Instruction issuing method and system based on regulation and control cloud development distributed group regulation and group control

Abstract

The invention provides an instruction issuing method and system based on regulation and control cloud development distributed group regulation and group control, and the method comprises the steps: collecting the real-time operation data of each photovoltaic node and a power grid, and forming a state vector; generating a regulation and control instruction set for regulating active output based on an optimization target by adopting active and frequency control logic; distributing an active power regulation and control instruction according to a proportion; bus voltage and reactive power and power factor control logic are adopted, and reactive power regulation and control instructions of reactive power output of an inverter and SVG compensation are distributed; continuously collecting actual output and voltage data after the photovoltaic equipment executes the instruction, comparing the actual output and voltage data with the expected physical behavior model, and constructing a response difference atlas; matching a correction strategy for the equipment with the deviation according to the deviation type, and generating a compensation instruction; analyzing the improvement degree of instruction execution on the global index of the power grid; and checking a distributed photovoltaic actual output curve and a line load rate curve of the target line through a visual interface. According to the invention, the observable, measurable, adjustable and controllable capabilities of distributed photovoltaic power generation are improved.

Description

Technical Field

[0001] This invention relates to the field of distributed photovoltaic group control technology, and more specifically, to a method and system for issuing instructions for distributed group control based on a control cloud. Background Technology

[0002] With the rapid growth of distributed photovoltaic (PV) installations, their decentralized, random, and intermittent characteristics have brought numerous challenges to the safe and stable operation of the power grid. These challenges include changes in power flow direction (power flow turbulence) caused by distributed PV grid connection, line overload, local voltage exceeding limits, and relay protection malfunctions. The main manifestations and causes of these problems are as follows:

[0003] 1. Traditional power grid power flow is "unidirectional radial" (transmission from substations to the user side), while after distributed photovoltaic grid connection, the power flow becomes "multi-source and multi-directional," leading to power grid power flow disorder. The main reasons include:

[0004] (1) Mismatch between power generation and load in time and space: Photovoltaic output fluctuates in real time with the intensity of light (such as a sudden increase in output at noon on sunny days and a sudden drop on cloudy days or at night), while user load (such as residential electricity consumption) shows a "morning peak and evening peak" pattern. When the peaks and valleys of the two do not coincide, it will lead to reverse power flow or violent fluctuations in the line.

[0005] (2) Lack of global coordination and control: Distributed photovoltaic power generation is mostly self-generated and self-consumed by users and is not included in the unified dispatch of the power grid. Its output regulation (such as start-up and shutdown, power control) is independent of the power grid load demand. When "over-generation" occurs in local areas (such as remote rural transformer areas), the power flow cannot be redistributed through global optimization, causing local congestion or reverse impact.

[0006] 2. Line overload refers to the actual current exceeding the line's rated current carrying capacity. Causes include: a sudden increase in output exceeding the line's maximum capacity, and reverse current flow leading to "bidirectional overload." Prolonged line overload can cause overheating, insulation aging, and even burnout.

[0007] 3. Voltage exceeding limits: After distributed power sources are connected to the power system, their "reaction" to voltage regulation is mainly reflected in intermittent output fluctuations and improper reactive power control, which may cause sudden voltage rises or falls. Photovoltaic output leads to "voltage rise" in the line, resulting in higher voltage. The superposition of sudden output drop and load fluctuation leads to "voltage drop" in the line, resulting in lower voltage.

[0008] 4. Data silos exist in the scheduling side, the load management system, and the user acquisition system, lacking a real-time collaboration mechanism, making it impossible to achieve holographic perception of distributed power sources.

[0009] 5. Existing control instructions rely on human experience for formulation, lack standardized verification logic for review, have an unclosed execution chain, and have high response latency.

[0010] 6. The lack of a unified framework for large-scale photovoltaic management and control at the provincial level results in low efficiency in data processing and command issuance, failing to meet the needs of precise regulation and control. Summary of the Invention

[0011] In view of this, the purpose of this invention is to propose a method and system for issuing instructions for distributed group control based on a control cloud, focusing on the research of distributed photovoltaic group control technology. It establishes a full-process processing mechanism for online monitoring, early warning, and handling on the control cloud, employing active power and frequency control logic to generate a set of control instructions for adjusting active power output based on optimization objectives. Based on equipment adjustment capabilities and response speed, it prioritizes inverters to undertake frequency regulation tasks and allocates active power control instructions proportionally. It also employs bus voltage, reactive power, and power factor control logic to allocate reactive power control instructions for inverter reactive power output and SVG compensation. This provides auxiliary decision-making for problems such as line overload caused by distributed photovoltaic power generation, improving the observability, measurability, adjustability, and controllability of distributed photovoltaics, ensuring the safe and stable operation of the power grid, and promoting the efficient local consumption of distributed new energy. It realizes group control of distributed photovoltaics to solve the low-voltage power grid operation safety problems caused by large-scale distributed photovoltaic grid connection, improve the power supply quality and equipment resilience of the low-voltage side of the distribution area, and help improve the power regulation and safe and stable operation capabilities of the large power grid.

[0012] This invention provides a method for issuing commands based on a control cloud-based distributed group control system, comprising the following steps:

[0013] S1. Before issuing control commands, collect real-time operating data (active / reactive power output, voltage, frequency, etc.) of each photovoltaic node and the power grid to form a state vector;

[0014] By collecting real-time data on active and reactive power output, voltage, and frequency from photovoltaic nodes, the current operating status of the power grid can be accurately grasped, providing a dynamic basis for control commands. For example, reactive power compensation strategies can be optimized based on real-time voltage data to prevent voltage exceedances.

[0015] State vectors contain key parameters such as frequency and voltage, which help identify potential risks such as low-frequency grid oscillations and allow for proactive stabilization measures. For example, when the frequency is below 48Hz, the inverter's fast response mechanism can be triggered. Combined with state vectors, dispatching agencies can implement hierarchical control of photovoltaic power plants, achieving precise active / reactive power tracking by adjusting inverter output or starting / stopping equipment.

[0016] S2. Using active power and frequency control logic, a set of control instructions for adjusting photovoltaic active power output is generated based on optimization objectives, and the instructions are verified to be within the equipment adjustment range of the photovoltaic equipment. The optimization objectives include: minimum grid loss and qualified voltage. The equipment adjustment range includes: maximum apparent power of the inverter and SVG capacity limit.

[0017] By optimizing for minimum network losses, active power losses in transmission lines can be effectively reduced, thus improving the overall operating efficiency of the power grid. For example, in active distribution networks, network losses can be significantly reduced by coordinating and optimizing photovoltaic active power output and reactive power compensation equipment.

[0018] With voltage compliance as the optimization goal, the active power output of photovoltaic systems can be dynamically adjusted to avoid voltage exceedance issues caused by power fluctuations. For example, in grid connection tests of photovoltaic power plants, it is necessary to verify reactive power / voltage control capabilities to ensure that the voltage remains stable within the qualified range after grid connection.

[0019] By verifying whether the instructions are within the device's adjustment range (such as the inverter's maximum apparent power and SVG capacity limit), overload or damage to the device can be prevented.

[0020] Under special operating conditions such as power curtailment, this logic can adjust the photovoltaic output strategy to balance grid constraints and power generation efficiency.

[0021] S3. Based on the equipment's adjustment capability and response speed, prioritize inverters with fast adjustment rates and sufficient capacity to undertake frequency regulation tasks (support grid frequency stability); predict the inverter's current maximum power generation capacity through a power generation capacity assessment model, and make corrections based on reactive power and AC voltage factors, and allocate active power control commands proportionally.

[0022] Inverters are fast-response (millisecond level) and highly accurate in regulation, enabling them to quickly track grid frequency fluctuations and effectively suppress frequency deviations.

[0023] By using a power generation capacity assessment model (considering factors such as solar irradiance and temperature) to predict the inverter's maximum power generation capacity and incorporating correction factors such as reactive power and AC voltage, overload or under-generation can be avoided. For example:

[0024] Reactive power correction: When the inverter needs to provide reactive power support, its active power output capability is limited by the maximum apparent power (e.g., in PQ mode 1, the maximum AC output power is equal to the maximum apparent power).

[0025] Voltage correction: When the grid voltage is abnormal, the inverter needs to reduce its output to ensure equipment safety. The model can dynamically adjust the distribution ratio.

[0026] The proportional allocation command can coordinate the work of multiple inverters to avoid overload of a single unit and achieve precise power control.

[0027] The power allocation strategy is dynamically adjusted and corrected based on real-time power generation capacity (such as fluctuations in photovoltaic output) to ensure that grid demand matches equipment capacity. Precise allocation of active power reduces grid losses and transmission line losses. Combined with reactive power optimization (such as SVC / DVC compensation), voltage quality can be improved, inverters can avoid long-term over-limit operation, and equipment lifespan can be extended.

[0028] The system employs control logic based on bus voltage, reactive power, and power factor to allocate reactive power control commands for inverter reactive power output and SVG reactive power compensation, thereby maintaining stable bus voltage and optimizing the power factor.

[0029] By monitoring the bus voltage deviation in real time and combining reactive power and power factor data, the reactive power output distribution between the inverter and the SVG is dynamically adjusted. The SVG's millisecond-level response capability can quickly suppress voltage fluctuations, while the inverter adjusts the reactive power support voltage, forming a dual protection mechanism. For example, in a photovoltaic grid-connected system, this strategy can suppress bus voltage fluctuations caused by sudden changes in sunlight.

[0030] Through closed-loop power factor control, the system power factor is ensured to be close to 1 (such as the State Grid's requirement of 0.9 or above). The SVG compensates for the lagging reactive power generated by inductive loads, and the inverter adjusts the reactive power output according to the real-time power generation capacity (such as photovoltaic output), reducing ineffective current flow and lowering line losses by approximately 15%-30%.

[0031] Reactive power commands are allocated based on equipment characteristics: SVG (Static Var Generator) handles high-frequency dynamic compensation (such as suppressing voltage flicker), while the inverter provides basic reactive power support. This division of labor maximizes equipment utilization; for example, SVG prioritizes responding to rapid fluctuations, while the inverter handles steady-state reactive power demands, reducing the capacity requirements of the SVG.

[0032] SVG can suppress 3rd / 5th / 7th harmonics while compensating for reactive power, and the inverter reduces output harmonics through PWM control. The synergy of both can reduce voltage distortion (THD<3%), avoid the resonance risk of traditional capacitor banks, and extend equipment life.

[0033] S4. Execute control commands, continuously collect actual output and voltage data of photovoltaic equipment after executing commands, compare with the expected physical behavior model, and construct a response difference map; for photovoltaic equipment with deviations, match correction strategies according to the type of deviation (such as equipment response lag, communication delay) and generate compensation commands.

[0034] By comparing actual output with model expectations in real time, deviation types such as equipment response lag and communication delay can be accurately identified. For example, for inverter MPPT tracking errors, the step size parameter of the disturbance observation method can be dynamically adjusted to control power fluctuations within ±0.5%. This closed-loop control mechanism significantly reduces power oscillations caused by environmental changes (such as sudden changes in irradiance) and improves grid voltage stability.

[0035] Difference maps can quickly locate equipment anomalies, such as component mismatch or communication network congestion. By analyzing voltage-current characteristic deviations, it can distinguish between non-mismatch faults (such as hot spots) and mismatch faults (such as string blockage), with a diagnostic accuracy rate of over 90%. Combined with compensation commands, it can automatically isolate faulty equipment and adjust operating parameters, reducing manual inspection costs by more than 30%.

[0036] Based on the deviation type matching strategy, the reactive power compensation ratio between the SVG and the inverter can be dynamically allocated. For example, in communication delay scenarios, the local inverter is given priority to provide fast reactive power support (response time ≤ 100ms), while the SVG compensates for steady-state reactive power demand, reducing line losses by 15%-30%. At the same time, the output power curve is smoothed through compensation commands, reducing reliance on the energy storage system.

[0037] S5. Analyze the degree of improvement of the overall power grid indicators (voltage qualification rate, frequency deviation, network loss) by the execution of the analysis instructions;

[0038] By executing commands (such as adjusting the tap changer of the distribution transformer, load balancing, and multi-level voltage regulation), the problem of low voltage caused by uneven load or equipment overload can be effectively solved.

[0039] The improvement of frequency deviation depends on the precise execution of power grid dispatch commands. Command execution reduces network losses through both technical and management approaches.

[0040] S6. Through the visualization interface, view the actual output curve of the distributed photovoltaic system on the target line and the line load rate curve, and observe whether the curve meets the control requirements within the execution time range. The control requirements include: the actual output does not exceed the power limit and the load rate is stable within the safe range; display the full life cycle status of the instruction in real time (not submitted for review, under review, approved, issued, etc.), and combine the real-time power of the line and the regional load data to indirectly judge the local photovoltaic consumption situation. If there is abnormal output or insufficient consumption, trigger an early warning in time and assist in formulating an adjustment plan.

[0041] The visualization interface can display the actual photovoltaic output curve and the line load rate curve in real time, intuitively verifying whether the control requirements of "actual output not exceeding the power limit and load rate stable within the safe range" are met.

[0042] The interface integrates command status (such as not submitted for review, under review, or issued), and combined with real-time line power and regional load data, it can indirectly assess the local photovoltaic consumption situation and achieve full lifecycle tracking of command status. For example, if the command status is "issued" but the consumption data is abnormal, an early warning can be triggered and the inverter output strategy can be adjusted accordingly.

[0043] When photovoltaic output suddenly increases or the load rate deviates from the safe range, an early warning is automatically triggered, and historical data is correlated to assist in the analysis of the cause (such as weather changes or equipment failure). For example, harmonic fluctuations or voltage flicker problems can be quickly located through abnormal curves.

[0044] Based on the early warning information, the visual interface can recommend flexible adjustment solutions (such as dynamic adjustment of inverter power) to avoid the problem of curtailment of solar power caused by traditional grid outages.

[0045] By integrating data such as photovoltaic output, load factor, and command status, multi-dimensional data fusion analysis is performed to optimize data-driven decision-making. For example, by analyzing load patterns through historical curves, more precise power dispatch plans can be formulated.

[0046] Furthermore, the method for allocating the reactive power output of the inverter and the reactive power control command of the SVG reactive power compensation in step S3 includes:

[0047] The reactive power allocation between the inverter and the SVG is based on the principle of prioritizing inverter allocation. Without affecting active power output, the reactive power regulation capability of the inverter is maximized first, and the remaining reactive power demand is supplemented by the SVG. The allocation formula is as follows:

[0048] Qinverse = min(Qadjustable, Qtotal × k),

[0049] QSVG = Qtotal - Qinverse;

[0050] Wherein, Q_adjustable is the current adjustable reactive power of the inverter (Q_adjustable is determined by the active dead zone range and apparent power limit), Q_inverse is the reactive power of the inverter, Q_SVG is the reactive power of the SVG, Q_total is the total reactive power, and k is the allocation coefficient (k is dynamically corrected to adapt to equipment characteristics).

[0051] SVG itself consumes a relatively high amount of power (approximately 2%-3% of its rated capacity), while the energy consumption of inverter reactive power regulation is almost negligible. Prioritizing the use of inverter reactive power regulation can reduce the commissioning time of SVG, reduce SVG operating losses, and significantly reduce the total system energy consumption.

[0052] Inverters can regulate reactive power while injecting active power (e.g., a grid-connected photovoltaic inverter can output reactive power equal to ±0.8 times the active power). Prioritizing the use of the inverter's reactive power capacity can avoid over-configuration of SVG and reduce initial investment costs.

[0053] Inverters offer faster response times (milliseconds), quickly compensating for fluctuating loads and making them suitable for handling rapidly fluctuating reactive power demands from sources like photovoltaic systems. SVG, on the other hand, serves as a supplement, providing continuous adjustment when inverter capacity is insufficient, forming a "tiered compensation" mechanism to improve system dynamic performance.

[0054] Furthermore, the inverter prioritizes compensation for fundamental reactive power, while the SVG can centrally manage harmonics (such as the 3rd / 5th / 7th harmonics generated by charging piles). The combination of the two can achieve synergistic optimization of reactive power compensation and harmonic management.

[0055] Furthermore, the method for constructing a response difference map by comparing the S4 step with the expected physical behavior model includes:

[0056] Based on mechanical equations, circuit models, physical models, or data-driven machine learning models trained on historical data, construct a desired physical behavior model that reflects the behavior of photovoltaic equipment under ideal or specific operating conditions.

[0057] Through parameter estimation and model calibration, the expected physical behavior model can accurately reflect the behavior of real equipment under ideal or specific working conditions, ensuring the effectiveness of the comparison.

[0058] The actual data collected in real time is compared with the expected value predicted by the expected physical behavior model. The difference between the actual data and the expected value is calculated, and a difference map is constructed based on the difference analysis.

[0059] The difference map visually presents the distribution of deviations between actual data and model predictions, enabling rapid identification of abnormal areas. The physical equation-based model accurately simulates the IV characteristics of photovoltaic equipment under standard test conditions, while the machine learning model captures the nonlinear effects of complex environmental factors. This dual-validation mechanism significantly reduces the false alarm rate, for example, distinguishing between power degradation caused by dust obstruction and anomalies caused by inverter malfunctions.

[0060] By estimating parameters and updating the model, the adaptive capability of the model is enhanced, and it is expected that the physical behavior model can continuously track the performance degradation of the device. This dynamic calibration mechanism enables the difference map to not only reflect instantaneous anomalies, but also reveal long-term performance degradation trends.

[0061] The time-series analysis function of the difference map supports performance evaluation at multiple time scales. By combining meteorological data with historical operating records, the system can predict the probability of potential failures, providing a basis for preventative maintenance decisions. Practical applications show that this method can reduce unplanned downtime by approximately 30% and improve the overall profitability of the power plant.

[0062] Difference mapping technology transforms abstract numerical differences into visualized spatial distribution characteristics, providing a comprehensive monitoring solution for photovoltaic systems from the component level to the system level. It is an important technical support for achieving intelligent operation and maintenance and precise management.

[0063] Furthermore, the method for calculating the difference between the actual data and the expected value, and constructing a difference map based on the difference analysis, includes:

[0064] Calculate the absolute error, relative error, or dynamic response deviation between the actual output, voltage parameters, and model predictions;

[0065] By utilizing the hierarchical framework of knowledge graphs, devices, parameters, commands, difference value elements, and their relationships are stored in a structured manner and visualized to form a response difference graph. The graph intuitively displays the difference patterns of each parameter under different commands, helping to quickly locate anomalies.

[0066] By using indicators such as absolute error (e.g., the actual output differs from the predicted value by 5kW), relative error (e.g., voltage deviation ±2%), and dynamic response deviation (e.g., response delay of 0.5s under a step command), the deviation of the actual behavior of the equipment from the expected model is quantified, avoiding the limitations of a single indicator.

[0067] By combining historical data to set dynamic thresholds (such as the 3σ principle), normal fluctuations and abnormal deviations can be distinguished, thereby reducing the false alarm rate.

[0068] A knowledge graph-based structured storage method is used to achieve complex association analysis based on element association mapping. For example, elements such as equipment (e.g., photovoltaic inverters), parameters (voltage, frequency), commands (power regulation commands), and difference values ​​are stored in the form of nodes and edges to establish multi-level relationships (e.g., "Equipment A - Voltage Deviation - Command X - Difference Value 0.8%").

[0069] By using graph queries (such as SPARQL), abnormal patterns can be quickly located, enabling cross-domain correlation analysis. For example, discovering that "equipment with voltage deviation >5% is located in the same area" may indicate a regional power grid problem.

[0070] Visualizing the hierarchical structure of differential maps can accelerate root cause identification. An example of a hierarchical structure is shown below:

[0071] Macro level: Global device difference heatmap, quickly identifying areas with high deviation;

[0072] Mid-level: The instruction-parameter association network of the device cluster, highlighting abnormal nodes;

[0073] Micro level: Time series difference curves for single devices, comparing predicted and actual values.

[0074] Interactive exploration: Supports drill-down analysis (such as clicking on an abnormal node to view historical data of related parameters), helping maintenance personnel to quickly locate the source of the fault (such as inverter aging causing continuously low output).

[0075] By tracing the abnormal propagation path through graphs (such as "voltage fluctuation → protection action → power output reduction"), the fault chain can be identified.

[0076] Preferably, the high-frequency deviation region is fed back to the physical model (e.g., to correct the photovoltaic efficiency curve) or the data-driven model (e.g., to supplement training data) to improve prediction accuracy.

[0077] Furthermore, the graph structure of knowledge graphs is naturally adapted to multi-source heterogeneous data (such as SCADA data and meteorological data), facilitating the expansion to new devices or parameters. Through API integration with existing monitoring systems (such as EMS and SCADA), existing data streams can be reused, enabling cross-system integration.

[0078] Furthermore, the method for generating compensation instructions by matching the correction strategy according to the deviation type in step S4 includes:

[0079] For inverters that experience response lag due to physical characteristics or control algorithm delays, a dynamic compensation strategy is used to correct the lag. A predictive model incorporating delay elements is constructed to calculate control quantities in advance to offset the lag effect. Compensation commands are output based on the predictive model. For example, in frequency regulation, the active power output command of the inverter is adjusted in advance; in voltage control, reactive power compensation commands are injected in advance based on the grid voltage change rate.

[0080] To address the mismatch in control cycles caused by communication delays, timing correction is performed through timing synchronization and data caching mechanisms; timestamps are cached during data acquisition, and delays are compensated for through interpolation or prediction algorithms; for example, in frequency regulation, the triggering timing of a primary frequency modulation command is dynamically adjusted based on cached data.

[0081] For multi-mode (such as multi-inverter collaboration and grid interaction) deviation collaborative compensation, a time alignment mechanism is used to coordinate multi-source data streams and generate joint compensation commands; in voltage-frequency collaborative control, composite commands are generated by combining the response characteristics of SVG and inverters; the input compensation (ICM) mechanism dynamically adjusts the deviation value according to the compensation method (addition / subtraction / replacement) and generates correction commands.

[0082] Specifically, by coordinating the timestamps of the data streams from multiple inverters, control deviations caused by communication delays or asynchronous sampling are eliminated, ensuring the synchronization of joint compensation commands.

[0083] The rapid dynamic compensation capability of SVG (Static Var Generator) (response time ≤ 50μs) combined with the active power output characteristics of the inverter can simultaneously achieve voltage stabilization and frequency regulation. For example, in a photovoltaic grid-connected system, SVG suppresses voltage flicker through continuous reactive power compensation, while the inverter optimizes active power output through the MPPT algorithm.

[0084] The reactive power compensation of the SVG and the active power of the inverter are dynamically allocated according to the grid demand, avoiding the problem of discontinuous switching of traditional capacitor banks.

[0085] Furthermore, the method for assessing the degree of improvement in global power grid indicators by executing the analysis instructions in step S5 includes:

[0086] Acquire power grid operation data before and after command execution, including node voltage, frequency, and branch power flow;

[0087] The power grid operation data is cleaned to remove outliers (such as jump data caused by faults), and typical operating sections (such as peak load and off-peak load periods) are selected to compare the data differences before and after the command is executed.

[0088] The average pass rate of the entire network is calculated by statistically analyzing the percentage of time that the voltage of each node is within the allowable deviation range (e.g., ±10%). The effect of commands on suppressing voltage deviation is analyzed by combining the voltage reactive power control (AVC) adjustment sensitivity.

[0089] Calculate the root mean square deviation (RMSD) of the system frequency before and after command execution to assess frequency stability; compare the dynamic matching degree between the active power output command and the actual response.

[0090] Based on the power flow calculation results, the changes in active power loss (network loss) before and after the execution of the command are compared, and the contribution of reactive power optimization command to reducing line loss is analyzed.

[0091] Preferably, statistical methods (such as t-tests) are used to verify the significance of the improvement results.

[0092] The data cleaning process effectively removes fault-related data fluctuations, ensuring the accuracy of the analysis results. Comparisons using typical operating sections such as peak and off-peak loads reflect the adaptability of the control strategy under different load conditions. Statistical analysis of the consistent percentage of voltage deviations at each node within the allowable range quantifies the overall pass rate of voltage control. Combined with AVC regulation sensitivity analysis, the effectiveness of commands in suppressing voltage deviations can be accurately evaluated.

[0093] Using the root mean square deviation of the system frequency as an evaluation index can objectively reflect the degree of improvement in frequency stability; comparing the matching degree between the active power output command and the actual response can verify the response accuracy of the control strategy.

[0094] By comparing the changes in active power loss before and after command execution based on power flow calculation results, the actual contribution of reactive power optimization to reducing line losses can be directly quantified.

[0095] This method provides comprehensive data support for power grid operation optimization by systematically evaluating the multifaceted impact of control commands on voltage, frequency, and network losses, which helps to improve the capacity for renewable energy absorption and the economic efficiency of system operation.

[0096] This invention also provides a command issuance system for distributed group control and regulation based on a control cloud, implementing the command issuance method for distributed group control and regulation based on a control cloud as described above, including:

[0097] Data acquisition and integration module: used to collect real-time operating data of each photovoltaic node and the power grid before the control command is issued, and form a state vector;

[0098] Instruction generation and verification module: This module uses active power and frequency control logic to generate a set of control instructions for adjusting the active power output of photovoltaic systems based on optimization objectives, and verifies whether the instructions are within the equipment adjustment range of the photovoltaic equipment. The optimization objectives include: minimum grid loss and qualified voltage. The equipment adjustment range includes: maximum apparent power of the inverter and SVG capacity limit.

[0099] Command allocation module: Based on the equipment's adjustment capability and response speed, it prioritizes inverters with fast adjustment rates and sufficient capacity to undertake frequency regulation tasks; it estimates the inverter's current maximum power generation capacity through a power generation capacity assessment model, and corrects it by combining reactive power and AC voltage factors, and allocates active power control commands proportionally; it uses the control logic of bus voltage, reactive power, and power factor to allocate reactive power control commands for inverter reactive power output and SVG reactive power compensation, maintaining bus voltage stability while optimizing the power factor;

[0100] Command tracking and compensation module: used to execute control commands, continuously collect actual output and voltage data of photovoltaic equipment after command execution, compare with the expected physical behavior model, and construct response difference map; for equipment with deviations, match correction strategies according to the deviation type and generate compensation commands; analyze the degree of improvement of the overall power grid indicators by command execution;

[0101] The scheduling and monitoring module is used to view the actual output curve and load rate curve of the distributed photovoltaic system on the target line through a visual interface. It observes whether the curve meets the control requirements within the execution time range. The control requirements include: the actual output does not exceed the power limit and the load rate is stable within the safe range. It displays the status of the entire life cycle of the command in real time. Combined with the real-time power of the line and regional load data, it indirectly judges the local photovoltaic consumption. If abnormal output or insufficient consumption occurs, it promptly triggers an early warning and assists in formulating adjustment plans.

[0102] Furthermore, the instruction tracking and compensation module includes:

[0103] Difference Calculation Unit: Used to calculate the absolute error, relative error, or dynamic response deviation between the actual output, voltage parameters, and model prediction values;

[0104] Knowledge Graph Construction Unit: This unit utilizes the hierarchical framework of knowledge graphs to structure and visualize devices, parameters, commands, difference value elements, and their relationships, forming a response difference graph. The graph visually displays the difference patterns of parameters under different commands, helping to quickly locate anomalies.

[0105] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, it implements the steps of the instruction issuance method for distributed group control based on the control cloud as described above.

[0106] The present invention also provides a computer device, the computer device including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the steps of the instruction issuance method for distributed group control based on the control cloud as described above.

[0107] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0108] This invention provides a method and system for issuing commands for distributed group control based on a control cloud, focusing on the research of distributed photovoltaic group control technology. It establishes a full-process processing mechanism for online monitoring, early warning, and handling on the control cloud. Employing active power and frequency control logic, it generates a set of control commands for adjusting active power output based on optimization targets. Based on equipment adjustment capabilities and response speeds, it prioritizes inverters to undertake frequency regulation tasks and allocates active power control commands proportionally. Using bus voltage, reactive power, and power factor control logic, it allocates reactive power control commands for inverter reactive power output and SVG compensation. This provides auxiliary decision-making for problems such as line overload caused by distributed photovoltaic power generation, improving the observability, measurability, adjustability, and controllability of distributed photovoltaics, ensuring the safe and stable operation of the power grid, and promoting the efficient local consumption of distributed new energy. It realizes group control of distributed photovoltaics, effectively solving the low-voltage power grid operation safety problems caused by large-scale distributed photovoltaic grid connection, improving the power supply quality and equipment resilience of the low-voltage side of the distribution area, and contributing to the improvement of the power regulation and safe and stable operation capabilities of the large power grid. Attached Figure Description

[0109] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0110] In the attached diagram:

[0111] Figure 1 This is a graph showing the actual output of distributed photovoltaic power in an embodiment of the present invention;

[0112] Figure 2 This is a load factor curve of a distributed photovoltaic line according to an embodiment of the present invention;

[0113] Figure 3 This is a flowchart of the instruction issuance method for distributed group control based on the control cloud according to an embodiment of the present invention;

[0114] Figure 4 This is a schematic diagram of the configuration of a computer device according to an embodiment of the present invention. Detailed Implementation

[0115] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and products consistent with some aspects of this disclosure as detailed in the appended claims.

[0116] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0117] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0118] The embodiments of the present invention will be described in further detail below.

[0119] This invention provides a method for issuing commands for distributed group control and dispatching based on a control cloud platform. See [link to relevant documentation]. Figure 3 As shown, it includes the following steps:

[0120] S1. Before issuing control commands, collect real-time operating data (active / reactive power output, voltage, frequency, etc.) of each photovoltaic node and the power grid to form a state vector;

[0121] S2. Using active power and frequency control logic, a set of control instructions for adjusting photovoltaic active power output is generated based on optimization objectives, and the instructions are verified to be within the equipment adjustment range of the photovoltaic equipment. The optimization objectives include: minimum grid loss and qualified voltage. The equipment adjustment range includes: maximum apparent power of the inverter and SVG capacity limit.

[0122] S3. Based on the equipment's adjustment capability and response speed, prioritize inverters with fast adjustment rates and sufficient capacity to undertake frequency regulation tasks (support grid frequency stability); predict the inverter's current maximum power generation capacity through a power generation capacity assessment model, and make corrections based on reactive power and AC voltage factors, and allocate active power control commands proportionally.

[0123] The system employs control logic based on bus voltage, reactive power, and power factor to allocate reactive power control commands for inverter reactive power output and SVG reactive power compensation, thereby maintaining stable bus voltage and optimizing the power factor.

[0124] The method for distributing reactive power control commands for inverter reactive power output and SVG reactive power compensation is as follows:

[0125] The reactive power allocation between the inverter and the SVG is based on the principle of prioritizing inverter allocation. Without affecting active power output, the reactive power regulation capability of the inverter is maximized first, and the remaining reactive power demand is supplemented by the SVG. The allocation formula is as follows:

[0126] Qinverse = min(Qadjustable, Qtotal × k),

[0127] QSVG = Qtotal - Qinverse;

[0128] Where Q_adjustable is the current adjustable reactive power of the inverter, Q_inverse is the reactive power of the inverter, Q_SVG is the reactive power of the SVG, Q_total is the total reactive power, and k is the allocation coefficient.

[0129] S4. Execute control commands, continuously collect actual output and voltage data of photovoltaic equipment after executing commands, compare with the expected physical behavior model, and construct a response difference map; for photovoltaic equipment with deviations, match correction strategies according to the type of deviation (equipment response lag, communication delay, etc.) and generate compensation commands.

[0130] Compared with the expected physical behavior model, the method for constructing the response difference map is as follows:

[0131] Based on mechanical equations, circuit models, physical models, or data-driven machine learning models trained on historical data, construct a desired physical behavior model that reflects the behavior of photovoltaic equipment under ideal or specific operating conditions.

[0132] Through parameter estimation and model calibration, the expected physical behavior model can accurately reflect the behavior of real equipment under ideal or specific working conditions, ensuring the effectiveness of the comparison.

[0133] The actual data collected in real time is compared with the expected value predicted by the expected physical behavior model. The difference between the actual data and the expected value is calculated, and a difference map is constructed based on the difference analysis.

[0134] The method for calculating the difference between actual data and expected values, and constructing a difference map based on difference analysis, is as follows:

[0135] Calculate the absolute error, relative error, or dynamic response deviation between the actual output, voltage parameters, and model predictions;

[0136] By utilizing the hierarchical framework of knowledge graphs, devices, parameters, commands, difference value elements, and their relationships are stored in a structured manner and visualized to form a response difference graph. The graph intuitively displays the difference patterns of each parameter under different commands, helping to quickly locate anomalies.

[0137] The method for generating compensation instructions based on the deviation type matching correction strategy is as follows:

[0138] To address inverter response lag caused by physical characteristics or control algorithm delays, a dynamic compensation strategy is employed. A predictive model incorporating delay elements is constructed to pre-calculate control quantities to offset the lag effect. Compensation commands are output based on the predictive model. In frequency regulation, the inverter's active power output command is adjusted in advance; in voltage control, reactive power compensation commands are injected in advance based on the grid voltage change rate.

[0139] To address control cycle mismatches caused by communication delays, timing correction is achieved through timing synchronization and data caching mechanisms. Timestamps are cached during data acquisition, and delays are compensated for using interpolation or prediction algorithms. During frequency regulation, the triggering timing of primary frequency modulation commands is dynamically adjusted based on the cached data.

[0140] For multi-mode (multi-inverter collaboration, grid interaction) deviation collaborative compensation, a time alignment mechanism is used to coordinate multi-source data streams and generate joint compensation commands; in voltage-frequency collaborative control, composite commands are generated by combining the response characteristics of SVG and inverters; the input compensation (ICM) mechanism dynamically adjusts the deviation value according to the compensation method (addition / subtraction / replacement) and generates correction commands.

[0141] S5. Analyze the degree of improvement of the overall power grid indicators (voltage qualification rate, frequency deviation, network loss) by the execution of the analysis instructions;

[0142] Acquire power grid operation data before and after command execution, including node voltage, frequency, and branch power flow;

[0143] The power grid operation data is cleaned to remove outliers, and typical operating sections (peak load, off-peak load periods, etc.) are selected to compare the data differences before and after the command is executed.

[0144] The average pass rate of the entire network was calculated by statistically analyzing the percentage of time that the voltage of each node was within the allowable deviation range (±10%); the effect of the command on suppressing voltage deviation was analyzed by combining the voltage reactive power control (AVC) adjustment sensitivity.

[0145] Calculate the root mean square deviation (RMSD) of the system frequency before and after command execution to assess frequency stability; compare the dynamic matching degree between the active power output command and the actual response.

[0146] Based on the power flow calculation results, the changes in active power loss (network loss) before and after the execution of the command are compared, and the contribution of reactive power optimization command to reducing line loss is analyzed.

[0147] Use statistical methods (such as t-test) to verify the significance of the improvement results.

[0148] S6. View the actual output curve of the distributed photovoltaic system on the target line through the visualization interface (e.g., Figure 1 As shown), the line load rate curve (as shown) Figure 2 As shown in the figure, observe whether the curve meets the control requirements within the execution time range. The control requirements include: the actual output does not exceed the power limit and the load rate is stable within the safe range; display the full life cycle status of the instruction in real time (not submitted for review, under review, approved, issued, etc.), and combine the real-time power of the line and the regional load data to indirectly judge the local photovoltaic consumption. If there is abnormal output or insufficient consumption, trigger an early warning in time and assist in the formulation of adjustment plans.

[0149] This invention also provides an instruction issuance system for distributed group control and dispatching based on a control cloud, implementing the instruction issuance method for distributed group control and dispatching based on a control cloud as described above, including:

[0150] Data acquisition and integration module: used to collect real-time operating data of each photovoltaic node and the power grid before the control command is issued, and form a state vector;

[0151] Instruction generation and verification module: This module uses active power and frequency control logic to generate a set of control instructions for adjusting the active power output of photovoltaic systems based on optimization objectives, and verifies whether the instructions are within the equipment adjustment range of the photovoltaic equipment. The optimization objectives include: minimum grid loss and qualified voltage. The equipment adjustment range includes: maximum apparent power of the inverter and SVG capacity limit.

[0152] Command allocation module: Based on the equipment's adjustment capability and response speed, it prioritizes inverters with fast adjustment rates and sufficient capacity to undertake frequency regulation tasks; it estimates the inverter's current maximum power generation capacity through a power generation capacity assessment model, and corrects it by combining reactive power and AC voltage factors, and allocates active power control commands proportionally; it uses the control logic of bus voltage, reactive power, and power factor to allocate reactive power control commands for inverter reactive power output and SVG reactive power compensation, maintaining bus voltage stability while optimizing the power factor;

[0153] Command tracking and compensation module: This module executes control commands, continuously collects actual output and voltage data from photovoltaic equipment after command execution, compares this data with the expected physical behavior model, and constructs a response difference map. For equipment with deviations, it matches correction strategies based on the deviation type and generates compensation commands. It analyzes the degree of improvement that command execution brings to the overall power grid indicators. The command tracking and compensation module includes:

[0154] Difference Calculation Unit: Used to calculate the absolute error, relative error, or dynamic response deviation between the actual output, voltage parameters, and model prediction values;

[0155] Knowledge Graph Construction Unit: This unit utilizes the hierarchical framework of knowledge graphs to structure and visualize devices, parameters, commands, difference value elements, and their relationships, forming a response difference graph. The graph visually displays the difference patterns of parameters under different commands, helping to quickly locate anomalies.

[0156] The scheduling and monitoring module is used to view the actual output curve and load rate curve of the distributed photovoltaic system on the target line through a visual interface. It observes whether the curve meets the control requirements within the execution time range. The control requirements include: the actual output does not exceed the power limit and the load rate is stable within the safe range. It displays the status of the entire life cycle of the command in real time. Combined with the real-time power of the line and regional load data, it indirectly judges the local photovoltaic consumption. If abnormal output or insufficient consumption occurs, it promptly triggers an early warning and assists in formulating adjustment plans.

[0157] In this embodiment, the approved control instructions are pushed to the load management system in the form of a message queue through the data exchange platform interface of the control cloud. The load management system monitors the changes in the message queue in real time. After receiving the instructions, it forwards the distributed photovoltaic control requirements of the control instructions to the user acquisition system, triggers the control operation, and updates the instruction status to: "order issued", forming a closed-loop execution link between the scheduling side, the load management system, and the user acquisition system.

[0158] Inverter data is collected by the data collector and sent to the marketing department's data collection system. The marketing department then synchronizes all the measurement data to the load management center platform. The load management center platform analyzes and filters the data to select all user power generation data and adjustable power generation data, which are then pushed to the data platform. The data platform then distributes the data to the control cloud.

[0159] The dispatcher sends instructions from the control cloud to the management center platform, the management center platform feeds back the execution results to the marketing department, the marketing department feeds back the execution results to the control cloud, and the control cloud platform displays the group control result curve for monitoring.

[0160] An example of the system data interaction process in this embodiment is as follows:

[0161] (1) Data content from the dispatching side to the negative control system:

[0162] A. Information on the regulating unit:

[0163] The name of the control unit, such as "XX City Power Control Center" or "XX County Power Dispatch Office", should be clearly stated, specifying the source of the data and the responsible party.

[0164] B. Adjusting the event date:

[0165] The control specifies the exact date of the event, accurate to the day. For example, "2025-07-17" is used to accurately record the date the event occurred, facilitating subsequent time series analysis and statistics.

[0166] C. Regulation time range:

[0167] The start and end times of the control are accurate to the minute. For example, "2025-07-17 10:45:00" is used to specify the time range of the event. The start and end times must be whole 5-minute intervals from each of the 288 frequency monitoring points.

[0168] D. Output upper limit:

[0169] The upper limit of output refers to the maximum power output of distributed photovoltaic (PV) systems on the line that needs to be regulated. For example, "32.06 kVA". This value cannot exceed the total rated installed capacity of distributed PV users on that line.

[0170] E. Line number and line name:

[0171] Line number, for example, “00021151XX”; line name, for example, “10kVXX59”.

[0172] F. Data Format:

[0173] Data format examples are shown in Table 1:

[0174] Table 1

[0175]

[0176] (2) Data content from the pipeline system to the dispatching side:

[0177] Data type:

[0178] The pipeline system provides the following data to the dispatching system: archived data such as district / county name (number), line name (number), transformer area name (number), and substation name (number), as well as real-time power generation data.

[0179] This embodiment deploys basic platform functions to meet the collaborative needs of distributed photovoltaic group dispatch and control operations, supporting the construction of a standardized, resource-sharing, and highly efficient dispatch and management system. The basic platform mainly includes:

[0180] 1. IaaS (Infrastructure as a Service) Layer: The IaaS layer is the system's hardware infrastructure layer, providing physical or virtual resource support for upper layers, including:

[0181] Virtual servers: They are responsible for virtualizing computing resources and supporting the operation of various applications and services.

[0182] Switch: Enables network connection and data exchange between various devices and modules within the system.

[0183] Disconnecting switches: ensure electrical isolation and safe switching in the power system.

[0184] Firewalls: Build network security barriers to prevent malicious external attacks and unauthorized access.

[0185] Virtual storage devices: provide virtualized resources for data storage to meet the storage needs of massive amounts of data.

[0186] Auxiliary equipment: This includes other hardware facilities that ensure the stable operation of the system (such as power supply and heat dissipation equipment).

[0187] 2. PaaS (Platform as a Service) Layer: The PaaS layer is the platform service layer. Based on the resources provided by the IaaS layer, it encapsulates common technologies and business capabilities, and consists of two parts:

[0188] Public technology platform services:

[0189] Unified permissions: Enables centralized management of user and role permissions within the system, ensuring operational security.

[0190] Data services: Provide basic services such as data storage, querying, and analysis to support data flow.

[0191] Process services: Standardized management and automated execution of business processes (such as instruction review and issuance processes).

[0192] Task scheduling: Automatically schedules various tasks (such as data collection tasks and control tasks) according to rules.

[0193] Bus service: Construct a communication bus between modules within the system to achieve efficient information exchange.

[0194] Public business platform services:

[0195] Model Management / Service / Data Platform: Manages the physical and mathematical models of photovoltaic systems, provides model services and maintains model data, and provides model support for regulation and control decisions.

[0196] Operational data platform: collects, stores, and manages real-time operational data of photovoltaic systems.

[0197] File interaction / service interaction / data exchange platform: Enables file transfer, inter-service interaction, and cross-system data exchange, ensuring information interoperability between different modules and systems.

[0198] 3. SaaS (Software as a Service) Layer: The SaaS layer is the software application layer, directly providing users with the functional applications of distributed photovoltaic group control and dispatching, including:

[0199] Data acquisition: Collect real-time operating data (such as power generation, voltage, current, etc.) of photovoltaic power plants and equipment.

[0200] Data integration: Cleaning, organizing, and merging collected multi-source data to form a unified data view.

[0201] Command editing: Edit specific control commands (such as power adjustment commands) according to control requirements.

[0202] Command review: Review the compliance and rationality of the edited commands to ensure that the commands are safe and effective.

[0203] Instruction Issuance: The approved instructions are issued to the photovoltaic equipment or control terminal.

[0204] Control and monitoring: Real-time monitoring of the execution of control commands and the operating status of photovoltaic systems, providing visual support for control decisions.

[0205] 4. Load Management Center (Usage Collection 2.0): As the external coordination layer of the system, the load management center is responsible for connecting with the power system's power consumption collection and load management system to realize the coordinated regulation of distributed photovoltaic and grid loads. On the one hand, it receives load demand and regulation instructions from the grid side; on the other hand, it feeds back the photovoltaic system's operating data and regulation capabilities to the grid to support the overall scheduling and load balancing of the grid.

[0206] This invention also provides a computer device. Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention; see the accompanying drawings. Figure 4 As shown, the computer device includes: an input device 23, an output device 24, a memory 22, and a processor 21; the memory 22 is used to store one or more programs; when the one or more programs are executed by the one or more processors 21, the one or more processors 21 implement the instruction issuance method for distributed group control based on the control cloud provided in the above embodiment; wherein the input device 23, the output device 24, the memory 22, and the processor 21 can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0207] The memory 22, as a read / write storage medium for a computing device, can be used to store software programs and computer-executable programs, such as the program instructions corresponding to the instruction issuance method for distributed group control based on the control cloud described in this embodiment of the invention. The memory 22 may mainly include a program storage area and a data storage area. 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. Furthermore, the memory 22 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, the memory 22 may further include memory remotely located relative to the processor 21, 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.

[0208] Input device 23 can be used to receive input digital or character information, and generate key signal inputs related to user settings and function control of the device; output device 24 may include display devices such as a display screen.

[0209] The processor 21 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 22, thereby realizing the above-mentioned instruction issuance method for distributed group control based on the control cloud.

[0210] The computer equipment provided above can be used to execute the instruction issuance method for distributed group control and dispatch based on the control cloud provided in the above embodiments, and has corresponding functions and beneficial effects.

[0211] This invention also provides a storage medium containing computer-executable instructions. When executed by a computer processor, these instructions are used to execute the instruction distribution method for distributed group control and dispatching based on the control cloud provided in the above embodiments. The storage medium can be any type of memory device or storage device, including: mounting media such as CD-ROM, floppy disk, or magnetic tape; 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; the storage medium may also include other types of memory or combinations thereof; furthermore, the 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 storage medium includes two or more storage media that can reside in different locations (e.g., in different computer systems connected via a network). The storage medium can store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0212] Of course, the computer-executable instructions provided in the embodiments of the present invention are not limited to the instruction issuance method for distributed group control and dispatching based on the control cloud as described in the above embodiments, but can also execute related operations in the instruction issuance method for distributed group control and dispatching based on the control cloud provided in any embodiment of the present invention.

[0213] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0214] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for issuing instructions for distributed group control and dispatch based on a control cloud, characterized in that: Includes the following steps: S1. Before the control command is issued, collect real-time operating data of each photovoltaic node and the power grid to form a state vector; S2. Using active power and frequency control logic, a set of control instructions for adjusting photovoltaic active power output is generated based on optimization objectives, and the instructions are verified to be within the equipment adjustment range of the photovoltaic equipment. The optimization objectives include: minimum grid loss and qualified voltage. The equipment adjustment range includes: maximum apparent power of the inverter and SVG capacity limit. S3. Based on the equipment's adjustment capability and response speed, prioritize inverters with fast adjustment rates and sufficient capacity to undertake frequency regulation tasks; estimate the inverter's current maximum power generation capacity through a power generation capacity assessment model, and make corrections based on reactive power and AC voltage factors, and allocate active power control commands proportionally. The system employs control logic based on bus voltage, reactive power, and power factor to allocate reactive power control commands for inverter reactive power output and SVG reactive power compensation, thereby maintaining stable bus voltage and optimizing the power factor. S4. Execute control commands, continuously collect actual output and voltage data of photovoltaic equipment after executing commands, compare with the expected physical behavior model, and construct a response difference map; for photovoltaic equipment with deviations, match correction strategies according to the deviation type and generate compensation commands; S5. Analyze the degree to which the execution of the analysis instructions improves the overall indicators of the power grid; S6. Through the visualization interface, view the actual output curve of the distributed photovoltaic system and the line load rate curve of the target line, and observe whether the curve meets the control requirements within the execution time range. The control requirements include: the actual output does not exceed the power limit and the load rate is stable within the safe range; display the status of the entire life cycle of the instruction in real time, and combine the real-time power of the line and the regional load data to indirectly judge the local photovoltaic consumption. If there is an abnormal output or insufficient consumption, trigger an early warning in time and assist in formulating an adjustment plan.

2. The instruction issuance method for distributed group control and dispatching based on a control cloud development system according to claim 1, characterized in that, The method for allocating reactive power output from the inverter and reactive power control commands for SVG reactive power compensation in step S3 includes: The reactive power allocation between the inverter and the SVG is based on the principle of prioritizing inverter allocation. Without affecting active power output, the reactive power regulation capability of the inverter is maximized first, and the remaining reactive power demand is supplemented by the SVG. The allocation formula is as follows: Qinverse = min(Qadjustable, Qtotal × k), QSVG = Qtotal - Qinverse; Where Q_adjustable is the current adjustable reactive power of the inverter, Q_inverse is the reactive power of the inverter, Q_SVG is the reactive power of the SVG, Q_total is the total reactive power, and k is the allocation coefficient.

3. The instruction issuance method for distributed group control and dispatching based on a control cloud development system according to claim 2, characterized in that, The method for constructing the response difference map by comparing the S4 step with the expected physical behavior model includes: Based on mechanical equations, circuit models, physical models, or data-driven machine learning models trained on historical data, construct a desired physical behavior model that reflects the behavior of photovoltaic equipment under ideal or specific operating conditions. Through parameter estimation and model calibration, the expected physical behavior model can accurately reflect the behavior of real equipment under ideal or specific working conditions, ensuring the effectiveness of the comparison. The actual data collected in real time is compared with the expected value predicted by the expected physical behavior model. The difference between the actual data and the expected value is calculated, and a difference map is constructed based on the difference analysis.

4. The instruction issuance method for distributed group control and dispatching based on a control cloud development system according to claim 3, characterized in that, The method for calculating the difference between actual data and expected values, and constructing a difference map based on difference analysis, includes: Calculate the absolute error, relative error, or dynamic response deviation between the actual output, voltage parameters, and model predictions; By utilizing the hierarchical framework of knowledge graphs, devices, parameters, commands, difference value elements, and their relationships are stored in a structured manner and visualized to form a response difference graph. The graph intuitively displays the difference patterns of each parameter under different commands, helping to quickly locate anomalies.

5. The instruction issuance method for distributed group control and dispatching based on a control cloud development system according to claim 1, characterized in that, The method for generating compensation instructions by matching the correction strategy according to the deviation type in step S4 includes: For inverters that experience response lag due to physical characteristics or control algorithm delays, a dynamic compensation strategy is used to correct the lag; a predictive model incorporating delay elements is constructed to pre-calculate control quantities to offset the lag effect; and compensation commands are output based on the predictive model. To address the mismatch in control cycles caused by communication delays, timing correction is performed through timing synchronization and data caching mechanisms; timestamps are cached during data acquisition, and delays are compensated for through interpolation or prediction algorithms. For multi-modal deviation collaborative compensation, a time alignment mechanism is used to coordinate multi-source data streams and generate joint compensation commands; in voltage-frequency collaborative control, composite commands are generated by combining the response characteristics of SVG and inverter; and an input compensation mechanism is used to dynamically adjust the deviation value according to the compensation method and generate correction commands.

6. The instruction issuance method for distributed group control and dispatching based on a control cloud development system according to claim 1, characterized in that, The method for assessing the degree of improvement in global power grid indicators by executing the analysis instructions in step S5 includes: Acquire power grid operation data before and after command execution, including node voltage, frequency, and branch power flow; The power grid operation data is cleaned to remove outliers, and typical operating sections are selected to compare the data differences before and after the command is executed. The percentage of time each node's voltage is within the allowable deviation range is statistically analyzed to calculate the average pass rate of the entire network; combined with the voltage reactive power control adjustment sensitivity, the suppression effect of commands on voltage deviation is analyzed. Calculate the root mean square deviation of the system frequency before and after command execution to evaluate frequency stability; compare the dynamic matching degree between the active power output command and the actual response. Based on the power flow calculation results, the changes in active power loss before and after the execution of the command are compared, and the contribution of the reactive power optimization command to reducing line loss is analyzed.

7. A command issuance system for distributed group control and dispatching based on a control cloud, implementing the command issuance method for distributed group control and dispatching based on a control cloud as described in any one of claims 1-6, characterized in that, include: Data acquisition and integration module: used to collect real-time operating data of each photovoltaic node and the power grid before the control command is issued, and form a state vector; Instruction generation and verification module: This module uses active power and frequency control logic to generate a set of control instructions for adjusting the active power output of photovoltaic systems based on optimization objectives, and verifies whether the instructions are within the equipment adjustment range of the photovoltaic equipment. The optimization objectives include: minimum grid loss and qualified voltage. The equipment adjustment range includes: maximum apparent power of the inverter and SVG capacity limit. Command allocation module: Based on the equipment's adjustment capability and response speed, it prioritizes inverters with fast adjustment rates and sufficient capacity to undertake frequency regulation tasks; it estimates the inverter's current maximum power generation capacity through a power generation capacity assessment model, and corrects it by combining reactive power and AC voltage factors, and allocates active power control commands proportionally; it uses the control logic of bus voltage, reactive power, and power factor to allocate reactive power control commands for inverter reactive power output and SVG reactive power compensation, maintaining bus voltage stability while optimizing the power factor; Command tracking and compensation module: used to execute control commands, continuously collect actual output and voltage data of photovoltaic equipment after command execution, compare with the expected physical behavior model, and construct response difference map; for equipment with deviations, match correction strategies according to the deviation type and generate compensation commands; analyze the degree of improvement of the overall power grid indicators by command execution; The scheduling and monitoring module is used to view the actual output curve and load rate curve of the distributed photovoltaic system on the target line through a visual interface. It observes whether the curve meets the control requirements within the execution time range. The control requirements include: the actual output does not exceed the power limit and the load rate is stable within the safe range. It displays the status of the entire life cycle of the command in real time. Combined with the real-time power of the line and regional load data, it indirectly judges the local photovoltaic consumption. If abnormal output or insufficient consumption occurs, it promptly triggers an early warning and assists in formulating adjustment plans.

8. The instruction issuance system for distributed group control and dispatching based on a control cloud as described in claim 7, characterized in that, The instruction tracking and compensation module includes: Difference Calculation Unit: Used to calculate the absolute error, relative error, or dynamic response deviation between the actual output, voltage parameters, and model prediction values; Knowledge Graph Construction Unit: This unit utilizes the hierarchical framework of knowledge graphs to structure and visualize devices, parameters, commands, difference value elements, and their relationships, forming a response difference graph. The graph visually displays the difference patterns of parameters under different commands, helping to quickly locate anomalies.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the instruction issuance method for the distributed group control and dispatching based on the control cloud as described in any one of claims 1-6.

10. A computer device, the computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the instruction issuance method for distributed group control and dispatching based on the control cloud as described in any one of claims 1-6.

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