Intelligent virtual power plant energy control system

Abstract

The application relates to the technical field of power system power supply and distribution collaborative control, and discloses an intelligent virtual power plant energy control system, which can more accurately identify local voltage risks in a power distribution network and device real-time support boundaries by constructing a node coupling graph, correcting a reactive voltage sensitivity matrix, generating a node voltage safety margin index and a reactive power support priority sequence, and implementing active and reactive closed-loop collaborative control in combination with direction-related available reactive power, so that the directional support capability of the virtual power plant for high-risk nodes is improved, the occurrence probability of local node voltage out-of-limit, reactive power compensation mismatch and device invalid adjustment is reduced, and the executability, operation stability and power distribution network support effect of the collaborative control instruction are improved.

Description

Technical Field

[0001] This application relates to the field of power system power supply and distribution coordinated control technology, and in particular to an intelligent virtual power plant energy control system. Background Technology

[0002] With the continuous integration of distributed photovoltaic (PV) systems, user-side energy storage, charging facilities, and adjustable loads into the park's and substation's distribution networks, virtual power plants are gradually evolving from a dispatching model focused on total power aggregation to a control model focused on supporting distribution network operation. In actual operation, a large number of inverter-type resources are connected to low-voltage or medium-voltage distribution networks through feeders with different impedance characteristics. Significant differences exist in the voltage support capacity, reactive power regulation capacity, and power carrying capacity of different nodes. Simultaneously, rapid changes in sunlight can cause short-term fluctuations in distributed PV output, concentrated charging load connections can lead to sharp increases in local loads, and changes in the operating status of the upper-level grid can be transmitted to the bus voltage side, thus causing continuous changes in the power flow distribution, node voltage levels, and reactive power exchange relationships within the park. In such scenarios… Virtual power plants not only need to complete the task of aggregated power regulation, but also need to take into account the output coordination between various parallel systems, local voltage stability, and safe operation of the distribution network. With the increasing marketization of virtual power plants, in park-type virtual power plants, integrated energy parks, and integrated source-grid-load-storage scenarios, there are also emerging application demands for multiple virtual power plants to participate in main market transactions, user-side point-to-point transactions, and coordinated optimization of electricity, heat and carbon emission indicators. For such scenarios, upper-level joint transaction decisions usually need to comprehensively consider equipment output, electricity and heat supply and demand balance, carbon cost constraints, and differences in transaction preferences. However, whether the relevant transaction results can be actually implemented still depends on the operating conditions such as the voltage status of the underlying distribution network nodes, reactive power support boundaries, equipment response capabilities, and local safety margins.

[0003] In existing technologies, control methods for distributed power sources and energy storage devices often employ fixed droop control, static power factor control, or simplified power flow verification methods based on station-level controllers to achieve voltage regulation of individual devices or local reactive power compensation. For the regulation of aggregated resources in virtual power plants, the focus is primarily on the total active power response target, uniformly allocating adjustable resources according to available capacity. These solutions can meet basic operational requirements in scenarios with small-scale single-device access, simple network structures, and weak node coupling. However, in park-type or transformer-type virtual power plant scenarios, existing solutions typically fail to fully consider the differences in voltage sensitivity of different nodes to active and reactive power regulation, fail to fully reflect the impact of inverter capacity limits, thermal constraints, and reactive power margins of energy storage converters on the regulation boundary, and lack consideration for... The real-time collaborative allocation mechanism of node voltage safety margin is prone to situations where power regulation at the aggregation level meets the target, but local nodes experience increased reverse power flow, voltage exceeding limits, or reactive power compensation mismatch. This is especially true in scenarios where multiple virtual power plants participate in the main market and point-to-point transactions in parallel. If regulation tasks are allocated only from the perspective of aggregated power or transaction targets, without synchronous characterization of node-level operational support capabilities and local voltage safety constraints, problems can easily arise where the upper-level joint transaction plan has economically optimized results, but the lower-level equipment experiences local exceeding limits, support mismatch, or insufficient actual executability during execution. Therefore, how to establish a control mechanism that coordinates active power regulation, reactive power support, and node safety margin in response to the spatial distribution characteristics of the distribution network has become an urgent technical problem to be solved in the process of refined control of virtual power plant parallel resources. Summary of the Invention

[0004] This application proposes an intelligent virtual power plant energy control system to address the problems mentioned in the background art.

[0005] To achieve the above objectives, this application adopts the following technical solution: an intelligent virtual power plant energy control system, comprising: The data acquisition module is used to acquire the distribution network operation data and adjustable equipment status data of the distribution network to which the virtual power plant belongs, and to construct a node coupling diagram based on the distribution network operation data; The sensitivity and capacity assessment module determines the initial reactive voltage sensitivity based on distribution network operation data, and corrects the initial reactive voltage sensitivity based on the node coupling diagram to obtain the corrected reactive voltage sensitivity matrix; it also determines the directional available reactive power capacity of each adjustable device based on the status data of adjustable devices. The safety margin and priority generation module determines the disturbance prediction result based on the distribution network operation data, and determines the node prediction voltage of each node in the distribution network based on the distribution network operation data, the corrected reactive voltage sensitivity matrix and the disturbance prediction result. Based on the node prediction voltage of each node in the distribution network, it generates the corresponding node voltage safety margin index, and generates the reactive power support priority sequence of each adjustable device based on the node voltage safety margin index, the corrected reactive voltage sensitivity matrix and the directional correlation available reactive power capacity. The collaborative control module selects the main regulating device based on the reactive power support priority sequence, and determines the active power regulation and reactive power regulation of each adjustable device based on the node voltage safety margin index, the corrected reactive power voltage sensitivity matrix, and the directional correlation available reactive power capacity. It outputs active and reactive power collaborative control commands. After the control is executed, it updates the corrected reactive power voltage sensitivity matrix, the node voltage safety margin index, and the reactive power support priority sequence based on the node voltage prediction residual between the actual measured voltage and the node predicted voltage.

[0006] Furthermore, the data acquisition module is specifically used to acquire voltage data of each node in the distribution network, current data of each branch, active and reactive power output data of each adjustable device, switch status data, feeder topology data, line impedance parameters, and transformer operating parameters when acquiring distribution network operation data and adjustable device status data. Acquire temperature rise data of each inverter device, health data and response delay data of each adjustable device, and acquire state of charge data when the adjustable device is an energy storage device. The obtained voltage, current, power, and capacity are standardized to a uniform per-unit value.

[0007] Furthermore, the sensitivity and capacity assessment module is specifically used to perform power flow linearization calculations at the current operating point based on distribution network operation data when determining the initial reactive voltage sensitivity, so as to obtain the initial reactive voltage sensitivity. The initial reactive voltage sensitivity is corrected based on the node coupling diagram and historical power flow samples to obtain the corrected reactive voltage sensitivity matrix.

[0008] Furthermore, the sensitivity and capability assessment module is specifically used to determine the dynamic available functional capacity of each adjustable device when determining the directionally relevant available functional capacity of each adjustable device, based on the rated apparent capacity, current active power output, temperature rise, device health and response delay of each adjustable device and the state of charge of the energy storage device. It also determines the injectable functional capacity and absorbable functional capacity of each adjustable device as the directionally relevant available functional capacity of each adjustable device.

[0009] Furthermore, the safety margin and priority generation module is specifically used to determine the output disturbance prediction results and load disturbance prediction results of distributed power sources based on the distribution network operation data when determining the disturbance prediction results, so as to serve as the disturbance prediction results.

[0010] Furthermore, the safety margin and priority generation module is specifically used to generate the node voltage safety margin index based on the node predicted voltage, upper allowable voltage limit, lower allowable voltage limit, and node neighborhood anomaly propagation status that reflects the degree of voltage deviation coupling between adjacent nodes when generating the node voltage safety margin index.

[0011] Furthermore, the safety margin and priority generation module is specifically used to generate a reactive power support priority sequence based on the directional available reactive power of each adjustable device, the voltage sensitivity coefficient of each node in the distribution network corresponding to the access node where each adjustable device is located in the corrected reactive power voltage sensitivity matrix, the node voltage safety margin index, and the line loss increment caused by each adjustable device participating in regulation.

[0012] Furthermore, the collaborative control module is specifically used to select adjustable devices that have a directional available reactive power capacity greater than a preset capacity threshold and a response delay less than a preset delay threshold when selecting the main regulating device, according to the sorting result of the reactive power support priority sequence, and to determine the adjustable devices that are sorted first as the main regulating devices.

[0013] Furthermore, the collaborative control module is specifically used to collaboratively solve for the active and reactive power regulation of each adjustable device when determining the active and reactive power regulation of each adjustable device, under the conditions of satisfying node voltage constraints, line current carrying constraints, device apparent capacity constraints, direction-dependent available reactive power constraints, energy storage device state of charge constraints, and device output change rate constraints, so as to obtain the active and reactive power regulation of each adjustable device.

[0014] Furthermore, the collaborative control module is specifically used to perform online correction and update of the corrected reactive voltage sensitivity matrix, node voltage safety margin index, and reactive power support priority sequence when updating the corrected reactive voltage sensitivity matrix, node voltage safety margin index, and reactive power support priority sequence, based on the node voltage prediction residual between the actual measured voltage and the predicted voltage of each node in the distribution network obtained in the next sampling period.

[0015] The beneficial effects of this invention are as follows: By constructing a node coupling diagram, correcting the reactive power-voltage sensitivity matrix, generating a node voltage safety margin index and a reactive power support priority sequence, and combining this with directionally correlated available reactive power capacity to implement active and reactive power closed-loop coordinated control, local voltage risks and real-time equipment support boundaries in the distribution network can be identified more accurately. This improves the virtual power plant's directional support capability for high-risk nodes, reduces the probability of local node voltage exceeding limits, reactive power compensation mismatch, and ineffective equipment regulation, and enhances the executability, operational stability, and distribution network support effect of coordinated control commands. Furthermore, the node voltage safety margin index, directionally correlated available reactive power capacity, and online-updated reactive power support priority sequence formed by this invention can also serve as the underlying operational constraint input and execution verification basis for virtual power plants participating in electricity-heat-carbon coordinated optimization and main market and point-to-point transaction coordinated decision-making. This provides support for the executability of transaction plans, consistency of operational boundaries, and safe and stable operation of the system in multi-virtual power plant joint transaction scenarios. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort: Figure 1 This is a system framework diagram of the present invention; Figure 2 This is a schematic diagram of the safety margin and priority generation module of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0018] Example

[0019] like Figure 1 and Figure 2 As shown, this invention discloses an intelligent virtual power plant energy control system, comprising: The data acquisition module is used to acquire the distribution network operation data and adjustable equipment status data of the distribution network to which the virtual power plant belongs, and to construct a node coupling diagram based on the distribution network operation data; The sensitivity and capacity assessment module determines the initial reactive voltage sensitivity based on distribution network operation data, and corrects the initial reactive voltage sensitivity based on the node coupling diagram to obtain the corrected reactive voltage sensitivity matrix; it also determines the directional available reactive power capacity of each adjustable device based on the status data of adjustable devices. The safety margin and priority generation module determines the disturbance prediction result based on the distribution network operation data, and determines the node prediction voltage of each node in the distribution network based on the distribution network operation data, the corrected reactive voltage sensitivity matrix and the disturbance prediction result. Based on the node prediction voltage of each node in the distribution network, it generates the corresponding node voltage safety margin index, and generates the reactive power support priority sequence of each adjustable device based on the node voltage safety margin index, the corrected reactive voltage sensitivity matrix and the directional correlation available reactive power capacity. The collaborative control module selects the main regulating device based on the reactive power support priority sequence, and determines the active power regulation and reactive power regulation of each adjustable device based on the node voltage safety margin index, the corrected reactive power voltage sensitivity matrix, and the directional correlation available reactive power capacity. It outputs active and reactive power collaborative control commands. After the control is executed, it updates the corrected reactive power voltage sensitivity matrix, the node voltage safety margin index, and the reactive power support priority sequence based on the node voltage prediction residual between the actual measured voltage and the node predicted voltage.

[0020] In this embodiment, the data acquisition module is used to acquire the distribution network operation data and adjustable equipment status data of the distribution network to which the virtual power plant belongs, and to construct a node coupling diagram based on the distribution network operation data. Specifically, when acquiring the distribution network operation data and adjustable equipment status data, the data acquisition module is used to acquire the voltage data of each node in the distribution network, the current data of each branch, the active power output data and reactive power output data of each adjustable equipment, the switch status data, the feeder topology data, the line impedance parameters, and the transformer operation parameters; acquire the temperature rise data of each inverter-type equipment, the equipment health data and the response delay data of each adjustable equipment, and acquire the state of charge data when the adjustable equipment is an energy storage device; and perform unified per-unit processing on the acquired voltage, current, power and capacity.

[0021] In existing technologies, virtual power plants typically collect operational data through distribution automation master stations, station-end controllers, edge gateways, smart meters, inverter monitoring units, and energy storage management units to achieve output monitoring and dispatch execution. This part of the data acquisition link is a conventional technical means in this field. This implementation does not impose special limitations on the data acquisition hardware, as long as it can stably output node-level, branch-level, and equipment-level data. Unlike existing solutions, this implementation does not only collect the total active power, total reactive power, or available equipment capacity at the aggregation level of the virtual power plant, but focuses on establishing an input data system oriented towards the spatial distribution characteristics of nodes, addressing the technical problems of voltage over-limit, reverse power flow, and reactive power compensation mismatch at local nodes in the distribution network.

[0022] Specifically, this implementation uses voltage data of each node, current data of each branch, feeder topology data, line impedance parameters, and transformer operating parameters in the distribution network as the main input quantities characterizing the electrical state of the distribution network. Temperature rise data, state of charge data, equipment health data, and response delay data are used as state input quantities characterizing the dynamic support capability of adjustable equipment. The two types of data are associated with the same node coupling diagram within the same control cycle, thereby providing a unified data foundation for the subsequent determination of initial reactive voltage sensitivity, generation of the corrected reactive voltage sensitivity matrix, generation of node voltage safety margin index, and generation of reactive power support priority sequence. Through this processing, the problem of existing schemes ignoring the electrical differences between nodes by controlling only based on total active power response or equipment static capacity can be avoided.

[0023] In practical implementation, the data acquisition module first performs hierarchical mapping of the data acquisition objects according to the distribution network topology. For distribution network operation data, bus nodes, branch nodes, common connection points, and load access points in the distribution network are used as node objects, and feeders, branches, and switch-controlled connections are used as branch objects. The association between nodes, branches, and adjustable equipment is established respectively. For voltage data, node voltage amplitude is preferred; for current data, branch current amplitude is preferred; for active and reactive power output data of each adjustable equipment, real-time injected power at the grid connection point is preferred; for switch status data, closed, open, and fault blocking status indicators are preferred; for feeder topology data, line impedance parameters, and transformer operating parameters, they are preferred to be read from the distribution automation master station, topology analysis module, and ledger parameter library.

[0024] For adjustable equipment status data, the temperature rise data of inverter equipment can be obtained from the temperature sensor or the temperature detection value of the heat dissipation component inside the equipment; the state of charge data of energy storage equipment can be obtained from the state of charge estimate output by the energy storage management system; the equipment health data can be generated by the operation and maintenance system based on historical failure rate, attenuation rate and recent alarm status; and the response delay data can be determined by the time difference between the time of control command issuance and the time of equipment status feedback. It should be noted that the temperature rise data, state of charge data, equipment health data and response delay data do not directly participate in the calculation of node voltage or branch power flow superposition, but are used to characterize the dynamic support boundary of each adjustable device in the current control cycle. This division of labor can maintain the consistency between electrical state quantities and equipment capacity quantities in a physical sense, and avoid directly splicing data of different natures, which would lead to a lack of interpretability in subsequent control results.

[0025] To ensure that data from different sources can be effectively integrated within the same control period, the data acquisition module also performs time synchronization processing and validity screening processing.

[0026] Preferably, the control cycle can be set to 1s to 300s. In a park-type virtual power plant, when photovoltaic output and charging load fluctuate frequently, the control cycle is preferably set to 1s to 30s. In a transformer substation-type virtual power plant, when the controlled objects are mainly transformer substation-level energy storage and flexible loads, the control cycle is preferably set to 5s to 60s. In scenarios where load changes are slow and communication bandwidth is limited, the control cycle can be relaxed to 60s to 300s. Correspondingly, the synchronization deviation of the data acquisition timestamp is preferably controlled within 1% to 10% of one control cycle. When the control cycle is 10s, the synchronization deviation is preferably no more than 100ms to 500ms. The reason for setting the above range is that if the synchronization deviation is too large, it will cause the voltage data, branch current data and equipment output data to be inconsistent, thereby affecting the node coupling diagram update and subsequent sensitivity correction. If the synchronization control is too strict, it will increase the communication and computing burden, which is not conducive to engineering implementation.

[0027] In terms of data validity screening, it is preferable to statistically analyze the proportion of missing data in each control cycle. When the key data of the same node or the same device is missing for more than 3 consecutive control cycles, or when the proportion of missing key fields in the current control cycle exceeds 20%, the node or device is marked as a low-confidence object, and cache compensation, replacement of the most recent valid value, or removal processing is triggered. Through this processing, the interference of abnormal data on the predicted voltage of subsequent nodes and the node voltage safety margin index can be reduced without disrupting the overall control closed loop.

[0028] After data acquisition and synchronization are completed, the data acquisition module performs uniform per-unit processing on the acquired voltage, current, power and capacity. Preferably, the voltage base value is determined according to the rated voltage of the corresponding voltage level in the distribution network, typically taking the corresponding level value from 0.4kV, 10kV, 35kV; the power base value is determined according to the regulation scale of the distribution network to which the virtual power plant belongs, preferably taking 10kVA to 100MVA; the current base value and apparent capacity base value are calculated according to the selected voltage base value and power base value.

[0029] For the same implementation scenario, it is preferable to adopt a unified base value system across the entire distribution network analysis scope. This ensures that node voltage data, branch current data, active power output data, reactive power output data of each adjustable device, and rated apparent capacity parameters are expressed under the same dimensional system. The reason for this unified per-unit processing is that this implementation method subsequently needs to use the node coupling diagram to correct the initial reactive power voltage sensitivity and generate the node voltage safety margin index and reactive power support priority sequence based on the corrected reactive power voltage sensitivity matrix. If the input quantities still maintain different engineering units, it will be difficult to perform cross-node comparisons, and the impact of different device capacity levels on sensitivity correction will be difficult to express on the same scale. Compared to the local normalization processing of single devices in existing technologies, this implementation method uses unified per-unit processing covering nodes, branches, and adjustable devices, ensuring that subsequent node coupling analysis and reactive power voltage sensitivity correction have a consistent data scale. This is a crucial supporting condition for ensuring the stability of the subsequent control model.

[0030] After completing the unified per-unit processing, the data acquisition module constructs a node coupling diagram based on the distribution network operation data.

[0031] Specifically, the effective connection relationships in the distribution network within the current control cycle are first determined based on feeder topology data, switch status data, and line impedance parameters. For branches where the switch is in the open position, the corresponding connection edges are removed. For branches where the switch is in the closed position and the branch parameters are complete, the corresponding connection edges are retained. For branches in the fault-blocked state or in the parameter missing state, their corresponding connection edges are marked as branches that cannot participate in the coupling analysis of this control cycle.

[0032] Based on this, the equivalent electrical distance between nodes is calculated by taking nodes as vertices in the graph and effective branches as edges in the graph, combined with line impedance parameters and transformer operating parameters, and the adjacency relationship in the node coupling graph is constructed accordingly.

[0033] Preferably, the node coupling graph includes a set of nodes, a set of edges, and an adjacency relation matrix or adjacency weight set reflecting the electrical coupling strength between nodes. It should be noted that the purpose of constructing the node coupling graph in this embodiment is not merely to graphically represent the topology, but to explicitly map the strength of electrical coupling between nodes to reflect the differentiated impact on the voltage of each node in the distribution network when the same reactive power support action falls on different access nodes. In the prior art, aggregation control often treats each adjustable device as a homogeneous resource and schedules it only according to capacity or total active power deficit, which makes it difficult to reflect the differences in line impedance between nodes and the neighborhood propagation effect. This embodiment, by introducing a node coupling graph, integrates feeder topology data, line impedance parameters, transformer operating parameters, and switch status data into a unified graph structure. This allows the subsequent sensitivity and capacity assessment module to no longer determine the initial reactive voltage sensitivity based solely on static network parameters, but to correct the initial reactive voltage sensitivity under the constraints of the node coupling graph, thereby improving the accuracy of the corrected reactive voltage sensitivity matrix in representing local voltage changes in the actual distribution network.

[0034] To ensure that the node coupling diagram accurately reflects the current operating status of the distribution network, the data acquisition module preferably triggers node coupling diagram reconstruction under the following circumstances: First, the switch status data changes, causing a switchover of the feeder topology; Second, the online identification results of line impedance parameters deviate from the ledger parameters by more than 5% to 20%; Third, the transformer tapping status changes, causing a change in the voltage reference relationship of downstream nodes; Fourth, the voltage data of key nodes or the current data of key branches are continuously missing for more than a preset periodic threshold.

[0035] The aforementioned deviation threshold range of 5% to 20% is determined by comprehensively considering the measurement errors of distribution network parameters, the changes in conductor impedance caused by temperature rise, and the computational overhead in engineering implementation: when the deviation threshold is below 5%, the node coupling graph reconstruction is too frequent, which will increase the consumption of computational resources; when the deviation threshold is above 20%, it may cause the graph structure to deviate too much from the actual network state, thereby affecting the reliability of the subsequently corrected reactive voltage sensitivity matrix. By setting the above triggering conditions and threshold range, a balance can be achieved between the timeliness of model updates and the stability of system operation.

[0036] Furthermore, after constructing the node coupling graph, the data acquisition module also outputs the mapping relationship between node indexes and adjustable device access locations, which serves as the basis for the subsequent sensitivity and capability assessment module to determine the access nodes where each adjustable device is located.

[0037] In other words, the data acquisition module not only completes data aggregation but also data structuring and organization, ensuring that the column indices in the corrected reactive voltage sensitivity matrix correspond one-to-one with the access nodes of each adjustable device, and that the node numbers in the node voltage safety margin index correspond one-to-one with the node predicted voltage, voltage data, and branch relationships. Through this unified indexing process, calculation deviations caused by inconsistencies in node numbers and device access points between subsequent modules can be avoided. This ensures a continuous and stable data transmission chain from the data acquisition module to the sensitivity and capacity assessment module, the safety margin and priority generation module, and the collaborative control module. The node coupling diagram output by the data acquisition module and the distribution network operation data after unified per-unit processing also serve as the input basis for the sensitivity and capacity assessment module to determine the initial reactive voltage sensitivity and generate the corrected reactive voltage sensitivity matrix.

[0038] In summary, the data acquisition module in this embodiment acquires distribution network operation data and adjustable equipment status data, performs unified per-unit processing, constructs a node coupling diagram, and establishes a mapping relationship between nodes and the access locations of adjustable equipment. This enables a unified representation of the spatial distribution characteristics of the distribution network and the dynamic capability boundaries of equipment. Compared with the coarse-grained acquisition method oriented towards total active power response in the prior art, the data foundation formed by this embodiment can more accurately support the subsequent correction of initial reactive voltage sensitivity, the generation of node voltage safety margin index, and the determination of reactive power support priority sequence. This provides a foundation for suppressing local node voltage over-limits, reducing reactive power compensation mismatch, and improving the collaborative support capability of virtual power plants for the distribution network.

[0039] In this embodiment, the sensitivity and capacity assessment module is used to determine the initial reactive voltage sensitivity based on distribution network operation data, and to correct the initial reactive voltage sensitivity based on the node coupling diagram to obtain the corrected reactive voltage sensitivity matrix; and to determine the directional available reactive power capacity of each adjustable device based on the adjustable device status data. Further, the sensitivity and capacity assessment module is specifically used to perform power flow linearization calculations at the current operating point based on distribution network operation data when determining the initial reactive voltage sensitivity, to obtain the initial reactive voltage sensitivity, and to correct the initial reactive voltage sensitivity based on the node coupling diagram and historical power flow samples to obtain the corrected reactive voltage sensitivity matrix; the sensitivity and capacity assessment module is also specifically used to determine the dynamic available reactive power capacity of each adjustable device based on the rated apparent capacity, current active power output, temperature rise, device health and response delay of each adjustable device, and the state of charge of the energy storage device when determining the directional available reactive power capacity of each adjustable device, and to determine the injectable reactive power capacity and absorbable reactive power capacity of each adjustable device respectively, as the directional available reactive power capacity of each adjustable device.

[0040] This implementation first uses a sensitivity and capacity assessment module to generate two key intermediate quantities: the corrected reactive power-voltage sensitivity matrix and the directionally dependent available reactive power capacity. The corrected reactive power-voltage sensitivity matrix characterizes the differentiated impact of reactive power changes at different access nodes on the voltage of each node in the distribution network under the current topology and operating conditions. The directionally dependent available reactive power capacity characterizes the actual support boundary that each adjustable device can use to inject or absorb reactive power under the current conditions. With these two intermediate quantities, the subsequent safety margin and priority generation module no longer relies on static network parameters and static capacity data, but can instead generate the node voltage safety margin index and the reactive power support priority sequence under the current operating scenario.

[0041] In practical implementation, the sensitivity and capacity assessment module first performs power flow linearization calculations at the current operating point based on distribution network operation data to obtain the initial reactive voltage sensitivity. Preferably, the distribution network operation data includes node voltage data, branch current data, active and reactive power output data of each adjustable device, feeder topology data, line impedance parameters, and transformer operating parameters after unified per-unit processing. Based on the above data, the AC power flow equation can be linearized in the vicinity of the operating state corresponding to the current control cycle, thereby establishing an approximate mapping relationship between node reactive power injection changes and node voltage changes. The initial reactive voltage sensitivity obtained in this way essentially reflects the basic influence relationship of reactive power regulation changes of each access node on the voltage changes of each node in the distribution network near the current operating point. To ensure the initial reactive voltage sensitivity... The physical effectiveness of the degree is preferably satisfied before linearization, under the following boundary conditions: the feeder topology remains unchanged during the current control cycle, the deviation of the voltage of each node in the distribution network from the rated voltage preferably does not exceed 0.03 pu to 0.10 pu, and the fluctuation of the branch current relative to the rated current preferably does not exceed 10% to 30%. When the node voltage deviation exceeds the above range, or when the switch state changes and causes the feeder topology to switch, it is preferable to re-perform the power flow calculation based on the current operating state and update the initial reactive voltage sensitivity, instead of directly using the result of the previous control cycle. The reason for adopting the above processing method is that the initial reactive voltage sensitivity is based on the premise of linear approximation. If it deviates too far from the current operating point, the approximate linear relationship between the node reactive power change and the node voltage change will be weakened, thereby affecting the accuracy of the subsequently corrected reactive voltage sensitivity matrix.

[0042] It should be noted that using power flow linearization to calculate the initial reactive voltage sensitivity is a reasonable use of existing power system analysis methods and is not the core innovation of this implementation method. The key to this implementation method is not whether linearization is performed, but rather that after obtaining the initial reactive voltage sensitivity, it is further corrected by combining the node coupling diagram and historical power flow samples. In other words, the initial reactive voltage sensitivity can be understood as the physical basis sensitivity obtained based on the current operating point, and its main function is to provide the starting matrix for subsequent corrections. The real technical means to solve the problem that offline sensitivity cannot reflect the time-varying nature of node coupling in the background technology is the subsequent graph constraint correction process.

[0043] After obtaining the initial reactive voltage sensitivity, the sensitivity and capacity assessment module corrects the initial reactive voltage sensitivity based on the node coupling diagram and historical power flow samples to obtain the corrected reactive voltage sensitivity matrix. Preferably, the node coupling diagram is output by the data acquisition module and includes a set of nodes, a set of edges, and an adjacency relationship matrix reflecting the electrical coupling strength between nodes. The historical power flow samples are preferably selected from historical operating samples within the last 1 to 90 days that are consistent with or similar to the current feeder topology, have similar load levels, and similar distributed generation penetration rates. To make the selection of historical power flow samples executable, a historical sample similarity index is preferably constructed based on topology consistency, total load deviation, distributed generation penetration rate deviation, and node voltage distribution differences. The samples are then selected based on the historical sample similarity index. Specifically, when a candidate historical sample meets the condition of consistency or equivalence with the current operating state in terms of topological consistency, and the total load deviation does not exceed 5% to 20%, the distributed power source penetration deviation does not exceed 5% to 20%, and the node voltage distribution difference does not exceed 0.01 pu to 0.05 pu, the candidate historical sample is determined to be a valid sample with high similarity to the current operating state. The reason for adopting the above method is that if the historical power flow sample differs too much from the current operating state, the sensitivity correction term extracted from it will lose its reference value. By introducing the historical sample similarity index to screen historical power flow samples, the adaptability of the sensitivity correction term to the current operating condition can be improved, thereby improving the reliability of the corrected reactive power voltage sensitivity matrix.

[0044] Preferably, the modified reactive voltage sensitivity matrix can be determined according to the following formula: ; in, Indicates time The corrected reactive voltage sensitivity matrix is ​​as follows. Indicates time Initial reactive voltage sensitivity, This represents the sensitivity correction term extracted based on the node coupling graph and historical power flow samples. The initial reactive voltage sensitivity is consistent with the dimension of the sensitivity correction term, which represents the correction weight. The advantage of this formula is that the initial reactive voltage sensitivity retains the physical interpretability based on the current operating point, while the sensitivity correction term is used to compensate for the local node coupling differences and historical operating deviations that the initial reactive voltage sensitivity fails to fully reflect. This makes the corrected reactive voltage sensitivity matrix more consistent with the actual operating scenario. Compared with the existing technology of pre-generating a fixed sensitivity table offline, this implementation method corrects the initial reactive voltage sensitivity online or quasi-online through node coupling diagrams and historical power flow samples, which can significantly improve the identification accuracy of the voltage support effect of different access nodes on high-risk nodes.

[0045] Among them, the correction weight The preferred value is 0 to 1, and more preferably 0.2 to 0.8. Setting it to 0 indicates that only the initial reactive voltage sensitivity is used, while setting it to 1 indicates that the initial reactive voltage sensitivity is corrected entirely according to the sensitivity correction term. The larger the correction weight, the stronger the correction effect of the node coupling diagram and historical power flow samples on the initial reactive voltage sensitivity. In engineering applications, the correction weight is preferably adaptively determined based on the data completeness of the current control cycle, the recent node voltage prediction residuals, and the similarity between the historical power flow samples and the current operating state. When data completeness is high, historical trend samples are highly similar to current operating conditions, and recent prediction residuals are small, the correction weight can be appropriately increased; when the current operating status changes drastically, historical sample matching is low, or there are many missing samples, it is preferable to reduce the correction weight to avoid over-correction. (Sensitivity correction term) Preferably, the matrix is ​​of the same dimension as the initial reactive voltage sensitivity, and its matrix elements can represent the sensitivity correction increment of the corresponding access node to the corresponding target node. In order to prevent the correction magnitude from being too large and causing distortion of physical meaning, the absolute value of the element corresponding to the sensitivity correction term is preferably limited to 5% to 50% of the absolute value of the element corresponding to the initial reactive voltage sensitivity, and more preferably limited to 10% to 30%. If the correction magnitude is less than 5%, the compensation effect on the local node coupling difference is not obvious; if the correction magnitude is greater than 50%, the corrected result may become overly dependent on historical deviations, weakening the stability of the physical basis sensitivity. Through the above processing, the corrected reactive voltage sensitivity matrix retains the physical interpretability under the current operating point, and introduces the local coupling characteristics represented by the node coupling diagram and historical power flow samples, which can more accurately support the generation of subsequent node voltage safety margin index and reactive power support priority sequence.

[0046] After obtaining the corrected reactive voltage sensitivity matrix, the sensitivity and capability assessment module further determines the directional available reactive power of each adjustable device. Specifically, when determining the directional available reactive power of each adjustable device, the sensitivity and capability assessment module first determines the dynamic available reactive power of each adjustable device based on the rated apparent capacity, current active power output, temperature rise, device health and response delay, and the state of charge of the energy storage device. Then, it determines the injectable reactive power and absorbable reactive power of each adjustable device as the directional available reactive power of each adjustable device. Compared with the existing technology that directly represents reactive power regulation capability by device nameplate capacity or fixed power factor boundary, this implementation emphasizes the joint constraint of the device's current active power occupancy, temperature rise status, health status, response delay, and energy storage device state of charge on the available reactive power, so that the directional available reactive power on which the subsequent safety margin and priority generation module is based is more in line with the device's real-time executable boundary.

[0047] Preferably, the dynamically available reactive power capacity can be determined in the following ways: First, the remaining reactive power capacity space is determined based on the rated apparent capacity and current active power output of each adjustable device. For inverter devices and energy storage devices, their reactive power capacity is usually limited by the upper limit of apparent capacity. Therefore, as the current active power output gradually approaches the rated apparent capacity, the remaining space available for reactive power regulation will gradually decrease. Preferably, when the current active power output accounts for less than 70% of the rated apparent capacity, the device can be considered to have sufficient reactive power regulation margin. When this ratio is between 70% and 95%, it is preferable to gradually reduce the reactive power regulation margin. When this ratio exceeds 95%, it is preferable to retain only the minimum reactive power capacity required for safety support or mark the device as a low-priority capacity device. Second, based on temperature rise, equipment health... The remaining reactive power capacity space is dynamically corrected by the health and response delay. The dynamic available reactive power capacity can be obtained by sequentially applying temperature rise correction, health correction, response delay correction and energy storage device state of charge correction to the remaining reactive power capacity space. For temperature rise, it is preferable to set the temperature rise correction coefficient according to the degree of proximity of the inverter equipment temperature rise to the equipment rated temperature rise threshold. The temperature rise correction coefficient is preferably 0 to 1, more preferably 0.8 to 1 when the temperature rise is lower than the first threshold, reduced to 0.3 to 0.8 in a linear or piecewise manner when the temperature rise is between the first threshold and the second threshold, and further reduced to 0 to 0.3 when the temperature rise is higher than the second threshold. The first threshold is preferably 70% to 90% of the equipment rated temperature rise alarm threshold, and the second threshold is preferably 90% to 100% of the equipment rated temperature rise alarm threshold.

[0048] The reason for setting the above range is that excessive temperature rise will increase the thermal stress and failure risk of the device, and its priority in participating in high-frequency reactive power regulation should be reduced. For the health of the equipment, it is preferable to set the health correction coefficient to 0.3 to 1. When the health is close to 1, it means that the equipment is in good operating condition and can participate more fully in reactive power support. When the health is close to 0.3, it means that the equipment has a high risk of aging or failure, and its reactive power support allocation should be reduced. For the response delay, it is preferable to set the delay reduction coefficient according to the time difference between the issuance of the equipment control command and the status response. The response delay is preferably controlled between 50ms and 30s. When the response delay is in the range of 50ms to 1s, the equipment can be considered to have good real-time support capability. When the response delay exceeds 5s to 30s, it is preferable to significantly reduce its dynamic available reactive power capability, or exclude it in the subsequent screening of main regulating equipment. The reason for adopting the above treatment is that node voltage risk usually has short-term change characteristics. If the equipment response is too slow, even if its static capacity is sufficient, it is difficult to form effective support.

[0049] For energy storage devices, the state of charge (SOC) should also be included in the dynamic available reactive power assessment. The SOC of the energy storage device is preferably represented by a ratio of 0 to 1. Preferably, when the SOC of the energy storage device is between 0.2 and 0.8, the DC side is considered to be relatively stable, allowing it to participate more fully in the directional reactive power allocation. When the SOC is between 0.1 and 0.2 or between 0.8 and 0.9, its dynamic available reactive power should preferably be appropriately reduced. When the SOC is below 0.1 or above 0.9, it is preferable to retain only the necessary safety support capacity or temporarily exclude it from the high priority support set. The reason for adopting this setting is that although the energy storage device can provide reactive power support through the converter, the DC side operating stability and subsequent active power regulation capability will be affected under extremely low or extremely high SOC conditions. If it is still forcibly involved in large-scale reactive power support, it may weaken the overall system regulation elasticity.

[0050] After completing the dynamic available reactive power assessment, the sensitivity and capability assessment module further determines the injectable reactive power and absorbable reactive power for each adjustable device. Preferably, for adjustable devices with symmetrical reactive power boundaries, the dynamic available reactive power can be allocated symmetrically into injectable and absorbable reactive power. For adjustable devices with asymmetrical boundaries, such as those constrained by power factor, control strategy, or grid connection specifications, the injectable and absorbable reactive power are preferably determined according to their forward and reverse reactive power output boundaries, respectively. By outputting injectable and absorbable non-functional capacity separately, the shortcomings of existing technologies that only provide a single available capacity and cannot distinguish between overvoltage and undervoltage scenarios can be avoided. Subsequently, when the safety margin and priority generation module identifies that a high-risk node mainly exhibits an overvoltage trend, absorbable non-functional capacity can be preferentially called; when it identifies that a high-risk node mainly exhibits an undervoltage trend, injectable non-functional capacity can be preferentially called. Thus, the direction-dependent available non-functional capacity is no longer an abstract total amount, but an executable support boundary directly facing different risk directions in the distribution network.

[0051] To ensure the stable output of the corrected reactive voltage sensitivity matrix and directionally dependent available reactive power within the same control cycle, it is preferable to keep the sensitivity and capacity assessment module and the data acquisition module updated synchronously. Specifically, the node coupling diagram, the distribution network operation data after unified per-unit processing, and the mapping relationship between node indices and adjustable equipment access locations output by the data acquisition module serve as direct inputs to the sensitivity and capacity assessment module. The corrected reactive voltage sensitivity matrix and directionally dependent available reactive power output by the sensitivity and capacity assessment module serve as direct inputs to the subsequent safety margin and priority generation module. The corrected reactive voltage sensitivity matrix is ​​used to characterize the support relationship between nodes, and the directionally dependent available reactive power is used to characterize the real-time executable edges of the equipment. Both the data acquisition module and the safety margin and priority generation module serve as inputs to the subsequent safety margin and priority generation module. In other words, the sensitivity and capability assessment module in this embodiment is not an independent mathematical analysis unit, but rather an intermediate bridge connecting the data acquisition module and the safety margin and priority generation module. On the one hand, the sensitivity correction under the node coupling graph constraint allows the subsequent node voltage safety margin index to be established on a more accurate node support relationship. On the other hand, the direction-dependent available reactive power capability allows the subsequent reactive power support priority sequence to be established on a more realistic real-time equipment support boundary. Thus, the reactive power support priority sequence generated by the subsequent module can simultaneously reflect which access node is more effective and which adjustable equipment is currently more available, rather than relying solely on static network parameters or static capacity data.

[0052] In summary, the sensitivity and capability assessment module in this embodiment generates a corrected reactive voltage sensitivity matrix by correcting the initial reactive voltage sensitivity under node coupling graph constraints. It then combines the rated apparent capacity of the adjustable device, current active power output, temperature rise, device health, response delay, and the state of charge of the energy storage device to generate directionally relevant available reactive power capabilities. This simultaneously characterizes the voltage support relationship between nodes and the real-time executable boundary of the device. Compared with existing technologies, this embodiment enables the subsequently generated node voltage safety margin index and reactive power support priority sequence to better reflect actual operating scenarios and provides a foundation for the collaborative control module to output more executable active and reactive power collaborative control commands.

[0053] In this embodiment, the safety margin and priority generation module is used to determine the disturbance prediction result based on the distribution network operation data, and to determine the node prediction voltage of each node in the distribution network based on the distribution network operation data, the corrected reactive voltage sensitivity matrix and the disturbance prediction result. Based on the node prediction voltage of each node in the distribution network, the module generates the corresponding node voltage safety margin index, and generates the reactive power support priority sequence of each adjustable device based on the node voltage safety margin index, the corrected reactive voltage sensitivity matrix and the directionally related available reactive power. Furthermore, the safety margin and priority generation module is specifically used to determine the output disturbance prediction results and load disturbance prediction results of distributed power sources based on the distribution network operation data when determining the disturbance prediction results, and to use these as the disturbance prediction results; when generating the node voltage safety margin index, it generates the node voltage safety margin index based on the node prediction voltage, upper allowable voltage limit, lower allowable voltage limit of each node in the distribution network, and the node neighborhood anomaly propagation situation reflecting the degree of voltage deviation coupling between adjacent nodes; when generating the reactive power support priority sequence, it generates the reactive power support priority sequence based on the directional related available reactive power capacity of each adjustable device, the voltage sensitivity coefficient of each adjustable device in the distribution network corresponding to each node in the access node of each adjustable device in the corrected reactive power voltage sensitivity matrix, the node voltage safety margin index, and the line loss increment caused by the participation of each adjustable device in regulation.

[0054] In existing technologies, virtual power plants typically first obtain the total power change trend for future periods based on load forecasting and distributed generation forecasting results, and then allocate regulation tasks according to preset rules or a single optimization objective. This type of scheme can support active power tracking at the aggregation level, but because it does not further map the forecast results to the voltage changes of each node in the distribution network and the risk propagation relationships between nodes, it is prone to situations where the total active power regulation meets the target, but local nodes still experience voltage exceedances, exacerbated reverse power flows, or reactive power compensation mismatches. To address this problem, this implementation does not directly sort resources based on disturbance forecasting results. Instead, it first converts the disturbance forecasting results into the node forecast voltages of each node in the distribution network based on the modified reactive power voltage sensitivity matrix, and then further generates a node voltage safety margin index. This node voltage safety margin index is then coupled with the directional available reactive power capacity of each adjustable device, the voltage sensitivity coefficient in the modified reactive power voltage sensitivity matrix, and the line loss increment to generate a reactive power support priority sequence.

[0055] In practical implementation, the safety margin and priority generation module first determines the disturbance prediction results based on the distribution network operation data. The disturbance prediction results include the distributed generation output disturbance prediction results and the load disturbance prediction results. The distributed generation output disturbance prediction results are preferably composed of at least one of the photovoltaic output change prediction value and the wind power output change prediction value. The load disturbance prediction results are preferably composed of at least one of the charging load change prediction value, the air conditioning load change prediction value, the industrial adjustable load change prediction value, and the general load change prediction value.

[0056] It should be noted that the prediction model for the disturbance prediction result can be implemented using conventional prediction methods in the field, such as time series prediction, regression prediction, or neural network prediction. This implementation does not limit the specific prediction model. The key is not which prediction algorithm is used, but rather to use the predicted output changes and load changes of distributed power sources as the input basis for the subsequent generation of node predicted voltage and node voltage safety margin index.

[0057] To balance real-time performance and prediction accuracy, the prediction window for disturbance prediction results is preferably set to 1 to 10 control cycles. When the control cycle is 5 to 30 seconds, the prediction window is preferably set to 10 to 300 seconds. When the control cycle is 60 to 300 seconds, the prediction window is preferably set to 5 to 30 minutes. The reason for setting these ranges is that if the prediction window is too short, the predicted node voltage will not adequately reflect upcoming node risk changes. If the prediction window is too long, the uncertainty of distributed power output fluctuations and load changes will increase, which will reduce the reliability of the node voltage safety margin index.

[0058] After obtaining the disturbance prediction results, the safety margin and priority generation module determines the node prediction voltage of each node in the distribution network based on the distribution network operation data, the corrected reactive voltage sensitivity matrix and the disturbance prediction results.

[0059] Specifically, the real-time voltage of each node in the distribution network during the current control cycle can be used as the baseline state. Combined with the distributed generation output disturbance prediction results, load disturbance prediction results, and the corrected reactive power voltage sensitivity matrix, the voltage change trend of each node in the distribution network under disturbance in the next prediction window can be calculated, thus obtaining the corresponding node predicted voltage. The node predicted voltage can characterize the voltage change trend and voltage risk evolution direction of each node in the distribution network under disturbance within the prediction window corresponding to the current control cycle. It should be noted that the corrected reactive power voltage sensitivity matrix has been obtained by the sensitivity and capacity assessment module after correcting the initial reactive power voltage sensitivity based on the node coupling diagram. Therefore, it can characterize the differentiated impact of reactive power changes at different access nodes on the voltage of each node in the distribution network. Based on this, the disturbance prediction results are then mapped to the node... Regarding the predicted voltage at the nodes, this approach avoids the problem in existing technologies where the overall operating status is inferred solely from changes in total power, while ignoring differences in coupling between nodes and voltage support. To ensure the availability of the predicted voltage at the nodes, it is preferable to truncate abnormal prediction samples before disturbance mapping to prevent extreme prediction errors from being directly amplified into the subsequent node voltage safety margin index. Specifically, the variation range of the predicted output disturbance and the predicted load disturbance can preferably be limited to 80% to 150% of the historical statistical fluctuation range from the most recent 1 hour to 24 hours. When the predicted value exceeds this range, it can be marked as a low-confidence disturbance and subjected to amplitude limiting or rollback to the most recent valid predicted value. The reason for this setting is that the safety margin and priority generation module mainly serves short-term control decisions, and its input emphasizes the identifiability of short-term risks rather than the global prediction accuracy over long-term scales.

[0060] After obtaining the predicted node voltage, the safety margin and priority generation module generates the node voltage safety margin index. The node voltage safety margin index is used to characterize the remaining safety space of each node in the distribution network from the allowable voltage boundary, and at the same time characterizes the risk strength of the propagation of abnormal states between adjacent nodes to the target node. Compared with the existing technology, which only uses the simple difference between the current node voltage and the upper and lower limits of the allowable voltage to describe the safety margin, this embodiment further introduces the node neighborhood abnormal propagation situation, which reflects the degree of voltage deviation coupling between adjacent nodes, into the generation process of the node voltage safety margin index.

[0061] Preferably, the propagation of anomalies in the neighborhood of a node is determined jointly based on the adjacency matrix in the node coupling graph and the voltage deviation of adjacent nodes in the current control cycle. More preferably, the set of adjacent nodes of the target node can be determined first, and then the voltage deviation of adjacent nodes relative to the center value of their respective allowable voltage ranges can be combined with the corresponding weight elements in the adjacency matrix to perform normalized weighted accumulation, thereby obtaining the propagation of anomalies in the neighborhood of the target node. The reason for adopting this method is that if the adjacent nodes around a node have significant voltage deviations and have strong electrical coupling with the node, the node is more likely to be affected by propagation in subsequent control cycles, even if it has not yet exceeded the limit. Preferably, the propagation of anomalies in the neighborhood is limited to a dimensionless quantity between 0 and 3; when its value is close to 0, it indicates that the impact of neighborhood anomaly propagation is weak; when its value increases, it indicates that the neighborhood risk propagation effect is enhanced. By incorporating the propagation of anomalies in the node's neighborhood into the node voltage safety margin index, the safety margin assessment can no longer only reflect the node's own state, but also characterize the potential risk of anomalies from neighboring nodes spreading to the target node in advance. Preferably, the node voltage safety margin index can be determined according to the following formula: ; in, Indicates time The node voltage safety margin index of node m in the sub-distribution network. Represents a node Node predicted voltage, Indicates the upper limit of the allowable voltage. Indicates the lower limit of the allowable voltage. This represents the voltage normalized reference value. Represents a node The abnormal propagation situation in the node's neighborhood, Indicates taking The numerator is limited to 0, which is a larger value between 0 and 0. The advantage of this formula is that when the predicted voltage of a node exceeds the upper or lower limit of the allowable voltage, the numerator is limited to 0, thus causing the node voltage safety margin index to directly enter the danger zone. When the predicted voltage of a node is still within the allowable range but the anomaly propagation in the neighborhood is strong, the denominator increases, which will reduce the node voltage safety margin index, thereby raising the risk level of the corresponding node in advance. Thus, the node voltage safety margin index no longer only reflects the static remaining voltage space of a single node, but also reflects the risk propagation characteristics of the electrical neighborhood in which the node is located. The smaller the node voltage safety margin index, the lower the voltage safety margin and the higher the risk level of the corresponding node.

[0062] The upper and lower limits of the allowable voltage are preferably determined according to the distribution network operation specifications and the equipment tolerance range. For low-voltage distribution networks, the upper limit of the allowable voltage is preferably 1.03 to 1.10 times the rated voltage, and the lower limit is preferably 0.90 to 0.97 times the rated voltage. For medium-voltage distribution networks, the upper limit of the allowable voltage is preferably 1.02 to 1.07 times the rated voltage, and the lower limit is preferably 0.93 to 0.98 times the rated voltage. The voltage normalization reference value is preferably 0.01 pu to 0.10 pu, and more preferably 0.03 pu to 0.05 pu. When the voltage normalization reference value is too small, the node voltage safety margin index is too sensitive to small disturbances, which can easily cause frequent switching of the main regulating equipment. When the voltage normalization reference value is too large, it will weaken the risk identification ability when the local node voltage is close to exceeding the limit. The node neighborhood anomaly propagation situation is preferably determined based on the adjacency relationship matrix or adjacency weight set in the node coupling graph and the voltage deviation of adjacent nodes, according to the preset propagation evaluation rules. Its value is preferably a dimensionless quantity, with a typical range of 0 to 5, and more preferably 0 to 2. The larger the value, the stronger the coupling degree of voltage deviation between adjacent nodes, and the easier it is for the abnormal state to propagate to the target node. Incorporating the node neighborhood anomaly propagation situation into the node voltage safety margin index can enable this embodiment to identify potential risk nodes that have not yet exceeded the limit but are about to be affected by neighborhood anomalies and tend to exceed the limit in advance. This is an important difference from the prior art.

[0063] After obtaining the node voltage safety margin index, the safety margin and priority generation module further generates a reactive power support priority sequence. The generation of the reactive power support priority sequence is not simply based on the capacity of each adjustable device, but rather combines the directional available reactive power capacity of each adjustable device, the voltage sensitivity coefficient of each node in the distribution network corresponding to the access node where each adjustable device is located in the corrected reactive power voltage sensitivity matrix, the node voltage safety margin index, and the line loss increment caused by the participation of each adjustable device in regulation. This yields a differentiated support ranking result oriented towards node risk mitigation and network cost control. Preferably, the reactive power support priority sequence can be generated based on the following priority scores: ; in, Indicates time Adjustable device Priority score, Indicates adjustable device Direction-related factors can be used with non-functional forces. This represents the reactive power normalization reference value. Represents the set of nodes in a distribution network. This represents the adjustable devices in the corrected reactive voltage sensitivity matrix. Access Node For nodes The voltage sensitivity coefficient, Represents a node The node voltage safety margin index, This indicates the prevention of small positive numbers with a denominator of 0. Indicates adjustable device The increase in line loss caused by adjustment This represents the line loss suppression weight. The formula indicates that if an adjustable device has a large directionally dependent available reactive power capacity, and its connected nodes have high voltage sensitivity coefficients to multiple low-safety-margin nodes, while the line loss increment caused by its participation in regulation is low, then the adjustable device has a higher priority score and is ranked higher. Conversely, if an adjustable device has a certain capacity, but its support for high-risk nodes is weak, or it may cause a large line loss increment, then its priority score will decrease. The larger the line loss increment, the more significant the decrease in the priority score of the corresponding adjustable device. This approach avoids the problems of low local support efficiency and high network costs caused by prioritizing larger capacity devices in existing technologies. In the reactive power support priority sequence, the larger the priority score, the higher the ranking of the corresponding adjustable device.

[0064] Among them, the available reactive power is preferably determined based on the voltage risk direction currently exhibited by the target node. When the high-risk node mainly exhibits an overvoltage trend, the reactive power that can be absorbed by each adjustable device is preferred. When the high-risk node mainly exhibits an undervoltage trend, the reactive power that can be injected by each adjustable device is preferred. The reactive power normalization reference value is preferably taken as 1 kvar to 1000 kvar, or as 10% to 100% of the average rated reactive power of all adjustable devices in the virtual power plant, so as to ensure that the priority scores of devices of different capacity levels can be compared on the same scale.

[0065] tiny positive numbers The preferred value is 0.001 to 0.05, and more preferably 0.005 to 0.02. This setting aims to prevent the priority score from diverging when the node voltage safety margin index of a certain node approaches 0, thus affecting the line loss suppression weight. The preferred value is 0.1 to 10, and more preferably 0.5 to 3. If the line loss suppression weight is too small, the line loss increment will not have a significant suppressive effect on the priority score, which may lead to high-loss equipment being over-prioritized. If the line loss suppression weight is too large, the line loss factor will suppress the node risk factor, which may cause the most effective support equipment for high-risk nodes to be ranked lower. Preferably, the line loss increment can be represented by the change in branch loss caused by the access of adjustable equipment, the change in normalized loss, or the equivalent loss evaluation quantity, as long as it can reflect the relative impact of each adjustable equipment participating in the adjustment on network loss.

[0066] To improve the stability of the reactive power support priority sequence, it is preferable to smooth the change in priority scores between adjacent control cycles after generating priority scores in each control cycle. Specifically, the priority change suppression threshold can be set to 5% to 30% of the average priority scores over the most recent 3 to 10 control cycles. When the change in priority score of a certain adjustable device in the current control cycle is less than the priority change suppression threshold, its ranking position relative to the previous control cycle remains unchanged. When the change exceeds the priority change suppression threshold, the ranking is updated. The reason for adopting this approach is that in a virtual power plant scenario, node voltage and equipment capacity are often affected by short-term noise and measurement errors. If the ranking is directly based on the priority score of a single cycle, it may cause frequent switching of the main regulating equipment, thereby increasing communication overhead and the number of equipment actions. By introducing ranking smoothing, the timing stability of the reactive power support priority sequence can be improved without weakening risk responsiveness.

[0067] In this embodiment, the safety margin and priority generation module is directly coupled with the data acquisition module and the sensitivity and capability assessment module. Specifically, the distribution network operation data output by the data acquisition module provides the basic input for generating disturbance prediction results and node predicted voltages. The corrected reactive voltage sensitivity matrix and directional correlation available reactive power output by the sensitivity and capability assessment module provide key parameters for generating the node voltage safety margin index and reactive power support priority sequence. In other words, the safety margin and priority generation module does not perform risk assessment in isolation, but rather, based on the corrected reactive voltage sensitivity matrix formed under the constraints of the node coupling diagram, it uniformly couples the disturbance prediction results, voltage risk assessment results, and equipment support capability results into a sorting output for the subsequent collaborative control module. Therefore, the node voltage safety margin index and reactive power support priority sequence in this embodiment are not two independent intermediate quantities, but rather constitute a continuous chain from node risk identification to equipment differentiated support sorting. The node voltage safety margin index and reactive power support priority sequence output by the safety margin and priority generation module serve as the input basis for the collaborative control module to select the main regulating equipment and determine the active power regulation and reactive power regulation of each adjustable equipment.

[0068] In summary, the safety margin and priority generation module in this embodiment determines the disturbance prediction result based on the distribution network operation data, determines the node prediction voltage of each node in the distribution network based on the distribution network operation data, the corrected reactive voltage sensitivity matrix, and the disturbance prediction result, generates the node voltage safety margin index based on the node prediction voltage, and generates the reactive power support priority sequence based on the node voltage safety margin index, the corrected reactive voltage sensitivity matrix, and the directionally correlated available reactive power capacity. This achieves a unified characterization of the node risk magnitude, the degree of anomaly propagation between nodes, and the actual support effectiveness of equipment.

[0069] In this embodiment, the collaborative control module is used to select the main regulating device based on the reactive power support priority sequence, and to determine the active power regulation and reactive power regulation of each adjustable device based on the node voltage safety margin index, the corrected reactive power voltage sensitivity matrix, and the directional correlation available reactive power capacity, and output active and reactive power collaborative control commands; after the control is executed, the corrected reactive power voltage sensitivity matrix, the node voltage safety margin index, and the reactive power support priority sequence are updated based on the node voltage prediction residual between the actual measured voltage and the node predicted voltage. Furthermore, the collaborative control module is specifically used to, when selecting the main regulating equipment, screen adjustable equipment with directional available reactive power capacity greater than a preset capacity threshold and response delay less than a preset delay threshold according to the ranking result of the reactive power support priority sequence, and determine the adjustable equipment with the highest ranking after screening as the main regulating equipment; the collaborative control module is also specifically used to, when determining the active power regulation and reactive power regulation of each adjustable equipment, determine the active power regulation and reactive power regulation of each adjustable equipment under the conditions of satisfying node voltage constraints, line current carrying capacity constraints, equipment apparent capacity constraints, directional available reactive power capacity constraints, energy storage equipment state of charge constraints, and equipment output change rate constraints; the collaborative control module is also specifically used to, when updating the corrected reactive power voltage sensitivity matrix, node voltage safety margin index, and reactive power support priority sequence, perform online correction and update of the corrected reactive power voltage sensitivity matrix, node voltage safety margin index, and reactive power support priority sequence based on the node voltage prediction residual between the actual measured voltage and the predicted voltage of each node in the distribution network obtained in the next sampling period.

[0070] In practical implementation, the collaborative control module first selects the main regulating equipment based on the reactive power support priority sequence. It should be noted that the reactive power support priority sequence has already been generated by the safety margin and priority generation module based on the node voltage safety margin index, the corrected reactive power voltage sensitivity matrix, directionally dependent available reactive power capacity, and line loss increment. Therefore, it comprehensively reflects the support effectiveness of each adjustable device for high-risk nodes and the network cost. Based on this, when selecting the main regulating equipment, the collaborative control module prefers not to indiscriminately participate in the solution for all adjustable devices, but rather first selects those with directionally dependent available reactive power capacity greater than a preset capacity threshold according to the ranking results of the reactive power support priority sequence. Adjustable devices with response delays less than a preset delay threshold are selected, and the top-ranked adjustable devices are identified as the main control devices. The reason for this approach is that if all adjustable devices are included in the main control set for each control cycle, it would not only significantly increase the optimization dimensions and communication burden, but also cause some directionally relevant devices with insufficient reactive power or slow response to participate in ineffective solutions, thereby weakening the executability of the control results. By first sorting them according to the reactive power support priority sequence, and then combining the preset capacity threshold and preset delay threshold for selection, the main control device set can be more concentrated on devices that are more effective for high-risk nodes and whose current state is more suitable for participation in regulation.

[0071] The preset capability threshold is preferably determined based on the statistical distribution of directionally dependent available non-functional capacity. It can be taken as 5% to 50% of the average directionally dependent available non-functional capacity of all candidate adjustable devices, and more preferably 10% to 30%. If the preset capability threshold is too low, devices with limited directionally dependent available non-functional capacity will be included in the main control device set, thereby increasing the solution dimension and contributing little to the suppression of node risk. If the preset capability threshold is too high, some devices with small capacity that play an important supporting role for local high-risk nodes may be excluded. The preset time delay threshold is preferably determined based on the control cycle and node risk response requirements. When the control cycle is 1s to 30s, the preset time delay threshold is preferably 50ms to 2s. When the control cycle is 30s to 300s, the preset time delay threshold is preferably 0.5s to 10s. If the preset time delay threshold is set too strictly, it may result in an insufficient number of devices that can participate in the adjustment. If the preset time delay threshold is set too loosely, devices with slow response may enter the main control device set, weakening the ability of the control action to suppress short-time voltage risk. Preferably, the number of main regulating devices can be dynamically determined based on the number of high-risk nodes in the current distribution network, the priority score distribution of reactive power support, and computational resource constraints. For example, the top 10% to 50% of the selected devices can be chosen, or the top 3 to 20 selected devices can be selected. This setting method can achieve a balance between solution complexity and support coverage.

[0072] After selecting the main regulating equipment, the collaborative control module further determines the active and reactive power regulation of each adjustable device and outputs active and reactive power collaborative control commands. It should be noted that although the reactive power support priority sequence is mainly aimed at generating reactive power support effectiveness, the virtual power plant still needs to consider aggregated active power targets, local voltage safety, and equipment operation safety in actual operation. Therefore, the collaborative control module does not only solve for reactive power regulation, but jointly determines the active and reactive power regulation of each adjustable device. Specifically, the collaborative control module preferably determines the active and reactive power regulation of each adjustable device under the conditions of satisfying node voltage constraints, line current carrying capacity constraints, equipment apparent capacity constraints, direction-dependent available reactive power capacity constraints, energy storage device state of charge constraints, and equipment output change rate constraints. The collaborative control module preferably prioritizes node voltage deviation suppression, satisfying aggregated active power targets, and... The comprehensive control effect of network loss suppression serves as the solution basis. Under various constraints, the active and reactive power regulation quantities of each adjustable device are determined. Node voltage constraints are used to ensure that the voltage of each node in the distribution network remains between the upper and lower allowable voltage limits after control execution. Line current carrying constraints are used to ensure that the current of each branch does not exceed the corresponding current carrying boundary after control execution. Equipment apparent capacity constraints are used to ensure that each adjustable device still meets the rated apparent capacity limit after executing active and reactive power regulation quantities. Direction-dependent available reactive power capacity constraints are used to ensure that reactive power output does not exceed the true support boundary under the current voltage risk direction. Energy storage device state of charge constraints are used to prevent energy storage devices from deviating from the safe operating range due to excessive participation in active and reactive power coordinated regulation. Equipment output change rate constraints are used to suppress excessively rapid output changes of equipment in adjacent control cycles, reducing the risk of action shocks and frequent switching.

[0073] Preferably, the upper and lower allowable voltage limits in the node voltage constraints can be the same as those used in the safety margin and priority generation module to maintain consistency between the risk assessment and control execution boundaries throughout the entire process. For low-voltage distribution networks, it is preferred to control the node voltage constraints within the range of 0.90 to 1.10 times the rated voltage, more preferably within the range of 0.93 to 1.07 times the rated voltage; for medium-voltage distribution networks, it is preferred to control them within the range of 0.93 to 1.07 times the rated voltage, more preferably within the range of 0.95 to 1.05 times the rated voltage. The line current carrying constraint is preferably taken as 80% to 100% of the branch's rated current carrying capacity, more preferably 90% to 100%, to ensure... Under the premise of safe operation, necessary control margins should be maintained. The apparent capacity constraint of the equipment is preferably set at the upper limit of the rated apparent capacity of each adjustable device. When the adjustable device is already in a high active power output occupancy state, it is preferable to further compress its reactive power regulation space that can participate in the active and reactive power co-solution. The output change rate constraint of the equipment is preferably set according to the equipment type. For inverter equipment and energy storage equipment, the upper limit of its active and reactive power output change rate is preferably determined according to the equipment manual, thermal management capability and control execution capability. When the control cycle is short, a smaller upper limit of output change rate is preferred to reduce command jitter in short cycles. When the control cycle is long, the output change rate constraint can be appropriately relaxed to improve the overall regulation efficiency.

[0074] To enable active and reactive power regulation to more directly serve node risk mitigation, this implementation prefers to assign higher control priority to nodes with smaller voltage safety margin indices during the solution process. In other words, the collaborative control module can prioritize allocating more effective reactive power regulation actions to nodes with low safety margins while ensuring the aggregated active power target is met. Based on the modified reactive power voltage sensitivity matrix, the control quantity is preferentially issued to access nodes with higher voltage sensitivity coefficients to high-risk nodes. In this way, the collaborative control module no longer applies regulation to all nodes on an average basis, but rather makes the active and reactive power collaborative control commands more concentrated on the node regions with higher risks and more effective support. Compared with the existing technology's regulation method that only requires the total amount to be met, this implementation introduces the node voltage safety margin index and the modified reactive power voltage sensitivity matrix, making the control commands have clear node risk targeting and network support orientation.

[0075] After determining the active and reactive power regulation values ​​of each adjustable device, the collaborative control module outputs active and reactive power collaborative control commands. Preferably, these commands may include active power setpoints, reactive power setpoints, power factor setpoints, or reactive power control mode switching commands for inverter and energy storage devices; commands for adjusting the switching status or reactive power setpoints of reactive power compensation devices; and commands for active power reduction, peak shifting, or start / stop control of adjustable loads. The distribution of these commands can be achieved through conventional control methods in the field, such as centralized distribution from the main station, forwarding by the edge controller, or local closed-loop execution. In this implementation method, no specific communication protocol or distribution path is limited. The key is that the coordinated control command is determined based on the reactive power support priority sequence, node voltage safety margin index, modified reactive power voltage sensitivity matrix, and directional correlation available reactive power capacity, rather than being directly generated by static rules or a single capacity parameter. The active power adjustment amount and reactive power adjustment amount in the active power and reactive power coordinated control command correspond to the control output of the main regulating device or other adjustable devices participating in the constraint solution. In this way, the control execution result can simultaneously reflect the node risk level, network coupling relationship, and current device status.

[0076] After control execution, the collaborative control module further updates the corrected reactive voltage sensitivity matrix, node voltage safety margin index, and reactive power support priority sequence online based on the node voltage prediction residual between the actual measured voltage and the node predicted voltage. Specifically, after obtaining the actual measured voltage of each node in the distribution network in the next sampling cycle, it can be compared with the node predicted voltage under the corresponding prediction window of the previous control cycle to obtain the node voltage prediction residual. The node voltage prediction residual is preferably defined as the difference between the actual measured voltage and the node predicted voltage of the same node at the same prediction time. The reason for using this residual for online correction and update is that even if the preceding module has corrected the initial reactive voltage sensitivity using the node coupling diagram and historical power flow samples, and has generated the reactive power support priority sequence using the directional correlation available reactive power capacity and the node voltage safety margin index, the prediction results may still differ from the actual results due to communication delays, local disturbances, model approximation errors, and equipment execution deviations in actual operation. If the node voltage prediction residual is not corrected by feedback, subsequent control cycles may still repeatedly generate control commands based on intermediate quantities with large deviations, thereby affecting the closed-loop control accuracy.

[0077] Preferably, the online correction and update can be performed in three layers. The first layer updates the corrected reactive power voltage sensitivity matrix, that is, corrects the sensitivity correction terms or correction weights according to the node voltage prediction residuals, so that the voltage sensitivity representation of each access node to each target node in the subsequent control cycle is closer to the actual operating results. The second layer updates the node voltage safety margin index, that is, re-estimates the remaining safety space of each node in the distribution network from the allowable voltage boundary according to the actual measured voltage and the new risk propagation state. The third layer updates the reactive power support priority sequence, that is, recalculates or adjusts the priority scores according to the updated and corrected reactive power voltage sensitivity matrix, node voltage safety margin index and equipment status, so that the selection of main regulating equipment is more in line with the latest operating status.

[0078] In engineering implementation, online correction updates are preferably completed within one control cycle. When computational resources are limited, a full update can be performed every 2 to 5 control cycles, while only partial incremental updates are performed in other control cycles. By adopting the above processing method, a balance can be achieved between the timeliness of correction and computational load. Online correction updates are preferably implemented using incremental correction or weighted correction methods, and the correction magnitude is preferably positively correlated with the absolute value of the node voltage prediction residual, so that the update results can reflect the actual deviation and maintain the temporal stability of the control process.

[0079] To avoid overcorrection caused by node voltage prediction residuals, it is preferable to set a residual trigger threshold and a correction upper limit for online correction updates. Preferably, when the absolute value of the node voltage prediction residual is less than 0.001 pu to 0.01 pu, the current prediction error can be considered to have a small impact on subsequent control, and it is preferable to perform only a slight update or maintain the original value. When the absolute value of the node voltage prediction residual exceeds 0.01 pu to 0.05 pu, it is preferable to trigger a significant update to quickly correct the preceding intermediate quantity. When the node voltage prediction residual exceeds 0.05 pu, it is preferable to mark this period as an abnormal correction period and combine it with topology changes, equipment failures, and disturbance abrupt changes for joint discrimination to prevent a single extreme anomaly from directly causing unreasonable jumps in intermediate quantities. The correction upper limit is preferably determined based on the historical fluctuation range of the corrected reactive power voltage sensitivity matrix, the node voltage safety margin index, and the reactive power support priority sequence, thereby ensuring that the online correction update can reflect the actual deviation without destroying the timing stability of the control system.

[0080] In this embodiment, the collaborative control module and the preceding module form a closed-loop coupling. Specifically, the node voltage safety margin index and reactive power support priority sequence output by the safety margin and priority generation module serve as the input basis for the collaborative control module to select the main regulating equipment and determine the active power regulation and reactive power regulation of each adjustable equipment. The node voltage prediction residual output by the collaborative control module, in turn, affects the updating of the corrected reactive power voltage sensitivity matrix, node voltage safety margin index, and reactive power support priority sequence. Thus, the collaborative control module is not merely a terminal command issuing unit, but a closed-loop control unit that transforms the risk information, sensitivity information, and equipment capability information output by the preceding module into actual control actions and uses the control execution results to perform reverse correction on the intermediate quantities of the preceding module. Through this closed-loop structure, control mismatch caused by long-term drift of the preceding model can be avoided, and the adaptive capability of the system under continuous control cycles can be improved.

[0081] In summary, the collaborative control module in this embodiment selects the main regulating equipment based on the reactive power support priority sequence. Under the constraints of node voltage, line current carrying capacity, equipment apparent capacity, directional available reactive power capacity, energy storage equipment state of charge, and equipment output change rate, it determines the active and reactive power regulation amounts of each adjustable equipment. Based on the node voltage prediction residual, it performs online correction and update of the corrected reactive power voltage sensitivity matrix, node voltage safety margin index, and reactive power support priority sequence. This forms an active and reactive power closed-loop collaborative control mechanism oriented towards node risk suppression and equipment real-time executable boundaries. Compared with the prior art, this embodiment can more effectively constrain local node voltage over-limit and reactive power compensation mismatch while meeting the aggregate regulation target, and improve the executability and operational stability of virtual power plant collaborative control commands.

[0082] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent virtual power plant energy control system, characterized in that, include: The data acquisition module is used to acquire the distribution network operation data and adjustable equipment status data of the distribution network to which the virtual power plant belongs, and to construct a node coupling diagram based on the distribution network operation data; The sensitivity and capacity assessment module determines the initial reactive voltage sensitivity based on the distribution network operation data, and corrects the initial reactive voltage sensitivity based on the node coupling diagram to obtain the corrected reactive voltage sensitivity matrix. Based on the status data of adjustable devices, determine the direction-related available nonfunctional capacity of each adjustable device; The safety margin and priority generation module determines the disturbance prediction result based on the distribution network operation data, and determines the node prediction voltage of each node in the distribution network based on the distribution network operation data, the corrected reactive voltage sensitivity matrix and the disturbance prediction result. Based on the node prediction voltage of each node in the distribution network, it generates the corresponding node voltage safety margin index, and generates the reactive power support priority sequence of each adjustable device based on the node voltage safety margin index, the corrected reactive voltage sensitivity matrix and the directional correlation available reactive power capacity. The collaborative control module selects the main regulating device based on the reactive power support priority sequence, and determines the active power regulation and reactive power regulation of each adjustable device based on the node voltage safety margin index, the corrected reactive power voltage sensitivity matrix, and the directional correlation available reactive power capacity. It outputs active and reactive power collaborative control commands. After the control is executed, it updates the corrected reactive power voltage sensitivity matrix, the node voltage safety margin index, and the reactive power support priority sequence based on the node voltage prediction residual between the actual measured voltage and the node predicted voltage.

2. The intelligent virtual power plant energy control system according to claim 1, characterized in that, The data acquisition module is specifically used to acquire voltage data of each node in the distribution network, current data of each branch, active and reactive power output data of each adjustable device, switch status data, feeder topology data, line impedance parameters, and transformer operating parameters when acquiring distribution network operation data and adjustable device status data. Acquire temperature rise data of each inverter device, health data and response delay data of each adjustable device, and acquire state of charge data when the adjustable device is an energy storage device. The obtained voltage, current, power, and capacity are standardized to a uniform per-unit value.

3. The intelligent virtual power plant energy control system according to claim 2, characterized in that, The sensitivity and capacity assessment module is specifically used to perform power flow linearization calculations at the current operating point based on distribution network operation data when determining the initial reactive voltage sensitivity, so as to obtain the initial reactive voltage sensitivity. The initial reactive voltage sensitivity is corrected based on the node coupling diagram and historical power flow samples to obtain the corrected reactive voltage sensitivity matrix.

4. The intelligent virtual power plant energy control system according to claim 3, characterized in that, The sensitivity and capability assessment module is specifically used to determine the dynamic available functional capacity of each adjustable device when determining the directional available functional capacity of each adjustable device. This is based on the rated apparent capacity, current active power output, temperature rise, device health and response delay of each adjustable device, as well as the state of charge of the energy storage device. The module also determines the injectable functional capacity and absorbable functional capacity of each adjustable device, which are used as the directional available functional capacity of each adjustable device.

5. The intelligent virtual power plant energy control system according to claim 4, characterized in that, The safety margin and priority generation module is specifically used to determine the output disturbance prediction results and load disturbance prediction results of distributed power sources based on the distribution network operation data when determining the disturbance prediction results, so as to serve as the disturbance prediction results.

6. The intelligent virtual power plant energy control system according to claim 5, characterized in that, The safety margin and priority generation module is specifically used to generate the node voltage safety margin index based on the node predicted voltage, upper allowable voltage limit, lower allowable voltage limit, and node neighborhood anomaly propagation status that reflects the degree of voltage deviation coupling between adjacent nodes when generating the node voltage safety margin index.

7. The intelligent virtual power plant energy control system according to claim 6, characterized in that, The safety margin and priority generation module is specifically used to generate a reactive power support priority sequence based on the directional available reactive power of each adjustable device, the voltage sensitivity coefficient of each node in the distribution network corresponding to the access node where each adjustable device is located in the corrected reactive power voltage sensitivity matrix, the node voltage safety margin index, and the line loss increment caused by each adjustable device participating in regulation.

8. The intelligent virtual power plant energy control system according to claim 7, characterized in that, The collaborative control module is specifically used to select main regulating equipment by sorting the reactive power support priority sequence, filtering adjustable equipment with directional available reactive power capacity greater than a preset capacity threshold and response delay less than a preset delay threshold, and determining the adjustable equipment with the highest sorting score as the main regulating equipment.

9. The intelligent virtual power plant energy control system according to claim 8, characterized in that, The collaborative control module is specifically used to collaboratively solve for the active and reactive power regulation of each adjustable device when determining the active and reactive power regulation of each adjustable device, under the conditions of satisfying node voltage constraints, line current carrying constraints, device apparent capacity constraints, direction-dependent available reactive power constraints, energy storage device state of charge constraints, and device output change rate constraints, so as to obtain the active and reactive power regulation of each adjustable device.

10. The intelligent virtual power plant energy control system according to claim 9, characterized in that, Specifically, the collaborative control module is used to perform online correction and update of the corrected reactive voltage sensitivity matrix, node voltage safety margin index, and reactive power support priority sequence when updating the corrected reactive voltage sensitivity matrix, node voltage safety margin index, and reactive power support priority sequence, based on the node voltage prediction residual between the actual measured voltage and the predicted voltage of each node in the distribution network obtained in the next sampling period.

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