A method and apparatus for voltage control command distribution and correction

Abstract

The application provides a voltage control instruction distribution and correction method and device, wherein the method comprises the following steps: a voltage control system model of a distribution network dynamic cluster is established, and parameters are initialized to obtain real-time state data of each distributed power supply DER; based on an instruction distribution model of voltage sensitivity, reactive power distribution weight coefficients of each DER are calculated according to the real-time state data of each DER, and reactive power regulation instructions are distributed to each DER; a rolling optimization model is established, a voltage control objective function is taken as an optimization objective, and the reactive power regulation instructions distributed to each DER are optimized in combination with preset constraint conditions to obtain an optimal reactive power regulation instruction sequence of each DER; and the optimal reactive power regulation instruction sequence is issued through an MPC executor cooperative strategy. Through accurate calculation of voltage sensitivity coefficients of each node and reasonable design of reactive power distribution weights, the rationality and control accuracy of reactive power distribution can be significantly improved.

Description

Technical Field

[0001] This application belongs to the field of power grid control technology, and in particular relates to a method and apparatus for voltage control command allocation and correction. Background Technology

[0002] With the widespread integration of distributed generation (DER) into distribution networks, the output of renewable energy sources on the power generation side exhibits strong volatility and randomness, while the energy demand on the load side shows significant time-varying characteristics and a degree of randomness. In this context, the uncertainty of source and load factors leads to increased fluctuations in system frequency, and the uncertainty of DER output results in more complex system power flow and voltage distribution, potentially causing frequent voltage fluctuations or even exceeding limits within the system. This inevitably threatens the safe and stable operation of the power grid. While resources such as sources, storage, and loads within a distribution virtual power plant can be centrally controlled and coordinated to respond to grid dispatch commands, balancing the temporal and spatial differences in power between the power generation and load sides, many challenges remain in voltage and frequency control.

[0003] Existing voltage and frequency control systems mainly have the following problems: 1) Although existing voltage control algorithms can achieve basic voltage regulation, they are insufficient in handling communication latency and will show obvious limitations under high-proportion distributed resource access. 2) Existing methods are mostly applied to single controlled objects, and their application in multi-dimensional distributed resource coordination and control is relatively limited; 3) The uncertainty of power output from new energy sources such as photovoltaics will significantly affect control accuracy, further exacerbating the difficulty of system regulation; 4) Existing voltage control methods often fail to guarantee the stability and accuracy of the control system when faced with communication delays, seriously threatening the safe and stable operation of the power distribution network.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] The purpose of this application is to provide a voltage control command allocation and correction method and device, which can significantly improve the rationality of reactive power allocation and control accuracy, and can still maintain the continuity and stability of control under communication delay conditions.

[0006] This application provides a voltage control command allocation and correction method and apparatus, which are implemented as follows: A voltage control command allocation and correction method, the method comprising: Establish a voltage control system model for the dynamic distribution network cluster and initialize the parameters; Import the current operating status information of the system into the voltage control system model to obtain the real-time status data of each distributed power source (DER). The command allocation model based on voltage sensitivity calculates the reactive power allocation weight coefficient of each DER based on the real-time status data of each DER. Based on the reactive power allocation weighting coefficient of each DER, the current voltage deviation, and the control target, reactive power adjustment commands are assigned to each DER. A rolling optimization model is established, with the voltage control objective function as the optimization objective. Combined with preset constraints, the reactive power regulation commands for each DER are optimized to obtain the optimal reactive power regulation command sequence for each DER. The optimal reactive power regulation command sequence is issued through the MPC actuator coordination strategy.

[0007] In one implementation, the voltage control system model of the voltage regulating device connected to the i-th node of the system is represented as: ; in, This represents the voltage at the i-th node in steady state. , Let represent the quantities at position (i, j) in the sensitivity matrices SUP and SUQ, respectively. This represents the change in active power at the j-th node. This represents the change in reactive power at the j-th node. This indicates the number of DERs with reactive power regulation capabilities within the cluster.

[0008] In one implementation, the reactive power allocation weighting coefficient for each DER is calculated according to the following formula: ; in, Let i be the reactive power allocation weighting coefficient for the i-th DER. , These are the coefficients before each term. The reactive voltage sensitivity from network node i to the grid connection point. Let m be the normalized upper limit of reactive power regulation for the DER located at network node i, where m is the number of network nodes. ; in, This indicates the proportion of reactive power used to track external AVC commands. This represents the maximum reactive power regulation capacity of the adjustable reactive power device connected to network node i.

[0009] In one implementation, the objective function is expressed as: ; in, This represents the weighting matrix used to minimize the AGC tracking error. This represents the weighting matrix used to minimize adjustment costs. z m The controlled output of the system. ,in, G pv , G bess , G cp This is the weighted coefficient matrix. u m For the active power control quantities of each DER in the secondary frequency regulation auxiliary service; The preset constraints include: voltage constraints of network nodes and capacity constraints of voltage regulating equipment, wherein the capacity constraints of voltage regulating equipment include: reactive power output constraints of various distributed power sources and regulation rate constraints of various distributed power sources. The voltage constraint of the network node is expressed as: ; in, This represents the voltage amplitude of the i-th network node in real-time operation. This represents the lower voltage limit of network node i. This represents the upper voltage limit of network node i.

[0010] In one implementation, after issuing the optimal reactive power regulation command sequence through the MPC actuator coordination strategy, the method further includes: After executing the optimal reactive power regulation command sequence, the system's operating status is monitored in real time, and a Kalman filter is used to estimate the state of the monitoring data. When a voltage limit violation is detected in the distribution network, the voltage sensitivity matrix is ​​updated based on the real-time voltage sensitivity and the latency of the communication network, and the latency data is managed through a memory buffer.

[0011] In one implementation, the memory buffer stores M×N fields, wherein: ; in, τ represents the total amount of resources. max For the maximum allowable delay, Sampling time, The ratio of measurement samples performed for each Kalman filter operation.

[0012] In one implementation, the delay is determined according to the following formula: ; Where d is the delay amount, The sampling period is For the delay of the total actuator, The remaining fractional delay component of the total actuator delay.

[0013] In one implementation, the optimal reactive power regulation command sequence is issued through an MPC actuator coordination strategy, including: The MPC actuator dynamically determines the control input for the current execution through a cooperative strategy: Control inputs take effect immediately upon arrival of new data; If no new data is received at the expected time, cache instructions are executed iteratively along the predicted trajectory in the control time domain; When increased communication delay is detected, control continuity in the time domain is maintained based on the predicted trajectory.

[0014] A voltage control command distribution and correction device, comprising: A module is established to build a voltage control system model for a dynamic distribution network cluster and to initialize the parameters. The import module is used to import the current operating status information of the system into the voltage control system model in order to obtain the real-time status data of each distributed power source (DER). The calculation module is used for the voltage sensitivity-based instruction allocation model to calculate the reactive power allocation weight coefficient of each DER based on the real-time status data of each DER. The allocation module is used to allocate reactive power adjustment commands to each DER based on the reactive power allocation weighting coefficient, the current voltage deviation, and the control target. The optimization module is used to establish a rolling optimization model. Taking the voltage control objective function as the optimization objective, and combined with preset constraints, it optimizes the reactive power regulation commands assigned to each DER to obtain the optimal reactive power regulation command sequence for each DER. The distribution module is used to distribute the optimal reactive power regulation command sequence through the MPC executor coordination strategy.

[0015] An electronic device includes a processor and a memory for storing processor-executable instructions, wherein the processor, when executing the instructions, implements the steps of the method described above.

[0016] A computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0017] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0018] The voltage control command allocation and correction method provided in this application establishes a voltage control system model of a dynamic distribution network cluster and initializes the parameters; imports the current operating status information of the system into the voltage control system model to obtain the real-time status data of each distributed power source (DER); based on a voltage sensitivity-based command allocation model, calculates the reactive power allocation weight coefficient of each DER according to the real-time status data of each DER; allocates reactive power adjustment commands to each DER based on the reactive power allocation weight coefficient of each DER, the current voltage deviation, and the control target; establishes a rolling optimization model, using the voltage control objective function as the optimization objective and combining preset constraints, to optimize the reactive power adjustment command allocation for each DER, obtaining the optimal reactive power adjustment command sequence for each DER; and issues the optimal reactive power adjustment command sequence through an MPC actuator collaborative strategy. In other words, the voltage sensitivity-based command allocation strategy, by accurately calculating the voltage sensitivity coefficient of each node and rationally designing the reactive power allocation weight, can significantly improve the rationality and control accuracy of reactive power allocation, avoiding the poor control effect caused by average allocation in existing methods. Furthermore, by adopting an MPC actuator coordination strategy, the complete control input trajectory is transmitted to the resource side, which enables the continuity and stability of control to be maintained even under communication delay conditions. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of one embodiment of the voltage control command allocation and correction method provided in this application; Figure 2 This is a schematic diagram of the secondary frequency control method architecture for a distribution virtual power plant that considers network voltage security, provided in this application. Figure 3 This is a hardware structure block diagram of an electronic device for a voltage control command allocation and correction method provided in this application; Figure 4 This is a schematic diagram of the module structure of one embodiment of the voltage control command allocation and correction device provided in this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0022] This example presents a method for voltage control command allocation and correction. In a distribution network system, each voltage regulating device node is written into the control model, establishing a unified reactive power-voltage control model for various distributed power sources (which may include: AGC command allocation model and resource command allocation models). A secondary frequency regulation model and a unified reactive power-voltage control model are constructed. Furthermore, a voltage sensitivity analysis method is established, deriving the system sensitivity matrix based on a matrix, designing a PI control unit to track specified reactive power, defining the reactive power allocation weight coefficients for each adjustable DER, and comprehensively considering the adjustable capacity characteristics of resources and the impact of reactive power regulation on internal voltage. Further, based on the unified reactive power-voltage control model for each resource... The voltage control model establishes a discretized state-space model, designs an adaptive control law to handle model uncertainties caused by photovoltaic power fluctuations, and adopts a time delay sensing module based on Kalman filtering to handle measurement delay and execution delay. The control continuity is maintained through the MPC actuator coordination strategy. The distribution network scheduling model, main grid scheduling model and microgrid scheduling model are established. With the goal of minimizing intraday operating costs, multiple objectives such as power purchase cost, grid loss cost, curtailment cost and electric vehicle compensation cost are comprehensively considered. A complete constraint system is set, including thermal power unit operation constraints, distributed photovoltaic operation constraints, charging station constraints and tie line power constraints. The optimal power regulation command sequence of each DER is solved through rolling optimization.

[0023] Figure 1 This is a flowchart of one embodiment of the voltage control command allocation and correction method provided in this application. Although this application provides method operation steps or device structures as shown in the following embodiments or figures, more or fewer operation steps or module units may be included in the method or device based on conventional or non-inventive effort. In steps or structures where there is no logically necessary causal relationship, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure described in the embodiments and figures of this application. When the method or module structure is applied in actual devices or terminal products, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or figures (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed processing environment).

[0024] Specifically, such as Figure 1As shown, the voltage control command allocation and correction method described above may include the following steps: Step 101: Establish a voltage control system model for the distribution network dynamic cluster and initialize the parameters; Specifically, each voltage regulating device node can be written into the control model to establish a unified reactive power-voltage control model for various distributed power sources (which may include: AGC command allocation model and resource command allocation models), and construct a secondary frequency regulation model and a unified reactive power-voltage control model. The voltage control system model of the voltage regulating device connected to the i-th node of the system can be represented as: ; in, This represents the voltage at the i-th node in steady state. , Let represent the quantities at position (i, j) in the sensitivity matrices SUP and SUQ, respectively. This represents the change in active power at the j-th node. This represents the change in reactive power at the j-th node. This indicates the number of DERs with reactive power regulation capabilities within the cluster.

[0025] Step 102: Import the current operating status information of the system into the voltage control system model to obtain the real-time status data of each distributed power source (DER). Step 103: Based on the voltage sensitivity-based command allocation model, calculate the reactive power allocation weight coefficient of each DER according to the real-time status data of each DER. Specifically, the reactive power allocation weighting coefficient of each DER can be calculated according to the following formula; ; in, Let i be the reactive power allocation weighting coefficient for the i-th DER. , These are the coefficients before each term. The reactive voltage sensitivity from network node i to the grid connection point. Let m be the normalized upper limit of reactive power regulation for the DER located at network node i, where m is the number of network nodes. ; in, This represents the proportion of reactive power capacity used to track external AVC commands, defined as the percentage that needs to be reserved (1-) during the DLVPP execution of external reactive power command tracking. k AVC The reactive power capacity is used to cope with internal voltage fluctuations, in order to meet the real-time voltage control requirements for adjustable reactive power capacity. This represents the maximum reactive power regulation capacity of the adjustable reactive power device connected to network node i.

[0026] Step 104: Assign reactive power adjustment commands to each DER based on the reactive power allocation weighting coefficient, current voltage deviation, and control target; Step 105: Establish a rolling optimization model, take the voltage control objective function as the optimization objective, and combine the preset constraints to optimize the reactive power regulation command allocation for each DER, so as to obtain the optimal reactive power regulation command sequence for each DER. Step 106: Issue the optimal reactive power regulation command sequence through the MPC actuator collaborative strategy.

[0027] The objective function described above can be expressed as: ; in, This represents the weighting matrix used to minimize the AGC tracking error. This represents the weighting matrix used to minimize adjustment costs. z m The controlled output of the system. ,in, G pv , G bess , G cp This is the weighted coefficient matrix. u m For the active power control quantities of each DER in the secondary frequency regulation auxiliary service; The aforementioned preset constraints may include: voltage constraints of network nodes and capacity constraints of voltage regulating equipment. The capacity constraints of the voltage regulating equipment may include: reactive power output constraints of various distributed power sources and regulation rate constraints of various distributed power sources. Specifically, the voltage constraints of network nodes can be expressed as: ; in, This represents the voltage amplitude of the i-th network node in real-time operation. This represents the lower voltage limit of network node i. This represents the upper voltage limit of network node i.

[0028] After issuing the optimal reactive power regulation command sequence through the MPC actuator collaborative strategy, the system's operating status can be monitored in real time after the optimal reactive power regulation command sequence is executed. A Kalman filter is used to estimate the state of the monitoring data. When it is determined that the voltage exceeds the limit in the distribution network, the voltage sensitivity matrix is ​​updated based on the real-time sensed voltage sensitivity and the latency of the communication network, and the latency data is managed through a memory buffer.

[0029] The memory buffer can store M×N fields, where: ; in, τ represents the total amount of resources. max For the maximum allowable delay, Sampling time, The ratio of measurement samples performed for each Kalman filter operation.

[0030] In practical implementation, the delay can be determined using the following formula: ; Where d is the delay amount, The sampling period is For the delay of the total actuator, The remaining fractional delay component of the total actuator delay.

[0031] Specifically, the optimal reactive power regulation command sequence issued through the MPC actuator collaborative strategy can include: the MPC actuator dynamically decides the control input to be executed based on the collaborative strategy: 1) the control input takes effect immediately when new data arrives; 2) if no new data is received at the expected time, cached instructions are iteratively executed along the predicted trajectory in the control time domain; 3) when communication delay is detected to be worsening, control continuity in the time domain is maintained according to the predicted trajectory. That is, the MPC actuator collaborative strategy dynamically decides the control input ui to be executed currently. When new data arrives, the control input takes effect immediately; if no new data is received at the expected time t+Ts, cached instructions are iteratively executed along the predicted trajectory Hu in the control time domain; when communication delay worsens, the resource actuator can maintain control continuity in the Hu time domain according to the latest optimized trajectory.

[0032] Specifically, a voltage-sensitivity-based command allocation model is established, and reasonable power command allocation is achieved through reactive power allocation weighting coefficient design. A model predictive controller considering communication delay is designed, and an adaptive control law is used to handle the uncertainty of renewable energy output, improving control robustness. A Kalman filter-based delay sensing module is introduced, and the impact of communication delay is effectively handled through memory buffers and state estimation methods. An MPC actuator coordination strategy is adopted to maintain control continuity under communication delay conditions, achieving stable control of dynamic cluster voltage. A complete online correction mechanism is established, dynamically adjusting based on real-time sensed voltage sensitivity, thereby ensuring the safe and stable operation of the system.

[0033] The above method will be described below with reference to a specific embodiment. However, it should be noted that this specific embodiment is only for better illustration of this application and does not constitute an improper limitation of this application.

[0034] While existing voltage regulation methods based on traditional PI control are simple in structure and easy to implement, they are extremely sensitive to communication delays. When there is a significant communication delay, control oscillations or even instability can easily occur, and their control accuracy is limited, making it difficult to meet the requirements of high-precision voltage regulation. Voltage regulation strategies based on distributed control, by distributing control decisions to various nodes, can effectively reduce dependence on communication networks and avoid communication delay problems to some extent. However, this method lacks global optimization capabilities, and the lack of effective coordination between nodes makes it difficult to achieve optimal control at the system level, resulting in poor performance under complex power grid operating conditions. Robust control methods, by designing controllers with certain anti-interference capabilities, can resist the effects of communication delays and system uncertainties to a certain extent. However, this method is usually highly conservative, often sacrificing control performance to ensure system stability, resulting in insufficient economy, and still has limitations when dealing with large time delay variations. Based on this, a superior MPC control method is proposed in this example. Combining Kalman filtering delay sensing technology and adaptive control law design, it not only ensures control accuracy but also has good delay adaptability and robustness. It can achieve stable and reliable voltage control under the condition of dynamic changes in communication delay, thus achieving better control effect.

[0035] This example presents a method for allocating and online correcting dynamic cluster voltage control commands for distribution networks that takes into account communication delays. This method enables collaborative optimization control of distributed resources in distribution networks under communication delay conditions. While ensuring the safe and stable operation of the system, it improves frequency regulation capability and voltage control accuracy. This is a dynamic cluster voltage and frequency control scheme suitable for distribution networks with a high proportion of distributed resources connected to the network.

[0036] Specifically, a dynamic cluster voltage control system model for the distribution network can be established first. Each voltage regulating device node in the distribution network system is written into the control model, and a unified reactive power-voltage control model for various distributed power sources is established, including an AGC command allocation model and command allocation models for various resources. Then, a command allocation model based on voltage sensitivity is established. Reactive power allocation strategies are designed through voltage sensitivity analysis and the AGC command allocation model to reduce the impact of reactive power regulation on the voltage within the distribution network. Furthermore, a model predictive controller considering communication delay is established. A discretized state-space model is established based on the unified reactive power-voltage control model for each resource. An adaptive control law is designed to handle model uncertainties caused by photovoltaic power fluctuations, and a Kalman filter-based delay sensing module is used to handle measurement and execution delays. Finally, objective functions and constraints are designed. A dual objective function is designed for secondary frequency regulation involving the cluster, and a frequency regulation economic benefit model is established, comprehensively considering network node voltage constraints, various DER output constraints, and system operation constraints.

[0037] like Figure 2 The diagram illustrates a framework for a dynamic cluster voltage control command allocation and online correction method for distribution networks, taking into account communication latency. This diagram only shows the logical sequence of the method described in this embodiment. In other possible embodiments of the invention, different methods may be used, provided they do not conflict. Figure 2 The steps shown or described are completed in the indicated sequence. The architecture of the above-mentioned distribution virtual power plant secondary frequency control method considering network voltage safety may include: a secondary frequency regulation control module, adaptive control law design, a delay sensing module, and a model predictive controller. The secondary frequency regulation control module may include: AGC allocation, AVC allocation, a secondary frequency module, and a reactive power-voltage model; the adaptive control law design may include: feasible parameter set, parameter set prediction, and online parameter set update; the delay sensing module may include: a Kalman filter, prediction model improvement, and MPC actuator cooperative strategy; the model predictive controller may include: a system prediction model, objective function design, constraints, and optimization solution. Figure 2 In Chinese, DLVPP stands for Distribution-Level Virtual Power Plant. The active power regulation command issued by the upper-level power grid to the DLVPP is used for objective function design (as a tracking target, the optimization objective is to minimize the tracking error). This represents the electrical state of the energy storage system, indicating the proportion of currently stored electrical energy to the total capacity. This represents the voltage deviation or voltage regulation target value of the i-th network node, used in formulas for voltage control models, objective functions, and voltage constraints. For example... Figure 2 As shown in the example, the method for allocating and online correcting dynamic cluster voltage control commands for distribution networks, taking into account communication delays, may include the following steps: Step 1: Establish a dynamic cluster voltage control system model for the distribution network and construct a unified reactive power-voltage control model for various distributed power sources, including: an AGC command allocation model and a secondary frequency regulation model for various resources; Step 2: Establish a command allocation model based on voltage sensitivity, and reduce the impact of reactive power regulation on the voltage inside the distribution network by using voltage sensitivity analysis methods and reactive power allocation weight coefficient design. Step 3: Establish a model predictive controller that takes into account communication delay, and design an adaptive control law and a delay sensing module based on Kalman filtering to handle photovoltaic power fluctuations and communication delays. Step 4: Establish a multi-objective optimization control strategy and design an online correction mechanism. Solve for the optimal power regulation command sequence of each DER to achieve precise control and online correction of the dynamic cluster voltage of the distribution network.

[0038] Specifically, the provided method for allocating and online correcting dynamic trunking voltage control commands for distribution networks while considering communication delays may include the following steps: Step S1: Establish a dynamic cluster voltage control system model for the distribution network. In the distribution network system, each voltage regulating device node is written into the control model to establish a unified reactive power-voltage control model for various distributed power sources: The dynamic voltage control model for the voltage regulating device at node i in the connected system can be expressed as: ; in, This represents the voltage at the i-th node in steady state. , Let represent the quantities at position (i, j) in the sensitivity matrices SUP and SUQ, respectively. This represents the change in active power at the j-th node. This represents the change in reactive power at the j-th node. This indicates the number of DERs with reactive power regulation capabilities within the cluster.

[0039] Step S2: Establish a command allocation model based on voltage sensitivity: Voltage sensitivity analysis: To reduce the impact of reactive power regulation on the voltage within the distribution network, a voltage sensitivity analysis method is established. This method may include: The relationship between the voltage amplitude change matrix ΔU, the voltage phase angle change matrix Δθ, and the active and reactive power change matrices ΔP and ΔQ in the system is expressed in matrix form as follows: ; Where H, N, J, and L are coefficient matrices in the matrix, the system sensitivity matrix is ​​then derived through matrix inversion: ; in, , , and This is the sensitivity coefficient matrix.

[0040] Reactive power allocation strategy design: Define the reactive power distribution weighting coefficient of the adjustable DER connected to node i. for: ; in, , These are the coefficients for each item, which can be selected according to requirements; The reactive-voltage sensitivity from network node i to the grid connection point; This is the normalized upper limit of reactive power regulation for the adjustable DER located at node i. Where: ; in, This represents the maximum reactive power regulation capacity of the adjustable reactive power device connected to node i.

[0041] Finally, set the reactive power distribution coefficient for each adjustable reactive power device. : ; Step S3: Establish a model predictive controller that considers communication delay: Establish a predictive model: Based on the unified reactive power-voltage control model for each resource, this model is discretized to derive the overall discretized state-space model for distribution network voltage control: ; in, For the cluster participating in secondary frequency modulation control, the overall state variable is... ) represents the system control variable. For system disturbance variables, For the system's control output variables, Output variables as constraints for the system. , , , , These are various parameter matrices.

[0042] Design an adaptive control law: Adaptive control is achieved by progressively updating the set of feasible parameters with uncertainties: at the initial time t=0, the uncertain power disturbance of the photovoltaic is defined as... At the same time, assume Its feasible initialization parameter set is: Θ0= : ; Among them, H θ The matrix represents the inequality constraint matrix, h. θ This is the constraint vector.

[0043] Delay sensing module based on Kalman filtering: A state estimation method based on a Kalman filter is adopted, which is implemented through a memory buffer containing M×N fields: ; in, The total quantity of resources; prior to t-τ in time sequence. max The measured value will be discarded from the estimate, i.e., τmax The maximum allowable delay; parameter The ratio of measurement samples performed by the Kalman filter each time can be determined by the sampling time. express.

[0044] Handling execution delays: Using an improved prediction model and an MPC executor coordination strategy, the executor latency is defined as: ; When the delay d is the sampling period The integer multiple of τ'a, where τ'a is the total actuator delay. The remaining fraction of the delayed component.

[0045] MPC executor coordination strategy: The MPC actuator's collaborative strategy dynamically determines the control input ui to be executed. When new data arrives, the control input takes effect immediately. If no new data is received at the expected time t+Ts, the cached instructions are executed iteratively along the predicted trajectory Hu in the control time domain. When communication delays worsen, the resource actuator can maintain control continuity in the Hu time domain based on the latest optimized trajectory.

[0046] Step S4: Optimization model and solution process for dynamic cluster voltage control in distribution network: 1. Design the objective function: Considering the objective function of voltage control, voltage constraints are set for network nodes to ensure the system operates within a safe range. Capacity constraints for voltage regulation equipment are also set, including reactive power output constraints and regulation rate constraints for various distributed power sources. The objective function considering voltage control is expressed as: ; 2. Set constraints: Set network node voltage constraints: ; To ensure that the system operates within a safe range.

[0047] Furthermore, the constraints can also include: voltage regulation equipment capacity constraints, including reactive power output constraints and regulation rate constraints for various distributed power sources; and system operation constraints to ensure the internal voltage safety of the distribution network system during regulation.

[0048] To achieve the dynamic cluster voltage control objective of the distribution network considering communication delay, a complete solution process is formed based on an improved model predictive control method, which may include: S1: System Initialization. Establish the dynamic cluster voltage control system model for the distribution network, and initialize the MPC controller parameters, Kalman filter parameters, memory buffer, and adaptive parameter set.

[0049] S2: Status Information Acquisition. Import the current operating status information of the system, obtain real-time status data of each DER, assess the communication network latency, and update the voltage sensitivity matrix.

[0050] S3: Command Allocation Calculation. Based on voltage sensitivity analysis, the reactive power allocation weight coefficient of each adjustable DER is calculated, and reactive power adjustment commands are allocated to each DER according to the current voltage deviation and control target.

[0051] S4: Delay-aware processing. A Kalman filter is used to estimate the state of the measurement data, and a memory buffer is used to manage delay data and update the system state prediction.

[0052] S5: MPC Optimization Solution. A rolling optimization model is established, using the voltage control objective function as the optimization objective, considering various constraints, to solve for the optimal reactive power regulation command sequence for each DER.

[0053] S6: Control command issuance. Control command sequences are issued through the MPC actuator coordination strategy. When new data arrives, the control input takes effect immediately, and when communication delays increase, control continuity is maintained based on the predicted trajectory.

[0054] S7: Online Correction Feedback. The system's operating status is monitored in real time. When voltage exceeds limits in the distribution network, online correction is performed based on the real-time voltage sensitivity, control parameters are updated, and the process returns to step S2 to execute a new round of optimization.

[0055] In the example above, addressing the voltage control issues arising from large-scale distributed resource access in the distribution network, a dynamic cluster voltage control method considering communication delay was established, constructing a complete voltage control system. This system achieves coordinated control across different stages, from command allocation to online correction. A command allocation strategy based on voltage sensitivity, by accurately calculating the voltage sensitivity coefficients of each node and rationally designing reactive power allocation weights, significantly improves the rationality and control accuracy of reactive power allocation, avoiding the poor control performance caused by average allocation in traditional methods. Regarding delay handling capabilities, a delay-aware mechanism based on Kalman filtering, using a memory buffer to store historical data and state estimation methods, effectively addresses the negative impact of communication delay on control performance, resolving the control instability problem caused by communication delay in traditional centralized control systems. Simultaneously, an MPC actuator coordination strategy is adopted to transmit the complete control input trajectory to the resource side, maintaining control continuity and stability even under communication delay conditions. Control inputs take effect immediately upon arrival of new data, and control continuity is maintained based on the latest optimized trajectory when communication delay worsens. This ensures the safe and stable operation of the distribution network and achieves the goal of precise voltage regulation and optimized control under high-proportion distributed resource access.

[0056] In the example above, a unified reactive power and voltage control model was used to achieve coordinated control of various heterogeneous distributed resources, including photovoltaics, energy storage, charging piles, and distributed wind turbines, fully leveraging the complementary advantages of different resource types. Adaptive control law design enabled real-time adaptation to changes in photovoltaic output, reducing AGC command tracking error by 64.98% in photovoltaic power fluctuation scenarios, significantly improving control accuracy. A Kalman filter-based delay sensing module and MPC actuator coordination strategy effectively addressed communication delay issues, maintaining control continuity within a time-varying communication delay range of 230ms-350ms. While ensuring frequency regulation performance, the system also considered voltage safety within the distribution network. Coordinated reactive power and voltage control stabilized the voltage of critical nodes within a safe range of 0.95-1.05 pu, effectively suppressing voltage exceedances and achieving economic optimization. The total system cost increase during frequency regulation was reduced by approximately 28% compared to a fully capacity-based allocation scheme and by approximately 17% compared to a scheme without considering network loss constraints, significantly improving economic efficiency.

[0057] The methods and embodiments provided in the above-described embodiments of this application can be executed in a mobile terminal, computer terminal, or similar computing device. Taking operation on an electronic device as an example... Figure 3 This is a hardware structure block diagram of an electronic device for a voltage control command allocation and correction method provided in this application. Figure 3 As shown, the electronic device 10 may include one or more (only one is shown in the figure) processors 02 (processors 02 may include, but are not limited to, processing devices such as microprocessors (MCUs) or programmable logic devices (FPGAs), a memory 04 for storing data, and a transmission module 06 for communication functions. Those skilled in the art will understand that... Figure 3 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, electronic device 10 may also include... Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown.

[0058] The memory 04 can be used to store software programs and modules of application software, such as the program instructions / modules corresponding to the voltage control instruction allocation and correction method in this embodiment. The processor 02 executes various functional applications and data processing by running the software programs and modules stored in the memory 04, that is, implementing the voltage control instruction allocation and correction method of the application described above. The memory 04 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 04 may further include memory remotely located relative to the processor 02, and these remote memories can be connected to the electronic device 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0059] The transmission module 06 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the electronic device 10. In one example, the transmission module 06 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission module 06 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0060] At the software level, the aforementioned voltage control command allocation and correction device can be as follows: Figure 4 As shown, it includes: Module 401 is used to establish a voltage control system model for a dynamic distribution network cluster and initialize the parameters. Import module 402 is used to import the current operating status information of the system into the voltage control system model in order to obtain the real-time status data of each distributed power source (DER). The calculation module 403 is used for the voltage sensitivity-based instruction allocation model to calculate the reactive power allocation weight coefficient of each DER based on the real-time status data of each DER. The allocation module 404 is used to allocate reactive power adjustment commands to each DER based on the reactive power allocation weight coefficient, the current voltage deviation, and the control target. The optimization module 405 is used to establish a rolling optimization model. Taking the voltage control objective function as the optimization objective, and combining the preset constraints, it optimizes the reactive power regulation instructions allocated to each DER to obtain the optimal reactive power regulation instruction sequence for each DER. The issuing module 406 is used to issue the optimal reactive power regulation command sequence through the MPC actuator coordination strategy.

[0061] In one implementation, the voltage control system model of the voltage regulating device connected to the i-th node of the system can be represented as: ; in, This represents the voltage at the i-th node in steady state. , Let represent the quantities at position (i, j) in the sensitivity matrices SUP and SUQ, respectively. This represents the change in active power at the j-th node. This represents the change in reactive power at the j-th node. This indicates the number of DERs with reactive power regulation capabilities within the cluster.

[0062] In one implementation, the calculation module 403 can specifically calculate the reactive power allocation weighting coefficient of each DER according to the following formula: ; in, Let i be the reactive power allocation weighting coefficient for the i-th DER. , These are the coefficients before each term. The reactive voltage sensitivity from network node i to the grid connection point. Let m be the normalized upper limit of reactive power regulation for the DER located at network node i, where m is the number of network nodes. ; in, This indicates the proportion of reactive power used to track external AVC commands. This represents the maximum reactive power regulation capacity of the adjustable reactive power device connected to network node i.

[0063] In one implementation, the objective function is expressed as: ; in, This represents the weighting matrix used to minimize the AGC tracking error. This represents the weighting matrix used to minimize adjustment costs. z m The controlled output of the system. ,in, G pv , G bess , G cp This is the weighted coefficient matrix. u m For the active power control quantities of each DER in the secondary frequency regulation auxiliary service; The preset constraints include: voltage constraints of network nodes and capacity constraints of voltage regulating equipment, wherein the capacity constraints of voltage regulating equipment include: reactive power output constraints of various distributed power sources and regulation rate constraints of various distributed power sources. The voltage constraint of the network node is expressed as: ; in, This represents the voltage amplitude of the i-th network node in real-time operation. This represents the lower voltage limit of network node i. This represents the upper voltage limit of network node i.

[0064] In one implementation, after issuing the optimal reactive power regulation command sequence through the MPC actuator coordination strategy, the following may also be included: After executing the optimal reactive power regulation command sequence, the system's operating status is monitored in real time, and a Kalman filter is used to estimate the state of the monitoring data. When a voltage limit violation is detected in the distribution network, the voltage sensitivity matrix is ​​updated based on the real-time voltage sensitivity and the latency of the communication network, and the latency data is managed through a memory buffer.

[0065] In one implementation, the memory buffer stores M×N fields, wherein: ; in, τ represents the total amount of resources. max For the maximum allowable delay, Sampling time, The ratio of measurement samples performed for each Kalman filter operation.

[0066] In one implementation, the delay is determined according to the following formula: ; Where d is the delay amount, The sampling period is For the delay of the total actuator, The remaining fractional delay component of the total actuator delay.

[0067] In one implementation, the issuing module 406 can specifically control the issuance of the optimal reactive power regulation instruction sequence through the MPC actuator coordination strategy. The MPC actuator dynamically decides the control input to be executed at the current time through the coordination strategy: when new data arrives, the control input takes effect immediately; if no new data is received at the expected time, the cached instructions are executed iteratively along the predicted trajectory in the control time domain; when the communication delay is detected to be worsening, the control continuity in the time domain is maintained according to the predicted trajectory.

[0068] This application also provides a specific implementation of an electronic device capable of implementing all steps of the voltage control command allocation and correction method in the above embodiments. The electronic device specifically includes: a processor, a memory, a communication interface, and a bus; wherein the processor, memory, and communication interface communicate with each other via the bus; the processor is used to call a computer program in the memory, and when the processor executes the computer program, it implements all steps of the voltage control command allocation and correction method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: Step 1: Establish a voltage control system model for the distribution network dynamic cluster and initialize the parameters; Step 2: Import the current operating status information of the system into the voltage control system model to obtain the real-time status data of each distributed power source (DER). Step 3: Based on the voltage sensitivity-based command allocation model, calculate the reactive power allocation weighting coefficient of each DER according to the real-time status data of each DER; Step 4: Assign reactive power adjustment commands to each DER based on its reactive power allocation weighting coefficient, current voltage deviation, and control target; Step 5: Establish a rolling optimization model, take the voltage control objective function as the optimization objective, and combine the preset constraints to optimize the reactive power regulation command allocation for each DER, so as to obtain the optimal reactive power regulation command sequence for each DER. Step 6: Issue the optimal reactive power regulation command sequence through the MPC actuator collaborative strategy.

[0069] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the voltage control command allocation and correction method in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the voltage control command allocation and correction method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: Step 1: Establish a voltage control system model for the distribution network dynamic cluster and initialize the parameters; Step 2: Import the current operating status information of the system into the voltage control system model to obtain the real-time status data of each distributed power source (DER). Step 3: Based on the voltage sensitivity-based command allocation model, calculate the reactive power allocation weighting coefficient of each DER according to the real-time status data of each DER; Step 4: Assign reactive power adjustment commands to each DER based on its reactive power allocation weighting coefficient, current voltage deviation, and control target; Step 5: Establish a rolling optimization model, take the voltage control objective function as the optimization objective, and combine the preset constraints to optimize the reactive power regulation command allocation for each DER, so as to obtain the optimal reactive power regulation command sequence for each DER. Step 6: Issue the optimal reactive power regulation command sequence through the MPC actuator collaborative strategy.

[0070] As described above, this application embodiment establishes a voltage control system model for a dynamic distribution network cluster and initializes the parameters; imports the current operating status information of the system into the voltage control system model to obtain the real-time status data of each distributed generation (DER); based on a voltage sensitivity-based instruction allocation model, calculates the reactive power allocation weight coefficient of each DER according to the real-time status data of each DER; allocates reactive power adjustment instructions to each DER based on the reactive power allocation weight coefficient of each DER, the current voltage deviation, and the control objective; establishes a rolling optimization model, using the voltage control objective function as the optimization objective, and optimizes the reactive power adjustment instructions allocated to each DER in combination with preset constraints to obtain the optimal reactive power adjustment instruction sequence for each DER; and issues the optimal reactive power adjustment instruction sequence through an MPC actuator collaborative strategy. In other words, the voltage sensitivity-based instruction allocation strategy, by accurately calculating the voltage sensitivity coefficient of each node and rationally designing the reactive power allocation weight, can significantly improve the rationality and control accuracy of reactive power allocation, avoiding the problem of poor control effect caused by average allocation in existing methods. Furthermore, by adopting an MPC actuator coordination strategy, the complete control input trajectory is transmitted to the resource side, which enables the continuity and stability of control to be maintained even under communication delay conditions.

[0071] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, hardware + program embodiments are relatively simple in description because they are fundamentally similar to method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0072] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0073] While this application provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0074] While this specification provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual device or end product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, product, 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, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in the process, method, product, or apparatus that includes said elements is not excluded.

[0075] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing the embodiments of this specification, the functions of each module can be implemented in one or more software and / or hardware components, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0076] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0077] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0080] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0081] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0082] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0083] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of computer program products implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] The embodiments described in this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. The embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0085] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, system embodiments are basically similar to method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. In the description of this specification, the terms "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 embodiments in this specification. 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 can 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 and the features of different embodiments or examples.

[0086] The above description is merely an embodiment of the present specification and is not intended to limit the embodiments of the present specification. For those skilled in the art, various modifications and variations can be made to the embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of the present specification should be included within the scope of the claims of the embodiments of the present specification.

Claims

1. A method for voltage control command allocation and correction, characterized in that, The method includes: Establish a voltage control system model for the dynamic distribution network cluster and initialize the parameters; Import the current operating status information of the system into the voltage control system model to obtain the real-time status data of each distributed power source (DER). The command allocation model based on voltage sensitivity calculates the reactive power allocation weight coefficient of each DER based on the real-time status data of each DER. Based on the reactive power allocation weighting coefficient of each DER, the current voltage deviation, and the control target, reactive power adjustment commands are assigned to each DER. A rolling optimization model is established, with the voltage control objective function as the optimization objective. Combined with preset constraints, the reactive power regulation commands for each DER are optimized to obtain the optimal reactive power regulation command sequence for each DER. The optimal reactive power regulation command sequence is issued through the MPC actuator coordination strategy.

2. The method according to claim 1, characterized in that, The voltage control system model of the voltage regulating device at node i in the access system is represented as follows: ； in, This represents the voltage at the i-th node in steady state. , Let represent the quantities at position (i, j) in the sensitivity matrices SUP and SUQ, respectively. This represents the change in active power at the j-th node. This represents the change in reactive power at the j-th node. This indicates the number of DERs with reactive power regulation capabilities within the cluster.

3. The method according to claim 1, characterized in that, The reactive power allocation weighting coefficient for each DER is calculated using the following formula: ； in, Let i be the reactive power allocation weighting coefficient for the i-th DER. , These are the coefficients before each term. The reactive voltage sensitivity from network node i to the grid connection point. Let m be the normalized upper limit of reactive power regulation for the DER located at network node i, where m is the number of network nodes. ； in, This indicates the proportion of reactive power used to track external AVC commands. This represents the maximum reactive power regulation capacity of the adjustable reactive power device connected to network node i.

4. The method according to claim 1, characterized in that, The objective function is expressed as: ； in, This represents the weighting matrix used to minimize the AGC tracking error. This represents the weighting matrix used to minimize adjustment costs. z m The controlled output of the system. ,in, G pv , G bess , G cp This is the weighted coefficient matrix. u m For the active power control quantities of each DER in the secondary frequency regulation auxiliary service; The preset constraints include: voltage constraints of network nodes and capacity constraints of voltage regulating equipment, wherein the capacity constraints of voltage regulating equipment include: reactive power output constraints of various distributed power sources and regulation rate constraints of various distributed power sources. The voltage constraint of the network node is expressed as: ； in, This represents the voltage amplitude of the i-th network node in real-time operation. This represents the lower voltage limit of network node i. This represents the upper voltage limit of network node i.

5. The method according to claim 1, characterized in that, After issuing the optimal reactive power regulation command sequence through the MPC actuator coordination strategy, the following is also included: After executing the optimal reactive power regulation command sequence, the system's operating status is monitored in real time, and a Kalman filter is used to estimate the state of the monitoring data. When a voltage limit violation is detected in the distribution network, the voltage sensitivity matrix is ​​updated based on the real-time voltage sensitivity and the latency of the communication network, and the latency data is managed through a memory buffer.

6. The method according to claim 5, characterized in that, The memory buffer stores M×N fields, where: ； in, τ represents the total amount of resources. max For the maximum allowable delay, Sampling time, The ratio of measurement samples performed for each Kalman filter operation.

7. The method according to claim 5, characterized in that, Determine the time delay using the following formula: ； Where d is the delay amount, The sampling period is For the delay of the total actuator, The remaining fractional delay component of the total actuator delay.

8. The method according to claim 1, characterized in that, The optimal reactive power regulation command sequence is issued through the MPC actuator coordination strategy, including: The MPC actuator dynamically determines the control input for the current execution through a cooperative strategy: Control inputs take effect immediately upon arrival of new data; If no new data is received at the expected time, cache instructions are executed iteratively along the predicted trajectory in the control time domain; When increased communication delay is detected, control continuity in the time domain is maintained based on the predicted trajectory.

9. A voltage control command allocation and correction device, characterized in that, include: A module is established to build a voltage control system model for a dynamic distribution network cluster and to initialize the parameters. The import module is used to import the current operating status information of the system into the voltage control system model in order to obtain the real-time status data of each distributed power source (DER). The calculation module is used for the voltage sensitivity-based instruction allocation model to calculate the reactive power allocation weight coefficient of each DER based on the real-time status data of each DER. The allocation module is used to allocate reactive power adjustment commands to each DER based on the reactive power allocation weighting coefficient, the current voltage deviation, and the control target. The optimization module is used to establish a rolling optimization model. Taking the voltage control objective function as the optimization objective, and combined with preset constraints, it optimizes the reactive power regulation commands assigned to each DER to obtain the optimal reactive power regulation command sequence for each DER. The distribution module is used to distribute the optimal reactive power regulation command sequence through the MPC executor coordination strategy.

10. An electronic device comprising a processor and a memory for storing processor-executable instructions, characterized in that, When the processor executes the instructions, it implements the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 8.

12. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 8.

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