategy includes: If the absolute value of the output tracking deviation is greater than 5% of the preset rated output but less than 15% of the preset rated output, the preset instruction issuance cycle will be shortened to two-thirds to determine the correction instruction cycle. Based on the execution instruction scheduling simulation, the instantaneous deviation value of the node is calculated; the deviation trend coefficient is determined based on the instantaneous deviation value of the node; the deviation trend coefficient is multiplied by the correction instruction cycle to form the feedforward compensation value, which is used as an enhanced scheduling strategy.

9. The method for constructing a distributed power supply collaborative scheduling platform according to claim 6, characterized in that, If the absolute value of the output tracking deviation is greater than 5% of the preset rated output, the emergency dispatch strategy includes: If the absolute value of the output tracking deviation is greater than 15% of the preset rated output, the preset instruction issuance cycle will be shortened to half, and the node output hit rate will be recorded; scheduling nodes with output hit rates lower than the preset hit rate threshold will be selected based on the node output hit rate. Collect inverter fault signals from the scheduling nodes; determine inverter fault codes based on the inverter fault signals; configure emergency scheduling strategies based on the inverter fault codes.

10. The method for constructing a distributed power supply collaborative scheduling platform according to claim 9, characterized in that, Configuring emergency dispatch strategies based on inverter fault codes includes: Parse the inverter fault codes to determine the over-temperature fault type; calculate the power deficit value based on the over-temperature fault type; allocate compensation power shares based on the power deficit value; and send the compensation power shares to the preset backup node through a high-priority communication link. If the power deviation of the standby node is lower than the preset power deviation for five consecutive cycles, a 20% arithmetic decrease in the compensation power share will be used until the power deviation is less than 5%, at which point the cluster compensation will be terminated to complete the emergency scheduling strategy.

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