Distributed voltage control method based on edge intelligent terminal

Abstract

The invention relates to a distributed voltage control method based on an edge intelligent terminal, and aims at solving the problems that in an existing distributed optimization control method, nodes can only depend on local measurement information and cannot sense the global state in real time, and voltage out-of-limit and local overload are likely to be caused. The method specifically comprises the steps that firstly, historical operation data of all nodes under the same feeder line are collected through a novel edge terminal, a time sequence correlation model of node load power is constructed, and the current power state and voltage regulation action of all the nodes are estimated; secondly, the edge terminal quickly estimates power changes and corresponding voltage responses of other nodes based on local real-time measurement data and a historical load curve and a calculation model; and finally, each node dynamically adjusts the voltage regulation and control strategy on the basis of accurately predicting the control actions of other nodes, an asynchronous coordination regulation and control mode is adopted, the nodes with different capacities operate in a staggered manner according to different control periods, and the problems of power oscillation and voltage out-of-limit caused by simultaneous regulation of multiple nodes are avoided.

Description

Technical Field

[0001] This invention relates to the field of voltage control technology for power system distribution networks, specifically a distributed voltage control method based on edge intelligent terminals. Background Technology

[0002] The large-scale integration of distributed photovoltaic (PV) power into the distribution network has caused severe overvoltage problems and bidirectional overloads, seriously impacting the operational safety of the distribution network. Coordinating and regulating various distributed resources within the distribution network, including distributed PV and energy storage devices, can effectively alleviate the problems caused by distributed power sources and improve the operational control level of the distribution network. This is also an inevitable requirement for the development of new power systems. However, the highly random and rapid power fluctuations of distributed power sources make it difficult to quickly adapt to these fluctuations through dispatch commands. Local regulation of distributed power sources can achieve second-level voltage control and quickly smooth voltage fluctuations, a technique that has gained widespread acceptance. However, controlling the voltage of numerous dispersed distributed resources in the distribution network solely through local voltage control can easily lead to power oscillations, further amplifying voltage exceedance problems and causing heavy overloads in distribution network branches.

[0003] However, these methods each have their shortcomings: centralized control requires sending field data to the master station for centralized calculation of voltage regulation commands, which involves communication delays, bandwidth pressure, and the risk of single-point failures, making it difficult to respond promptly to rapidly changing photovoltaic output; local droop control does not require communication, but each node operates independently, lacking coordination. When multiple photovoltaic inverters simultaneously use droop curve regulation, mutual interference may occur, leading to control oscillations or steady-state deviations, failing to guarantee that the entire network voltage remains within a safe range; distributed cooperative control algorithms (such as consensus algorithms based on multiple agents or distributed optimization algorithms) achieve coordination to some extent through neighborhood communication, but typically require frequent information exchange and iterative calculations between nodes, resulting in slow convergence speeds in large-scale node scenarios. Moreover, unstable communication may cause control oscillations or even failure to converge. Existing technologies struggle to simultaneously meet the requirements of rapid response, stable coordination, and low communication overhead. Therefore, a new voltage control method is urgently needed that can fully utilize the radial topology of the distribution network and historical operating data to autonomously achieve rapid and stable voltage regulation at the edge side, avoiding oscillation problems caused by multi-node interactive control, thereby ensuring the voltage safety and stability of the distribution network under high-penetration distributed power source access. Summary of the Invention

[0004] This invention addresses the shortcomings of existing technologies by providing a distributed voltage control method based on edge intelligent terminals.

[0005] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0006] A distributed voltage control method based on an edge intelligent terminal includes the following steps:

[0007] Step 1: Sensitivity modeling of the relationship between node injected power and voltage amplitude.

[0008] By utilizing the radial structure of the distribution network, and based on the topology information and branch impedance parameter matrix of the radial distribution network, a linearized sensitivity mapping relationship is constructed to quantify the impact of injected power at different nodes on the voltage of different nodes in the network.

[0009] In a radial distribution network, by modeling the sensitivity matrix, the voltage impact of any node injected with power on other nodes can be quantified based on topology information and branch impedance parameters.

[0010] For the linearized model under small perturbation conditions, the voltage and power changes at each node approximately satisfy a linear relationship:

[0011] Δv≈S P2V ΔP+S Q2V ΔQ;

[0012] In the formula, ΔP, ΔQ, and Δv represent the active power injected into nodes PQ of the distribution network, the reactive power injected into nodes, and the voltage magnitude of nodes, respectively; matrix S P2V S P2V These represent the sensitivity coefficients of the active power injected into the node and the reactive power injected into the node to the square of the node voltage amplitude, respectively. The state space uses the sensitivity of the square of the voltage amplitude.

[0013] Define a matrix A representing the upstream and downstream relationship coefficients of nodes in a radial distribution network. When the element in the i-th row and j-th column of the matrix is ​​A... ij When = 1, it indicates that node j is a downstream node of node i; by defining the upstream and downstream relationships of nodes, the sensitivity matrix S of the node is... P2V S Q2V It can be expressed as the following formula:

[0014]

[0015] In the formula, A T R represents the transpose of matrix A; d X d These represent diagonal matrices constructed with the resistance and reactance of the upstream branches of a node as diagonal elements in a radial distribution network.

[0016] Step 2: Establish a local voltage regulation mechanism based on node power correlation.

[0017] A distributed coordination and control method based on historical data from edge terminals and a local lightweight computing model:

[0018] 1) By utilizing a novel edge terminal to aggregate historical operating data from various edge terminals on the same feeder, a time-series correlation model of load power among nodes is constructed. The correlation coefficient of the load is then used to predict the power data of different nodes at the current moment, where the historical power data X of node i is... i Compared with the operating power data Y at the current operating moment i As shown below:

[0019]

[0020] In the formula, P T,i Q T,i These represent the historical active power load data for node i; t now Indicates the current time value, 1:t now-1 This represents the n historical data sections from the past at the current moment.

[0021] It also constructs historical power data X for all nodes collected locally, and current local power data Y for each node. i And the nonlinear state space Ψ(Y) of their product i The mapping relationship between the basic vector formed by X and Y, and the current operating power data Y of all nodes:

[0022] X = [X1, X2, ..., X] N ], Y = [Y1, Y2, ..., Y N ]

[0023] [X,Y i ,Ψ(Y i ·X)]→Y.

[0024] 2) The edge terminal estimates the source load power state of other nodes at the current moment by using local real-time measurement data and the historical load curve model, and further predicts the autonomous voltage control actions that each node may take; in the current voltage distribution situation, through...

[0025] v now =v last +Δv load

[0026] Δv load =[S P2V S Q2V ](YY last )

[0027] In the formula, v now v last These represent the estimated voltage measurement value at the current moment and the voltage data at the previous comprehensive measurement section, respectively; Δv load Indicates v now vlast The estimated difference between the two is calculated by comparing the estimated current operating power data Y with the operating power data Y of the previous measurable section. last Calculated.

[0028] 3) Based on accurate prediction of the response of other nodes, this node dynamically adjusts its own voltage control strategy or control curve to proactively avoid negative impacts on other nodes due to local regulation behavior, ensuring that the entire distribution network does not experience local voltage exceedances or exacerbated overload problems; by solving for the optimal droop control parameters of node i, the optimal distributed voltage regulation of all nodes is achieved. Its objective function has two levels: minimizing the droop control parameter adjustment amount and the amplitude of the droop control parameter itself compared to the previous cycle.

[0029]

[0030] In the formula, K represents the power-voltage droop control parameter for all nodes with edge terminals. i,last Kv represents the power-voltage droop parameter at a fully measurable section on node i. now This represents the power regulation amount at each node, v min v max These represent the upper and lower limits of the node voltage constraints, respectively; P max P min Q max Q min These represent the upper and lower limits of active power and reactive power for all adjustable resources, respectively.

[0031] By controlling the power-voltage droop parameters of each node, the overall network regulation is minimized to solve the overvoltage problem.

[0032] Step 3: Asynchronous scheduling and coordination.

[0033] The edge terminals of different nodes execute voltage control cycles asynchronously, and different control cycles are set according to the differences in the capacity of the distributed power supply connected to each node.

[0034] Furthermore: In step one, the node includes a substation busbar, a branch point, or a photovoltaic grid-connected node.

[0035] Furthermore, in step three, the edge terminals of different nodes execute voltage control cycles asynchronously, including: the timing of the regulation execution of each terminal is staggered and performed asynchronously.

[0036] In step three, setting different control cycles based on the differences in the distributed power supply capacity of each node includes:

[0037] For high-capacity nodes, reaching 100kW or more, the power adjustment range is large, and the impact on system operation is significant. The control cycle setting time is 10-20 seconds, which serves as the adjustment benchmark value for low-capacity nodes.

[0038] For small-capacity nodes, below 100kW, the control cycle setting time is 1-10 seconds to achieve rapid adjustment without significantly altering the system's operating state.

[0039] Small-capacity nodes perform small, fine-tuning adjustments at a higher frequency, while large-capacity nodes implement significant control actions at the end of a longer period.

[0040] This asynchronous time-sharing control mechanism ensures that the timing of actions of each node does not overlap for most of the time. While some nodes are in an active control state, others are in a passive observation and waiting state, avoiding power oscillations that may occur when multiple nodes perform control actions simultaneously. When large-capacity photovoltaic nodes reduce active power to control local voltage, small-capacity nodes, having already undergone multiple fine-tuning adjustments, do not need to react drastically. They only need to make minor compensation adjustments based on the new voltage level within their own control cycle. Furthermore, the asynchronous control strategy does not require strict clock synchronization between nodes; each edge terminal can operate autonomously according to its locally set cycle, simplifying system implementation complexity and improving the robustness of the control system. After a period of continuous asynchronous cyclic control, the voltage of each node gradually converges, thereby achieving safe and stable operation of the entire distribution network voltage.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0042] This invention proposes a distributed voltage control method based on edge intelligent terminals. By utilizing the electrical characteristics of the radial structure of the distribution network and data-driven sensitivity analysis, a distributed, autonomous, and rapid voltage management method is achieved. This method coordinates the voltage control of multiple photovoltaic grid-connected nodes without relying on centralized communication, avoiding the oscillation and delay problems of traditional methods, and significantly improving the voltage stability and operating efficiency of high-penetration distributed power sources connected to the distribution network. The above embodiments are only used to illustrate the technical solution of this invention. All equivalent transformations or substitutions made under the guidance of the concept of this invention are covered within the protection scope of this invention. Attached Figure Description

[0043] Figure 1 This is a typical radial topology diagram of the power distribution network in the embodiments of the present invention;

[0044] Figure 2 This is a flowchart of a distributed coordination control method based on historical data from edge terminals and a local lightweight computing model, as described in an embodiment of the present invention. Detailed Implementation

[0045] The present invention will now be described in conjunction with specific embodiments, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar components or components having the same or similar functions throughout.

[0046] The directional terms used in this invention, such as up, down, left, right, front, back, inside, outside, front, back, side, etc., are merely for reference to the accompanying drawings. The embodiments and directional terms used in the following description with reference to the accompanying drawings are exemplary and are only used to explain this invention, and should not be construed as limiting this invention. Furthermore, the various specific processes and materials provided in this invention are examples that those skilled in the art will recognize for the application of other processes and / or the use of other materials.

[0047] A distributed voltage control method based on edge intelligent terminals is applied to a distribution network with multiple distributed photovoltaic grid-connected points. Please refer to [link / reference]. Figure 1 , Figure 1 This is a typical radial topology diagram of the power distribution network in an embodiment of the present invention.

[0048] Figure 1 It includes several nodes and branch lines. Nodes can be substation busbars, branch points, or photovoltaic grid-connected nodes, etc.

[0049] A distributed voltage control method based on an edge intelligent terminal includes the following steps:

[0050] Step 1: Sensitivity modeling of the relationship between node injected power and voltage amplitude.

[0051] By utilizing the radial structure of the distribution network, and based on the topology information and branch impedance parameter matrix of the radial distribution network, a linearized sensitivity mapping relationship is constructed to quantify the impact of injected power at different nodes on the voltage of different nodes in the network.

[0052] In a radial distribution network, by modeling the sensitivity matrix, the voltage impact of any node injected with power on other nodes can be quantified based on topology information and branch impedance parameters.

[0053] For the linearized model under small perturbation conditions, the voltage and power changes at each node approximately satisfy a linear relationship:

[0054] Δv≈S P2V ΔP+S Q2V ΔQ;

[0055] In the formula, ΔP, ΔQ, and Δv represent the active power injected into nodes PQ of the distribution network, the reactive power injected into nodes, and the voltage magnitude of nodes, respectively; matrix S P2V S P2V These represent the sensitivity coefficients of the active power injected into the node and the reactive power injected into the node to the square of the node voltage amplitude, respectively. The state space uses the sensitivity of the square of the voltage amplitude.

[0056] Define a matrix A representing the upstream and downstream relationship coefficients of nodes in a radial distribution network. When the element in the i-th row and j-th column of the matrix is ​​A... ij When = 1, it indicates that node j is a downstream node of node i; by defining the upstream and downstream relationships of nodes, the sensitivity matrix S of the node is... P2V S Q2V It can be expressed as the following formula:

[0057]

[0058] In the formula, A T R represents the transpose of matrix A; d X d These represent diagonal matrices constructed with the resistance and reactance of the upstream branches of a node as diagonal elements in a radial distribution network.

[0059] Step 2: Establish a local voltage regulation mechanism based on node power correlation.

[0060] To address the issue that in distributed optimization control, nodes can only rely on local measurement information for autonomous decision-making, lacking real-time global information and thus struggling to predict the impact of local adjustments on other nodes, especially in distribution networks with numerous distributed autonomous adjustment devices, this blindness can easily lead to local voltage exceedances or severe overloads, and even deteriorate operational conditions. Therefore, a distributed coordinated control method based on historical data from edge terminals and a local lightweight computing model is established.

[0061] Please see Figure 2 , Figure 2 This is a flowchart of a distributed coordination control method based on historical data from edge terminals and a local lightweight computing model, as described in an embodiment of the present invention.

[0062] A distributed coordination and control method based on historical data from edge terminals and a local lightweight computing model:

[0063] 1) By utilizing a novel edge terminal to aggregate historical operating data from various edge terminals on the same feeder, a time-series correlation model of load power among nodes is constructed. The correlation coefficient of the load is then used to predict the power data of different nodes at the current moment, where the historical power data X of node i is... i Compared with the operating power data Y at the current operating moment iAs shown below:

[0064]

[0065] In the formula, P T,i Q T,i These represent the historical active power load data for node i; t now Indicates the current time value, 1:t now-1 This represents the n historical data sections from the past at the current moment.

[0066] It also constructs historical power data X for all nodes collected locally, and current local power data Y for each node. i And the nonlinear state space Ψ(Y) of their product i The mapping relationship between the basic vector formed by X and Y, and the current operating power data Y of all nodes:

[0067] X = [X1, X2, ..., X] N ], Y = [Y1, Y2, ..., Y N ]

[0068] [X,Y i ,Ψ(Y i ·X)]→Y.

[0069] 2) The edge terminal estimates the source load power state of other nodes at the current moment by using local real-time measurement data and the historical load curve model, and further predicts the autonomous voltage control actions that each node may take; in the current voltage distribution situation, through...

[0070] v now =v last +Δv load

[0071] Δv load =[S P2V S Q2V ](YY last )

[0072] In the formula, v now v last These represent the estimated voltage measurement value at the current moment and the voltage data at the previous comprehensive measurement section, respectively; Δv load Indicates v now v last The estimated difference between the two is calculated by comparing the estimated current operating power data Y with the operating power data Y of the previous measurable section. last Calculated.

[0073] 3) Based on accurate prediction of the response of other nodes, this node dynamically adjusts its own voltage control strategy or control curve to proactively avoid negative impacts on other nodes due to local regulation behavior, ensuring that the entire distribution network does not experience local voltage exceedances or exacerbated overload problems; by solving for the optimal droop control parameters of node i, the optimal distributed voltage regulation of all nodes is achieved. Its objective function has two levels: minimizing the droop control parameter adjustment amount and the amplitude of the droop control parameter itself compared to the previous cycle.

[0074]

[0075] In the formula, K represents the power-voltage droop control parameter for all nodes with edge terminals. i,last Kv represents the power-voltage droop parameter at a fully measured section on node i. now This represents the power regulation amount at each node, v min v max These represent the upper and lower limits of the node voltage constraints, respectively; P max P min Q max Q min These represent the upper and lower limits of active power and reactive power for all adjustable resources, respectively.

[0076] By controlling the power-voltage droop parameters of each node, the overall network regulation is minimized to solve the overvoltage problem.

[0077] Step 3: Asynchronous scheduling and coordination.

[0078] The edge terminals at different nodes execute voltage control cycles asynchronously, with the timing of each terminal's regulation execution staggered and asynchronous. Different control cycles are set based on the differences in the distributed power supply capacity connected to each node.

[0079] For large-capacity nodes, reaching 100 kW or more, such as photovoltaic power plants with a capacity of 100 kW or more, the adjustment power range is large and has a significant impact on the system operation status. The adjustment cycle is set to 10-20 seconds as the adjustment benchmark value for small-capacity nodes.

[0080] For small-capacity nodes, below 100 kW, such as rooftop photovoltaic systems with a capacity of tens of kilowatts, the control cycle can be set to 1-10 seconds to achieve rapid adjustment without significantly altering the system's operating status.

[0081] Small-capacity nodes perform small, fine-tuning adjustments at a higher frequency, while large-capacity nodes implement significant control actions at the end of a longer period.

[0082] This asynchronous time-sharing control mechanism ensures that the timing of actions of each node does not overlap for most of the time. While some nodes are in an active control state, others are in a passive observation and waiting state, avoiding power oscillations that may occur when multiple nodes perform control actions simultaneously. When large-capacity photovoltaic nodes reduce active power to control local voltage, small-capacity nodes, having already undergone multiple fine-tuning adjustments, do not need to react drastically. They only need to make minor compensation adjustments based on the new voltage level within their own control cycle. Furthermore, the asynchronous control strategy does not require strict clock synchronization between nodes; each edge terminal can operate autonomously according to its locally set cycle, simplifying system implementation complexity and improving the robustness of the control system. After a period of continuous asynchronous cyclic control, the voltage of each node gradually converges, thereby achieving safe and stable operation of the entire distribution network voltage.

[0083] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0084] This invention proposes a distributed voltage control method based on edge intelligent terminals. By utilizing the electrical characteristics of the radial structure of the distribution network and data-driven sensitivity analysis, a distributed, autonomous, and rapid voltage management method is achieved. This method coordinates the voltage control of multiple photovoltaic grid-connected nodes without relying on centralized communication, avoiding the oscillation and delay problems of traditional methods, and significantly improving the voltage stability and operating efficiency of high-penetration distributed power sources connected to the distribution network. The above embodiments are only used to illustrate the technical solution of this invention. All equivalent transformations or substitutions made under the guidance of the concept of this invention are covered within the protection scope of this invention.

[0085] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

[0086] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A distributed voltage control method based on edge intelligent terminals, characterized in that: Includes the following steps: Step 1: Sensitivity modeling of the relationship between node injected power and voltage amplitude; By utilizing the radial structure of the distribution network, and based on the topology information and branch impedance parameter matrix of the radial distribution network, a linearized sensitivity mapping relationship is constructed to quantify the impact of different node injected power on the voltage of different nodes in the network. In a radial distribution network, by modeling the sensitivity matrix, the voltage impact of any node injected with power on other nodes can be quantified based on topology information and branch impedance parameters. For the linearized model under small perturbation conditions, the voltage and power changes at each node approximately satisfy a linear relationship: Δv≈S P2V ΔP+S Q2V ΔQ; In the formula, ΔP, ΔQ, and Δv represent the active power injected into nodes PQ of the distribution network, the reactive power injected into nodes, and the voltage magnitude of nodes, respectively; matrix S P2V S P2V These represent the sensitivity coefficients of the active power injected into the node and the reactive power injected into the node to the square of the node voltage amplitude, respectively, and the state space of the sensitivity using the square of the voltage amplitude is used. Define a matrix A representing the upstream and downstream relationship coefficients of nodes in a radial distribution network. When the element in the i-th row and j-th column of the matrix is ​​A... ij When = 1, it indicates that node j is a downstream node of node i; by defining the upstream and downstream relationships of nodes, the sensitivity matrix S of the node is... P2V S Q2V It can be expressed as the following formula: In the formula, A T R represents the transpose of matrix A; d X d These represent diagonal matrices constructed with diagonal elements representing the resistance and reactance of the upstream branches of a node in a radial distribution network. Step 2: Establish a local voltage regulation mechanism based on node power correlation; A distributed coordination and control method based on historical data from edge terminals and a local lightweight computing model: 1) By utilizing a novel edge terminal to aggregate historical operating data from various edge terminals on the same feeder, a time-series correlation model of load power among nodes is constructed. The correlation coefficient of the load is then used to predict the power data of different nodes at the current moment, where the historical power data X of node i is... i Compared with the operating power data Y at the current operating moment i As shown below: In the formula, P T,i Q T,i These represent the historical active power load data for node i; t now Indicates the current time value, 1:t now-1 This represents the n historical data sections from the past moment. It also constructs historical power data X for all nodes collected locally, and current local power data Y for each node. i And the nonlinear state space Ψ(Y) of their product i The mapping relationship between the basic vector formed by X and Y, and the current operating power data Y of all nodes: X=[X1,X2,...,X N ],Y=[Y1,Y2,...,Y N ] [X,Y i ,Ϩ(Y i ·X)]→Y; 2) The edge terminal estimates the source load power state of other nodes at the current moment by using local real-time measurement data and the historical load curve model, and further predicts the autonomous voltage control actions that each node may take; in the current voltage distribution situation, through... v now =v last +Δv load Δv load =[S P2V ,S Q2V ](Y-Y last ) In the formula, v now v last These represent the estimated voltage measurement value at the current moment and the voltage data at the previous comprehensive measurement section, respectively; Δv load Indicates v now v last The estimated difference between the two is calculated by comparing the estimated current operating power data Y with the operating power data Y of the previous measurable section. last Calculated; 3) Based on accurate prediction of the response of other nodes, this node dynamically adjusts its own voltage control strategy or control curve to proactively avoid negative impacts on other nodes due to local regulation behavior, ensuring that the entire distribution network does not experience local voltage exceedances or exacerbated overload problems; by solving for the optimal droop control parameters of node i, the optimal distributed voltage regulation of all nodes is achieved. Its objective function has two levels: minimizing the droop control parameter adjustment amount and the amplitude of the droop control parameter itself compared to the previous cycle. In the formula, K represents the power-voltage droop control parameter for all nodes with edge terminals. i,last Kv represents the power-voltage droop parameter at a fully measurable section on node i. now This represents the power regulation amount at each node, v min v max These represent the upper and lower limits of the node voltage constraints, respectively; P max P min Q max Q min These represent the upper and lower limits of active power and reactive power for all adjustable resources, respectively. By controlling the power-voltage droop parameters of each node, the overall network regulation is minimized to solve the overvoltage problem. Step 3: Asynchronous scheduling and coordination; The edge terminals of different nodes execute voltage control cycles asynchronously, and different control cycles are set according to the differences in the capacity of the distributed power supply connected to each node.

2. The distributed voltage control method based on an edge intelligent terminal according to claim 1, characterized in that: In step one, the node includes a substation busbar, a branch point, or a photovoltaic grid-connected node.

3. The distributed voltage control method based on an edge intelligent terminal according to claim 1, characterized in that: In step three, the edge terminals of different nodes execute voltage control cycles asynchronously, including: the timing of the regulation execution of each terminal is staggered and performed asynchronously.

4. The distributed voltage control method based on an edge intelligent terminal according to claim 1, characterized in that: In step three, setting different control cycles based on the differences in the distributed power supply capacity of each node includes: For high-capacity nodes, reaching 100kW or more, the power adjustment range is large and has a significant impact on the system operating status. The control cycle setting time is 10-20 seconds, which serves as the adjustment benchmark value for low-capacity nodes. For small-capacity nodes, below 100kW, the control cycle setting time is 1-10 seconds to achieve rapid adjustment without significantly changing the system's operating state; Small-capacity nodes perform small, fine-tuning adjustments at a higher frequency, while large-capacity nodes implement significant control actions at the end of a longer period.

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