A Multi-Parameter Fusion Method for Dynamic Aggregation and Zonal Voltage Control of Charging Load

Abstract

This invention discloses a multi-parameter fusion-based dynamic aggregation and zoning voltage control method for charging loads, relating to the field of distribution network operation control and voltage regulation technology. The method includes: acquiring the distribution network topology, operational data, and the location, adjustable capacity, response, and cost coefficients of distributed energy resources; constructing a power-voltage sensitivity matrix through linearized power flow, and fusing active and reactive power sensitivities to obtain a comprehensive sensitivity index; calculating the normalized adjustment weights of distributed energy resources, combining the sensitivity to construct a comprehensive electrical distance and clustering to form dynamic aggregation zones; and employing hierarchical voltage control, with the upper layer fixing zones and issuing coordination parameters, and the lower layer rapidly executing regional voltage control in each zone. This invention, through multi-parameter fusion and dynamic aggregation zoning, improves the coupling matching degree between controllable resources and voltage deviation, reduces computational and communication overhead, and enhances the stability, qualification rate, and operational economy of distribution network voltage control.

Description

Technical Field

[0001] This invention relates to the field of power distribution network operation control and voltage regulation technology, and in particular to a multi-parameter fusion method for dynamic aggregation and zoned voltage control of charging loads. Background Technology

[0002] As the penetration rate of distributed energy resources (DERs) such as flexible loads continues to increase in distribution networks, distribution networks exhibit characteristics such as rapid power flow fluctuations and frequent changes in operating status. Traditional voltage control methods that rely on fixed zones or empirical rules are difficult to maintain good voltage qualification rate and economy under different operating conditions.

[0003] Existing zoned voltage control methods typically divide zones based on topology, geographical location, or static electrical distance, ignoring differences in DER adjustable capacity, response speed, and adjustment costs. This leads to electrical coupling mismatch between controllable resources within a zone and the controlled voltage deviation, thereby reducing control effectiveness. Furthermore, if a zone is updated every step, it introduces high computational and communication overhead and may cause instability risks due to frequent switching of control strategies between different zones. Summary of the Invention

[0004] The main objective of this invention is to provide a method for dynamic aggregation and zoned voltage control of charging load based on multi-parameter fusion.

[0005] Another objective of this invention is to propose a multi-parameter fusion charging load dynamic aggregation zone voltage control device.

[0006] The third objective of this invention is to provide an electronic device.

[0007] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0008] To achieve the above objectives, a first aspect of the present invention proposes a multi-parameter fusion-based dynamic aggregation zoned voltage control method for charging load, comprising:

[0009] S1, acquires the topology of the distribution network, real-time operation measurement data, and location information, adjustable capacity, response speed coefficient and adjustment cost coefficient of each distributed energy source; S2, construct the power voltage sensitivity matrix based on linearized power flow calculation, and fuse the active power sensitivity and reactive power sensitivity according to preset weights to obtain a comprehensive sensitivity index characterizing the electrical coupling strength between nodes. S3 calculates the normalized adjustment weight based on the adjustable capacity, response speed coefficient and adjustment cost coefficient of each distributed energy source, and constructs the comprehensive electrical distance in combination with the comprehensive sensitivity index. The distributed energy nodes are then clustered using the comprehensive electrical distance as a metric to form multiple dynamic aggregation partitions. S4 executes hierarchical voltage control, maintaining the structure of the dynamic aggregation partition unchanged within the set upper-level coordination cycle and issuing coordination parameters. Within the set lower-level fast control cycle, each dynamic aggregation partition executes voltage control decisions within its region based on the coordination parameters.

[0010] Optionally, the topology of the distribution network, real-time operational measurement data, and location information, adjustable capacity, response speed coefficient, and adjustment cost coefficient of each distributed energy source can be obtained, including: Real-time operational measurement data are obtained by collecting bus voltage amplitude, bus voltage phase angle, line power flow data, switch status, and transformer tap status through a preset sampling period via a power distribution automation system or edge node. The data collected includes the location of the access bus, rated capacity, current output value and available adjustment margin of each distributed energy source. The response speed coefficient is determined by combining the preset controller bandwidth mapping relationship. The adjustment cost coefficient is constructed based on the cost of curtailment, reactive power loss or lifetime loss, forming a distributed energy characteristic dataset containing location information, adjustable capacity, response speed coefficient and adjustment cost coefficient.

[0011] Optionally, a power-voltage sensitivity matrix is ​​constructed based on linearized power flow calculation, and the active power sensitivity and reactive power sensitivity are fused according to preset weights to obtain a comprehensive sensitivity index characterizing the electrical coupling strength between nodes, including: Construct the Jacobian matrix based on the current running point and perform linearized power flow calculations to obtain the nodes. Changes in active power injection on nodes Effect of voltage amplitude and nodes Changes in reactive power injection on nodes Effect of voltage amplitude This forms a power voltage sensitivity matrix; Weighting coefficients are set online adaptively based on the average resistance-inductance ratio of the distribution network lines and historical operating statistics. and Calculate the comprehensive sensitivity index using the formula. : .

[0012] Optionally, normalized regulation weights are calculated based on the adjustable capacity, response speed coefficient, and regulation cost coefficient of each distributed energy source. A comprehensive electrical distance is then constructed using a comprehensive sensitivity index. Distributed energy nodes are clustered using this comprehensive electrical distance as a metric to form multiple dynamic aggregation partitions, including: Calculate nodes according to the formula Moderating contribution:

[0013] in, For nodes Adjustable capacity per unit For response speed coefficient, To adjust the cost coefficient; Based on the aforementioned regulation contribution and comprehensive sensitivity index, the node is calculated using the formula. For nodes Normalized adjustment weights :

[0014] in, It is the set of all distributed energy nodes; The comprehensive electrical distance is defined according to the formula. :

[0015] The K-means++ clustering algorithm is selected to cluster the distributed energy nodes using the comprehensive electrical distance as a metric. At the same time, partition size constraints are introduced to ensure that each partition contains at least a preset number of controllable distributed energy nodes and key buses, thereby forming multiple dynamic aggregated partitions.

[0016] Optionally, hierarchical voltage control is implemented. Within a set upper-level coordination cycle, the structure of the dynamic aggregation partition remains unchanged, and coordination parameters are issued. Within a set lower-level fast control cycle, each dynamic aggregation partition executes voltage control decisions within its region based on the coordination parameters, including: The upper-level coordination cycle is set to 15 minutes. During the upper-level coordination cycle, the topology of the dynamic aggregation partition remains unchanged. The upper-level global coordinator calculates and issues the target voltage bandwidth, adjustment budget or target weight of the partition as coordination parameters based on the voltage operation index of the entire network. The lower-level fast control cycle is set to 1 minute. Under the conditions of inverter apparent power constraints, energy storage state of charge constraints and flexible load operation constraints, the local agent intelligent agent in each dynamic aggregation zone solves the optimal control quantity with the optimization objectives of minimizing voltage deviation, minimizing regulation cost and minimizing network loss. The optimal control quantity is executed within the lower-level fast control cycle to achieve comprehensive optimization of voltage qualification and operation economy in the region.

[0017] To achieve the above objectives, a second aspect of the present invention provides a multi-parameter fusion charging load dynamic aggregation zone voltage control device, comprising: The data acquisition module is used to acquire the topology of the distribution network, real-time operation measurement data, location information of each distributed energy source, adjustable capacity, response speed coefficient and adjustment cost coefficient; The fusion module is used to construct a power voltage sensitivity matrix based on linearized power flow calculation, and fuse active power sensitivity and reactive power sensitivity according to preset weights to obtain a comprehensive sensitivity index characterizing the electrical coupling strength between nodes. The clustering module is used to calculate the normalized adjustment weight based on the adjustable capacity, response speed coefficient and adjustment cost coefficient of each distributed energy source, and to construct the comprehensive electrical distance in combination with the comprehensive sensitivity index. The distributed energy nodes are clustered using the comprehensive electrical distance as a metric to form multiple dynamic aggregation partitions. The control module is used to perform hierarchical voltage control. Within the set upper-level coordination cycle, it maintains the structure of the dynamic aggregation partitions unchanged and issues coordination parameters. Within the set lower-level fast control cycle, each dynamic aggregation partition executes voltage control decisions within its region based on the coordination parameters.

[0018] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0019] To achieve the above objectives, a third aspect of this application provides an electronic device, including a processor and a memory; wherein the processor runs a program corresponding to the executable program code stored in the memory, for implementing the multi-parameter fusion charging load dynamic aggregation partition voltage control method as described in the first aspect embodiment.

[0020] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-parameter fusion charging load dynamic aggregation partition voltage control method as described in the first aspect embodiment.

[0021] The embodiments of the present invention have the following beneficial effects: This invention constructs a dynamic aggregation partition by integrating electrical coupling strength and DER regulation performance, which solves the problem of low matching degree between controllable resources and voltage deviation in traditional static partitioning. Combined with a hierarchical control architecture, it effectively improves the accuracy, stability and economic efficiency of power distribution network voltage control while reducing computational and communication overhead. Attached Figure Description

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart of a multi-parameter fusion charging load dynamic aggregation zone voltage control method provided in an embodiment of the present invention; Figure 2This is a schematic diagram of dynamic aggregation partitioning based on comprehensive electrical distance provided in an embodiment of the present invention; Figure 3 A schematic diagram of a hierarchical voltage control architecture provided in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the voltage optimization comparison results provided in an embodiment of the present invention. Figure 5 This is a structural diagram of a multi-parameter fusion charging load dynamic aggregation zone voltage control device provided in an embodiment of the present invention. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] The following description, with reference to the accompanying drawings, describes a method and apparatus for dynamic aggregation and partitioned voltage control of charging load based on multi-parameter fusion, according to embodiments of the present invention.

[0026] Example 1 This invention provides a method for dynamic aggregation and zoned voltage control of charging load based on multi-parameter fusion, such as... Figure 1 As shown, the method includes the following steps: S1 acquires the topology of the distribution network, real-time operation measurement data, and the location information, adjustable capacity, response speed coefficient, and adjustment cost coefficient of each distributed energy source.

[0027] To provide accurate and reliable data support for subsequent steps and ensure the stability and economy of voltage control, this application accurately collects relevant information on distribution network operation and distributed energy resources to form a characteristic dataset that meets the requirements, laying the foundation for the smooth implementation of the entire voltage control method.

[0028] In this embodiment, S1 serves as the initial step of the hierarchical voltage control method for the entire power distribution network. It is the foundation for ensuring the smooth implementation of all subsequent control processes. Its core value lies in providing comprehensive and accurate data support for the entire control method, ensuring that subsequent steps can proceed in an orderly manner.

[0029] In this embodiment, the core task of S1 is to complete the collection and organization of basic information, providing data support for all subsequent control operations. Specifically, in this embodiment, S1 first focuses on acquiring the topology of the distribution network, clarifying the overall layout and connection relationships of the distribution network, and providing a basic framework for subsequent sensitivity calculation and zone control. At the same time, through the distribution automation system, according to the preset sampling period, it accurately collects real-time operating data of the distribution network, including bus voltage amplitude, bus phase angle, line power flow data, switch status, and transformer tap status, ensuring that each piece of data accurately reflects the operating status of the distribution network.

[0030] For the core information collection of each distributed energy source (DER), this embodiment focuses on acquiring the location information, adjustable capacity, response capability, and adjustment cost-related parameters of each DER. Among them, the adjustable capacity is dynamically adjusted in strict accordance with the real-time operation requirements of the distribution network to ensure that it is compatible with subsequent control strategies. The response speed-related parameters are set according to the actual operation requirements of the controller to ensure the feasibility of subsequent adjustment actions. The adjustment cost-related parameters are combined with the actual operation scenario, comprehensively considering factors such as curtailment and losses, to scientifically construct a cost system to ensure the rationality of subsequent control decisions.

[0031] In this embodiment, to further improve the accuracy and usability of the data, all collected information is rigorously verified, invalid data is removed, and valid information that meets the requirements is selected to form a complete basic data set. Simultaneously, in this embodiment, all collected information is synchronously transmitted to the corresponding control module to ensure data consistency between the upper-level coordination module and the lower-level partition control module, avoiding data deviations from affecting subsequent sensitivity calculations, partition clustering, and hierarchical control effects.

[0032] In this embodiment of the application, a complete feature dataset is formed by collecting basic operational information of the power distribution network and distributed energy, which lays the foundation for obtaining a comprehensive sensitivity index that characterizes the electrical coupling strength between nodes.

[0033] S2. Based on linearized power flow calculation, a power voltage sensitivity matrix is ​​constructed, and the active power sensitivity and reactive power sensitivity are fused according to preset weights to obtain a comprehensive sensitivity index characterizing the electrical coupling strength between nodes.

[0034] In order to accurately quantify the electrical coupling strength between distribution network nodes and provide a reliable basis for subsequent distributed energy clustering and partitioning, this application constructs and integrates active and reactive power sensitivity through linearized power flow calculation to obtain a comprehensive sensitivity index.

[0035] In this embodiment, S2 relies on the collected distribution network topology, line parameters, and real-time operation measurement data to construct a power-voltage sensitivity matrix based on linearized power flow calculation, ensuring that the sensitivity calculation results closely match the actual operating state of the distribution network.

[0036] In this embodiment, the Jacobian matrix is ​​first constructed based on the current operating point of the distribution network, and then linearized power flow calculation is performed on this basis. The distribution network strictly satisfies the node power balance constraint during operation, and the constraint relationship is shown in the formula:

[0037] in, and For nodes Injected active and reactive power, and For nodes Active and reactive loads, and Let be the real and imaginary parts of the nodal admittance matrix. For nodes and The voltage phase angle difference between them is the fundamental premise for linearized power flow calculation in this application.

[0038] In this embodiment, based on the above-mentioned node power balance constraints, the matrix relationship shown is obtained through linearized power flow calculation, thereby reflecting the correlation characteristics between power changes and voltage and phase angle changes. The matrix is ​​as follows:

[0039] In this embodiment of the application, nodes are further obtained through calculation. Changes in active power injection on nodes Effect of voltage amplitude And the effect of reactive power injection changes at node j on node Effect of voltage amplitude Based on this, a power-voltage sensitivity matrix as shown in the formula is formed, which fully characterizes the influence of power injection at each node on the voltage amplitude:

[0040] In this embodiment, the power voltage sensitivity matrix is ​​obtained by linearization calculation based on the Jacobian matrix. At the same time, this application sets the update period of the matrix to be consistent with the upper-level coordination period, that is, it is updated once in each upper-level coordination period. While ensuring the timeliness of the sensitivity index, it effectively reduces the overall calculation cost and improves the operating efficiency of the control method.

[0041] In this application embodiment, considering that the impact mechanisms of active power and reactive power on distribution network voltage are significantly different, and that reactive power regulation has a more prominent effect on voltage regulation, in order to comprehensively and reasonably reflect the electrical coupling characteristics between nodes, this application integrates active power sensitivity and reactive power sensitivity according to preset weights to obtain a comprehensive sensitivity index that can comprehensively characterize the electrical coupling strength between nodes.

[0042] In this embodiment of the application, the weighting coefficients are... and An online adaptive setting method is adopted, specifically determined based on the average resistance-to-inductance ratio of the distribution network lines and historical operating statistics, with weighting coefficients satisfying the following requirements. Constraint relationships, where It can be further calculated and determined using the formula:

[0043] Where Z is the average resistance-to-inductance ratio of the distribution network line, and k is the adjustment coefficient. In this embodiment, the empirical value is taken as 1.5 to ensure that the weight setting fits the actual network structure characteristics of the distribution network.

[0044] In this embodiment, the formula for calculating the comprehensive sensitivity index is as follows:

[0045] The higher the value of the comprehensive sensitivity index, the stronger the electrical coupling relationship between the corresponding nodes, and the more significant the effect of node power regulation on the target node voltage.

[0046] In this embodiment, the power voltage sensitivity matrix is ​​obtained by linearization calculation based on the Jacobian matrix. At the same time, this application sets the update period of the matrix to be consistent with the upper-level coordination period, that is, it is updated once in each upper-level coordination period. While ensuring the timeliness of the sensitivity index, it effectively reduces the overall calculation cost and improves the operating efficiency of the control method.

[0047] In the embodiments of this application, the weighting coefficient , Furthermore, it can be adaptively adjusted based on parameters such as line resistance reactance ratio and reactive power regulation availability, further enhancing the adaptability of the method in different grid structures of distribution networks. This ensures that the final comprehensive sensitivity index can accurately reflect the node coupling characteristics, providing solid and accurate quantitative support for the subsequent clustering and partitioning of distributed energy nodes, and guaranteeing the rationality and effectiveness of the subsequent partitioning results.

[0048] In this embodiment, a power-voltage sensitivity matrix is ​​constructed by linearizing power flow calculation and then weighted and fused to obtain a comprehensive sensitivity index, which provides a quantitative basis for subsequent distributed energy clustering and partitioning.

[0049] S3 calculates the normalized adjustment weight based on the adjustable capacity, response speed coefficient and adjustment cost coefficient of each distributed energy source, and constructs the comprehensive electrical distance in combination with the comprehensive sensitivity index. The distributed energy nodes are then clustered using the comprehensive electrical distance as a metric to form multiple dynamic aggregation partitions.

[0050] In order to combine the regulation capability of distributed energy with the electrical coupling strength between nodes, improve the effectiveness of partition aggregation, and provide a reliable partitioning basis for hierarchical voltage control, this application embodiment constructs regulation contribution, normalized regulation weight and comprehensive electrical distance, adopts K-means++ clustering algorithm and introduces partition size constraints to form multiple dynamic aggregation partitions.

[0051] In this embodiment, S3 is one of the core steps of the hierarchical voltage control method for distribution networks. Its core purpose is to combine the regulation capability of distributed energy sources with the electrical coupling strength between nodes to construct a scientifically reasonable comprehensive electrical distance. Then, multiple dynamic aggregation partitions are formed through clustering algorithms, providing a solid and reliable partitioning foundation for subsequent hierarchical voltage control execution. This ensures that subsequent voltage control can take into account both globality and speed, improving the overall effect of distribution network voltage control. The specific implementation process is as follows: First, it should be clarified in the embodiments of this application that the obtained power-voltage sensitivity can only reflect the degree of electrical coupling between the buses in the distribution network, and cannot reflect the actual regulation capability of each distributed energy source (DER) node. If zoning aggregation is performed solely based on this sensitivity, it will lead to a mismatch between the aggregated zoning and the actual regulation requirements, reducing the effectiveness of subsequent zoning control. To this end, this application proposes a weight allocation mechanism based on regulation performance. This mechanism comprehensively considers the adjustable capacity, response speed coefficient, and regulation cost coefficient of each DER node, and obtains the regulation contribution of each node through quantitative calculation. This compensates for the shortcomings of the prior art that only considers electrical coupling and ignores regulation capability, thereby improving the scientificity and effectiveness of aggregation and zoning control.

[0052] In this embodiment, the adjustment contribution of node j is calculated using formula (4), and the specific calculation formula is as follows:

[0053] in, For nodes In this embodiment of the application, the adjustable capacity per unit is preferentially selected from nodes. The current available reactive / active margin ensures that the regulation contribution can truly reflect the actual regulation potential of the node at present. For nodes The response speed coefficient can be determined by a preset controller bandwidth mapping relationship. Specifically, it can be calculated based on the bandwidth or response time of the DER node controller. The larger the response speed coefficient, the faster the node responds to the voltage regulation command and the stronger the regulation timeliness. For nodes In this embodiment, the adjustment cost coefficient can be constructed based on the cost of curtailment, reactive power loss, or equipment lifespan depreciation, comprehensively considering the economics of the node adjustment process and avoiding excessively high operating costs due to blind adjustment. It is understood that this adjustment contribution... Comprehensive reflection of nodes The actual capacity and economic efficiency of voltage regulation in the distribution network provide a core quantitative basis for subsequent weight calculations.

[0054] The moderating contribution calculated above Combined with the obtained comprehensive sensitivity index The embodiments of this application further compute nodes For nodes Normalized adjustment weights The core function of this normalized adjustment weight is to characterize the effect of each DER node on the target bus (node). The relative contribution of voltage deviation mitigation, in this embodiment, indicates that the larger the weight value, the stronger the node's contribution. For nodes The stronger the voltage regulation capability and the higher the collaborative control value, the more efficiently it can participate in the voltage deviation management of node i. The specific calculation formula is as follows:

[0055] in, The set of all distributed energy nodes, with the denominator being all DER node pairs. The sum of the products of the moderating contribution and the overall sensitivity, its core function is to normalize the weights and ensure that the normalized moderating weights are applied. The value range is between 0 and 1, which facilitates the subsequent calculation of the comprehensive electrical distance, while ensuring the comparability and rationality of the weights of each node.

[0056] In this embodiment, in order to simultaneously characterize the "electrical coupling strength" and "available regulation capability" between distribution network nodes, and to overcome the shortcomings of existing electrical distance methods that can only reflect electrical coupling but not regulation capability, this application is based on the aforementioned comprehensive sensitivity index. With normalized adjustment weights Defined the comprehensive electrical distance The specific calculation formula is as follows:

[0057] in, From node To the node In this embodiment of the application, the magnitude of the comprehensive electrical distance is negatively correlated with the collaborative adjustment value between nodes; that is, the smaller the comprehensive electrical distance, the stronger the busbar coordination value. busbar With stronger voltage influence capability and higher available regulation contribution, such nodes should be prioritized to be aggregated into the same partition during the clustering process to ensure that the nodes in the same partition have strong electrical coupling and matching regulation capability, laying the foundation for subsequent autonomous control of the partition.

[0058] After obtaining the comprehensive electrical distance between all nodes, this embodiment of the application selects the K-means++ clustering algorithm, using the comprehensive electrical distance as the metric, to cluster all distributed energy nodes in the distribution network. Compared with the traditional K-means algorithm, the K-means++ algorithm can effectively avoid the clustering results from getting trapped in local optima by optimizing the selection of the initial cluster centers, thereby improving the accuracy and stability of clustering and better meeting the dynamic aggregation and partitioning requirements of this application.

[0059] Meanwhile, in this embodiment of the application, in order to ensure the controllability and operational stability of each aggregation partition and avoid the situation where the number of controllable distributed energy nodes in the partition is insufficient, this application introduces partition size constraints. These constraints clearly stipulate that each dynamic aggregation partition must contain at least a preset number of controllable distributed energy nodes and key buses, ensuring that each partition has the ability to independently complete voltage regulation within the region.

[0060] Meanwhile, in this embodiment, in order to ensure the controllability and operational stability of each aggregation zone and avoid the situation where there is insufficient number of controllable DER nodes in the zone and the inability to achieve effective voltage control, this application introduces a zone size constraint. This constraint clearly stipulates that each dynamic aggregation zone shall contain at least a preset number of controllable distributed energy nodes and key buses. The preset number can be flexibly set according to the actual scale of the distribution network, the distribution of DER nodes and voltage control requirements, to ensure that each zone has the ability to independently complete voltage regulation within the region.

[0061] Through the above clustering process, multiple dynamic aggregated partitions are ultimately formed, such as Figure 2 As shown in the figure, the specific process of dynamic partitioning in this application embodiment is clearly illustrated. In this application embodiment, DER nodes with strong electrical coupling and matching adjustment capabilities are aggregated into a partition by a clustering algorithm, so that the nodes in each partition have similar electrical characteristics and coordinated adjustment capabilities, effectively realizing the decoupling of the distribution network.

[0062] In this embodiment, by constructing the adjustment contribution, normalized adjustment weight, and comprehensive electrical distance, the K-means++ clustering algorithm is used and a partition size constraint is introduced to form a dynamic aggregated partition, which lays the foundation for subsequent hierarchical voltage control.

[0063] S4 executes hierarchical voltage control, maintaining the structure of the dynamic aggregation partition unchanged within the set upper-level coordination cycle and issuing coordination parameters. Within the set lower-level fast control cycle, each dynamic aggregation partition executes voltage control decisions within its region based on the coordination parameters.

[0064] In order to balance the rapid response capability and global optimization of distribution network voltage control, and to achieve comprehensive optimization of voltage compliance and operational economy, the embodiments of this application adopt a two-layer hierarchical voltage control strategy based on dynamic aggregation partitioning, and complete the precise optimization of distribution network voltage through upper and lower layer collaborative control.

[0065] In this embodiment, S4 is the final execution step of the distribution network hierarchical voltage control method. Its core lies in implementing a scientific and efficient hierarchical voltage control strategy based on the formed dynamic aggregation partition, taking into account both the speed and globality of distribution network voltage control, and ultimately achieving comprehensive optimization of distribution network voltage qualification and operational economy. This ensures that the voltage control method proposed in this application can be implemented and achieve the expected control effect. The specific implementation process is as follows: In this embodiment, the hierarchical voltage control architecture adopts a two-layer design of "upper-layer global coordination + lower-layer regional autonomous control." This design is one of the core innovations of this application. It can retain the rapid autonomous response capability of local areas to deal with voltage fluctuations in a timely manner, while ensuring the system optimization of the entire distribution network through upper-layer global coordination, avoiding the problem of regional control being disconnected from the global objective. Its architecture diagram is shown below. Figure 3 As shown in the figure, the overall structure of the hierarchical voltage control architecture of this application and the connection relationship of each part are clearly illustrated.

[0066] In this embodiment of the application, the hierarchical architecture consists of three core components: the bottom-level aggregation area autonomous control, the upper-level global coordination, and the time synchronization mechanism connecting the two layers. The core function of the time coordination mechanism is to ensure the precise time scale alignment between the upper-level global coordination and the lower-level partition autonomous control, promote efficient functional collaboration between the fast-responding local agents and the global coordinator, avoid the chaos in the execution of control commands due to time scale deviation, and ensure the orderly advancement of hierarchical control.

[0067] In this embodiment, the specific control process is divided into an upper-level coordination cycle and a lower-level rapid control cycle. The two cycles operate in coordination and cooperate with each other to jointly realize the hierarchical voltage control of the distribution network. The specific details are as follows: In this embodiment, the upper-level coordination cycle is set to 15 minutes. This cycle is determined based on a combination of the stability requirements of the distribution network operation and computational efficiency. This ensures the scientific nature of the upper-level coordination decisions while avoiding excessive computational burden due to an excessively short cycle. Within the upper-level coordination cycle, the topology of the formed dynamic aggregation partition remains fixed to ensure the stability and relevance of the upper-level coordination parameters. The upper-level global coordinator assumes the responsibility for network-wide coordination decisions. Specifically, the upper-level global coordinator comprehensively calculates and distributes coordination parameters to each dynamic aggregation partition based on network-wide voltage operation indicators, including but not limited to network-wide voltage deviation, number of nodes exceeding limits, and total network loss.

[0068] In this embodiment, the coordination parameters specifically include partition target voltage bandwidth, regulation budget, or target weight, etc. The core function of these coordination parameters is to guide the control decisions of lower-level partitions, ensuring that the control behavior of lower-level partitions conforms to the optimal goal of the entire network. Meanwhile, to maintain system scalability and adapt to large-scale distribution network application scenarios, in this embodiment, the global coordinator does not process fine-grained data from each node, but operates based on aggregated statistical summaries reported by each partition. This abstraction strategy, while retaining key operational information of the entire network, significantly compresses the dimensionality of the state space, effectively reducing the computational complexity of the global coordinator and improving the efficiency of coordination decisions.

[0069] In this application embodiment, the core objective of upper-layer coordination is to ensure reasonable power distribution and system-level voltage quality across the entire network. To achieve this objective, this application constructs a coordination reward function. This function prioritizes overall network performance indicators and guides the optimization of upper-layer coordination decisions by quantitatively evaluating the overall network operating status. Its specific expression is as follows:

[0070] in, As a global voltage quality reward, in this embodiment of the application, the reward is calculated based on the voltage deviation and over-limit situation of all nodes. The smaller the voltage deviation and the fewer the over-limit nodes, the higher the global voltage quality reward. The core purpose of the inter-regional power balance reward is to promote the consistency of power balance in all dynamic aggregation zones, avoid the situation where some regions have a large power surplus while other regions have a serious power shortage, and ensure that the power allocation of the entire network is reasonable. The reward is for losses across the entire network, taking into account the total active power loss of all lines in the distribution network. The smaller the network loss, the higher the reward. , To coordinate the reward weighting coefficient, in this embodiment of the application, the weighting coefficient can be flexibly set according to the actual operation requirements of the distribution network and the voltage control priority, so as to ensure that the reward function can accurately match the control target of the entire network.

[0071] In this embodiment, the lower-level fast control cycle is set to 1 minute. This cycle is primarily designed to rapidly suppress voltage fluctuations, respond promptly to voltage changes within the region, and compensate for the longer upper-level coordination cycle. After receiving coordination parameters from the upper-level global coordinator, the local agent agent within each dynamic aggregation zone acts as the execution entity for lower-level control. Under various operational constraints, it executes voltage control decisions within the region. These constraints include, but are not limited to, inverter apparent power constraints, energy storage state of charge (SOC) constraints, and flexible load operation constraints. These constraints are designed to ensure the safety of the voltage control process and the stability of equipment operation, preventing equipment damage or operational abnormalities due to over-adjustment. In this embodiment, the local agent agent focuses on minimizing voltage deviation, adjustment costs, and network losses as core optimization objectives. It solves for the optimal control quantity using corresponding optimization algorithms and executes this optimal control quantity promptly within the lower-level fast control cycle, ultimately achieving comprehensive optimization of voltage compliance and operational economy within the region.

[0072] In this embodiment, the underlying autonomous control serves as the execution foundation of the layered architecture. Through the partitioning in step S3, the entire distribution network is decoupled into multiple electrically coupled, relatively independent subsystems. Each subsystem corresponds to a dynamic aggregation partition, and each subsystem deploys an independent agent intelligent body specifically responsible for voltage control within its designated area. This design allows regional agents to handle only the operating status and control actions within their respective areas, without needing to consider fine-grained data from the entire network. This significantly reduces the decision-making complexity of a single controller and enables advanced control methods such as deep reinforcement learning to be successfully applied to voltage control in large-scale distribution networks, thereby improving the level of control intelligence.

[0073] In this embodiment, the control objective of each region is not only to optimize the voltage quality and operational economy of its own region, but also to minimize the negative impact on adjacent regions and avoid voltage fluctuations in adjacent regions caused by control actions within the region. To this end, the regional agent learns the control strategy through an independent network π(s, a) with the goal of maximizing the expected cumulative reward, and continuously optimizes the control decision. The specific expression of the regional reward function is as follows:

[0074] in, The regional voltage quality reward is calculated based on the average voltage deviation and the number of over-limit nodes in the region, and directly reflects the effectiveness of voltage control in the region. The regional network loss reward is based on the active power loss of the lines in the region, reflecting the economic efficiency of regional operation. The regional operating cost incentive primarily considers the cost input during the adjustment process of DER nodes within the region.

[0075] In this embodiment, the complete implementation of the aforementioned hierarchical voltage control strategy enables precise control and comprehensive optimization of the distribution network voltage. Figure 4 This diagram illustrates the voltage optimization comparison results provided in the embodiments of this application. It clearly shows that after adopting the hierarchical voltage control method proposed in this application, the voltage deviation of the distribution network is significantly reduced, voltage exceedance is effectively improved, and the operational economy of the distribution network is significantly enhanced. This fully verifies the effectiveness and practicality of the hierarchical voltage control method in the embodiments of this application, which can meet the voltage control requirements of large-scale distribution networks and solve problems such as slow voltage control response, insufficient global optimization, and poor operational economy in the prior art. Example 2 This invention provides a multi-parameter fusion charging load dynamic aggregation zone voltage control device 10, such as... Figure 5 As shown, the device includes: The data acquisition module 100 is used to acquire the topology of the distribution network, real-time operation measurement data, location information, adjustable capacity, response speed coefficient and adjustment cost coefficient of each distributed energy source; The fusion module 200 is used to construct a power voltage sensitivity matrix based on linearized power flow calculation, and fuse the active power sensitivity and reactive power sensitivity according to preset weights to obtain a comprehensive sensitivity index characterizing the electrical coupling strength between nodes. Clustering module 300 is used to calculate normalized adjustment weights based on the adjustable capacity, response speed coefficient and adjustment cost coefficient of each distributed energy source, and to construct a comprehensive electrical distance in combination with the comprehensive sensitivity index. The distributed energy nodes are clustered using the comprehensive electrical distance as a metric to form multiple dynamic aggregation partitions. The control module 400 is used to perform hierarchical voltage control. Within the set upper-level coordination cycle, the structure of the dynamic aggregation partition remains unchanged and coordination parameters are issued. Within the set lower-level fast control cycle, each dynamic aggregation partition executes voltage control decisions within its region based on the coordination parameters.

[0076] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0077] Example 3 To implement the methods of the above embodiments, the present invention also provides an electronic device, which includes a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the various steps of the methods described above.

[0078] Example 4 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0079] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0080] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0081] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for dynamic aggregation and zoned voltage control of charging load using multi-parameter fusion, characterized in that, include: S1, acquires the topology of the distribution network, real-time operation measurement data, and location information, adjustable capacity, response speed coefficient and adjustment cost coefficient of each distributed energy source; S2, construct the power voltage sensitivity matrix based on linearized power flow calculation, and fuse the active power sensitivity and reactive power sensitivity according to preset weights to obtain a comprehensive sensitivity index characterizing the electrical coupling strength between nodes. S3 calculates the normalized adjustment weight based on the adjustable capacity, response speed coefficient and adjustment cost coefficient of each distributed energy source, and constructs the comprehensive electrical distance in combination with the comprehensive sensitivity index. The distributed energy nodes are then clustered using the comprehensive electrical distance as a metric to form multiple dynamic aggregation partitions. S4 executes hierarchical voltage control, maintaining the structure of the dynamic aggregation partition unchanged within the set upper-level coordination cycle and issuing coordination parameters. Within the set lower-level fast control cycle, each dynamic aggregation partition executes voltage control decisions within its region based on the coordination parameters.

2. The method according to claim 1, characterized in that, The acquisition of the distribution network topology, real-time operational measurement data, and location information, adjustable capacity, response speed coefficient, and adjustment cost coefficient of each distributed energy source includes: Real-time operational measurement data are obtained by collecting bus voltage amplitude, bus voltage phase angle, line power flow data, switch status, and transformer tap status through a preset sampling period via a power distribution automation system or edge node. The data collected includes the location of the access bus, rated capacity, current output value and available adjustment margin of each distributed energy source. The response speed coefficient is determined by combining the preset controller bandwidth mapping relationship. The adjustment cost coefficient is constructed based on the cost of curtailment, reactive power loss or lifetime loss, forming a distributed energy characteristic dataset containing location information, adjustable capacity, response speed coefficient and adjustment cost coefficient.

3. The method according to claim 1, characterized in that, The power voltage sensitivity matrix is ​​constructed based on linearized power flow calculation, and the active power sensitivity and reactive power sensitivity are fused according to preset weights to obtain a comprehensive sensitivity index characterizing the electrical coupling strength between nodes, including: Construct the Jacobian matrix based on the current running point and perform linearized power flow calculations to obtain the nodes. Changes in active power injection on nodes Effect of voltage amplitude and nodes Changes in reactive power injection on nodes Effect of voltage amplitude This forms a power voltage sensitivity matrix; Weighting coefficients are set online adaptively based on the average resistance-inductance ratio of the distribution network lines and historical operating statistics. and Calculate the comprehensive sensitivity index using the formula. : 。 4. The method according to claim 1, characterized in that, The normalized adjustment weight is calculated based on the adjustable capacity, response speed coefficient, and adjustment cost coefficient of each distributed energy source. A comprehensive electrical distance is constructed using a comprehensive sensitivity index. Distributed energy nodes are then clustered using this comprehensive electrical distance as a metric to form multiple dynamic aggregation partitions, including: Calculate nodes according to the formula Moderating contribution: in, For nodes Adjustable capacity per unit For response speed coefficient, To adjust the cost coefficient; Based on the aforementioned regulation contribution and comprehensive sensitivity index, the node is calculated using the formula. For nodes Normalized adjustment weights : in, It is the set of all distributed energy nodes; The comprehensive electrical distance is defined according to the formula. : The K-means++ clustering algorithm is selected to cluster the distributed energy nodes using the comprehensive electrical distance as a metric. At the same time, partition size constraints are introduced to ensure that each partition contains at least a preset number of controllable distributed energy nodes and key buses, thereby forming multiple dynamic aggregated partitions.

5. The method according to claim 1, characterized in that, The hierarchical voltage control is implemented by maintaining the structure of the dynamic aggregation partitions unchanged and issuing coordination parameters within a set upper-level coordination cycle, and by making voltage control decisions within each dynamic aggregation partition based on the coordination parameters within a set lower-level fast control cycle, including: The upper-level coordination cycle is set to 15 minutes. During the upper-level coordination cycle, the topology of the dynamic aggregation partition remains unchanged. The upper-level global coordinator calculates and issues the target voltage bandwidth, adjustment budget or target weight of the partition as coordination parameters based on the voltage operation index of the entire network. The lower-level fast control cycle is set to 1 minute. Under the conditions of inverter apparent power constraints, energy storage state of charge constraints and flexible load operation constraints, the local agent intelligent agent in each dynamic aggregation zone solves the optimal control quantity with the optimization objectives of minimizing voltage deviation, minimizing regulation cost and minimizing network loss. The optimal control quantity is executed within the lower-level fast control cycle to achieve comprehensive optimization of voltage qualification and operation economy in the region.

6. A multi-parameter fusion charging load dynamic aggregation zone voltage control device, characterized in that, include: The data acquisition module is used to acquire the topology of the distribution network, real-time operation measurement data, location information of each distributed energy source, adjustable capacity, response speed coefficient and adjustment cost coefficient; The fusion module is used to construct a power voltage sensitivity matrix based on linearized power flow calculation, and fuse active power sensitivity and reactive power sensitivity according to preset weights to obtain a comprehensive sensitivity index characterizing the electrical coupling strength between nodes. The clustering module is used to calculate the normalized adjustment weight based on the adjustable capacity, response speed coefficient and adjustment cost coefficient of each distributed energy source, and to construct the comprehensive electrical distance in combination with the comprehensive sensitivity index. The distributed energy nodes are clustered using the comprehensive electrical distance as a metric to form multiple dynamic aggregation partitions. The control module is used to perform hierarchical voltage control. Within the set upper-level coordination cycle, it maintains the structure of the dynamic aggregation partitions unchanged and issues coordination parameters. Within the set lower-level fast control cycle, each dynamic aggregation partition executes voltage control decisions within its region based on the coordination parameters.

7. An electronic device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method as described in any one of claims 1-5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-5.

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