A distributed photovoltaic aggregation control method, device, equipment and medium

Abstract

The application discloses a kind of distributed photovoltaic aggregation control method, device, equipment and medium, it is related to system control technical field.The method comprises: obtaining the operating state data of distributed photovoltaic system access point, according to operating state data determine the feature vector and key voltage node of photovoltaic inverter in each distributed photovoltaic system access point;According to feature vector, photovoltaic inverter is divided into at least two photovoltaic aggregation areas, according to preset condition, key voltage node is distributed to corresponding photovoltaic aggregation area;According to the voltage operating condition information of each key voltage node, determine the voltage risk index of each photovoltaic aggregation area and the comprehensive voltage sensitivity of photovoltaic inverter, according to operating state data, determine the reactive power margin of photovoltaic inverter;According to voltage risk index, comprehensive voltage sensitivity and reactive power margin, determine control correction amount, determine the control instruction of each photovoltaic inverter based on control correction amount, according to control instruction, adjust the reactive power of distributed photovoltaic system access point.

Description

Technical Field

[0001] This invention relates to the field of system control technology, and in particular to a distributed photovoltaic aggregation control method, device, equipment and medium. Background Technology

[0002] Currently, the scale of residential rooftop photovoltaic (PV), industrial and commercial PV, and park PV systems connected to the distribution network continues to expand. Distributed PV is characterized by its dispersed connection points, small individual capacity, and significant impact on output from sunlight. Its large-scale integration alters the traditional unidirectional power flow structure of the distribution network, easily leading to problems such as backflow, node voltage rise, increased voltage fluctuations, and local voltage exceedances. Especially during peak PV generation and low load periods at midday, high voltage exceedances are prone to occur at feeder ends, low-voltage sides of transformer substations, and areas with concentrated PV connections. Conversely, during periods of cloud cover, rapid decline in PV output, or sudden load increases, low voltage risks may arise, affecting the safe and stable operation of the distribution network.

[0003] Current power grid voltage regulation mainly relies on on-load tap-changing transformers, capacitor banks, and static var generators (SVG). However, these devices typically have slow regulation speeds and limited operational frequency, making them ill-suited to the rapid fluctuations in distributed photovoltaic (PV) output. Limiting PV active power output to suppress voltage exceedances would reduce the level of renewable energy integration. In contrast, PV inverters possess a certain reactive power regulation capability, participating in voltage control by absorbing or generating reactive power. They offer advantages such as fast response, utilization of existing equipment, and low retrofit costs, thus becoming an important means of voltage management in high-penetration distributed PV scenarios.

[0004] Currently, most photovoltaic inverters adopt reactive power-voltage control based on local voltage, meaning they absorb reactive power when the voltage rises and generate reactive power when the voltage falls. This method is simple to implement, but it still has shortcomings: On the one hand, a single inverter usually operates independently based on its own grid connection point voltage, lacking unified coordination at the distribution area or feeder level, which can easily lead to local overcompensation, undercompensation, or uneven utilization of reactive power resources; on the other hand, parameters such as reactive power gain, voltage action threshold, and dead zone width are mostly fixed settings, making it difficult to dynamically adjust with changes in photovoltaic output, load level, topology status, voltage risk, and inverter reactive power margin.

[0005] Furthermore, the impact of photovoltaic inverters at different connection locations on the voltage of critical voltage nodes varies. Inverters at the end of feeders, concentrated photovoltaic connection points, or in areas with weak voltage typically exhibit more significant reactive power regulation; while inverters with lower voltage sensitivity may have limited effects even if they participate in regulation. Applying uniform parameters to all inverters not only makes it difficult to fully utilize the regulation capabilities of high-sensitivity inverters but may also cause frequent operation of some devices, increasing the risk of control oscillations. Simultaneously, the available reactive power capacity of inverters is constrained by the current active power output. When photovoltaic output approaches its rated capacity, the reactive power regulation margin decreases significantly, and fixed parameter control can easily lead to commands becoming unexecutable or unstable regulation effects. Therefore, how to adaptively fine-tune reactive power to achieve coordinated, stable, and reliable reactive power and voltage control of distributed photovoltaic clusters has become an urgent problem to be solved. Summary of the Invention

[0006] This invention provides a distributed photovoltaic aggregation control method, apparatus, equipment, and medium to solve the problem of the inability to coordinate reactive power control of distributed photovoltaic groups in the prior art.

[0007] According to one aspect of the present invention, a distributed photovoltaic aggregation control method is provided, wherein the method includes: Obtain operational status data of distributed photovoltaic system access points, and determine the feature vector and key voltage nodes of photovoltaic inverters in each distributed photovoltaic system access point based on the operational status data; The photovoltaic inverter is divided into at least two photovoltaic aggregation regions based on the feature vector, and key voltage nodes are assigned to the corresponding photovoltaic aggregation regions according to preset conditions. Determine the voltage condition information of each key voltage node, determine the voltage risk index of each photovoltaic aggregation area and the comprehensive voltage sensitivity of the photovoltaic inverter based on the voltage condition information, and determine the reactive power margin of the photovoltaic inverter according to the operating status data; The control correction amount is determined based on the voltage risk index, the comprehensive voltage sensitivity, and the reactive power margin. The control command for each photovoltaic inverter is determined based on the control correction amount. The reactive power of the distributed photovoltaic system access point is adjusted according to the control command.

[0008] According to another aspect of the present invention, a distributed photovoltaic aggregation control device is provided, wherein the device comprises: The data determination module is used to acquire the operating status data of the distributed photovoltaic system access points, and determine the feature vector and key voltage node of the photovoltaic inverter in each distributed photovoltaic system access point based on the operating status data. The region division module is used to divide the photovoltaic inverter into at least two photovoltaic aggregation regions according to the feature vector, and to allocate key voltage nodes to the corresponding photovoltaic aggregation regions according to preset conditions. The parameter determination module is used to determine the voltage condition information of each of the key voltage nodes, determine the voltage risk index of each of the photovoltaic aggregation regions and the comprehensive voltage sensitivity of the photovoltaic inverter based on the voltage condition information, and determine the reactive power margin of the photovoltaic inverter according to the operating status data. The parameter control module is used to determine the control correction amount based on the voltage risk index, the comprehensive voltage sensitivity and the reactive power margin, and to determine the control parameters of each photovoltaic inverter based on the control correction amount.

[0009] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to execute a distributed photovoltaic aggregation control method according to any embodiment of the present invention.

[0010] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement a distributed photovoltaic aggregation control method according to any embodiment of the present invention.

[0011] According to another aspect of the present invention, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements a distributed photovoltaic aggregation control method according to any embodiment of the present invention.

[0012] The technical solution of this invention acquires the operating status data of the distributed photovoltaic system access points, determines the feature vector and key voltage nodes of the photovoltaic inverters in each distributed photovoltaic system access point based on the operating status data, divides the photovoltaic inverters into at least two photovoltaic aggregation regions based on the feature vectors, and assigns key voltage nodes to the corresponding photovoltaic aggregation regions according to preset conditions. It then determines the voltage condition information of each key voltage node, determines the voltage risk index of each photovoltaic aggregation region and the comprehensive voltage sensitivity of the photovoltaic inverters based on the voltage condition information, determines the reactive power margin of the photovoltaic inverters according to the operating status data, determines the control correction amount based on the voltage risk index, comprehensive voltage sensitivity, and reactive power margin, determines the control commands for each photovoltaic inverter based on the control correction amount, and adjusts the reactive power of the distributed photovoltaic system access points according to the control commands. This achieves regional coordination and adaptive precise voltage regulation of distributed photovoltaics across the entire region, effectively suppressing voltage exceedances and fluctuations in the distribution network, and significantly improving the voltage stability level of the distribution network.

[0013] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of a distributed photovoltaic aggregation control method according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of a distributed photovoltaic aggregation control method according to Embodiment 2 of the present invention; Figure 3 This is a flowchart of a distributed photovoltaic aggregation control method provided in Embodiment 3 of the present invention; Figure 4 This is a flowchart of a distributed photovoltaic aggregation control method according to Embodiment 4 of the present invention; Figure 5 This is a schematic diagram of the fine-tuning logic of the reactive power-voltage curve according to Embodiment 4 of the present invention; Figure 6 This is a schematic diagram of the structure of a distributed photovoltaic aggregation control device according to Embodiment 5 of the present invention; Figure 7This is a schematic diagram of the structure of an electronic device that implements a distributed photovoltaic aggregation control method according to an embodiment of the present invention. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] Example 1 Figure 1 This is a flowchart of a distributed photovoltaic aggregation control method according to Embodiment 1 of the present invention. This embodiment is applicable to the coordinated reactive power control of distributed photovoltaic groups. The method can be executed by a distributed photovoltaic aggregation control device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S110. Obtain the operating status data of the distributed photovoltaic system access points, and determine the feature vector and key voltage nodes of the photovoltaic inverters in each distributed photovoltaic system access point based on the operating status data.

[0019] In this context, the distributed photovoltaic (PV) system access point refers to the electrical node in the distribution network where distributed PV inverters are connected to the grid. This node serves as the execution point for data acquisition and reactive power regulation. Generally, each distributed PV system access point corresponds to one PV inverter. A PV inverter is a power electronic device in a PV power generation system that converts the direct current (DC) generated by PV modules into alternating current (AC). Operating status data refers to the set of electrical parameters of the PV inverter in real-time operation. In actual operation, this data may include, but is not limited to, the PV inverter's active power output, reactive power output, PV inverter capacity and reserved reactive power margin, network topology parameters, and the node voltage of the distribution network electrical node corresponding to each distributed PV system access point. The feature vector refers to a multi-dimensional feature parameter vector used to quantify the electrical characteristics and voltage regulation capabilities of the PV inverter. For example, the feature vector may include parameters such as inverter reactive power margin, reactive power voltage sensitivity, PV inverter capacity, and electrical distance. Critical voltage nodes can be understood as weak electrical nodes in the distribution network that experience severe voltage fluctuations, are prone to high and low voltage over-limits, and are highly sensitive to load changes.

[0020] In this embodiment, real-time data can be collected on the active and reactive power outputs of the photovoltaic inverters at each distributed photovoltaic (PV) system access point, the inverter capacity and reserved reactive power margin, the node voltage of the distribution network electrical nodes corresponding to each PV system access point, and network topology parameters. Parameters such as the historical voltage overrun count, voltage fluctuation amplitude, and node type of the distribution network electrical nodes corresponding to the PV system access point are determined. These parameters are used as operational status data. Combining the distribution network topology, historical voltage fluctuation data, and real-time overrun information, weak voltage nodes across the entire network are identified and marked as critical voltage nodes. Generally, criteria for identifying critical voltage nodes can be predetermined. For example, distribution network electrical nodes with a historical voltage overrun count exceeding a preset overrun count condition, a voltage fluctuation amplitude exceeding a preset fluctuation amplitude condition, or belonging to a preset node type can be designated as critical voltage nodes. The core electrical characteristics of each photovoltaic inverter are extracted to construct a multi-dimensional feature vector. For example, the equivalent impedance of each photovoltaic inverter to the distribution transformer or feeder head can be determined by network topology parameters. The reactive voltage sensitivity of each photovoltaic inverter to the key voltage node can be determined by combining reactive power output and node voltage of key voltage nodes. The equivalent impedance, reactive voltage sensitivity, and photovoltaic inverter capacity are used as the feature vector of the photovoltaic inverter.

[0021] S120. Divide the photovoltaic inverter into at least two photovoltaic aggregation regions according to the feature vector, and allocate the key voltage node to the corresponding photovoltaic aggregation region according to the preset conditions.

[0022] In this context, a photovoltaic (PV) aggregation region can be understood as a clustered management area formed by dividing PV inverters with similar voltage node impact patterns and electrical distances using clustering algorithms. Generally, PV inverters within the same aggregation region possess similar voltage regulation capabilities. Preset allocation conditions refer to the conditions pre-set for dividing key voltage nodes. In actual operation, the preset allocation conditions can be optimized by maximizing the sum of reactive voltage sensitivity of the equivalent central inverter in each PV aggregation region to all key voltage nodes, thus matching and allocating key voltage nodes to the PV aggregation region with the best voltage regulation adaptability.

[0023] In this embodiment, the feature vectors of each photovoltaic inverter can be used as input, and clustering algorithms such as K-means and fuzzy C-means can be employed to group all photovoltaic inverters. Inverters with similar electrical characteristics or voltage regulation characteristics are grouped into the same cluster, forming at least two independent photovoltaic aggregation regions. For each photovoltaic aggregation region, the photovoltaic inverter with the closest Euclidean distance to the virtual cluster center is selected as the equivalent center inverter, and the reactive voltage sensitivity of the equivalent center inverter characterizes the overall voltage regulation capability of the region. With maximizing the sum of the regional sensitivities of all key voltage nodes as the core optimization objective, all key voltage nodes are traversed, and each key voltage node is assigned to the photovoltaic aggregation region with the optimal voltage regulation capability, thus achieving precise matching between photovoltaic aggregation regions and key voltage nodes.

[0024] S130. Determine the voltage condition information of each key voltage node, determine the voltage risk index of each photovoltaic aggregation area and the comprehensive voltage sensitivity of the photovoltaic inverter based on the voltage condition information, and determine the reactive power margin of the photovoltaic inverter according to the operating status data.

[0025] Voltage condition information refers to the set of parameters representing the real-time voltage operating status of key voltage nodes. For example, voltage condition information may include, but is not limited to, real-time voltage, upper voltage limit, lower voltage limit, maximum allowable voltage deviation, predicted voltage at the next moment, duration of high voltage exceedance, and duration of low voltage exceedance. Voltage risk indicators are used to quantify the overall voltage runaway degree of a single photovoltaic aggregation area, including regional low-voltage risk indicators and regional high-voltage risk indicators. Higher indicator values ​​indicate a greater probability of voltage exceedance in that area. Comprehensive voltage sensitivity can be understood as the impact information of a single photovoltaic inverter on key voltage nodes within its aggregation area. Comprehensive voltage sensitivity can be calculated by weighting the reactive power voltage sensitivity of the photovoltaic inverter with the weights of the key voltage nodes, reflecting the voltage regulation influence of the inverter. Reactive power margin includes the available reactive power margin of a single photovoltaic inverter and the total available reactive power margin of all photovoltaic aggregation areas. Generally, the available reactive power margin of a photovoltaic inverter is the upper and lower limits of reactive power that a single inverter can currently flexibly adjust; the total available reactive power margin of a region is the cumulative value of the individual reactive power margins of all inverters within the same photovoltaic aggregation area.

[0026] In this embodiment, real-time voltage, voltage threshold, voltage deviation, predicted voltage, and high / low voltage over-limit duration data for each key voltage node can be collected and integrated to form complete node voltage condition information. Based on this voltage condition information, the low-voltage risk value and high-voltage risk value of each key voltage node are quantitatively calculated. Then, according to the aggregation region affiliation of the key voltage nodes, the high and low voltage risk values ​​of all nodes within the same region are summed by mean square to obtain the high and low voltage risk indicators for each photovoltaic aggregation region, i.e., the regional voltage risk index. Next, the reactive power voltage sensitivity of each photovoltaic inverter to each key voltage node within the region is extracted and weighted by a weighted sum based on the preset weights of each key voltage node to quantify the comprehensive voltage sensitivity of each photovoltaic inverter. Based on the active power output, photovoltaic inverter, and reserved reactive power margin of the photovoltaic inverters, the available reactive power margin of a single inverter is calculated. Simultaneously, the reactive power margins of all single units within the same aggregation region are accumulated to obtain the total available reactive power margin for the region.

[0027] S140. Determine the control correction amount based on the voltage risk index, comprehensive voltage sensitivity and reactive power margin, determine the control command for each photovoltaic inverter based on the control correction amount, and adjust the reactive power of the distributed photovoltaic system access point according to the control command.

[0028] Among them, the control correction quantity refers to the regulation correction parameters calculated jointly based on regional voltage risk, inverter voltage regulation influence, and cluster reactive power reserve capacity. These parameters are used to dynamically correct the core reactive power regulation parameters of the photovoltaic inverter, achieving adaptive voltage regulation. In actual operation, the control correction quantity can include at least voltage reactive power gain, high-voltage action threshold fine-tuning quantity, and low-voltage action threshold fine-tuning quantity. The control command refers to the reactive power regulation command finally issued to the photovoltaic inverter after parameter correction and capacity limiting. It is the direct execution basis for the photovoltaic inverter to perform reactive power generation or absorption.

[0029] In this embodiment, the voltage risk index of each photovoltaic aggregation area, the comprehensive voltage sensitivity of each photovoltaic inverter, and the reactive power margin are used as inputs to calculate the reactive power gain fine-tuning amount, high voltage threshold fine-tuning amount, and high voltage threshold fine-tuning amount. These three amounts are then used as control correction quantities. Based on the control correction quantities, the inverter's basic voltage regulation parameters are adaptively corrected to obtain parameters such as dynamic voltage reactive power gain, target high voltage threshold, and target low voltage threshold. The real-time voltage at the photovoltaic inverter's grid connection point is collected, and combined with the corrected voltage regulation parameters, candidate reactive power control commands for the inverter are calculated in segments. Simultaneously, the candidate commands are limited by the upper and lower limits of the inverter's reactive power margin to obtain the final executable control command. The final control command is then sent to the corresponding photovoltaic inverter to adjust the reactive power at the distributed photovoltaic system's connection point in real time. In actual operation, if it is determined that the real-time voltage at the grid connection point is greater than the target high-voltage threshold, the difference between the real-time voltage and the target high-voltage threshold can be determined as the first difference, and the product of the first difference and the dynamic voltage reactive power gain can be determined as the first product. The negative of the first product is used as the candidate control command. If it is determined that the real-time voltage at the grid connection point is less than the target low-voltage threshold, the difference between the target low-voltage threshold and the real-time voltage at the grid connection point is determined as the second difference, and the product of the second difference and the dynamic voltage reactive power gain is determined as the second product. The second product is used as the candidate control command. When it is determined that the real-time voltage at the grid connection point is greater than or equal to the target low-voltage threshold and less than or equal to the target high-voltage threshold, the candidate control command is determined to be zero. At this point, it can be determined whether the reactive power command is within the normal range. When the reactive power command is greater than the current maximum adjustable reactive power amplitude of the photovoltaic inverter, the current maximum adjustable reactive power amplitude of the photovoltaic inverter can be used as the final control command. When the reactive power command is less than the current minimum adjustable reactive power amplitude of the photovoltaic inverter, the current minimum adjustable reactive power amplitude of the photovoltaic inverter can be used as the final control command. When the reactive power command is less than or equal to the current maximum adjustable reactive power amplitude of the photovoltaic inverter, and greater than or equal to the current minimum adjustable reactive power amplitude of the photovoltaic inverter, the reactive power command can be used as the final control command.

[0030] In this embodiment of the invention, by acquiring the operating status data of the access points of a distributed photovoltaic (PV) system, the feature vectors and key voltage nodes of the PV inverters in each access point are determined based on the operating status data. The PV inverters are then divided into at least two PV aggregation regions based on the feature vectors, and key voltage nodes are assigned to the corresponding PV aggregation regions according to preset conditions. The voltage condition information of each key voltage node is determined, and the voltage risk index and comprehensive voltage sensitivity of each PV aggregation region are determined based on the voltage condition information. The reactive power margin of the PV inverters is determined according to the operating status data. Control correction amounts are determined based on the voltage risk index, comprehensive voltage sensitivity, and reactive power margin. Control commands for each PV inverter are determined based on the control correction amounts. The reactive power of the access points of the distributed PV system is adjusted according to the control commands. This achieves regional coordination and adaptive precise voltage regulation of the entire distributed PV system, effectively suppressing voltage exceedances and fluctuations in the distribution network, and significantly improving the voltage stability level of the distribution network.

[0031] In one embodiment, after determining the control correction amount based on the voltage risk index, comprehensive voltage sensitivity, and reactive power margin, and determining the control parameters of each photovoltaic inverter based on the control correction amount, the method further includes: After each photovoltaic inverter performs parameter adjustments according to the control parameters, it re-collects voltage change information for each key voltage node. If under-regulation is determined based on voltage change information, then in the next cycle, increase the dynamic voltage reactive power gain, increase the target high voltage threshold and / or the target low voltage threshold. If over-voltage regulation is determined based on voltage change information, then in the next cycle, the dynamic voltage reactive power gain is reduced, and the target high voltage threshold and / or the target low voltage threshold are decreased.

[0032] Parameter adjustment refers to the process by which the photovoltaic inverter adjusts the reactive power of the distributed photovoltaic system's access points based on the dynamic voltage reactive power gain, target high-voltage threshold, and target low-voltage threshold obtained in the current cycle. Voltage change information refers to the real-time voltage fluctuations, voltage offsets, voltage recovery rates, steady-state voltage deviations, and residual over-limit durations generated at each key voltage node after the photovoltaic inverter parameters are updated and reactive power adjustment is performed. Under-regulation refers to a condition where the voltage regulation effect is insufficient, meaning that after reactive power regulation in the current cycle, key voltage nodes still experience persistent voltage over-limits, voltage offsets that have not returned to the allowable range, or excessively slow voltage recovery rates. Over-regulation can be understood as an excessive voltage regulation condition, meaning that after reactive power regulation in the current cycle, the originally offset voltage exhibits reverse deviations, voltage oscillations, or excessive reactive power regulation redundancy.

[0033] In this embodiment, after loading the dynamic voltage reactive power gain, target high voltage threshold, and target low voltage threshold calculated for the current cycle onto each photovoltaic inverter, the historical control parameters can be replaced. After the parameters are updated, the photovoltaic inverter calculates the reactive power control command in real time according to the updated control parameters, executes reactive power generation or absorption operations, and completes the voltage regulation for the current cycle. After the regulation stabilizes, real-time voltage data of each key voltage node is collected across the entire domain, compared with the voltage state before regulation, and the node voltage offset, voltage change amplitude, steady-state deviation, and over-limit status are statistically analyzed to obtain the voltage change information of each key voltage node. The voltage change information of the key voltage nodes after regulation is compared with the preset voltage qualified range. If the key voltage nodes still have voltage over-limit, residual voltage offset exceeding the standard, or voltage recovery speed not reaching the preset standard after regulation, it is determined that there is an under-regulation problem in the current regulation. In the next regulation cycle, the dynamic voltage reactive power gain can be appropriately increased to improve the inverter voltage response sensitivity and increase the reactive power regulation amplitude under the same voltage deviation; at the same time, the target high voltage threshold and target low voltage threshold can be appropriately increased. In actual operation, photovoltaic inverters with larger reactive power margins and stronger voltage regulation capabilities at key voltage nodes should be given priority in the next cycle to undertake reactive power regulation tasks.

[0034] If, based on the voltage change information at key voltage nodes, a reverse voltage shift or excessive reactive power regulation redundancy occurs after the current reactive power regulation, it is determined that the current regulation has an over-regulation problem. Initiating a weakened voltage regulation strategy in the next regulation cycle can appropriately reduce the dynamic voltage reactive power gain, decreasing the reactive power regulation amount per unit voltage deviation. Simultaneously, the target high-voltage threshold and target low-voltage threshold are reduced. In one embodiment, the regulation weight of photovoltaic inverters with larger reactive power margins and stronger voltage regulation capabilities at key voltage nodes can be reduced in the next cycle. This avoids the shortcomings of traditional fixed-parameter regulation, such as under-regulation, over-regulation, and poor adaptability, achieving refined, adaptive, and long-term stable regulation of the entire voltage domain.

[0035] Example 2 Figure 2 This is a flowchart of a distributed photovoltaic aggregation control method according to Embodiment 2 of the present invention. This embodiment is a further optimization and extension based on the above embodiments, and can be combined with various optional technical solutions in the above embodiments. Figure 2 As shown, the method includes: S201. Collect the active power output, reactive power output, photovoltaic inverter capacity and reserved reactive power margin of the photovoltaic inverter in each distributed photovoltaic system access point, as well as the node voltage of the distribution network electrical node corresponding to each distributed photovoltaic system access point as electrical status parameters.

[0036] Among these, active power output refers to the active power output of the photovoltaic inverter to the distribution network at the current moment, which is the effective work power generated by the photovoltaic system. Reactive power output refers to the reactive power that the photovoltaic inverter sends to or absorbs from the distribution network at the current moment. Photovoltaic inverter capacity refers to the rated capacity of the photovoltaic inverter, which is the maximum power limit that the photovoltaic inverter is allowed to operate stably for a long period of time. Reserved reactive power margin refers to the remaining reactive power regulation capacity that the photovoltaic inverter can continue to generate or absorb, which can be determined by the rated capacity of the photovoltaic inverter and the current active and reactive power operating conditions. Node voltage refers to the bus voltage of the electrical node of the distribution network connected to the distributed photovoltaic system at the current moment. Electrical state parameters can be understood as basic electrical data that characterize the real-time operating conditions of the distributed photovoltaic inverter and the voltage status of the grid-connected nodes.

[0037] In this embodiment, real-time data can be collected on the equipment operating parameters and node voltage parameters of all distributed photovoltaic (PV) system access points within the distribution network, such as the active power output, reactive power output, rated capacity of each PV inverter, and the equipment's reserved reactive power margin. Simultaneously, the node voltage of the corresponding distribution network electrical node for each PV system access point is also collected. All collected parameters are integrated and summarized into a unified global electrical status parameter.

[0038] S202. Determine the historical voltage over-limit number, voltage fluctuation amplitude, and node type of the distribution network electrical nodes corresponding to each distributed photovoltaic system access point as node voltage characteristic parameters, and designate the distribution network electrical nodes that meet at least one of the preset over-limit number conditions, preset fluctuation amplitude conditions, or preset node type conditions as key voltage nodes.

[0039] Among them, node voltage characteristic parameters can be understood as statistical characteristic parameters characterizing the voltage stability, fluctuation characteristics, and node attributes of distribution network electrical nodes, including the number of historical voltage over-limit occurrences, historical voltage fluctuation amplitude, and node type. The number of historical voltage over-limit occurrences can be understood as the cumulative number of times the real-time voltage of a distribution network electrical node exceeds the upper or lower voltage limit specified in the regulations within a preset historical statistical period. Voltage fluctuation amplitude refers to the range of voltage fluctuation relative to the rated voltage of a distribution network electrical node within a preset statistical period. Node type can be understood as the functional attribute classification of distribution network electrical nodes. For example, node types may include, but are not limited to, feeder end nodes, load concentration nodes, high photovoltaic penetration nodes, transformer area backbone nodes, and ordinary distribution nodes. The preset over-limit occurrence condition refers to the pre-configured voltage over-limit judgment threshold, which is the cumulative number of times the node voltage is higher than the upper voltage limit or lower than the lower voltage limit within the statistical period reaching the set threshold. The preset fluctuation amplitude condition refers to the pre-configured voltage fluctuation judgment threshold, which can be the maximum voltage deviation amplitude of the node voltage exceeding the set threshold within a specified statistical period. The preset node type condition refers to the pre-set node type that meets the criteria for critical voltage nodes.

[0040] In this embodiment, historical operating data of each distribution network electrical node can be retrieved to statistically analyze the cumulative number of voltage exceedances and the maximum voltage fluctuation amplitude of each node within a preset statistical period. Simultaneously, the node type of each distribution network electrical node is marked, forming node voltage characteristic parameters corresponding to each distribution network electrical node. When a distribution network electrical node is determined to meet any one of the preset exceedance count condition, preset fluctuation amplitude condition, or preset critical voltage node type condition, the node's voltage characteristic parameters can be identified as a voltage-weak node, and the voltage-weak node can be designated as a critical voltage node.

[0041] S203. Collect the network topology parameters of the distribution network where each distributed photovoltaic system access point is located, and use the electrical status parameters, node voltage characteristic parameters and network topology parameters as operating status data.

[0042] Network topology parameters are parameters relating to the physical architecture and electrical connections of a distribution network. Generally, network topology parameters may include information such as feeder branch structure, line impedance parameters, distribution transformer locations, node connection relationships, and line segment lengths.

[0043] In this embodiment, all network topology parameters of the distribution network can be collected, including feeder structure, line segment impedance, transformer installation location, node connection relationship, and line length, among other topology information. The collected network topology parameters, electrical status parameters, and node voltage characteristic parameters are used as operational status data.

[0044] S204. Determine the equivalent impedance of each photovoltaic inverter to the distribution transformer or feeder head end by segmenting and accumulating the network topology parameters, and determine the reactive voltage sensitivity of each photovoltaic inverter to the critical voltage node according to the reactive power output and the node voltage of the critical voltage node.

[0045] In this context, the feeder start-up refers to the power supply starting point of the same main outgoing line. Equivalent impedance is the total line impedance obtained by accumulating it segment by segment along the distribution network line from the photovoltaic inverter's grid connection point to the feeder start-up or distribution transformer. The value of equivalent impedance characterizes the electrical distance between the photovoltaic inverter and the main grid node; the smaller the equivalent impedance and the closer the electrical distance, the stronger the voltage regulation capability. Reactive power voltage sensitivity characterizes the degree of influence of a single photovoltaic inverter's unit reactive power change on the voltage amplitude of key voltage nodes; the larger the sensitivity value, the stronger the inverter's voltage regulation capability and the higher its voltage regulation contribution to the corresponding node.

[0046] In this embodiment, the path from each photovoltaic inverter to the distribution transformer or feeder head can be determined based on network topology parameters. A segmented impedance accumulation algorithm is used to accumulate the line impedance segment by segment from the grid connection point of each photovoltaic inverter towards the distribution transformer or feeder head, calculating the equivalent grid impedance corresponding to each photovoltaic inverter. Simultaneously, the reactive power output of each inverter and the real-time voltage changes at key voltage nodes are considered to determine the reactive power voltage sensitivity of each photovoltaic inverter to each key voltage node. In actual operation, if the photovoltaic inverter is low-voltage grid connected, the equivalent impedance from the photovoltaic inverter to the distribution transformer can be selected; if the photovoltaic inverter is medium-voltage grid connected, the equivalent impedance from the photovoltaic inverter to the feeder head can be selected. In one embodiment, the reactive power voltage sensitivity can be calculated as follows: ;in, For the reactive power output of the Xth photovoltaic inverter; , ; The node voltage of the critical voltage node; The number of critical voltage nodes; Let X be the reactive voltage sensitivity of the Xth photovoltaic inverter.

[0047] S205. Normalize the equivalent impedance, voltage sensitivity, and photovoltaic inverter capacity, and use the normalized equivalent impedance, voltage sensitivity, and photovoltaic inverter capacity as the characteristic vector of the photovoltaic inverter.

[0048] In this embodiment, the equivalent impedance, reactive voltage sensitivity, and rated capacity of the photovoltaic inverter can be uniformly preprocessed using algorithms such as extreme value normalization, mapping parameters from different numerical ranges to the same standard range. The normalized equivalent impedance, voltage sensitivity, and photovoltaic inverter capacity are then used as the feature vectors of the photovoltaic inverter.

[0049] S206. Based on the feature vector, the K-means clustering algorithm is used to divide the photovoltaic inverter into at least two photovoltaic aggregation regions.

[0050] Among them, the K-means clustering algorithm is an unsupervised machine learning clustering algorithm. It takes the multi-dimensional feature vectors of each photovoltaic inverter as input, and divides photovoltaic inverters with high feature similarity into photovoltaic aggregation regions by iteratively updating the cluster centers and minimizing the intra-cluster Euclidean distance.

[0051] In this embodiment, the feature vectors of all photovoltaic inverters are used as input samples to initialize a preset number of cluster centers, and the K-means clustering algorithm is used for iterative calculations. By continuously updating the cluster centers, all photovoltaic inverters with similar feature vectors are automatically grouped into the same cluster, ultimately resulting in at least two independent photovoltaic aggregation regions.

[0052] S207. Select the photovoltaic inverter that is closest to the cluster center feature Euclidean distance within the photovoltaic aggregation area as the equivalent center inverter, and extract the reactive voltage sensitivity of each equivalent center inverter to each key voltage node as the first sensitivity.

[0053] The equivalent center inverter refers to the real photovoltaic inverter within the photovoltaic aggregation region that has the smallest Euclidean distance to the virtual cluster center of the region. The first sensitivity refers to the reactive voltage sensitivity of the equivalent center inverter in each aggregation region to each key voltage node within the photovoltaic aggregation region, used to characterize the overall voltage regulation capability of the corresponding photovoltaic aggregation region to key voltage nodes.

[0054] In the embodiment, for each photovoltaic aggregation region that has been divided, the Euclidean distance between the feature vector of each photovoltaic inverter in the region and the virtual cluster center of the region is calculated. The real photovoltaic inverter with the smallest distance is selected as the equivalent center inverter of the region. The reactive voltage sensitivity of the equivalent center inverter of each aggregation region to all key voltage nodes in the whole region is extracted as the first sensitivity.

[0055] S208. Taking the maximum sum of the first sensitivities corresponding to all key voltage nodes as the allocation optimization objective, traverse each key voltage node and allocate the key voltage node to the photovoltaic polymerization region with the largest corresponding first sensitivities value.

[0056] Among them, the allocation optimization objective refers to the objective of allocating key voltages. The optimal criterion is to maximize the sum of the first sensitivities corresponding to the key voltage nodes, so as to ensure that all voltage-weak nodes are matched to the photovoltaic polymerization region with the strongest voltage regulation capability.

[0057] In this embodiment, the core optimization criterion is to maximize the sum of the first sensitivities of all regions corresponding to the key voltage nodes. Every key voltage node in the entire domain is traversed. For the single key voltage node to be assigned, the first sensitivities of all photovoltaic aggregation regions to the key voltage node are compared one by one. The key voltage node is then uniquely assigned to the photovoltaic aggregation region with the largest first sensitivity value, thus completing the accurate matching of all key voltage nodes and photovoltaic aggregation regions.

[0058] S209. Determine the voltage condition information of each key voltage node, determine the voltage risk index of each photovoltaic aggregation area and the comprehensive voltage sensitivity of the photovoltaic inverter based on the voltage condition information, and determine the reactive power margin of the photovoltaic inverter according to the operating status data.

[0059] S210. Determine the control correction amount based on the voltage risk index, comprehensive voltage sensitivity and reactive power margin, determine the control command for each photovoltaic inverter based on the control correction amount, and adjust the reactive power of the distributed photovoltaic system access point according to the control command.

[0060] In this embodiment of the invention, the operating status data of the distributed photovoltaic system access point is determined, and key voltage nodes are identified according to preset over-limit number conditions, preset fluctuation amplitude conditions, or preset node types. The accuracy of key voltage node identification is ensured. By collecting network topology parameters and integrating global operating status data, equivalent impedance and reactive power voltage sensitivity are calculated to accurately determine feature vectors. Based on these feature vectors, a K-means clustering algorithm is used to divide the photovoltaic inverters into at least two photovoltaic aggregation regions. The photovoltaic inverter within each aggregation region that has the closest Euclidean distance to the cluster center is selected as the equivalent center inverter. The reactive voltage sensitivity of each equivalent central inverter to each key voltage node is extracted as the first sensitivity. The allocation optimization objective is to maximize the sum of the first sensitivities corresponding to all key voltage nodes. Each key voltage node is traversed and assigned to the photovoltaic aggregation area with the largest corresponding first sensitivity value. This achieves coordinated control of multiple inverters in the photovoltaic aggregation area by taking the photovoltaic aggregation area as the control object and comprehensively considering the area voltage risk, inverter reactive power margin and voltage sensitivity. This allows different inverters to undertake corresponding reactive power regulation tasks according to their regulation capabilities and effects, thereby improving the overall voltage support capability of the photovoltaic cluster.

[0061] Example 3 Figure 3 This is a flowchart of a distributed photovoltaic aggregation control method according to Embodiment 2 of the present invention. This embodiment is a further optimization and extension based on the above embodiments, and can be combined with various optional technical solutions in the above embodiments. Figure 3 As shown, the method includes: S301. Obtain the operating status data of the distributed photovoltaic system access points, and determine the feature vector and key voltage node of the photovoltaic inverter in each distributed photovoltaic system access point based on the operating status data.

[0062] S302. Divide the photovoltaic inverter into at least two photovoltaic aggregation regions according to the feature vector, and allocate the key voltage node to the corresponding photovoltaic aggregation region according to the preset conditions.

[0063] S303. Determine the real-time voltage, upper voltage limit, lower voltage limit, maximum allowable voltage deviation, predicted voltage at the next moment, high voltage over-limit duration, and low voltage over-limit duration of each key voltage node as voltage condition information.

[0064] Real-time voltage refers to the instantaneous value of the bus voltage collected at the current moment at key voltage nodes. The upper voltage limit is essentially the highest voltage threshold allowed for safe operation of the distribution network. The lower voltage limit refers to the lowest voltage threshold allowed for safe operation of the distribution network. The maximum permissible voltage deviation can be understood as the maximum voltage deviation range allowed by regulations, based on the rated voltage. The predicted voltage for the next moment is the future periodic node voltage extrapolated from historical voltage, photovoltaic output, and load time-series data, used to predict voltage runaway trends in advance. High-voltage over-limit duration refers to the cumulative duration for which the node voltage is continuously higher than the upper voltage limit. Low-voltage over-limit duration refers to the cumulative duration for which the node voltage is continuously lower than the lower voltage limit.

[0065] In this embodiment, the real-time voltage of each key voltage node can be collected, the pre-configured upper voltage limit, lower voltage limit, and maximum allowable voltage deviation can be retrieved, and the predicted voltage of each key voltage node at the next moment can be output through a time-series prediction model. The duration of high voltage exceedance and the duration of low voltage exceedance can be determined according to the real-time voltage, upper voltage limit, and lower voltage limit. The real-time voltage, upper voltage limit, lower voltage limit, maximum allowable voltage deviation, predicted voltage at the next moment, duration of high voltage exceedance, and duration of low voltage exceedance of the key voltage node are used as voltage condition information.

[0066] S304. Determine the node undervoltage risk and node overvoltage risk of each critical voltage node based on the voltage condition information.

[0067] Among them, the node undervoltage risk is a quantified undervoltage risk value for a single critical voltage node. It is calculated by weighting the node's real-time undervoltage offset, the duration of undervoltage exceeding the limit, and the predicted undervoltage deviation. The larger the value, the higher the undervoltage risk. The node overvoltage risk is a quantified overvoltage risk value for a single critical voltage node. It is calculated by weighting the node's real-time high-voltage offset, the duration of high-voltage exceeding the limit, and the predicted high-voltage deviation. The larger the value, the higher the overvoltage risk.

[0068] In this embodiment, the method for determining the node low-voltage risk of a critical voltage node can be as follows: ;in, Risk of low voltage at nodes; Let be the real-time voltage of node k at time t; This is the lower limit of voltage. This is the maximum permissible voltage deviation; For node k at time... The predicted voltage, To regulate the duration of the cycle; The duration of the low voltage over-limit at node k; , , For pre-set weighting coefficients; This is a real-time undervoltage risk item. To predict undervoltage risk items; The method for determining the high and low voltage risks at critical voltage nodes can be as follows: ;in, This is the upper limit of voltage. The high voltage over-limit duration at node k.

[0069] S305. The low voltage risk and high voltage risk of each node are respectively processed by mean square summation according to the photovoltaic aggregation region to which the critical voltage node belongs, to obtain the regional low voltage risk index and regional high voltage risk index of each photovoltaic aggregation region, and the regional low voltage risk index and regional high voltage risk index are used as the voltage risk index of each photovoltaic aggregation region.

[0070] The regional low-voltage risk index refers to the comprehensive index obtained by summing the low-voltage risks of all critical voltage nodes within a single photovoltaic aggregation area using the mean square method. The regional high-voltage risk index refers to the comprehensive index obtained by summing the high-voltage risks of all critical voltage nodes within a single photovoltaic aggregation area using the mean square method.

[0071] In this embodiment, the low-voltage risk and high-voltage risk of a node can be summed by mean square according to the photovoltaic aggregation region to which the critical voltage node belongs, to obtain the regional low-voltage risk index and regional high-voltage risk index for the photovoltaic aggregation region. These regional low-voltage risk index and regional high-voltage risk index are then used as the voltage risk index for each photovoltaic aggregation region. The regional low-voltage risk index can be... ;in, As a regional low voltage risk indicator, The set of all critical voltage nodes in the c-th photovoltaic aggregation region This represents the total number of critical voltage nodes participating in this statistical analysis within the c-th photovoltaic aggregation region. The regional high-voltage risk index can be... ; This serves as a regional high-voltage risk indicator.

[0072] S306. The reactive voltage sensitivity of each photovoltaic inverter to each key voltage node in its photovoltaic aggregation area is weighted and summed according to the preset weight of each key voltage node to obtain the comprehensive voltage sensitivity of each photovoltaic inverter.

[0073] Among them, the comprehensive voltage sensitivity can be understood as the contribution capability of a single photovoltaic inverter to the overall voltage regulation of its aggregated area. It is obtained by weighted summation of the reactive voltage sensitivity of the inverter to each key voltage node in the area and the node weight.

[0074] In this embodiment, the reactive voltage sensitivity of each photovoltaic inverter to each key voltage node within its photovoltaic aggregation region can be determined. The reactive voltage sensitivity is then weighted and summed according to preset weights for each key voltage node to obtain the comprehensive voltage sensitivity of each photovoltaic inverter. In actual operation, the comprehensive voltage sensitivity of the photovoltaic inverter can be... ;in, For critical voltage nodes k The voltage value; For photovoltaic inverters Unproductive efforts; This refers to the e-th photovoltaic inverter in the c-th photovoltaic aggregation region; For photovoltaic inverters Reactive power changes at critical voltage nodes k The effect of voltage on sensitivity; Weights for critical voltage nodes.

[0075] S307. Determine the available reactive power margin of each photovoltaic inverter based on the active power output, photovoltaic inverter capacity, and reserved reactive power margin in the operating status data.

[0076] The available reactive power margin refers to the remaining adjustable capacity range that each photovoltaic inverter can flexibly generate additional inductive reactive power and absorb capacitive reactive power under the current real-time operating conditions.

[0077] In the embodiment, the available reactive power margin of each photovoltaic inverter can be determined by... It is confirmed that, among them, Within photovoltaic aggregation region c, the first e Taiwanese photovoltaic inverter Rated capacity, Within photovoltaic aggregation region c, the first e Taiwanese photovoltaic inverters Their efforts and contributions; The photovoltaic aggregation area within region c is not the first e Taiwanese photovoltaic inverters Reserved reactive power margin.

[0078] S308. Determine the sum of the available reactive power margins of photovoltaic inverters belonging to the same photovoltaic aggregation area as the total available reactive power margin, and use the available reactive power margin and the total available reactive power margin as the reactive power margin.

[0079] In this embodiment, the photovoltaic aggregation area can be grouped, and the single-unit available reactive power margin of all photovoltaic inverters in the same area can be accumulated to obtain the total available reactive power margin of each photovoltaic aggregation area. The available reactive power margin and the total available reactive power margin are used as the reactive power margin.

[0080] S309. The sum of the regional low voltage risk index and the regional high voltage risk index of the voltage risk index in each photovoltaic aggregation region shall be taken as the voltage risk sum.

[0081] In the embodiments, the sum of the regional low voltage risk index and the regional high voltage risk index of the voltage risk index in each photovoltaic aggregation region can be determined as the voltage risk sum.

[0082] S310. Determine the ratio of the available reactive power margin of the photovoltaic inverter in each photovoltaic aggregation region to the total available reactive power margin as the reactive power margin ratio. Then, perform a weighted summation of the voltage risk, comprehensive voltage sensitivity and reactive power margin ratio corresponding to each photovoltaic inverter according to a preset fixed weight to obtain the reactive power gain fine-tuning amount.

[0083] The preset fixed weights can be understood as pre-set weighting coefficients for the sum of voltage risks, comprehensive voltage sensitivity, and reactive power margin ratio, which can be set according to business needs. The reactive power gain fine-tuning refers to the parameter of reactive power gain, which is a correction parameter calculated based on the weighted average of the sum of voltage risks, comprehensive voltage sensitivity, and reactive power margin ratio.

[0084] In this embodiment, the ratio of the available reactive power margin of each photovoltaic inverter in each photovoltaic aggregation region to the total available reactive power margin can be calculated as the reactive power margin ratio. The sum of voltage risks, comprehensive voltage sensitivity, and the reactive power margin ratio corresponding to each photovoltaic inverter are then weighted and summed according to a preset fixed weight. The result is used as the reactive power gain fine-tuning amount. For example, the reactive power gain fine-tuning amount can be... ;in, This represents the sum of voltage risks. To consider overall voltage sensitivity, This is the reactive power margin ratio. , and Preset fixed weights.

[0085] S311. Read the basic reactive power gain of each photovoltaic inverter and use the sum of the basic reactive power gain and the reactive power gain fine-tuning amount as the dynamic voltage reactive power gain.

[0086] Among them, dynamic voltage reactive power gain refers to the voltage reactive power gain that is updated in real time based on the current operating conditions.

[0087] In the embodiment, the dynamic voltage reactive power gain can be ;in, The base reactive power gain of the e-th photovoltaic inverter in the c-th photovoltaic aggregation region is given by [the relevant data point]. This refers to the dynamic voltage reactive power gain.

[0088] S312. Determine the average comprehensive voltage sensitivity of photovoltaic inverters belonging to the same photovoltaic aggregation area as the regional influence average, and determine the low voltage action threshold fine-tuning amount according to the regional influence average and the regional low voltage risk index in the voltage risk index.

[0089] Among them, the low-voltage action threshold fine-tuning amount refers to the correction amount of the calculated low-voltage action threshold, which is used to adaptively adjust the low-voltage regulation start threshold.

[0090] In the embodiment, the comprehensive voltage sensitivity of photovoltaic inverters belonging to the same photovoltaic aggregation region can be determined, the mean of the comprehensive voltage sensitivity can be used as the regional influence mean, and the regional low voltage risk index and the regional influence mean can be weighted and summed to obtain the low voltage action threshold fine adjustment amount.

[0091] S313. Determine the high voltage action threshold fine-tuning amount according to the regional impact mean and the regional high voltage risk index in the voltage risk index, and use the dynamic voltage reactive power gain, the high voltage action threshold fine-tuning amount and the low voltage action threshold fine-tuning amount as control correction amounts.

[0092] Among them, the high-voltage action threshold fine-tuning amount refers to the correction amount of the calculated high-voltage action threshold, which is used to adaptively adjust the high-voltage regulation start threshold.

[0093] In the embodiment, the high voltage risk index and the mean value of regional impact can be weighted and summed to obtain the high voltage action threshold fine-tuning amount, and the dynamic voltage reactive power gain, the high voltage action threshold fine-tuning amount, and the low voltage action threshold fine-tuning amount can be used as control correction amounts.

[0094] S314. The difference between the preset initial high-pressure threshold and the high-pressure action threshold fine-tuning amount is taken as the target high-pressure threshold, and the sum of the preset initial low-pressure threshold and the low-pressure action threshold fine-tuning amount is taken as the target low-pressure threshold.

[0095] Among them, the target high-voltage threshold refers to the final high-voltage judgment threshold used to generate control commands; the target low-voltage threshold refers to the final low-voltage judgment threshold used to generate control commands.

[0096] In the embodiment, the difference between the preset initial high-pressure threshold and the high-pressure action threshold adjustment amount, and the sum of the preset initial low-pressure threshold and the low-pressure action threshold adjustment amount can be calculated respectively. The difference between the preset initial high-pressure threshold and the high-pressure action threshold adjustment amount is used as the target high-pressure threshold, and the sum of the preset initial low-pressure threshold and the low-pressure action threshold adjustment amount is used as the target low-pressure threshold.

[0097] S315. Determine the control commands for each photovoltaic inverter according to the dynamic voltage reactive power gain, the target high voltage threshold, and the target low voltage threshold, and adjust the reactive power of the distributed photovoltaic system access point through the control commands.

[0098] In this embodiment, the dynamic voltage reactive power gain can be compared with the target high voltage threshold and the target low voltage threshold, respectively. The comparison result is used to generate corresponding control commands, which are then sent to the distributed photovoltaic system access point to adjust the reactive power of the distributed photovoltaic system access point. The reactive power of the distributed photovoltaic system access point is adjusted according to the control commands.

[0099] In one embodiment, the control commands for each photovoltaic inverter are determined according to the dynamic voltage reactive power gain, the target high voltage threshold, and the target low voltage threshold, including: Collect the real-time voltage at the grid connection point of the photovoltaic inverter in each distributed photovoltaic system access point; When it is determined that the real-time voltage at the grid connection point is greater than the target high-voltage threshold, the difference between the real-time voltage and the target high-voltage threshold is determined as the first difference, the product of the first difference and the dynamic voltage reactive power gain is determined as the first product, and the negative of the first product is taken as the candidate control command. When it is determined that the real-time voltage at the grid connection point is less than the target low-voltage threshold, the difference between the target low-voltage threshold and the real-time voltage at the grid connection point is determined as the second difference, the product of the second difference and the dynamic voltage reactive power gain is determined as the second product, and the second product is used as the candidate control command. When it is determined that the real-time voltage at the grid connection point is greater than or equal to the target low-voltage threshold and less than or equal to the target high-voltage threshold, the candidate control command is determined to be zero. When the reactive power command is greater than the current maximum adjustable reactive power amplitude of the photovoltaic inverter, the current maximum adjustable reactive power amplitude of the photovoltaic inverter will be used as the final control command. When the reactive power command is less than the current minimum adjustable reactive power amplitude of the photovoltaic inverter, the current minimum adjustable reactive power amplitude of the photovoltaic inverter will be used as the final control command. When the reactive power command is less than or equal to the current maximum adjustable reactive power amplitude of the photovoltaic inverter, and greater than or equal to the current minimum adjustable reactive power amplitude of the photovoltaic inverter, the reactive power command will be used as the final control command.

[0100] In this embodiment, the real-time voltage of each photovoltaic inverter's grid connection point can be collected in real time. Candidate control commands are calculated based on three operating conditions, including dynamic voltage reactive power gain, a target high-voltage threshold, and a target low-voltage threshold. When the real-time voltage at the grid connection point is greater than the target high-voltage threshold, a first difference between the real-time voltage and the target high-voltage threshold is calculated. The product of this first difference and the dynamic voltage reactive power gain is then multiplied by the negative value and used as the candidate control command. When the real-time voltage at the grid connection point is less than the target low-voltage threshold, a second difference between the target low-voltage threshold and the real-time voltage is calculated. The product of this second difference and the dynamic voltage reactive power gain is then used as the candidate control command. When the voltage is within the high and low voltage threshold range, the candidate control command is set to zero, and reactive power regulation is not required. The calculated candidate control commands undergo capacity limiting correction. If a candidate command exceeds the inverter's current maximum adjustable reactive power amplitude, it is used as the final control command to avoid equipment overload. If a candidate command is less than the inverter's current minimum adjustable reactive power amplitude, it is used as the final control command. If a candidate command is within the adjustable reactive power range, it is directly used as the final control command. Finally, the optimized control command is sent to the photovoltaic inverter to adjust the reactive power at the distributed photovoltaic system's connection point in real time, achieving adaptive and refined control of the distribution network voltage.

[0101] In this embodiment of the invention, by determining the voltage condition information of each key voltage node, the low-voltage risk and high-voltage risk of each key voltage node are determined according to the voltage condition information, thereby determining the regional low-voltage risk index and regional high-voltage risk index, accurately distinguishing between minor and major voltage hazards; by determining the ratio of the available reactive power margin of the photovoltaic inverter to the total available reactive power margin in each photovoltaic aggregation region as the reactive power margin ratio, the reactive power gain fine-tuning amount is obtained by weighting the sum of voltage risks, comprehensive voltage sensitivity, and reactive power margin ratio corresponding to each photovoltaic inverter according to a preset fixed weight; the basic reactive power gain of each photovoltaic inverter is read, and the basic reactive power gain and the reactive power gain fine-tuning amount are combined. The sum is used as the dynamic voltage reactive power gain. The average value of the comprehensive voltage sensitivity of photovoltaic inverters belonging to the same photovoltaic aggregation area is determined as the regional influence average. The high-voltage action threshold fine-tuning amount and low-voltage action threshold fine-tuning amount are determined according to the regional influence average and voltage risk index. The dynamic voltage reactive power gain, high-voltage action threshold fine-tuning amount and low-voltage action threshold fine-tuning amount are used as control correction amounts. The control commands of each photovoltaic inverter are determined according to the dynamic voltage reactive power gain, target high-voltage threshold and target low-voltage threshold. The reactive power of the distributed photovoltaic system access point is adjusted by the control commands to realize the dynamic adjustment of voltage reactive power gain, solve the defect of poor adaptability of traditional fixed parameters, and improve the adaptive capability of reactive power control parameters.

[0102] Example 3 Figure 4This is a flowchart of a distributed photovoltaic aggregation control method according to Embodiment 4 of the present invention. This embodiment uses electrical state variables as electrical state parameters as an example to further illustrate a distributed photovoltaic aggregation control method, such as... Figure 4 As shown, the method includes: S410. Collect the electrical status parameters of each distributed photovoltaic system access point.

[0103] Collect data from each distributed photovoltaic system access point. node voltage Contributing merit and effort Unproductive efforts Photovoltaic inverter capacity Reserve reactive power margin Voltage change rate and photovoltaic power output change rate As an electrical state quantity.

[0104] S420. Based on the electrical state parameters of the photovoltaic system access point and the network topology parameters of the distribution network where the photovoltaic system access point is located, determine the electrical distance, reactive voltage sensitivity, and photovoltaic inverter capacity. According to the electrical distance, reactive voltage sensitivity, and photovoltaic inverter capacity, use a clustering algorithm to divide the distributed photovoltaic system into several photovoltaic aggregation regions.

[0105] In one embodiment, two photovoltaic inverters are defined. and electrical distance Equivalent line impedance between grid-connected nodes: ; In the formula: Let x be the mutual impedance phasor between x and y of the photovoltaic inverter; Let x be the modulus of the equivalent impedance of the line between x and y of the photovoltaic inverter; For photovoltaic inverters x , y Equivalent resistance on the common electrical path between them; For photovoltaic inverters x , y Equivalent reactance on the common electrical path between them. The smaller the value, the closer the electrical distance between the two inverters, making them more suitable for being assigned to the same area.

[0106] To calculate reactive voltage sensitivity, critical voltage nodes are first defined. This invention defines a node as a critical voltage node that satisfies at least one of the following conditions: 1) ; 2) ; 3) Feeder terminal nodes, transformer substation terminal user nodes, and photovoltaic centralized access nodes; In the formula: The number of times the node's historical voltage exceeded the limit; The voltage fluctuation amplitude at the node; and These are the preset threshold for the number of times the limit is exceeded and the preset fluctuation threshold, respectively.

[0107] Define two photovoltaic inverters and Set of key voltage nodes The reactive voltage sensitivity is: ;in, For the reactive power output of the xth photovoltaic inverter; , ; The node voltage of the critical voltage node; The number of critical voltage nodes; Let be the reactive voltage sensitivity of the x-th photovoltaic inverter; For the reactive power output of the y-th photovoltaic inverter; Let be the reactive voltage sensitivity of the y-th photovoltaic inverter.

[0108] Difference in reactive voltage sensitivity between two photovoltaic inverters for: ; It is a set of critical voltage nodes; The smaller the value, the more similar the impact patterns of the two photovoltaic systems on the critical voltage node, and the more suitable they are to be assigned to the same region.

[0109] In summary, for each photovoltaic inverter Construct the feature vector: In the formula: , and They are respectively Normalized equivalent impedance to the head of the distribution transformer or feeder Normalized value of reactive voltage sensitivity at critical voltage node k and The capacity normalized value.

[0110] Based on this feature vector, the k-means clustering algorithm is used to cluster the distributed photovoltaic inverters, resulting in... N A photovoltaic aggregation area is denoted as: In the formula: For the first c A collection of photovoltaic inverters within a photovoltaic aggregation area; For the first c Within the first aggregation region, the firste Taiwan photovoltaic inverter; For the first c The number of photovoltaic inverters in a photovoltaic aggregation area.

[0111] Key voltage nodes that can represent the regional voltage operating status are assigned to each photovoltaic aggregation region.

[0112] In one embodiment, the assignment of critical voltage nodes can be determined by maximizing the sum of reactive voltage sensitivities of each photovoltaic cluster center to critical voltage nodes within the clustered area. This method aims to assign critical voltage nodes to photovoltaic clusters with the strongest voltage regulation capabilities, thereby significantly improving the overall voltage support performance of the system.

[0113] In the formula: Photovoltaic clustering center c For critical voltage nodes k The reactive voltage sensitivity. Finally, the photovoltaic polymerization region is obtained. c Set of key voltage nodes within : In the formula: For the first c The first photovoltaic aggregation area m Key voltage nodes; For the first c The number of critical voltage nodes within a photovoltaic aggregation area.

[0114] S430. Calculate the node-level voltage risk index based on the maximum permissible voltage deviation, voltage change rate, and over-limit duration of each critical voltage node.

[0115] For each critical voltage node voltage Calculate the high voltage risk and low voltage risk separately.

[0116] High voltage risk at nodes The calculation formula is as follows: ;in, Let be the real-time voltage of node k at time t; This is the upper limit of voltage; This is the maximum permissible voltage deviation; For node k at time... The predicted voltage, To regulate the duration of the cycle; The duration of the high-voltage over-limit at node k; , , For the pre-set weighting coefficients, .

[0117] Node low voltage risk The calculation formula is as follows: In the formula: This is the lower limit of voltage. The duration of the low voltage over-limit at node k.

[0118] In one embodiment, node risk indicators of multiple key voltage nodes within the same photovoltaic aggregation region can be aggregated to obtain regional high-voltage risk indicators and regional low-voltage risk indicators.

[0119] The regional high / low voltage risk index of this invention is obtained by summing the node risks of each key voltage node in the set of key voltage nodes through mean square, so as to highlight the impact of weak nodes on the overall region.

[0120] ;in, As a regional low voltage risk indicator, The set of all critical voltage nodes in the c-th photovoltaic aggregation region This represents the total number of critical voltage nodes participating in this statistical analysis within the c-th photovoltaic aggregation region. This serves as a regional high-voltage risk indicator.

[0121] S440. Calculate the available reactive power margin of the photovoltaic inverter and the comprehensive voltage sensitivity of the photovoltaic aggregation region.

[0122] For the c Within the first aggregation region, the first e Taiwanese photovoltaic inverters have available reactive power margins. for: In the formula, Within photovoltaic aggregation region c, the first e Taiwanese photovoltaic inverters Rated capacity, Within photovoltaic aggregation region c, the first e Taiwanese photovoltaic inverters Their efforts and contributions; e The photovoltaic aggregation area within region c is not the first e Taiwanese photovoltaic inverters Reserved reactive power margin.

[0123] Total available reactive power margin in the aggregation region for: ; This refers to the collection of photovoltaic inverters within photovoltaic aggregation zone c. It can determine how much reactive power the photovoltaic inverters in the area currently have available for voltage regulation, so as to guide the adjustment direction of dynamic voltage control parameters.

[0124] For the c Within the first aggregation region, the firste Taiwan Inverter Calculate the comprehensive voltage sensitivity of reactive power changes to key voltage nodes in the region. : In the formula: For critical voltage nodes k Node voltage; For photovoltaic inverters Unproductive efforts; For photovoltaic inverters Reactive power changes at critical voltage nodes k The effect of voltage on sensitivity; Weights for critical voltage nodes.

[0125] The reactive power gain fine-tuning amount and the voltage action threshold fine-tuning amount are determined as follows.

[0126] Traditional reactive power-voltage control typically uses fixed control parameters. This invention sets these parameters to be dynamically adjustable to improve the flexibility and voltage support capability of photovoltaic inverters. This is achieved through the aforementioned regional high-voltage risk indicators. Regional low voltage risk indicators Available reactive power margin Total available reactive power margin Overall voltage sensitivity Construct dynamic control parameters.

[0127] For the c Within the first aggregation region, the first e Taiwan Inverter Set its dynamic voltage reactive power gain for: In the formula: Based on reactive power gain, This is the reactive power gain fine-tuning amount.

[0128] According to regional high voltage risk indicators Regional low voltage risk indicators Available reactive power margin Total available reactive power margin Overall voltage sensitivity Sure: In the formula: , and The weights are preset to a fixed value. From the above formula, we can see that... , The higher, The larger, The larger; The higher, The larger, The larger; The larger, The larger, The larger.

[0129] In addition, for Set amplitude limit: In the formula: and They are respectively The upper and lower limits.

[0130] Set the dynamic high-voltage threshold (target high-voltage threshold). When the risk of high voltage in a region increases, the inverter should absorb reactive power in advance, therefore the high voltage action threshold should be lowered.

[0131] ; In the formula: The basic high-voltage action threshold; This is the fine-tuning amount for the high-voltage action threshold; For the overall voltage sensitivity of the region; and Here are the weighting coefficients. From the above formula, we can see that... The higher, The more it moves down; The higher the voltage, the sooner the photovoltaic inverter can enter the high-voltage support state.

[0132] Similarly, set the dynamic low-voltage threshold (target low-voltage threshold). When the risk of low voltage in the region increases, the inverter should be made to generate reactive power earlier, so the low voltage action threshold should be raised.

[0133] ; In the formula: The basic low-voltage action threshold; This is a fine-tuning amount for the low-voltage action threshold; and Here are the weighting coefficients. From the above formula, we can see that... The higher, The higher it moves; The higher the voltage, the sooner the photovoltaic inverter can enter the low-voltage support state.

[0134] In addition, for and Set amplitude limit: ; In the formula: and They are respectively and The maximum value.

[0135] S450 generates aggregated control parameters, and each inverter outputs reactive power based on the finely adjusted dynamic reactive-voltage curve.

[0136] Based on the above calculation results, the first... c The set of control parameters for each photovoltaic inverter within a single aggregation region: Inverter After receiving the aggregation control parameters, based on the local real-time voltage Perform dynamic reactive power-voltage control.

[0137] when Higher than At that time, the inverter absorbs reactive power. : ;when Below At that time, the inverter releases reactive power. : ; The final output must meet the following requirements: In the formula: For photovoltaic inverters The limit of reactive power output.

[0138] In one embodiment, Figure 5 This is a schematic diagram of the fine-tuning logic for the reactive power-voltage curve according to Embodiment 4 of the present invention. Figure 5 As shown, this represents the real-time voltage at the grid connection point of the e-th photovoltaic inverter within the c-th photovoltaic aggregation region. The vertical axis is The reactive power command for the e-th photovoltaic inverter within the c-th photovoltaic aggregation region. After control execution, the set of key voltage nodes is re-acquired. The voltage changes at key voltage nodes are monitored to assess the control effectiveness. If under-regulation exists, then in the next cycle: increase... Increase Prioritize calling , A c,e High-voltage inverter. If over-regulation exists, then in the next cycle: reduce... ; reduce ;reduce , High inverter regulation weight.

[0139] This embodiment improves the accuracy of voltage risk identification in distributed photovoltaic (PV) systems. Traditional methods often rely on the grid connection voltage of a single inverter or the regional average voltage, easily overlooking local risks such as feeder ends and weak voltage nodes. This invention selects key voltage nodes within the PV aggregation area and constructs regional high and low voltage risk indicators based on their real-time voltage, predicted voltage, and over-limit duration. This more accurately reflects the actual voltage operating status of the aggregation area, providing a reliable basis for subsequent control parameter adjustments. It also achieves coordinated reactive power control of the distributed PV cluster. Existing PV inverters typically employ local reactive power-voltage control, with each inverter operating independently, easily leading to overcompensation, undercompensation, or uneven utilization of reactive power resources. This invention uses the PV aggregation area as the control object, comprehensively considering regional voltage risk, inverter reactive power margin, and voltage sensitivity, to coordinate the control of multiple inverters within the area. This allows different inverters to undertake corresponding reactive power regulation tasks according to their regulation capabilities and effects, improving the overall voltage support capability of the PV cluster. Finally, it enhances the adaptive capability of reactive power control parameters. Traditional methods often use fixed settings for reactive power gain and voltage action thresholds, making it difficult to adapt to dynamic changes in photovoltaic output, load levels, and topology. This invention adaptively fine-tunes reactive power gain and voltage action thresholds, enabling inverters to absorb reactive power in advance when high-voltage risks increase and to release reactive power in advance when low-voltage risks increase, thereby improving the initiative and effectiveness of voltage regulation. In summary, this invention enables refined, coordinated, and adaptive reactive power and voltage control in distributed photovoltaic aggregation areas, significantly improving voltage exceedance mitigation capabilities and distribution network operational reliability.

[0140] Example 5 Figure 6 This is a schematic diagram of a distributed photovoltaic aggregation control device according to Embodiment 5 of the present invention. Figure 6 As shown, the device includes: a data determination module 61, a region division module 62, a parameter determination module 63, and a parameter control module 64.

[0141] Among them, the data determination module 61 is used to acquire the operating status data of the distributed photovoltaic system access point, and determine the feature vector and key voltage node of the photovoltaic inverter in each distributed photovoltaic system access point based on the operating status data.

[0142] The region division module 62 is used to divide the photovoltaic inverter into at least two photovoltaic aggregation regions based on the feature vector, and to allocate key voltage nodes to the corresponding photovoltaic aggregation regions according to preset conditions.

[0143] The parameter determination module 63 is used to determine the voltage condition information of each key voltage node, determine the voltage risk index of each photovoltaic aggregation area and the comprehensive voltage sensitivity of the photovoltaic inverter based on the voltage condition information, and determine the reactive power margin of the photovoltaic inverter according to the operating status data.

[0144] The parameter control module 64 is used to determine the control correction amount based on the voltage risk index, comprehensive voltage sensitivity and reactive power margin, and to determine the control parameters of each photovoltaic inverter based on the control correction amount.

[0145] The technical solution of this invention involves a data determination module acquiring operational status data of distributed photovoltaic (PV) system access points, a region division module determining the feature vectors and key voltage nodes of PV inverters in each PV system access point based on the operational status data, dividing the PV inverters into at least two PV aggregation regions based on the feature vectors, and allocating key voltage nodes to the corresponding PV aggregation regions according to preset conditions, a parameter determination module determining the voltage condition information of each key voltage node, determining the voltage risk index of each PV aggregation region and the comprehensive voltage sensitivity of the PV inverters based on the voltage condition information, and determining the reactive power margin of the PV inverters according to the operational status data, and a parameter control module determining the control correction amount based on the voltage risk index, comprehensive voltage sensitivity, and reactive power margin, determining the control commands for each PV inverter based on the control correction amount, and adjusting the reactive power of the distributed PV system access points according to the control commands. This achieves regional coordination and adaptive precise voltage regulation of distributed PV across the entire region, effectively suppressing voltage over-limits and fluctuations in the distribution network, and significantly improving the voltage stability level of the distribution network.

[0146] In one embodiment, the data determination module 61 includes: The data acquisition unit is used to collect the active power output, reactive power output, photovoltaic inverter capacity and reserved reactive power margin of the photovoltaic inverter in each distributed photovoltaic system access point, as well as the node voltage of the distribution network electrical node corresponding to each distributed photovoltaic system access point as electrical status parameters. The node determination unit is used to determine the historical voltage over-limit number, voltage fluctuation amplitude and node type of the distribution network electrical node corresponding to each distributed photovoltaic system access point as node voltage characteristic parameters, and to designate the distribution network electrical node that meets at least one of the preset over-limit number conditions, preset fluctuation amplitude conditions or belongs to the preset node type conditions as the key voltage node. The data determination unit is used to collect the network topology parameters of the distribution network where each distributed photovoltaic system access point is located, and uses electrical status parameters, node voltage characteristic parameters and network topology parameters as operating status data. The equivalent impedance of each photovoltaic inverter to the distribution transformer or feeder head end is determined by segmented accumulation of network topology parameters, and the reactive voltage sensitivity of each photovoltaic inverter to the critical voltage node is determined according to reactive power output and node voltage of critical voltage node. The vector determination unit is used to normalize the equivalent impedance, voltage sensitivity, and photovoltaic inverter capacity, and uses the normalized equivalent impedance, voltage sensitivity, and photovoltaic inverter capacity as the characteristic vector of the photovoltaic inverter.

[0147] In one embodiment, the region division module 62 includes: The region division unit is used to divide the photovoltaic inverter into at least two photovoltaic aggregation regions based on the feature vector and using the K-means clustering algorithm; The sensitivity determination unit is used to select the photovoltaic inverter with the closest Euclidean distance to the cluster center feature within the photovoltaic aggregation area as the equivalent center inverter, and extract the reactive voltage sensitivity of each equivalent center inverter to each key voltage node as the first sensitivity. The node partitioning unit is used to traverse each key voltage node and assign the key voltage node to the photovoltaic aggregation region with the largest corresponding first sensitivity value, with the goal of maximizing the sum of the first sensitivity values ​​of all key voltage nodes.

[0148] In one embodiment, the parameter determination module 63 includes: The operating condition determination unit is used to determine the real-time voltage, upper voltage limit, lower voltage limit, maximum allowable voltage deviation, predicted voltage at the next moment, high voltage over-limit duration, and low voltage over-limit duration of each key voltage node as voltage operating condition information. The risk determination unit is used to determine the node undervoltage risk and node overvoltage risk of each key voltage node based on voltage condition information. The indicator determination unit is used to perform mean square summation on the low voltage risk and high voltage risk of each node according to the photovoltaic aggregation region to which the key voltage node belongs, to obtain the regional low voltage risk index and regional high voltage index of each photovoltaic aggregation region, and use the regional low voltage index and regional high voltage index as the voltage risk index of each photovoltaic aggregation region. The summation unit is used to sum the reactive voltage sensitivity of each photovoltaic inverter to each key voltage node in its photovoltaic aggregation area according to the preset weight of each key voltage node, so as to obtain the comprehensive voltage sensitivity of each photovoltaic inverter. The available reactive power margin determination unit is used to determine the available reactive power margin of each photovoltaic inverter based on the active power output, photovoltaic inverter capacity and reserved reactive power margin in the operating status data. The reactive power margin determination unit is used to determine the sum of the available reactive power margins of photovoltaic inverters belonging to the same photovoltaic aggregation area as the total available reactive power margin, and to use the available reactive power margin and the total available reactive power margin as the reactive power margin.

[0149] In one embodiment, the parameter control module 64 includes: The risk sum determination unit is used to take the sum of the regional low voltage risk index and the regional high voltage index of the voltage risk index in each photovoltaic aggregation region as the voltage risk sum; The fine-tuning unit is used to determine the ratio of the available reactive power margin of the photovoltaic inverter in each photovoltaic aggregation region to the total available reactive power margin as the reactive power margin ratio. The unit also calculates the reactive power gain fine-tuning amount by weighting the sum of voltage risks, comprehensive voltage sensitivity and reactive power margin ratio corresponding to each photovoltaic inverter according to a preset fixed weight. The reactive power gain determination unit is used to read the basic reactive power gain of each photovoltaic inverter and use the sum of the basic reactive power gain and the reactive power gain fine-tuning amount as the dynamic voltage reactive power gain. The threshold determination unit is used to determine the average comprehensive voltage sensitivity of photovoltaic inverters belonging to the same photovoltaic aggregation area as the regional influence average, and to determine the low voltage action threshold fine-tuning amount according to the regional influence average and the regional low voltage risk index in the voltage risk index. The correction amount determination unit is used to determine the high voltage action threshold fine-tuning amount according to the regional influence average and the regional high voltage risk index in the voltage risk index, and uses the dynamic voltage reactive power gain, the high voltage action threshold fine-tuning amount and the low voltage action threshold fine-tuning amount as control correction amounts. The threshold adjustment unit is used to take the difference between the preset initial high voltage threshold and the high voltage action threshold fine adjustment amount as the target high voltage threshold, and the sum of the preset initial low voltage threshold and the low voltage action threshold fine adjustment amount as the target low voltage threshold. The parameter control unit is used to determine the control commands for each photovoltaic inverter according to the dynamic voltage reactive power gain, the target high voltage threshold, and the target low voltage threshold, and adjust the reactive power of the distributed photovoltaic system access point through the control commands.

[0150] In one embodiment, the parameter control unit is specifically used for: Collect the real-time voltage at the grid connection point of the photovoltaic inverter in each distributed photovoltaic system access point; When it is determined that the real-time voltage at the grid connection point is greater than the target high-voltage threshold, the difference between the real-time voltage and the target high-voltage threshold is determined as the first difference, the product of the first difference and the dynamic voltage reactive power gain is determined as the first product, and the negative of the first product is taken as the candidate control command. When it is determined that the real-time voltage at the grid connection point is less than the target low-voltage threshold, the difference between the target low-voltage threshold and the real-time voltage at the grid connection point is determined as the second difference, the product of the second difference and the dynamic voltage reactive power gain is determined as the second product, and the second product is used as the candidate control command. When it is determined that the real-time voltage at the grid connection point is greater than or equal to the target low-voltage threshold and less than or equal to the target high-voltage threshold, the candidate control command is determined to be zero. When the reactive power command is greater than the current maximum adjustable reactive power amplitude of the photovoltaic inverter, the current maximum adjustable reactive power amplitude of the photovoltaic inverter will be used as the final control command. When the reactive power command is less than the current minimum adjustable reactive power amplitude of the photovoltaic inverter, the current minimum adjustable reactive power amplitude of the photovoltaic inverter will be used as the final control command. When the reactive power command is less than or equal to the current maximum adjustable reactive power amplitude of the photovoltaic inverter, and greater than or equal to the current minimum adjustable reactive power amplitude of the photovoltaic inverter, the reactive power command will be used as the final control command.

[0151] In one embodiment, the distributed photovoltaic aggregation control device further includes: The voltage change determination module is used to re-collect voltage change information of each key voltage node after each photovoltaic inverter performs parameter adjustments according to the control parameters. The first adjustment module is used to increase the dynamic voltage reactive power gain, increase the target high voltage threshold and / or the target low voltage threshold in the next cycle if it is determined that there is under-regulation based on the voltage change information. The second adjustment module is used to reduce the dynamic voltage reactive power gain, decrease the target high voltage threshold and / or the target low voltage threshold in the next cycle if it is determined that there is over-voltage regulation based on the voltage change information.

[0152] The distributed photovoltaic aggregation control device provided in the embodiments of the present invention can execute the distributed photovoltaic aggregation control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0153] Example 6 Figure 7 This is a schematic diagram of an electronic device implementing a distributed photovoltaic aggregation control method according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0154] like Figure 7As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0155] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0156] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as distributed photovoltaic aggregation control methods.

[0157] In some embodiments, the distributed photovoltaic aggregation control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the distributed photovoltaic aggregation control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the distributed photovoltaic aggregation control method by any other suitable means (e.g., by means of firmware).

[0158] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0159] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0160] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0161] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0162] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0163] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0164] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the distributed photovoltaic aggregation control method of any embodiment of the present invention.

[0165] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0166] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0167] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A distributed photovoltaic aggregation control method, characterized in that, include: Obtain operational status data of distributed photovoltaic system access points, and determine the feature vector and key voltage nodes of photovoltaic inverters in each distributed photovoltaic system access point based on the operational status data; The photovoltaic inverter is divided into at least two photovoltaic aggregation regions based on the feature vector, and key voltage nodes are assigned to the corresponding photovoltaic aggregation regions according to preset conditions. Determine the voltage condition information of each key voltage node, determine the voltage risk index of each photovoltaic aggregation area and the comprehensive voltage sensitivity of the photovoltaic inverter based on the voltage condition information, and determine the reactive power margin of the photovoltaic inverter according to the operating status data; The control correction amount is determined based on the voltage risk index, the comprehensive voltage sensitivity, and the reactive power margin. The control command for each photovoltaic inverter is determined based on the control correction amount. The reactive power of the distributed photovoltaic system access point is adjusted according to the control command.

2. The method according to claim 1, characterized in that, The process of acquiring operational status data of distributed photovoltaic (PV) system access points and determining the feature vectors and key voltage nodes of PV inverters at each PV system access point based on the operational status data includes: The active power output, reactive power output, photovoltaic inverter capacity and reserved reactive power margin of the photovoltaic inverter in each distributed photovoltaic system access point, as well as the node voltage of the distribution network electrical node corresponding to each distributed photovoltaic system access point, are collected as electrical status parameters. The historical voltage over-limit number, voltage fluctuation amplitude, and node type of the distribution network electrical nodes corresponding to each distributed photovoltaic system access point are determined as node voltage characteristic parameters. Distribution network electrical nodes that meet at least one of the preset over-limit number conditions, preset fluctuation amplitude conditions, or preset node type conditions are designated as key voltage nodes. Collect the network topology parameters of the distribution network where each distributed photovoltaic system access point is located, and use the electrical status parameters, the node voltage characteristic parameters, and the network topology parameters as operating status data; The equivalent impedance from each photovoltaic inverter to the distribution transformer or feeder head end is determined by segmented accumulation of the network topology parameters, and the reactive voltage sensitivity of each photovoltaic inverter to the critical voltage node is determined according to the reactive power output and the node voltage of the critical voltage node. The equivalent impedance, voltage sensitivity, and photovoltaic inverter capacity are normalized, and the normalized equivalent impedance, voltage sensitivity, and photovoltaic inverter capacity are used as the feature vector of the photovoltaic inverter.

3. The method according to claim 1, characterized in that, The step of dividing the photovoltaic inverter into at least two photovoltaic aggregation regions based on the feature vector, and allocating key voltage nodes to the corresponding photovoltaic aggregation regions according to preset conditions, includes: Based on the feature vector, the photovoltaic inverter is divided into at least two photovoltaic aggregation regions using the K-means clustering algorithm; The photovoltaic inverter that is closest to the cluster center feature Euclidean distance within the photovoltaic aggregation area is selected as the equivalent center inverter, and the reactive voltage sensitivity of each equivalent center inverter to each key voltage node is extracted as the first sensitivity. With the goal of maximizing the sum of the first sensitivities corresponding to all key voltage nodes, the allocation optimization is carried out by traversing each key voltage node and assigning the key voltage node to the photovoltaic aggregation region with the largest corresponding first sensitivity value.

4. The method according to claim 1, characterized in that, The process of determining the voltage condition information of each key voltage node, determining the voltage risk index of each photovoltaic aggregation region and the comprehensive voltage sensitivity of the photovoltaic inverter based on the voltage condition information, and determining the reactive power margin of the photovoltaic inverter according to the operating status data includes: The real-time voltage, upper voltage limit, lower voltage limit, maximum allowable voltage deviation, predicted voltage at the next moment, high voltage over-limit duration, and low voltage over-limit duration of each key voltage node are determined as voltage condition information. Based on the voltage condition information, determine the node low voltage risk and node high voltage risk for each of the key voltage nodes; The low voltage risk and high voltage risk of each node are respectively processed by mean square summation according to the photovoltaic aggregation region to which the key voltage node belongs, to obtain the regional low voltage risk index and regional high voltage index of each photovoltaic aggregation region, and the regional low voltage index and regional high voltage index are used as the voltage risk index of each photovoltaic aggregation region. The reactive voltage sensitivity of each photovoltaic inverter to each key voltage node in its photovoltaic aggregation area is weighted and summed according to the preset weight of each key voltage node to obtain the comprehensive voltage sensitivity of each photovoltaic inverter. The available reactive power margin of each photovoltaic inverter is determined based on the active power output, photovoltaic inverter capacity and reserved reactive power margin in the operating status data. The sum of the available reactive power margins of photovoltaic inverters belonging to the same photovoltaic aggregation region is determined as the total available reactive power margin, and the available reactive power margin and the total available reactive power margin are used as the reactive power margin.

5. The method according to claim 1, characterized in that, The process of determining a control correction amount based on the voltage risk index, the comprehensive voltage sensitivity, and the reactive power margin; determining control commands for each photovoltaic inverter based on the control correction amount; and adjusting the reactive power at the distributed photovoltaic system access point according to the control commands includes: The sum of the regional low-voltage risk index and the regional high-voltage index of the voltage risk indicators in each photovoltaic aggregation region is taken as the voltage risk sum; The ratio of the available reactive power margin of the photovoltaic inverter in each photovoltaic aggregation region to the total available reactive power margin is determined as the reactive power margin ratio. The sum of voltage risk, comprehensive voltage sensitivity and reactive power margin ratio corresponding to each photovoltaic inverter are weighted and summed according to a preset fixed weight to obtain the reactive power gain fine adjustment amount. Read the basic reactive power gain of each photovoltaic inverter, and use the sum of the basic reactive power gain and the reactive power gain fine-tuning amount as the dynamic voltage reactive power gain. The average comprehensive voltage sensitivity of photovoltaic inverters belonging to the same photovoltaic aggregation region is determined as the regional influence average. The low voltage action threshold fine-tuning amount is determined according to the regional influence average and the regional low voltage risk index in the voltage risk index. The high voltage action threshold fine-tuning amount is determined according to the mean regional impact and the regional high voltage risk index in the voltage risk index, and the dynamic voltage reactive power gain, the high voltage action threshold fine-tuning amount and the low voltage action threshold fine-tuning amount are used as control correction amounts. The difference between the preset initial high-voltage threshold and the fine-tuning amount of the high-voltage action threshold is used as the target high-voltage threshold, and the sum of the preset initial low-voltage threshold and the fine-tuning amount of the low-voltage action threshold is used as the target low-voltage threshold. The control commands for each photovoltaic inverter are determined according to the dynamic voltage reactive power gain, the target high voltage threshold, and the target low voltage threshold, and the reactive power of the distributed photovoltaic system access point is adjusted through the control commands.

6. The method according to claim 5, characterized in that, The process of determining the control commands for each photovoltaic inverter based on the dynamic voltage reactive power gain, the target high-voltage threshold, and the target low-voltage threshold includes: Collect the real-time voltage at the grid connection point of the photovoltaic inverter in each distributed photovoltaic system access point; When it is determined that the real-time voltage at the grid connection point is greater than the target high voltage threshold, the difference between the real-time voltage and the target high voltage threshold is determined as the first difference, the product of the first difference and the dynamic voltage reactive power gain is determined as the first product, and the negative of the first product is taken as the candidate control command. When it is determined that the real-time voltage at the grid connection point is less than the target low-voltage threshold, the difference between the target low-voltage threshold and the real-time voltage at the grid connection point is determined as the second difference, the product of the second difference and the dynamic voltage reactive power gain is determined as the second product, and the second product is used as the candidate control command. When it is determined that the real-time voltage at the grid connection point is greater than or equal to the target low-voltage threshold and less than or equal to the target high-voltage threshold, the candidate control command is determined to be zero. When the reactive power command is greater than the current maximum adjustable reactive power amplitude of the photovoltaic inverter, the current maximum adjustable reactive power amplitude of the photovoltaic inverter shall be used as the final control command. When the reactive power command is less than the current minimum adjustable reactive power amplitude of the photovoltaic inverter, the current minimum adjustable reactive power amplitude of the photovoltaic inverter shall be used as the final control command. When the reactive power command is less than or equal to the current maximum adjustable reactive power amplitude of the photovoltaic inverter, and greater than or equal to the current minimum adjustable reactive power amplitude of the photovoltaic inverter, the reactive power command is used as the final control command.

7. The method according to claim 1, characterized in that, After determining the control correction amount based on the voltage risk index, the comprehensive voltage sensitivity, and the reactive power margin, and determining the control parameters of each photovoltaic inverter based on the control correction amount, the method further includes: After each photovoltaic inverter performs parameter adjustments according to the control parameters, it re-collects the voltage change information of each of the key voltage nodes; If under-regulation is determined based on the voltage change information, then in the next cycle, the dynamic voltage reactive power gain is increased, the target high voltage threshold and / or the target low voltage threshold are increased; If over-voltage regulation is determined based on the voltage change information, then in the next cycle, the dynamic voltage reactive power gain is reduced, the target high voltage threshold and / or the target low voltage threshold are decreased.

8. A distributed photovoltaic aggregation control device, characterized in that, include: The data determination module is used to acquire the operating status data of the distributed photovoltaic system access points, and determine the feature vector and key voltage node of the photovoltaic inverter in each distributed photovoltaic system access point based on the operating status data. The region division module is used to divide the photovoltaic inverter into at least two photovoltaic aggregation regions according to the feature vector, and to allocate key voltage nodes to the corresponding photovoltaic aggregation regions according to preset conditions. The parameter determination module is used to determine the voltage condition information of each of the key voltage nodes, determine the voltage risk index of each of the photovoltaic aggregation regions and the comprehensive voltage sensitivity of the photovoltaic inverter based on the voltage condition information, and determine the reactive power margin of the photovoltaic inverter according to the operating status data. The parameter control module is used to determine the control correction amount based on the voltage risk index, the comprehensive voltage sensitivity and the reactive power margin, and to determine the control parameters of each photovoltaic inverter based on the control correction amount.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the distributed photovoltaic aggregation control method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the distributed photovoltaic aggregation control method according to any one of claims 1-7.

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