Stock power distribution network charging power coordination control method and system based on dynamic bearing capacity

Abstract

The invention discloses a stock power distribution network charging power coordination control method and system based on dynamic bearing capacity, and relates to the technical field of stock power distribution network charging power coordination control, and the method comprises the steps: building a rolling optimization model based on the real-time state and nonlinear voltage-power characteristics of an end node; dynamically calculating the bearable power of the feeder line and generating a node-level charging power control instruction; performing dynamic clustering on the end nodes according to load characteristics and physical positions, calculating cluster-level power constraints, and completing priority charging distribution and power adjustment in clusters to form a cluster-level execution instruction; through the synergistic effect of a node-level control instruction and a cluster-level execution instruction, the node voltage stability of a residential area, the feeder load balance and the coordinated charging under the dynamic bearing capacity constraint under the condition of concentrated charging at night are realized; the problems that the node voltage fluctuation is large, the feeder load is easy to exceed the limit and the existing method cannot deal with the short-time power peak value in the residential area night EV concentrated charging scene are solved.

Description

Technical Field

[0001] This invention relates to the field of coordinated control technology for charging power in existing distribution networks, and more specifically, to a method and system for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity. Background Technology

[0002] Currently, in existing residential power distribution networks, research has proposed charging power coordination control methods based on dynamic load capacity to address the charging load issue caused by a large number of connected electric vehicles (EVs). These methods optimize the allocation of EV charging power by real-time monitoring of end-node voltage, feeder load, and network status, combined with node-level and feeder-level power constraints. This improves charging power utilization and system safety while ensuring stable node voltage and feeder load within limits. These methods typically rely on rolling optimization models to dynamically adjust the charging power of each node to adapt to changes in load and grid conditions.

[0003] However, existing technologies have significant shortcomings in application. Rolling optimization models typically allocate power based on fixed time slices, while electric vehicle charging loads and end-node power injection exhibit short-term fluctuations, leading to instantaneous peaks between optimization calculations and execution. These peaks may occur before the next time slice's optimization execution, increasing the risk of node voltage deviations or feeder load overloads, and reducing the accuracy and reliability of dynamic load capacity control.

[0004] To alleviate the above problems, we can draw on the concept of adaptive control of intelligent traffic signals. Similar to how traffic signals adjust the green light duration and cycle based on real-time traffic density and speed, the coordination of EV charging power in residential areas can also be achieved through dynamic feedback and continuous adjustment. This tightly integrates node-level and cluster-level power allocation with real-time network conditions, thereby quickly responding to instantaneous power fluctuations, mitigating the impact of short-term load peaks on the power grid, and achieving more refined power coordination control.

[0005] However, the method of adaptive control of intelligent traffic signals also brings new problems to power systems. Because it requires frequent rolling optimizations, each optimization involves solving large-scale nonlinear constraints, resulting in extremely high computational demands. For edge devices in residential areas or centralized computing servers, the high frequency of calculations may lead to insufficient system computing resources, delaying the generation of optimization results, affecting the real-time performance and executability of power allocation, and increasing the difficulty of practical applications.

[0006] While existing EV charging power coordination control methods based on dynamic load capacity can theoretically improve load allocation efficiency and grid security, they suffer from problems such as mismatch between time slices and instantaneous fluctuations, potential early occurrence of instantaneous peaks, high computation frequency compared to adaptive control, and limited edge computing resources. These shortcomings limit their application effectiveness and scalability in large-scale EV access scenarios in actual residential areas, necessitating the development of improved solutions that balance real-time performance, accuracy, and computability.

[0007] To address the above problems, this invention proposes a solution. Summary of the Invention

[0008] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity. Through a two-layer rolling optimization control mechanism based on node-level dynamic carrying capacity calculation and cluster-level coordinated power allocation, the method addresses the problems of large node voltage fluctuations, easy overload of feeder loads, and the inability of existing methods to cope with short-term power peaks in the scenario of concentrated EV charging at night in residential areas.

[0009] To achieve the above objectives, the present invention provides the following technical solution: The method for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity includes the following steps: A rolling optimization model is constructed based on the real-time status and nonlinear voltage-power characteristics of the end nodes in residential areas. The feeder power carrying capacity is dynamically calculated, and node-level charging power control commands are generated. These node-level charging power control commands are used as cluster-level optimization inputs. End nodes are dynamically grouped into load clusters according to load characteristics and physical location. Cluster-level power constraints are calculated, and priority charging allocation and power adjustment are performed within the cluster level to generate cluster-level execution commands. Through the coordinated application of node-level charging power control commands and cluster-level execution commands, node voltage stability, feeder load balancing, and charging coordination under dynamic carrying capacity constraints are achieved under the condition of centralized EV charging at night in residential areas.

[0010] The system for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity includes a charging power control command generation module, a cluster-level execution command generation module, and a charging coordination module. The charging power control command generation module is used to construct a rolling optimization model based on the real-time status and nonlinear voltage-power characteristics of the end nodes in residential areas, dynamically calculate the feeder power carrying capacity, and generate node-level charging power control commands. The cluster-level execution command generation module uses the node-level charging power control commands as cluster-level optimization inputs, dynamically groups end nodes into load clusters according to load characteristics and physical location, calculates cluster-level power constraints, and performs priority charging allocation and power adjustment within the cluster level to generate cluster-level execution commands. The charging coordination module is used to achieve node voltage stability, feeder load balancing, and charging coordination under dynamic carrying capacity constraints under the condition of centralized EV charging at night in residential areas through the coordinated application of node-level charging power control commands and cluster-level execution commands.

[0011] The technical effects and advantages of this invention, which is a method and system for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity, are as follows: 1. This invention achieves dynamic and accurate calculation of the upper limit of feeder power carrying capacity through a rolling optimization mechanism based on the real-time status of end nodes in residential areas and nonlinear voltage-power characteristics, and generates node-level charging power control commands accordingly. Compared with traditional power allocation methods that rely on fixed thresholds or static models, this invention can adjust power allocation in a timely manner when EV charging loads fluctuate, keeping node voltage stable under real-time changing conditions, thereby significantly reducing the risks of overvoltage, undervoltage, and feeder overload. Simultaneously, this invention employs an iterative correction mechanism at the node level using voltage simulation and feeder load simulation, ensuring that the charging power allocation of each feeder and node always matches the actual carrying capacity, improving the safety, available carrying capacity, and overall charging efficiency of low-voltage distribution networks in scenarios with a large number of EVs simultaneously connected at night.

[0012] 2. This invention employs a cluster-level load grouping and hierarchical optimization strategy to dynamically cluster end nodes based on their physical location, historical load characteristics, and EV behavior features. This achieves more stable and efficient regional load coordination than traditional single-node independent control. Prioritized charging allocation under cluster-level power constraints, combined with local voltage and feeder load correction, enables cluster-level execution commands to precisely adjust the charging power of each EV under constraints, mitigating local anomalies caused by instantaneous load peaks. Through the synergy of node-level and cluster-level commands, this invention can achieve multi-level power coordination across the entire network, allowing the node and cluster layers to compensate and constrain each other. Ultimately, this ensures that the charging power allocation across the entire residential network meets voltage constraints, feeder capacity constraints, and user priority requirements in each time slot, significantly improving the dynamic carrying capacity and controllability of the overall distribution network. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the method for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity, as described in this invention.

[0014] Figure 2 This is a schematic diagram of the structure of the existing power distribution network charging power coordination control system based on dynamic load capacity according to the present invention. Detailed Implementation

[0015] 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1, Figure 1 The present invention provides a method for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity, comprising the following steps: S1, based on the real-time status and nonlinear voltage-power characteristics of the end nodes in the residential area, a rolling optimization model is constructed to dynamically calculate the power carrying capacity of the feeder and generate node-level charging power control commands. S2, takes the node-level charging power control command as the cluster-level optimization input, dynamically groups the end nodes according to load characteristics and physical location to form load clusters, calculates the cluster-level power constraints, and performs priority charging allocation and power adjustment within the cluster-level range to form cluster-level execution commands; S3 achieves stable node voltage, balanced feeder load, and coordinated charging under dynamic load capacity constraints under the conditions of centralized EV charging at night in residential areas through the coordinated application of node-level charging power control commands and cluster-level execution commands.

[0017] In this embodiment, the step of constructing a rolling optimization model based on the real-time status and nonlinear voltage-power characteristics of the end nodes in the residential area to dynamically calculate the power carrying capacity of the feeder and generate node-level charging power control commands specifically involves: S11: Obtain the voltage, current, and EV charging status of the end nodes in the residential area, and generate network status data. In this embodiment, the step of acquiring the voltage, current, and EV charging status of the end nodes in the residential area and generating network status data specifically involves: The system acquires real-time voltage and current measurements at each end node of the low-voltage distribution network in the residential area, and collects charging status information of EVs at the access nodes, including charged capacity, charging power, and estimated off-grid time. The collected end-node voltage, current and EV charging status are formatted and converted into node state vectors. The initial network state matrix is ​​then generated according to the feeder order. Calculate the active and reactive power injection of each node based on the initial network state matrix, and generate a node power injection table; By combining the node power injection table with network topology information, a node voltage-power mapping relationship is established, generating network state data that can be used for the rolling optimization model.

[0018] In this embodiment, the terminal node of the residential low-voltage distribution network refers to the electrical node at the end of the low-voltage distribution line connecting the loads of each user in the residential area and the EV charging piles. Each node represents a physical access point, capable of collecting the node's voltage, current, and charging information of the connected EVs in real time, thus serving as the basic unit for network status monitoring and power distribution. This node is not only the terminal for power transmission but also the object of information collection and power regulation.

[0019] In this embodiment, the real-time voltage and current measurements refer to the voltage magnitude, current magnitude, and phase information collected instantaneously at each end node by distribution network monitoring devices or sensors. These measurements reflect the current load status of the nodes and the operating conditions of the power grid, providing an accurate data foundation for subsequent power injection calculations and optimization models.

[0020] In this embodiment, the EV charging status information refers to the current charging power, charged capacity, and estimated disconnection time of each electric vehicle under the access node. This information not only reflects the charging progress of a single vehicle but can also be used to determine the urgency of charging demand and the priority of node power allocation, making it an important parameter for the rolling optimization model.

[0021] In this embodiment, the node state vector refers to a unified information unit formed by formatting the collected voltage, current, and EV charging status data. Each node is represented by a vector, whose elements include voltage amplitude, current magnitude, charging power of each EV, remaining rechargeable capacity, and estimated off-grid time. The node state vector can be easily organized into a matrix according to the feeder order, providing standardized input for modeling the overall network state.

[0022] In this embodiment, the initial network state matrix refers to a matrix structure formed by arranging the node state vectors of all end nodes in feeder order. Each row in the matrix corresponds to the state vector of a node, and each column corresponds to a certain type of information (such as voltage, current, charging power, etc.). The matrix form can simultaneously represent the state of all end nodes in the entire residential area, realizing a holistic description of the mutual influence between nodes and the power distribution of the feeders.

[0023] In this embodiment, the node power injection table is a table formed by calculating the active and reactive power injection results of each node based on the initial network state matrix. The table records the active and reactive power values ​​injected or consumed by each node into the grid, providing data information for establishing the node voltage-power mapping relationship and serving as direct input for the rolling optimization model to perform power allocation calculations.

[0024] In this embodiment, the node voltage-power mapping relationship refers to the correspondence between voltage and power established by combining the node power injection table with network topology information and analyzing the impact of injected power on node voltage. This mapping relationship can describe the changing trend of each node voltage under different power injection conditions, thereby providing a predictive basis for rolling optimization and ensuring that the charging power allocation does not exceed the node voltage constraints.

[0025] In this embodiment, the network state data usable in the rolling optimization model refers to a unified dataset formed after processing node state vectors, initial network state matrices, node power injection tables, and voltage-power mapping relationships. This dataset contains voltage, current, EV charging status, and power constraint information for each end node in the residential area, and can be directly input into the rolling optimization model to achieve optimized calculation of power allocation per minute.

[0026] It should be noted that the frequency of network state data collection and update was not specified in the above description. In this embodiment, to ensure that the rolling optimization model can reflect instantaneous load changes, the network state data collection frequency needs to be set to once per minute, and the node state vector and the initial network state matrix need to be updated immediately after each collection to ensure that the input data of the rolling optimization model is timely and accurate.

[0027] It should be noted that the above text did not explicitly address how to handle the nonlinear relationship between node voltage and power. In this embodiment, by collecting historical load data and combining it with power flow calculations, voltage response curves for each node under different power injections are established. The nonlinear characteristics are discretized to generate lookup tables or function models that can be used for rolling optimization, ensuring that the optimization calculations can accurately predict node voltage changes.

[0028] S12, Construct a rolling optimization model based on the generated network state data, and calculate the upper limit of the power that each feeder can carry by combining the nonlinear voltage-power characteristics of the end node; In this embodiment, the step of constructing a rolling optimization model based on the generated network state data and calculating the upper limit of the power that each feeder can carry, combined with the nonlinear voltage-power characteristics of the end nodes, specifically involves: Based on the generated network status data, a rolling optimization model for each feeder is established to determine the adjustable range of charging power for each node in the next time slice. Input the voltage, power injection, and feeder load information of each node in the network status data into the nonlinear voltage-power calculation module to calculate the instantaneous power carrying capacity limit of each feeder in the current state and generate a feeder power constraint table.

[0029] The rolling optimization model in this embodiment refers to a continuous-time optimization method that allocates power to each feeder and its downstream EVs based on real-time network status data of the end nodes in the residential area. This model recalculates the node charging power every minute to cope with load fluctuations and voltage changes, achieving dynamic power allocation. The model's inputs include node voltage, current, charging status, and feeder load information; the output is the adjustable charging power range for each node in the current time slice.

[0030] In this embodiment, the adjustable charging power range for the next time slice refers to the upper and lower limits of the allocable power calculated for each end node's EV based on the current network state and the optimization model. This range takes into account node voltage constraints, feeder power constraints, and the allocated charging power to ensure that the node is neither overloaded nor causes voltage deviation during the charging process in the next minute.

[0031] In this embodiment, the nonlinear voltage-power calculation module is a calculation unit used to describe the nonlinear response of node voltage to power injection. After inputting node voltage, current, and power injection data, the module calculates the voltage response and power carrying capacity of each node and feeder under the current power distribution through power flow analysis or table lookup methods, forming a nonlinear constraint relationship between nodes and feeders.

[0032] In this embodiment, the upper limit of instantaneous power carrying capacity of a feeder refers to the maximum instantaneous power value that each feeder can withstand under the current network conditions. This upper limit takes into account the feeder's own thermal capacity, voltage sag constraints, node voltage limits, and the power requirements of the EV connected to the feeder, in order to ensure that no local overload or voltage exceedance occurs during the rolling optimization process.

[0033] The feeder power constraint table in this embodiment is a table that organizes the instantaneous power carrying capacity limits of each feeder. The table records the maximum allowable power value and corresponding time slice information of each feeder in the current state, providing constraints for node-level EV power allocation.

[0034] It should be noted that the above text does not explicitly explain how the nonlinear voltage-power model guarantees calculation accuracy. In this embodiment, to avoid calculation deviations during rolling optimization, the nonlinear calculation module combines historical measurement data and power flow simulation results, and uses a piecewise discretization method to describe the nonlinear relationship between power and voltage, ensuring that the power upper limit calculation for each node and feeder can accurately reflect actual operating conditions.

[0035] It should be noted that the time synchronization method between the node status data and the rolling optimization model was not explicitly stated above. In this embodiment, the network status data collected every minute is uniformly formatted before the rolling optimization model is calculated, ensuring that the model input is consistent with the actual node status. At the same time, the feeder power constraints are continuously updated during the optimization process to ensure that each power allocation is based on the latest network status data.

[0036] S13: For each EV on the feeder, allocate charging power according to the feeder's maximum power capacity and node voltage constraints, and generate node-level charging power control commands.

[0037] In this embodiment, the charging power allocation for each EV on each feeder is based on the feeder's maximum power capacity and node voltage constraints, and node-level charging power control commands are generated, specifically as follows: For each node in the preliminary EV power allocation table, perform node voltage simulation according to the network topology and feeder connection relationship, and calculate the voltage deviation value of each node; For nodes whose voltage deviates from the rated value by more than the allowable range, the charging power of the EVs belonging to them is reduced proportionally to no more than the power limit corresponding to the voltage constraint of that node, and the EV power allocation table is updated. Perform cumulative simulation of the feeder load to calculate the total charging power of all nodes on the feeder; For feeders whose total charging power exceeds the feeder's maximum capacity, the charging power of all EVs connected to the feeder will be reduced proportionally to make the total power equal to the feeder's maximum capacity, and the EV power allocation table will be updated. Repeat the above simulation and adjustment steps for node voltage and feeder load until all node voltages are within the allowable range and the total power of the feeder does not exceed the upper limit of the load capacity. Finally, the adjusted EV power allocation table is output to form the optimized node-level charging power control command.

[0038] In this embodiment, the node voltage simulation refers to the predictive calculation of the voltage of each end node based on the current EV charging power allocation and network topology. This simulation considers the effects of current flow between nodes, feeder impedance, and power injection on node voltage to determine whether each node meets the voltage constraints under the current power allocation.

[0039] In this embodiment, the voltage deviation value refers to the difference between the node voltage simulation result and the node's rated voltage. This value is used to determine whether the node exceeds the allowable voltage fluctuation range, thereby guiding the adjustment of the charging power of the associated EV.

[0040] In this embodiment, the proportional power reduction refers to reducing the charging power of the EV connected to that node proportionally according to the degree of exceedance when the voltage of a node exceeds the allowable range, so that the adjusted power can restore the node voltage to the allowable range. The proportional calculation takes into account the current power distribution of the node and the magnitude of the voltage deviation to achieve precise control.

[0041] In this embodiment, the feeder load accumulation simulation involves summing the charging power of all nodes along a feeder to calculate the total power load of the feeder, in order to determine whether it exceeds the feeder's instantaneous carrying capacity. This simulation relies on the node-level EV power allocation table and feeder topology information.

[0042] In this embodiment, the feeder load capacity limit power adjustment refers to proportionally reducing the charging power of all EVs connected to the feeder when the total feeder power exceeds the allowable limit, so that the total feeder power equals the load capacity limit. This adjustment also considers the original power allocation of each node and the node voltage constraint to ensure that the node voltage is not affected by excessive voltage drop.

[0043] In this embodiment, the optimized node-level charging power control command refers to recording the final executable EV charging power of each node after completing the simulation adjustment of node voltage and feeder load, forming a command table that can be directly used for actual charging control.

[0044] It should be noted that the above text does not explicitly define how to determine the convergence conditions for node voltage and feeder power adjustments. In this embodiment, by setting voltage deviation thresholds and feeder power over-limit thresholds, after each iteration, it is checked whether all nodes and feeders meet the requirements. If all requirements are met, convergence is determined; otherwise, the EV power is adjusted proportionally until convergence is achieved.

[0045] It should be noted that the above text did not explicitly address the potential impact of node voltage adjustments on the voltage and total power of other nodes on the same feeder. In this embodiment, voltage deviation adjustment and feeder total power adjustment are performed alternately in a cyclical manner. After each adjustment, the voltage and total power of the entire feeder node are resimulated to ensure that both node voltage constraints and feeder power limits are met simultaneously, thus resolving the nonlinear effects caused by node-feeder coupling.

[0046] In this embodiment, the node-level charging power control command is used as the cluster-level optimization input. End nodes are dynamically grouped into load clusters based on load characteristics and physical location. Cluster-level power constraints are calculated, and priority charging allocation and power adjustment are performed within the cluster level to form cluster-level execution commands. Specifically, this involves: S21, the node-level charging power control command is used as the cluster-level optimization input, and the end nodes are dynamically grouped into load clusters according to the physical feeder location of the nodes, historical load characteristics and EV plug-in probability, generating a cluster-level node list; In this embodiment, the step of using node-level charging power control commands as cluster-level optimization inputs, and dynamically grouping end nodes into load clusters based on the physical feeder location, historical load characteristics, and EV plug-in probability to generate a cluster-level node list, specifically involves: Read the optimized node-level charging power control instructions and obtain the physical feeder location, historical load curve and EV plug-in probability of each node; The terminal nodes are divided into preliminary clusters according to the physical feeder location, so that the nodes in the same cluster are located under adjacent feeders or the same feeder path; A similarity analysis is performed on the historical load curves of nodes within the initial cluster, and the node load similarity matrix is ​​calculated. By combining node load similarity and EV plug-in probability, nodes with high similarity are clustered into the same load cluster to generate a cluster-level node list; The member nodes, feeders, historical load characteristics, and EV plug-in probability of each load cluster are recorded in the cluster-level node list to form the final cluster-level node list.

[0047] S22, calculate the available power limit for each load cluster based on the cluster-level node list to obtain cluster-level power constraint information; In this embodiment, the calculation of the available power limit for each load cluster based on the cluster-level node list to obtain cluster-level power constraint information specifically involves: Read the cluster-level node list and extract the node-level charging power control commands and feeder load information of the member nodes in each load cluster; The power of each member node in each load cluster is summed to calculate the total planned charging power of the cluster. The total planned charging power of the cluster is compared with the power carrying capacity of each feeder in the cluster, node voltage constraints, and historical load peaks to determine the upper limit of the available power of the cluster. Record the available power limit and member node information for each load cluster to form cluster-level power constraint information.

[0048] In this embodiment, the node-level charging power control command refers to the set of EV charging power values ​​that each end node can execute after prior rolling optimization and node voltage / feeder load simulation adjustment. This command explicitly records the power range that each EV can be allocated in the current time slice, and includes the feeder information where the node is located and the corresponding voltage constraints.

[0049] In this embodiment, the physical feeder location refers to the position of each end node in the residential area distribution network within the actual feeder topology, including the feeder path where the node is located, its connection sequence to the busbar, and the distribution of adjacent nodes. This information is used for initial clustering, making nodes within the same cluster relatively concentrated in physical space, which facilitates power coordination.

[0050] In this embodiment, the historical load characteristics refer to the load variation curve of each end node over several past time periods, including peak and trough periods of EV charging load and the magnitude of load fluctuations. This characteristic is used to calculate the load similarity between nodes, ensuring consistency of nodes within a cluster in the load time series and improving the predictability of cluster-level power allocation.

[0051] In this embodiment, the EV plug-in probability refers to the estimated likelihood that each EV will actually connect to a charging station during a specific time slice. This probability is obtained based on historical charging data and user travel pattern statistics, and is used to optimize the grouping of nodes within a cluster, making it more likely that EVs within the same cluster will charge simultaneously during the same time period, thereby improving cluster-level optimization efficiency.

[0052] In this embodiment, the node load similarity matrix refers to the matrix generated by calculating the similarity of the historical load curves of each pair of nodes in the initial cluster set. Each element in the matrix represents the degree of matching between the load curves of the two nodes. This matrix is ​​used for subsequent intra-cluster node clustering to ensure that nodes with consistent load characteristics are assigned to the same load cluster.

[0053] In this embodiment, the cluster-level node list refers to a complete list that records the member nodes, feeders, historical load characteristics, and EV plug-in probabilities of each load cluster after dynamic grouping. This list clarifies the cluster-level partitioning results and serves as the foundational data for cluster-level power calculation and priority allocation.

[0054] In this embodiment, the cluster-level power constraint information refers to the upper limit of available power for each load cluster, calculated based on the node-level charging power, feeder carrying capacity, node voltage constraints, and historical load peak values ​​of the member nodes within the cluster. This information records the maximum charging power that each cluster can execute in the current time slice and includes detailed data on the member nodes within the cluster.

[0055] It should be noted that the above text did not explicitly address the possibility of extreme differences in node load similarity and EV plug-in probability during the grouping process. In this embodiment, a weighted similarity index is synthesized by calculating the node load similarity matrix and the EV plug-in probability to ensure the balance of node load curves and charging probabilities within the cluster. This prevents a single node from consuming excessive power within the cluster, which could lead to distortion of cluster-level power constraints.

[0056] It should be noted that the above text does not explain how to handle boundary issues such as node voltage or feeder exceeding limits after accumulating the total cluster power. In this embodiment, each time the total planned power of the cluster is calculated, the power is compared with the carrying capacity of each feeder in the cluster, the node voltage constraints, and the historical peak value. If any constraint is exceeded, the power of each node in the cluster is adjusted iteratively until the total cluster power meets all constraints, thereby forming the final cluster-level power constraint information.

[0057] S23, Under the cluster-level power constraint information, the EVs in the cluster-level node list are allocated power according to priority to generate an intra-cluster EV power allocation table; In this embodiment, the step of allocating power to EVs in the cluster-level node list according to priority under cluster-level power constraint information to generate an intra-cluster EV power allocation table specifically involves: Read the cluster-level node list and the corresponding cluster-level power constraint information; For each load cluster, the nodes are sorted according to the EV off-grid time, remaining rechargeable capacity and set priority of the member nodes to generate a cluster priority list; Based on cluster-level power constraint information, charging power is allocated sequentially to the highest priority EV to ensure that its power does not exceed the allocable range specified in the node-level charging power control instruction; Update the remaining available power of the cluster, and allocate the remaining power to the second highest priority EVs in the cluster in priority order. Repeat until all EVs in the cluster have been allocated or the remaining available power of the cluster is zero. Record the final allocated power of member nodes within each load cluster and generate an intra-cluster EV power allocation table; After completing the power allocation for all load clusters, output the complete EV power allocation table within the cluster.

[0058] S24, adjusts the allocation in the EV power allocation table within the cluster that may cause local voltage or feeder load overload, and generates cluster-level execution instructions.

[0059] In this embodiment, the adjustment of the allocation in the intra-cluster EV power allocation table that may lead to local voltage or feeder load overload, and the generation of cluster-level execution instructions, specifically involves: Read the complete intra-cluster EV power allocation table and corresponding cluster-level power constraint information; For each load cluster, the currently allocated EV charging power is simulated using a node voltage-power nonlinear model to calculate the predicted voltage and feeder load for each node. Identify nodes in the simulation results that exceed the node voltage limit or feeder power limit and their corresponding EV charging power allocation; According to the EV priority within the load cluster, the charging power is adjusted sequentially starting from the low priority node, reducing the allocated power to not exceed the node or feeder constraints, and the node voltage and feeder load calculated in the simulation are updated in real time. Repeat the adjustment until all node voltages and feeder loads meet the constraints. Record the final executable charging power of each EV in each load cluster and generate cluster-level execution instructions.

[0060] It should be noted that the following is a feasible example of calculating the initial intra-cluster power allocation matrix: ; In the formula, This is the initial intra-cluster power allocation matrix. The initial charging power is allocated to the first EV under node 1. The number of nodes within the cluster. This represents the number of EVs under node 1.

[0061] It should be noted that the following are feasible calculation examples for node voltage-power simulation: ; In the formula, Let be the predicted voltage at node i; This is the reference voltage for node i when there is no load. , Let i be the equivalent resistance and reactance of the feeder corresponding to node i; Let EVj be the reactive power at node i; It should be noted that the following is a feasible calculation example for adjusting power according to priority: Power allocation is gradually reduced based on EV priority, from low to high: ; ; In the formula, The power of EVj after adjustment at node i; The power is adjusted for this round; K is the adjustment coefficient.

[0062] It should be noted that the following is a feasible example of calculating cluster-level execution instructions: ; In the formula, The cluster-level execution instructions, i.e. the adjusted power matrix, record the final executable charging power of each EV within each load cluster.

[0063] In this embodiment, the coordinated application of node-level charging power control commands and cluster-level execution commands specifically refers to: Read node-level charging power control instructions and cluster-level execution instructions; For each end node, the node-level charging power control command is superimposed with the cluster-level execution command of the corresponding EV in the cluster to calculate the final charging power of the node in the current time slice. The node charging power is simulated using a node voltage-power nonlinear model and a feeder load model, and the predicted voltage and corresponding feeder load of each node are calculated. Nodes whose predicted voltage and feeder load exceed safety constraints are identified, and their final charging power is locally adjusted. The adjustment order is determined according to the priority of node-level and cluster-level instructions. Iteratively update the adjusted node power and re-simulate the voltage and feeder load until all node voltages and feeder loads meet the constraints. Record the final executable charging power of each node to form a collaborative charging power allocation table for the entire residential area network; The collaborative charging power allocation table is output for actual charging execution, achieving stable node voltage, balanced feeder load, and coordinated charging under dynamic load capacity constraints.

[0064] In this embodiment, the cluster-level power constraint information refers to the maximum available power value of each load cluster calculated in the preceding steps based on the node-level charging power control commands, feeder carrying capacity, node voltage constraints, and historical load peak values. This information includes the power upper limit of each cluster and the relevant constraint parameters of each member node within the cluster, used to guide the allocation of EV charging power within the cluster.

[0065] In this embodiment, the intra-cluster priority list refers to a list formed by sorting the EV nodes within each load cluster according to their off-grid time, remaining rechargeable capacity, and preset priority. This list clarifies the order of EV power allocation within the cluster, ensuring that charging power is allocated sequentially according to priority, guaranteeing that time-sensitive or capacity-constrained EVs receive priority charging.

[0066] In this embodiment, the intra-cluster EV power allocation table refers to the result table after EV charging power allocation is completed according to intra-cluster priority within the cluster-level power constraint. This table records the final allocated charging power value of each EV in each load cluster, as well as the remaining available power status of the cluster, ensuring that the total power does not exceed the cluster-level constraint.

[0067] The node voltage-power nonlinear model in this embodiment refers to a nonlinear relationship model used to predict the changes in node voltage and feeder load as EV charging power changes. This model considers the complex interactions of voltage drop, current flow, and feeder impedance in the distribution network, and can accurately reflect the impact of EV charging power adjustments on node voltage and feeder load.

[0068] In this embodiment, the cluster-level execution instruction refers to the set of instructions that, after simulation checks and gradual power adjustments, record the final executable charging power of all EVs within each load cluster. This instruction ensures that all node voltages are within allowable ranges, feeder loads do not exceed carrying capacity, and retains priority relationship information within the cluster.

[0069] It should be noted that the above description of power allocation from the cluster priority list did not explicitly explain the handling mechanism when high-priority EVs receive too much power, leaving low-priority EVs unable to receive any. In this embodiment, by updating the remaining available power of the cluster in real time and iteratively checking, it is ensured that low-priority EVs receive a proportional reduction in power allocation when available power is insufficient, while high-priority EVs still receive full allocation, thereby achieving power coordination and fairness within the cluster.

[0070] Example 2, Figure 2 This invention presents a dynamic load-bearing capacity-based charging power coordination control system for existing distribution networks, comprising a charging power control command generation module, a cluster-level execution command generation module, and a charging coordination module. The charging power control command generation module is used to construct a rolling optimization model based on the real-time status and nonlinear voltage-power characteristics of the end nodes in residential areas, dynamically calculate the feeder power carrying capacity, and generate node-level charging power control commands. The cluster-level execution command generation module uses the node-level charging power control commands as cluster-level optimization inputs, dynamically groups end nodes into load clusters according to load characteristics and physical location, calculates cluster-level power constraints, and performs priority charging allocation and power adjustment within the cluster-level range to generate cluster-level execution commands. The charging coordination module, through the coordinated application of node-level charging power control commands and cluster-level execution commands, achieves node voltage stability, feeder load balancing, and charging coordination under dynamic load-bearing capacity constraints under conditions of concentrated EV charging at night in residential areas.

[0071] It should be noted that the above text does not specifically explain how to ensure the iterative convergence of simulation calculations and power adjustments. In this embodiment, after each power adjustment, the node voltage and feeder load are immediately recalculated and compared with the constraint values. This cycle is repeated until all node voltages and feeder loads meet the constraint conditions, ensuring that cluster-level execution instructions can be directly executed in practical applications without causing local overruns.

[0072] In the embodiments provided by this invention, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0073] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0074] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0075] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0076] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0077] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0078] In the embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0079] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0080] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity, characterized in that, Includes the following steps: A rolling optimization model is constructed based on the real-time status and nonlinear voltage-power characteristics of the end nodes in the residential area. The power carrying capacity of the feeder is dynamically calculated and node-level charging power control commands are generated. The node-level charging power control command is used as the cluster-level optimization input. The end nodes are dynamically grouped into load clusters according to load characteristics and physical location. Cluster-level power constraints are calculated, and priority charging allocation and power adjustment are performed within the cluster-level range to form cluster-level execution commands. By coordinating the application of node-level charging power control commands and cluster-level execution commands, node voltage stability, feeder load balancing, and charging coordination under dynamic load capacity constraints are achieved under the condition of centralized EV charging at night in residential areas.

2. The method for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity according to claim 1, characterized in that, The rolling optimization model, constructed based on the real-time status and nonlinear voltage-power characteristics of the end nodes in the residential area, dynamically calculates the power carrying capacity of the feeder and generates node-level charging power control commands, specifically: Collect the voltage, current, and EV charging status of the end nodes in the residential area, and generate network status data; A rolling optimization model is constructed based on the generated network status data. The upper limit of the power that each feeder can carry is calculated by combining the nonlinear voltage-power characteristics of the end nodes, and the calculation results are used as the basis for power allocation. For each EV on a feeder, charging power is allocated based on the feeder's maximum power capacity and node voltage constraints, and node-level charging power control commands are generated.

3. The method for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity according to claim 2, characterized in that, The process involves using node-level charging power control commands as cluster-level optimization inputs, dynamically grouping end nodes into load clusters based on load characteristics and physical location, calculating cluster-level power constraints, and performing priority charging allocation and power adjustment within the cluster-level scope to generate cluster-level execution commands. Specifically: The node-level charging power control command is used as the cluster-level optimization input, and the end nodes are dynamically grouped into load clusters based on the physical feeder location of the nodes, historical load characteristics and EV plug-in probability, generating a cluster-level node list. The available power limit for each load cluster is calculated based on the cluster-level node list to obtain cluster-level power constraint information; Under cluster-level power constraint information, power is allocated to EVs in the cluster-level node list according to priority, and an intra-cluster EV power allocation table is generated. Adjustments are made to the allocations in the EV power allocation table within the cluster that may lead to local voltage or feeder load overload, and cluster-level execution instructions are generated.

4. The method for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity according to claim 3, characterized in that, The process of acquiring voltage, current, and EV charging status from the end nodes in the residential area and generating network status data specifically involves: The system acquires real-time voltage and current measurements at each end node of the low-voltage distribution network in the residential area, and collects charging status information of EVs at the access nodes, including charged capacity, charging power, and estimated off-grid time. The collected end-node voltage, current and EV charging status are formatted and converted into node state vectors. The initial network state matrix is ​​then generated according to the feeder order. Calculate the active and reactive power injection of each node based on the initial network state matrix, and generate a node power injection table; By combining the node power injection table with network topology information, a node voltage-power mapping relationship is established, generating network state data that can be used for the rolling optimization model.

5. The method for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity according to claim 4, characterized in that, For each EV on a feeder, charging power is allocated based on the feeder's maximum power capacity and node voltage constraints, and node-level charging power control commands are generated, specifically as follows: For each node in the preliminary EV power allocation table, perform node voltage simulation according to the network topology and feeder connection relationship, and calculate the voltage deviation value of each node; For nodes whose voltage deviates from the rated value by more than the allowable range, the charging power of the EVs belonging to them is reduced proportionally to no more than the power limit corresponding to the voltage constraint of that node, and the EV power allocation table is updated. Perform cumulative simulation of the feeder load to calculate the total charging power of all nodes on the feeder; For feeders whose total charging power exceeds the feeder's maximum capacity, the charging power of all EVs connected to the feeder will be reduced proportionally to make the total power equal to the feeder's maximum capacity, and the EV power allocation table will be updated. Repeat the above simulation and adjustment steps for node voltage and feeder load until all node voltages are within the allowable range and the total power of the feeder does not exceed the upper limit of the load capacity. Finally, the adjusted EV power allocation table is output to form the optimized node-level charging power control command.

6. The method for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity according to claim 5, characterized in that, The calculation of the available power limit for each load cluster based on the cluster-level node list yields cluster-level power constraint information, specifically as follows: Read the cluster-level node list and extract the node-level charging power control commands and feeder load information of the member nodes in each load cluster; The power of each member node in each load cluster is summed to calculate the total planned charging power of the cluster. The total planned charging power of the cluster is compared with the power carrying capacity of each feeder in the cluster, node voltage constraints, and historical load peaks to determine the upper limit of the available power of the cluster. Record the available power limit and member node information for each load cluster to form cluster-level power constraint information.

7. The method for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity according to claim 6, characterized in that, Under the cluster-level power constraint information, the EVs in the cluster-level node list are allocated power according to priority to generate an intra-cluster EV power allocation table, specifically as follows: Read the cluster-level node list and the corresponding cluster-level power constraint information; For each load cluster, the nodes are sorted according to the EV off-grid time, remaining rechargeable capacity and set priority of the member nodes to generate a cluster priority list; Based on cluster-level power constraint information, charging power is allocated sequentially to the highest priority EV to ensure that its power does not exceed the allocable range specified in the node-level charging power control instruction; Update the remaining available power of the cluster, and allocate the remaining power to the second highest priority EVs in the cluster in priority order. Repeat until all EVs in the cluster have been allocated or the remaining available power of the cluster is zero. Record the final allocated power of member nodes within each load cluster and generate an intra-cluster EV power allocation table; After completing the power allocation for all load clusters, output the complete EV power allocation table within the cluster.

8. The method for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity according to claim 7, characterized in that, The adjustment of the EV power allocation table within the cluster, which may lead to local voltage or feeder load overload, generates cluster-level execution instructions, specifically as follows: Read the complete intra-cluster EV power allocation table and corresponding cluster-level power constraint information; For each load cluster, the currently allocated EV charging power is simulated using a node voltage-power nonlinear model to calculate the predicted voltage and feeder load for each node. Identify nodes in the simulation results that exceed the node voltage limit or feeder power limit and their corresponding EV charging power allocation; According to the EV priority within the load cluster, the charging power is adjusted sequentially starting from the low priority node, reducing the allocated power to not exceed the node or feeder constraints, and the node voltage and feeder load calculated in the simulation are updated in real time. Repeat the adjustment until all node voltages and feeder loads meet the constraints. Record the final executable charging power of each EV in each load cluster and generate cluster-level execution instructions.

9. The method for coordinated control of charging power in existing distribution networks based on dynamic carrying capacity according to claim 8, characterized in that, The coordinated application of node-level charging power control commands and cluster-level execution commands specifically includes: Read node-level charging power control instructions and cluster-level execution instructions; For each end node, the node-level charging power control command is superimposed with the cluster-level execution command of the corresponding EV in the cluster to calculate the final charging power of the node in the current time slice. The node charging power is simulated using a node voltage-power nonlinear model and a feeder load model, and the predicted voltage and corresponding feeder load of each node are calculated. Nodes whose predicted voltage and feeder load exceed safety constraints are identified, and their final charging power is locally adjusted. The adjustment order is determined according to the priority of node-level and cluster-level instructions. Iteratively update the adjusted node power and re-simulate the voltage and feeder load until all node voltages and feeder loads meet the constraints. Record the final executable charging power of each node to form a collaborative charging power allocation table for the entire residential area network; The collaborative charging power allocation table is output for actual charging execution, achieving stable node voltage, balanced feeder load, and coordinated charging under dynamic load capacity constraints.

10. A system using the dynamic carrying capacity-based coordinated control method for charging power in existing distribution networks as described in any one of claims 1-9, characterized in that, It includes a charging power control instruction generation module, a cluster-level execution instruction generation module, and a charging coordination module; The charging power control command generation module is used to build a rolling optimization model based on the real-time status and nonlinear voltage-power characteristics of the end nodes in the residential area, dynamically calculate the power carrying capacity of the feeder, and generate node-level charging power control commands. The cluster-level execution instruction generation module is used to take the node-level charging power control instruction as the cluster-level optimization input, dynamically group the end nodes into load clusters according to load characteristics and physical location, calculate the cluster-level power constraints, and perform priority charging allocation and power adjustment within the cluster-level range to form cluster-level execution instructions. The charging coordination module is used to achieve node voltage stability, feeder load balancing, and charging coordination under dynamic load capacity constraints under the condition of centralized EV charging at night in residential areas through the coordinated application of node-level charging power control commands and cluster-level execution commands.

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