Power distribution network multi-time scale voltage regulation and control method adapting to topological change

Abstract

The invention provides a power distribution network multi-time scale voltage regulation and control method adapting to topological changes, and aims to solve many problems of traditional power distribution network voltage regulation and control. According to the method, firstly, key parameters of the power distribution network are monitored in real time, topological changes are recognized through a clustering analysis algorithm, and a node-branch model based on the graph theory is dynamically updated; secondly, designing long-time and short-time scale strategies, adjusting equipment based on load prediction and a power generation plan for a long time, and rapidly adjusting load and distributed power supply changes for a short time; and then sensitivity analysis is carried out, and a regulation and control strategy is optimized according to the influence weight of the equipment on the voltage. A distributed cooperative control framework is constructed, and a regional controller coordinates a photovoltaic inverter and an energy storage device according to a selected protocol and mechanism. Finally, deviation is calculated through real-time data collection, and a control strategy is adjusted in a self-adaptive mode. The method effectively deals with topological changes, integrates the advantages of multiple strategies, improves the voltage regulation and control precision, enhances the power supply reliability of the power distribution network and the distributed energy consumption capability, and guarantees the stable operation of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage regulation in distribution networks, and in particular to a multi-time-scale voltage regulation method for distribution networks that adapts to topology changes. Background Art

[0002] In the operation of traditional distribution networks, voltage regulation faces many challenges. With the widespread access of distributed power sources (such as photovoltaics, wind power, etc.) to the distribution network, the intermittent and uncertain output of distributed power sources has a great impact on voltage stability. Since the output power of distributed power sources will fluctuate greatly with natural factors such as light intensity and wind speed, it is difficult to maintain power balance in the distribution network, which in turn causes voltage deviation. For example, during periods of strong sunlight, the power of photovoltaic power generation rises sharply. If it cannot be adjusted in time, the voltage of the local power grid may rise beyond the allowable range.

[0003] At the same time, the topology of the distribution network is not static. In actual operation, the topology of the distribution network often changes due to line failures, equipment maintenance, or load migration. Traditional voltage control methods are mostly based on fixed power grid models, which are difficult to adapt to these topological changes quickly and accurately. When the topology changes, the power flow distribution and voltage distribution of the power grid will also change accordingly, but traditional methods cannot respond to these changes effectively in a timely manner, which can easily lead to problems such as voltage over-limit, affecting the safe and stable operation of the power system and the power quality of users.

[0004] In addition, existing voltage regulation strategies usually lack refined management of voltage fluctuations at different time scales. Power generation plans and equipment adjustments at long time scales often fail to fully consider the rapid load fluctuations and instant output changes of distributed power sources at short time scales, resulting in poor voltage regulation and failure to meet the strict requirements of modern power systems for power quality. Summary of the invention

[0005] In view of the above deficiencies in the prior art, the object of the present invention is to:

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0007] A multi-time scale voltage control method for a distribution network adapting to topology changes comprises the following steps:

[0008] S1. Detection of distribution network topology changes and updating of distribution network models. Real-time monitoring of key parameters of the distribution network. Use the key parameters as direct feedback of the distribution network operation status. Apply cluster analysis algorithm to process the key parameters. Identify topology changes in the distribution network through the processed key parameters. Dynamically update the distribution network model according to the topology changes.

[0009] S2. Multi-time scale voltage regulation strategy design. Based on the dynamically updated distribution network model, long-time scale and short-time scale voltage regulation strategies are designed. The long-time scale strategy focuses on equipment adjustment based on load forecasting and power generation plan, while the short-time scale strategy makes rapid adjustments to real-time load fluctuations and distributed power output changes.

[0010] S3. Sensitivity analysis and control strategy optimization. Based on the multi-time scale voltage control strategy designed in S2, sensitivity analysis is performed. The long-time scale and short-time scale voltage control strategies are optimized according to the results of sensitivity analysis. The optimization process considers the influence weight of the control equipment to achieve more accurate voltage control.

[0011] S4. Distributed collaborative control framework: Based on the voltage regulation strategy optimized in S3, a distributed collaborative control framework is constructed to achieve coordinated control of photovoltaic inverters and energy storage devices in each local control area through the distributed collaborative control framework;

[0012] S5. Real-time feedback and adaptive adjustment: collect voltage and power flow data of each control area in real time, and adaptively adjust the distributed collaborative control strategy in S4 based on the collected voltage and power flow data.

[0013] Furthermore, in S1, the method for detecting distribution network topology changes and updating distribution network models includes:

[0014] S11. Key parameter monitoring, key parameters include node voltage of the power grid, line power flow, etc. Key parameters are obtained by installing high-precision sensors on various key nodes and lines of the distribution network;

[0015] S12. Application of cluster analysis algorithm, using K-Means clustering algorithm to process key parameters, taking key parameter data as input, calculating according to the steps of K-Means algorithm, and updating cluster centers through continuous iteration to minimize the sum of squares of distances from key parameter values ​​to their cluster centers. Its objective function is:

[0016]

[0017] Among them, J is the total distance square sum, which is the objective function; xi is the key parameter value, uk is the center of the kth cluster; K is the number of clusters that is preset, and K is reasonably selected according to the scale and structural characteristics of the distribution network. For small distribution networks, the value is 3-5, for medium-sized distribution networks, the value is 5-8, and for large distribution networks, the value is 8-12;

[0018] S13. Identify topological changes in the distribution network. During the operation of the cluster analysis algorithm in S12, the cluster centers are continuously updated iteratively so that data points can be more reasonably divided into different clusters. When the topological structure of the distribution network changes, the data distribution of key parameters will change, and the changes in key parameters will be reflected in the clustering results, so that topological changes can be identified through cluster analysis results.

[0019] S14. The distribution network model is dynamically updated. A node-branch model based on graph theory is used to construct the distribution network model. In this model, nodes have properties such as node voltage and injected power; branches have properties such as resistance, reactance, and line power flow. When a topology change occurs, a branch is disconnected, and the connection relationship of the branch needs to be modified in the model, that is, the connection between the nodes related to the branch is marked as disconnected, and the attribute values ​​of the related nodes and branches are updated at the same time.

[0020] Furthermore, in S14, the method for constructing a distribution network model based on a node-branch model of graph theory includes:

[0021] S141. Node-branch association matrix construction: construct an m×n matrix to represent the connection relationship between nodes and branches; where m is the number of branches, n is the number of nodes, and the matrix element aij is defined as:

[0022] If branch j is connected to node i and the current direction is away from the node, then aij = 1;

[0023] If branch j is connected to node i and the current direction points to the node, then aij = -1;

[0024] If branch j is not connected to node i, then aij = 0;

[0025] S142. The node voltage equation is established. According to Kirchhoff's current law, for each node, the sum of the currents flowing into the node is equal to the sum of the currents flowing out of the node. The current is expressed by voltage and branch impedance.

[0026] The branch admittance connecting node and node is The voltage of node n is Vn, the voltage of node j is Vj, according to Ohm's law, the branch current Inj = Ynj (Vn-Vj);

[0027]

[0028] Since the current flowing into node n Equal to all branches connected to node n Substituting Inj=Ynj(Vn-Vj), where m is the number of branches connected to node n, we get:

[0029]

[0030] The injected current at node n Calculated from the injected power Pn and Qn and the node voltage Vn,

[0031] In steady-state conditions, according to KCL, The node voltage equation is obtained:

[0032]

[0033]

[0034] In actual calculations, for a distribution network with nodes, N node voltage equations are obtained. The voltage value of each node can be obtained by solving the equations simultaneously. The solution method uses numerical calculation methods such as Gauss-Seidel iteration method, and its iteration formula is:

[0035]

[0036] Where k represents the number of iterations; represents the voltage value of node n at the k+1th iteration, represents the voltage value of node j at the kth iteration; the iteration process ends when a certain convergence condition is met, such as when the absolute value of the node voltage difference between two adjacent iterations is less than the set threshold, and the node voltage value obtained at this time is the desired value;

[0037] S143. The power flow equation is established. For each branch, according to Ohm's law and the definition of power, the branch power flow equation is obtained:

[0038] The active power flow of the branch is:

[0039]

[0040] The reactive power flow of the branch is:

[0041]

[0042] Among them, Rij is the resistance of branch ij; gij is the conductance; bij is the susceptance; Vi is the voltage of node i;

[0043] Vj is the voltage at node j; θij is the phase difference between the voltages at node i and node j;

[0044] In actual calculations, for a distribution network with M branches, M branch power flow equations are obtained; by combining the node voltage equation and the branch power flow equation, the values ​​of the node voltage and branch power flow are continuously updated by calculation until the convergence conditions are met, thereby obtaining accurate distribution network operation status parameters.

[0045] Furthermore, in S2, the step of designing a multi-time scale voltage regulation strategy includes:

[0046] S21. Long-term strategy design:

[0047] Load forecasting uses the ARIMA model in the time series analysis method to forecast load. The formula is:

[0048]

[0049] Among them, p is the autoregressive order; q is the moving average order; q is the difference order; L is the lag operator; Yt is the time series; εt is the white noise series; is the autoregressive coefficient; θj is the moving average coefficient; θ0 is the constant term in the model; d is the difference order. In time series analysis, when the original time series is not stable, it is made stable through the difference operation;

[0050] Power generation plan and equipment adjustment, determining the active and reactive power of power generation equipment according to the power generation plan. The power generation plan is formulated based on factors such as the availability of energy resources, the operating requirements of the power grid, and the needs of the power market;

[0051] Compare the load demand and the output power of the power generation equipment. If the active load demand is greater than the active power generation of the power generation equipment, consider increasing the investment in adjustable equipment.

[0052] For the transformer tap, adjust the tap position reasonably according to the voltage deviation. The transformer ratio is k, the high-voltage side voltage is VH, and the low-voltage side voltage is VL. Change the voltage on the low-voltage side by adjusting k. If the voltage is too low, increase the transformer ratio k appropriately to increase the voltage on the low-voltage side.

[0053] For capacitor banks, capacitor banks of corresponding capacity are put into use according to the reactive power demand to provide reactive power support and improve voltage stability. The input capacity of the capacitor bank is calculated based on the reactive power deficit. If the reactive power deficit is ΔQ and the capacity of a single capacitor is C, then the number of capacitor banks required is

[0054] On the contrary, if the active load demand is less than the active power generation of the power generation equipment, the opposite operation is performed, and the equipment investment needs to be reduced or other adjustments need to be made;

[0055] S22. Short-term strategy: real-time monitoring of load fluctuations and changes in distributed generation output. The frequency of monitoring data collection should be high enough to accurately capture real-time changes.

[0056] When ΔPl(t)+ΔPg(t)>0, where ΔPl(t) represents the load power change at time t, and ΔPg(t) represents the power generation change at time t, the power is supplemented by controlling the rapid discharge of the energy storage device to maintain voltage stability;

[0057] When ΔPl(t)+ΔPg(t)<0, the energy storage device is controlled to charge or the output power of the distributed power source is adjusted.

[0058] Furthermore, in S3, the method of sensitivity analysis and control strategy optimization includes:

[0059] S31. Sensitivity analysis: For long-term strategies, calculate the sensitivity of equipment adjustment to voltage. Obtain the voltage change under different equipment adjustment amounts through actual measurement and data analysis. Perform multiple tests under different load levels and operating conditions. The calculation formula for the sensitivity of equipment adjustment to voltage is:

[0060]

[0061] Among them, SV-dev represents the sensitivity of device adjustment to voltage, ΔV represents the change in voltage; Δdev represents the device adjustment amount;

[0062] For the short-term strategy, the sensitivity of the energy storage device and distributed generation control to the voltage is calculated. For the energy storage device, the sensitivity of the energy storage device to the voltage SV-st is expressed as:

[0063]

[0064] ΔV′ is the voltage change caused by the charge and discharge operation of the energy storage device; ΔI is the current change;

[0065] For distributed power sources, the sensitivity of distributed power control to voltage SV-DG is expressed as:

[0066]

[0067] ΔV″ is the voltage change caused by power adjustment, and ΔP′ is the power adjustment amount;

[0068] S32. Control strategy optimization. When optimizing the long-term strategy, the influence weight of the control device is considered. The weight is determined based on the importance of the device to voltage regulation, response speed, adjustment range and other factors;

[0069] According to the sensitivity analysis results and weights, adjust the step size or decision rule of equipment adjustment. If the transformer tap adjustment is more sensitive to voltage and the transformer tap weight is also larger, it means that the transformer tap has a greater impact on voltage and is more sensitive. In this case, a smaller step size is used when adjusting the transformer tap. This can control the voltage more accurately and avoid excessive voltage fluctuations or instability caused by excessive adjustment.

[0070] If the transformer tap adjustment is less sensitive to voltage and the weight of the transformer tap is also small, a larger step size is used to improve the adjustment efficiency; for the adjustment of the capacitor bank, reasonable adjustments are also made according to its sensitivity and weight;

[0071] The calculation formula of step length is:

[0072]

[0073] Wherein, Δdevo is the initial step size; Δdevn is the new step size; SV-tap is the sensitivity of the transformer tap adjustment to voltage; ωtap is the weight of the transformer tap;

[0074] When optimizing the short-term strategy, the influence weights ωst and ωt of energy storage devices and distributed generation control are considered. DG , adjust the charging and discharging strategy of the energy storage device and the control strategy of the distributed power source according to the sensitivity analysis results and weights;

[0075] If SV-st is large and ωst is large, it means that the energy storage device has a greater impact on the voltage and is more sensitive. In this case, a more sophisticated control strategy and more precise current limit should be used when controlling the charging and discharging of the energy storage device to better maintain voltage stability and avoid excessive voltage fluctuations due to improper charging and discharging operations.

[0076] The current limiting formula is:

[0077]

[0078] Imax is the current limit, Inew is the new current limit;

[0079] If SV-st is large and ωst is small, a more relaxed control strategy is adopted; for distributed power supply control, reasonable adjustments are also made according to its sensitivity and weight.

[0080] Furthermore, in S4, the method of constructing a distributed collaborative control framework to achieve coordinated control of the photovoltaic inverter and the energy storage device in each local control area includes:

[0081] S41. Build the framework, select the communication protocol and data sharing mechanism; for the communication protocol, it needs to be configured according to its standard specifications, including setting parameters such as network address, port number, data transmission rate, etc.

[0082] Install a regional controller in each local control area and connect it to local photovoltaic inverters, energy storage devices, sensors and other equipment; the connection method uses wired Ethernet or wireless communication;

[0083] S42. Data collection and transmission. The regional controller collects local voltage and power flow data through sensors. The data collection frequency is set to once per second to ensure that the latest grid operation status information can be obtained in a timely manner. At the same time, it receives the operating parameters fed back by the photovoltaic inverter and energy storage device.

[0084] Then, according to the selected communication protocol, the data is encapsulated into a standard data frame format and sent to the adjacent regional controller and the superior control center; the data frame should contain key information such as data source, timestamp, data type, etc., so that the receiver can accurately parse and use the data;

[0085] S43. Coordinate the implementation of the control strategy. The regional controller receives data shared from other regional controllers and determines the control requirements of the local photovoltaic inverter and energy storage device by combining local data and optimized voltage regulation strategy;

[0086] For photovoltaic inverters, the power adjustment of photovoltaic inverters is calculated according to the voltage deviation and power demand. The calculation formula is:

[0087] ΔPp=ΔVp×SP-V;

[0088] The voltage deviation is ΔVp, the power-voltage sensitivity of the SP-V photovoltaic inverter, and ΔPp is the power adjustment amount;

[0089] Then a control signal is generated. The control signal format must comply with the communication protocol requirements of the photovoltaic inverter, including information such as device address, function code, data content, etc., and the control signal is sent to the photovoltaic inverter to achieve precise control of its output power;

[0090] For the energy storage device, the charging and discharging power and time of the energy storage device are determined according to the voltage control requirements and the state of the energy storage device; when the voltage is low and the SOC of the energy storage device is within a reasonable discharge range, the calculation formula for the discharge power is:

[0091]

[0092] Pdis is the discharge power, Pmax is the maximum discharge power of the energy storage device; SOCmin and SOCmax are the minimum and maximum remaining power allowed, respectively;

[0093] Then, corresponding control instructions are generated and sent to the energy storage device through the communication interface with the energy storage device to control its charging and discharging operations;

[0094] During the control process, the operating status of the photovoltaic inverter and energy storage device is monitored in real time. If any abnormal situation occurs, the control strategy is adjusted or an alarm is issued in time to ensure the safe and stable operation of the equipment.

[0095] Furthermore, the method of collecting voltage and power flow data of each control area in real time in S5 and adaptively adjusting the distributed collaborative control strategy in S4 includes:

[0096] S51. Collect voltage and power flow data of each control area in real time, and transmit the data to the regional controller through sensors and communication networks installed in each control area;

[0097] S52. Adaptive adjustment: Calculate voltage deviation and power deviation based on the collected voltage and power flow data. The voltage deviation and power deviation values ​​reflect the difference between the actual operation and the expected operation. Adaptively adjust the distributed collaborative control strategy based on the voltage deviation and power deviation.

[0098] The voltage deviation calculation formula is:

[0099] ΔVc(t)=Vc(t)-Vr(t);

[0100] ΔVc(t) is the voltage deviation; Vr(t) is the reference voltage value; Vc(t) is the actual measured voltage value;

[0101] The power deviation calculation formula is:

[0102] ΔPc(t)=Pc(t)-Pr(t);

[0103] ΔQc(t)=Qc(t)-Qr(t);

[0104] Wherein, ΔPc(t) is the active power deviation, Pr(t) is the reference active power value; Pc(t) is the actual measured active power value; ΔQc(t) is the reactive power deviation, Qr(t) is the reference reactive power value; Qc(t) is the actual measured reactive power value;

[0105] According to the voltage deviation and power deviation, the distributed collaborative control strategy is adaptively adjusted. If ΔVc(t)>0 and ΔPc(t)>0, it means that the actual voltage and power are higher than the expected values. It is necessary to reduce the output power of the photovoltaic inverter or increase the charging amount of the energy storage device to maintain the stability of voltage and power. If ΔVc(t)<0 and ΔPc(t)<0, it means that the actual voltage and power are lower than the expected values. It is necessary to increase the output power of the photovoltaic inverter or reduce the charging amount of the energy storage device. Through continuous adaptive adjustment, it is ensured that the distribution network can maintain a stable voltage and power state under different operating conditions.

[0106] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are:

[0107] The multi-time-scale voltage control method for distribution network of the present invention effectively solves the above problems. By real-time monitoring of key parameters of the distribution network and applying cluster analysis algorithm to timely identify topology changes, the distribution network model can be quickly updated so that it always accurately reflects the actual operation status of the power grid, thereby effectively coping with dynamic changes in the topology structure.

[0108] In terms of multi-timescale voltage control strategies, long-timescale load forecasting and power generation plan equipment adjustment are combined with short-timescale real-time adjustment, fully considering the voltage influencing factors at different time scales. The long-timescale strategy formulates a reasonable power generation plan based on energy resources, grid operation and power market demand, and adjusts the equipment according to the power comparison between load and power generation equipment, such as accurately adjusting the transformer tap and capacitor bank input capacity to ensure that the voltage is stable within a reasonable range for a long time. The short-timescale strategy effectively smooths short-term voltage fluctuations by high-frequency monitoring of load and distributed power output changes, timely controlling the charging and discharging of energy storage devices or adjusting the output power of distributed power sources.

[0109] Sensitivity analysis and control strategy optimization further improve the control accuracy. Through sensitivity analysis of different devices and control methods, and optimizing the control strategy based on their influence weights, the equipment and control parameters can be adjusted more reasonably according to the actual situation, avoiding voltage fluctuations caused by improper adjustment and achieving more accurate voltage control.

[0110] The distributed collaborative control framework achieves efficient coordination of photovoltaic inverters and energy storage devices. Regional controllers quickly exchange information and make collaborative decisions through reasonably configured communication protocols and data sharing mechanisms, ensuring that the overall situation can be fully considered during the voltage regulation process in different regions, thereby improving the voltage stability and power quality of the entire distribution network.

[0111] The real-time feedback and adaptive adjustment mechanism enables the control system to continuously optimize the control strategy according to the actual operation data. By continuously monitoring the voltage and power flow data, calculating the deviation and adaptively adjusting the control strategy, a closed-loop control is formed, which further enhances the system's adaptability to various complex operating conditions, ensuring that the distribution network can maintain a stable voltage and power state under different conditions, effectively improving the power supply reliability of the distribution network and its ability to absorb distributed energy, and improving the overall operating efficiency and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 It is a logic block diagram of a multi-time-scale voltage control method for distribution networks that adapts to topology changes;

[0113] Figure 2 A logical block diagram of a method for detecting distribution network topology changes and updating distribution network models;

[0114] Figure 3 It is the logic block diagram of the method for multi-time scale voltage regulation strategy;

[0115] Figure 4 Logic block diagram for sensitivity analysis and control strategy optimization;

[0116] Figure 5 A logic block diagram of a method for coordinated control of a photovoltaic inverter and an energy storage device;

[0117] Figure 6 Logic block diagram of the method for adaptively adjusting distributed collaborative control strategy. DETAILED DESCRIPTION

[0118] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in combination with the embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and are not used to limit the invention.

[0119] like Figure 1 As shown, a multi-time scale voltage control method for a distribution network adapting to topology changes includes the following steps:

[0120] S1. Detection of distribution network topology changes and updating of distribution network models. Real-time monitoring of key parameters of the distribution network. Use the key parameters as direct feedback of the distribution network operation status. Apply cluster analysis algorithm to process the key parameters. Identify topology changes in the distribution network through the processed key parameters. Dynamically update the distribution network model according to the topology changes.

[0121] S2. Multi-time scale voltage regulation strategy design. Based on the dynamically updated distribution network model, long-time scale and short-time scale voltage regulation strategies are designed. The long-time scale strategy focuses on equipment adjustment based on load forecasting and power generation plan, while the short-time scale strategy makes rapid adjustments to real-time load fluctuations and distributed power output changes.

[0122] S3. Sensitivity analysis and control strategy optimization. Based on the multi-time scale voltage control strategy designed in S2, sensitivity analysis is performed. The long-time scale and short-time scale voltage control strategies are optimized according to the results of sensitivity analysis. The optimization process considers the influence weight of the control equipment to achieve more accurate voltage control.

[0123] S4. Distributed collaborative control framework: Based on the voltage regulation strategy optimized in S3, a distributed collaborative control framework is constructed to achieve coordinated control of photovoltaic inverters and energy storage devices in each local control area through the distributed collaborative control framework;

[0124] S5. Real-time feedback and adaptive adjustment: collect voltage and power flow data of each control area in real time, and adaptively adjust the distributed collaborative control strategy in S4 based on the collected voltage and power flow data.

[0125] like Figure 2 As shown, in S1, the method for detecting distribution network topology changes and updating distribution network models includes:

[0126] S11. Key parameter monitoring, key parameters include node voltage of the power grid, line power flow, etc. Key parameters are obtained by installing high-precision sensors on various key nodes and lines of the distribution network;

[0127] S12. Application of cluster analysis algorithm, using K-Means clustering algorithm to process key parameters, taking key parameter data as input, calculating according to the steps of K-Means algorithm, and updating cluster centers through continuous iteration to minimize the sum of squares of distances from data points (key parameter values) to their cluster centers. Its objective function is:

[0128]

[0129] Among them, J is the total distance square sum, which is the objective function; xi is the data point (i.e. the key parameter value), uk is the center of the kth cluster; K is the number of clusters that is preset, and K is reasonably selected according to the scale and structural characteristics of the distribution network. For small distribution networks, the value is 3-5, for medium-sized distribution networks, the value is 5-8, and for large distribution networks, the value is 8-12;

[0130] S13. Identify topological changes in the distribution network. During the operation of the clustering analysis algorithm of S12, the cluster centers are continuously updated iteratively so that data points can be more reasonably divided into different clusters. When the topological structure of the distribution network changes, the data distribution of key parameters will change, and the changes in key parameters will be reflected in the clustering results, so that topological changes can be identified through clustering analysis results. In actual use, if a line is disconnected, the node voltage and line power flow data related to the line will change abnormally, which will be reflected in the clustering results as some data points that originally belonged to the same cluster will be divided into different clusters, or the position and value of the cluster center will change significantly. Through dynamic monitoring and analysis of the clustering results, topological changes can be identified in a timely and accurate manner.

[0131] S14. Dynamic update of distribution network model. A node-branch model based on graph theory is used to construct the distribution network model. In this model, nodes have properties such as node voltage and injected power; branches have properties such as resistance, reactance, line power flow, etc. When a topology change occurs, a branch is disconnected, and the connection relationship of the branch needs to be modified in the model, that is, the connection between the nodes related to the branch is marked as disconnected, and the attribute values ​​of the related nodes and branches are updated at the same time; for example, the injected power of the node connected to the disconnected branch will change, and its value needs to be recalculated; for node voltage, if a node loses part of its connection due to topology changes, its voltage value will be recalculated and updated according to Kirchhoff's law and circuit equivalence principle to ensure that the distribution network model can accurately reflect the actual grid topology and.

[0132] In S14, the method for constructing a distribution network model based on a node-branch model of graph theory includes:

[0133] S141. Node-branch association matrix construction: construct an m×n matrix to represent the connection relationship between nodes and branches; where m is the number of branches, n is the number of nodes, and the matrix element aij is defined as:

[0134] If branch j is connected to node i and the current direction is away from the node, then aij = 1;

[0135] If branch j is connected to node i and the current direction points to the node, then aij = -1;

[0136] If branch j is not connected to node i, then aij = 0;

[0137] S142. The node voltage equation is established. According to Kirchhoff's current law, for each node, the sum of the currents flowing into the node is equal to the sum of the currents flowing out of the node. The current is expressed by voltage and branch impedance.

[0138] The branch admittance connecting node and node is The voltage of node n is Vn, the voltage of node j is Vj, according to Ohm's law, the branch current Inj = Ynj (Vn-Vj);

[0139]

[0140] Since the current flowing into node n Equal to all branches connected to node n Substituting Inj=Ynj(Vn-Vj), where m is the number of branches connected to node n, we get:

[0141]

[0142] The injected current at node n Calculated from the injected power Pn and Qn and the node voltage Vn,

[0143] In steady-state conditions, according to KCL, The node voltage equation is obtained:

[0144]

[0145] In actual calculations, for a distribution network with nodes, N node voltage equations are obtained. The voltage value of each node can be obtained by solving the equations simultaneously. The solution method uses numerical calculation methods such as Gauss-Seidel iteration method, and its iteration formula is:

[0146]

[0147] Where k represents the number of iterations; represents the voltage value of node n at the k+1th iteration, represents the voltage value of node j at the kth iteration; the iteration process ends when a certain convergence condition is met, such as when the absolute value of the node voltage difference between two adjacent iterations is less than the set threshold (10-4), and the node voltage value obtained at this time is the desired value;

[0148] S143. The power flow equation is established. For each branch, according to Ohm's law and the definition of power, the branch power flow equation is obtained:

[0149] The active power flow of the branch is:

[0150]

[0151] The reactive power flow of the branch is:

[0152]

[0153] Among them, Rij is the resistance of branch ij; gij is the conductance; bij is the susceptance; Vi is the voltage of node i;

[0154] Vj is the voltage at node j; θij is the phase difference between the voltages at node i and node j;

[0155] In actual calculations, for a distribution network with M branches, M branch power flow equations are obtained; by combining the node voltage equation and the branch power flow equation, the values ​​of the node voltage and branch power flow are continuously updated by calculation until the convergence conditions are met (such as the power deviation is less than the set threshold), thereby obtaining accurate distribution network operation status parameters.

[0156] like Figure 3 As shown, in S2, the steps of designing a multi-time scale voltage regulation strategy include:

[0157] S21. Long-term strategy design:

[0158] S211. Load forecasting: ARI MA model in time series analysis method is used for load forecasting. The formula is:

[0159]

[0160] Among them, p is the autoregressive order; q is the moving average order; q is the difference order; L is the lag operator; Yt is the time series; εt is the white noise series; is the autoregressive coefficient; θj is the moving average coefficient; θ0 is the constant term in the model; d is the difference order. In time series analysis, when the original time series is not stable, it is made stable through the difference operation;

[0161] Select appropriate model parameters based on the characteristics of historical load data; for load data with obvious daily and weekly periodicity, set d to 1 (to handle daily periodicity) and 7 (to handle weekly periodicity) respectively. By analyzing historical load data and estimating model parameters, predict the active load demand and reactive load demand in the future (such as the next 24 hours);

[0162] S212. Power generation plan and equipment adjustment. The active power generation and reactive power generation of power generation equipment are determined according to the power generation plan. The power generation plan is formulated based on factors such as the availability of energy resources (such as coal reserves for thermal power generation and reservoir water levels for hydropower generation), the operation requirements of the power grid (such as total system load requirements and spare capacity requirements), and the needs of the power market (such as fluctuations in electricity prices and changes in peak and valley electricity demand);

[0163] Compare the load demand and the output power of the power generation equipment. If the active load demand is greater than the active power generation of the power generation equipment, consider increasing the investment in adjustable equipment.

[0164] For the transformer tap, adjust the tap position reasonably according to the voltage deviation. The transformer ratio is k, the high-voltage side voltage is VH, and the low-voltage side voltage is VL. By adjusting k, the voltage on the low-voltage side can be changed. If the voltage is too low, the transformer ratio k can be appropriately increased to increase the voltage on the low-voltage side.

[0165] For capacitor banks, capacitor banks of corresponding capacity are put into use according to the reactive power demand to provide reactive power support and improve voltage stability. The input capacity of the capacitor bank is calculated based on the reactive power deficit. If the reactive power deficit is ΔQ and the capacity of a single capacitor is C, then the number of capacitor banks required is (In actual calculation, factors such as the rated voltage of the capacitor and the system voltage need to be considered for correction).

[0166] On the contrary, if the active load demand is less than the active power generation of the power generation equipment, the opposite operation needs to be performed, which requires reducing the equipment input or making other adjustments, such as adjusting the transformer tap position to reduce the voltage, or reducing the input capacity of the capacitor bank;

[0167] S22. Short-term strategy: real-time monitoring of load fluctuations and changes in distributed power output (including distributed power sources such as photovoltaics and wind power). The frequency of monitoring data collection should be high enough (e.g., data collection every 5 minutes) to accurately capture real-time changes.

[0168] When ΔPl(t)+ΔPg(t)>0 (indicating system power shortage), where ΔPl(t) represents the load power change at time t, and ΔPg(t) represents the power generation change at time t, the power is supplemented by controlling the rapid discharge of the energy storage device to maintain voltage stability;

[0169] When ΔPl(t)+ΔPg(t)<0 (indicating excess system power), the energy storage device is controlled to charge or the output power of the distributed power source is adjusted (such as by controlling the power factor of the photovoltaic inverter, etc.).

[0170] like Figure 4 As shown, in S3, the method of sensitivity analysis and control strategy optimization includes:

[0171] S31. Sensitivity analysis: For long-term strategies, calculate the sensitivity of device adjustment to voltage. Obtain the voltage change under different device adjustment amounts through actual measurement and data analysis. Perform multiple tests under different load levels (such as light load, full load, etc.) and operating conditions (such as different distributed power supply output conditions). The calculation formula for the sensitivity of device adjustment to voltage is:

[0172]

[0173] Among them, SV-dev represents the sensitivity of equipment adjustment to voltage, ΔV represents the change in voltage, and Δdev represents the equipment adjustment amount. For example, when adjusting the transformer tap, Δdev is the adjustment amount of the tap. By measuring and analyzing the change in voltage under different adjustment amounts, the sensitivity of the transformer tap to voltage can be obtained.

[0174] For the short-term strategy, the sensitivity of the energy storage device and distributed generation control to the voltage is calculated. For the energy storage device, the sensitivity of the energy storage device to the voltage SV-st is expressed as:

[0175]

[0176] ΔV′ is the voltage change caused by the charge and discharge operation of the energy storage device; ΔI is the current change;

[0177] For distributed power sources, the sensitivity of distributed power control to voltage SV-DG is expressed as:

[0178]

[0179] ΔV″ is the voltage change caused by power adjustment, and ΔP′ is the power adjustment amount;

[0180] S32. Control strategy optimization. When optimizing the long-term strategy, the influence weight of the control equipment (such as the weight of the transformer tap, the weight of the capacitor bank, etc.) is considered. The weight is determined based on the importance of the equipment to voltage regulation, response speed, adjustment range and other factors. For example, the transformer tap adjustment has a greater impact on the voltage and a relatively slow response speed, so it is given a higher weight; the capacitor bank regulates reactive power, which plays an important role in voltage stability, but its regulation ability is limited, so it can be given a moderate weight.

[0181] According to the sensitivity analysis results and weights, adjust the step size or decision rule of equipment adjustment. If the transformer tap adjustment is more sensitive to voltage and the transformer tap weight is also larger, it means that the transformer tap has a greater impact on voltage and is more sensitive. In this case, a smaller step size is used when adjusting the transformer tap. This can control the voltage more accurately and avoid excessive voltage fluctuations or instability caused by excessive adjustment.

[0182] If the transformer tap adjustment is less sensitive to voltage and the weight of the transformer tap is also small, a larger step size is used to improve the adjustment efficiency; for the adjustment of the capacitor bank, reasonable adjustments are also made according to its sensitivity and weight;

[0183] The calculation formula of step length is:

[0184]

[0185] Wherein, Δdevo is the initial step size; Δdevn is the new step size; SV-tap is the sensitivity of the transformer tap adjustment to voltage; ωtap is the weight of the transformer tap;

[0186] When optimizing the short-term strategy, the influence weights ωst and ωt of energy storage devices and distributed generation control are considered. DG , adjust the charging and discharging strategy of the energy storage device and the control strategy of the distributed power source according to the sensitivity analysis results and weights;

[0187] If SV-st is large and ωst is large, it means that the energy storage device has a greater impact on the voltage and is more sensitive. In this case, a more sophisticated control strategy should be used when controlling the charging and discharging of the energy storage device, such as using a more accurate current limit, to better maintain voltage stability and avoid excessive voltage fluctuations due to improper charging and discharging operations.

[0188] The current limiting formula is:

[0189]

[0190] Imax is the current limit, Inew is the new current limit;

[0191] If SV-st is large and ωst is small, a more relaxed control strategy is adopted; for distributed power supply control, reasonable adjustments are also made according to its sensitivity and weight.

[0192] like Figure 5 As shown, in S4, the method of constructing a distributed collaborative control framework to achieve coordinated control of photovoltaic inverters and energy storage devices in each local control area includes:

[0193] S41. Build a framework, select a communication protocol (such as IEC 61850 protocol) and a data sharing mechanism; for the communication protocol, configure it according to its standard specifications, including setting parameters such as network address, port number, and data transmission rate; for example, set the IP address of the regional controller in the same subnet segment to ensure mutual communication, and set the data transmission rate to 100Mbps to meet the needs of fast data exchange;

[0194] Install a regional controller in each local control area and connect it to local photovoltaic inverters, energy storage devices, sensors and other equipment; the connection method can be wired Ethernet or wireless communication (such as Wi-Fi or ZigBee, selected according to the actual environment and device compatibility); for example, for photovoltaic inverters, connect to the regional controller through an RS485 serial port to Ethernet module to achieve data transmission and control command reception; for energy storage devices, if they support CAN bus communication, use a CAN to Ethernet gateway to connect to the regional controller;

[0195] S42. Data collection and transmission. The regional controller collects local voltage and power flow data through sensors. The data collection frequency is set to once per second to ensure that the latest grid operation status information can be obtained in a timely manner. At the same time, it receives the operating parameters fed back by the photovoltaic inverter and energy storage device, such as the output power, efficiency, and operating temperature of the photovoltaic inverter, and the remaining power (SOC), charge and discharge current, voltage and other information of the energy storage device;

[0196] Then, according to the selected communication protocol, the data is encapsulated into a standard data frame format and sent to the adjacent regional controller and the superior control center; the data frame should contain key information such as data source, timestamp, data type, etc., so that the receiver can accurately parse and use the data;

[0197] S43. Coordinate the implementation of the control strategy. The regional controller receives data shared from other regional controllers and determines the control requirements of the local photovoltaic inverter and energy storage device by combining local data and optimized voltage regulation strategy;

[0198] For photovoltaic inverters, the power adjustment of photovoltaic inverters is calculated according to the voltage deviation and power demand. The calculation formula is:

[0199] ΔPp=ΔVp×SP-V;

[0200] The voltage deviation is ΔVp, the power-voltage sensitivity of the SP-V photovoltaic inverter, and ΔPp is the power adjustment amount;

[0201] Then, a control signal is generated. The control signal format must comply with the communication protocol requirements of the photovoltaic inverter, including information such as device address, function code, data content (such as power adjustment value), etc., and the control signal is sent to the photovoltaic inverter to achieve precise control of its output power.

[0202] For the energy storage device, the charging and discharging power and time of the energy storage device are determined according to the voltage control requirements and the state of the energy storage device; when the voltage is low and the SOC of the energy storage device is within a reasonable discharge range, the calculation formula for the discharge power is:

[0203]

[0204] Pdis is the discharge power, Pmax is the maximum discharge power of the energy storage device; SOCmin and SOCmax are the minimum and maximum remaining power allowed, respectively;

[0205] Then, corresponding control instructions are generated and sent to the energy storage device through the communication interface with the energy storage device to control its charging and discharging operations;

[0206] During the control process, the operating status of the photovoltaic inverter and energy storage device is monitored in real time. If an abnormal situation occurs (such as a sudden change in the output power of the photovoltaic inverter or excessive temperature of the energy storage device), the control strategy is adjusted or an alarm is issued in time to ensure safe and stable operation of the equipment.

[0207] like Figure 6 As shown, in S5, the method for adaptively adjusting the distributed collaborative control strategy in S4 includes:

[0208] S51. Collect voltage and power flow data of each control area in real time, and transmit the data to the regional controller through sensors and communication networks installed in each control area;

[0209] S52. Adaptive adjustment: Calculate voltage deviation and power deviation based on the collected voltage and power flow data. The voltage deviation and power deviation values ​​reflect the difference between the actual operation and the expected operation. Adaptively adjust the distributed collaborative control strategy based on the voltage deviation and power deviation.

[0210] The voltage deviation calculation formula is:

[0211] ΔVc(t)=Vc(t)-Vr(t);

[0212] ΔVc(t) is the voltage deviation; Vr(t) is the reference voltage value; Vc(t) is the actual measured voltage value;

[0213] The power deviation calculation formula is:

[0214] ΔPc(t)=Pc(t)-Pr(t);

[0215] ΔQc(t)=Qc(t)-Qr(t);

[0216] Wherein, ΔPc(t) is the active power deviation, Pr(t) is the reference active power value; Pc(t) is the actual measured active power value; ΔQc(t) is the reactive power deviation, Qr(t) is the reference reactive power value; Qc(t) is the actual measured reactive power value;

[0217] According to the voltage deviation and power deviation, the distributed collaborative control strategy is adaptively adjusted. If ΔVc(t)>0 and ΔPc(t)>0, it means that the actual voltage and power are higher than the expected values. It is necessary to reduce the output power of the photovoltaic inverter or increase the charging amount of the energy storage device to maintain the stability of voltage and power. If ΔVc(t)<0 and ΔPc(t)<0, it means that the actual voltage and power are lower than the expected values. It is necessary to increase the output power of the photovoltaic inverter or reduce the charging amount of the energy storage device. Through continuous adaptive adjustment, it is ensured that the distribution network can maintain a stable voltage and power state under different operating conditions.

[0218] The above description is a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the invention should be included in the protection scope of the invention.

Claims

1. A multi-time scale voltage control method for a distribution network adapting to topology changes, characterized in that: The following steps are involved: S1. Detection of distribution network topology changes and updating of distribution network models. Real-time monitoring of key parameters of the distribution network. Use the key parameters as direct feedback of the distribution network operation status. Apply cluster analysis algorithm to process the key parameters. Identify topology changes in the distribution network through the processed key parameters. Dynamically update the distribution network model according to the topology changes. S2. Design of multi-time scale voltage regulation strategy: Based on the dynamically updated distribution network model, design long-time scale and short-time scale voltage regulation strategies; Long-term strategies focus on equipment adjustment based on load forecasts and power generation plans, while short-term strategies make rapid adjustments to real-time load fluctuations and changes in distributed power output. S3. Sensitivity analysis and control strategy optimization. Based on the multi-time scale voltage control strategy designed in S2, sensitivity analysis is performed. The long-time scale and short-time scale voltage control strategies are optimized according to the results of sensitivity analysis. The optimization process considers the influence weight of the control equipment to achieve more accurate voltage control. S4. Distributed collaborative control framework: Based on the voltage regulation strategy optimized in S3, a distributed collaborative control framework is constructed to achieve coordinated control of photovoltaic inverters and energy storage devices in each local control area through the distributed collaborative control framework; S5. Real-time feedback and adaptive adjustment: collect voltage and power flow data of each control area in real time, and adaptively adjust the distributed collaborative control strategy in S4 based on the collected voltage and power flow data.

2. A distribution network multi-time scale voltage control method adapting to topology changes according to claim 1, characterized in that: In S1, the method for detecting distribution network topology changes and updating distribution network models includes: S11. Key parameter monitoring, key parameters include node voltage and line power flow of the power grid. Key parameters are obtained by installing high-precision sensors at various key nodes and lines of the distribution network; S12. Application of cluster analysis algorithm, using K-Means clustering algorithm to process key parameters, taking key parameter data as input, calculating according to the steps of K-Means algorithm, and updating cluster centers through continuous iteration to minimize the sum of squares of distances from key parameter values ​​to their cluster centers. Its objective function is: Among them, J is the total distance square sum, which is the objective function; xi is the key parameter value, uk is the center of the kth cluster; K is the number of clusters that is preset, and K is reasonably selected according to the scale and structural characteristics of the distribution network. For small distribution networks, the value is 3-5, for medium-sized distribution networks, the value is 5-8, and for large distribution networks, the value is 8-12; S13. Identify topological changes in the distribution network. During the operation of the cluster analysis algorithm in S12, the cluster centers are continuously updated iteratively so that data points can be more reasonably divided into different clusters. When the topological structure of the distribution network changes, the data distribution of key parameters will change, and the changes in key parameters will be reflected in the clustering results, so that topological changes can be identified through cluster analysis results. S14. The distribution network model is dynamically updated. A node-branch model based on graph theory is used to construct the distribution network model. In this model, nodes have node voltage and injected power attributes; branches have resistance, reactance, and line power flow attributes. When a topology change occurs, a branch is disconnected, and the connection relationship of the branch needs to be modified in the model, that is, the connection between the nodes related to the branch is marked as disconnected, and the attribute values ​​of the related nodes and branches are updated at the same time.

3. The multi-time-scale voltage control method for a distribution network adapting to topology changes according to claim 1 is characterized in that: In S14, the method for constructing a distribution network model based on a node-branch model of graph theory includes: S141. Node-branch association matrix construction: construct an m×n matrix to represent the connection relationship between nodes and branches; where m is the number of branches, n is the number of nodes, and the matrix element aij is defined as: If branch j is connected to node i and the current direction is away from the node, then aij = 1; If branch j is connected to node i and the current direction points to the node, then aij = -1; If branch j is not connected to node i, then aij = 0; S142. The node voltage equation is established. According to Kirchhoff's current law, for each node, the sum of the currents flowing into the node is equal to the sum of the currents flowing out of the node. The current is expressed by voltage and branch impedance. The branch admittance connecting node and node is The voltage of node n is Vn, the voltage of node j is Vj, according to Ohm's law, the branch current Inj = Ynj (Vn-Vj); Since the current flowing into node n Equal to all branches connected to node n Substituting Inj=Ynj(Vn-Vj), where m is the number of branches connected to node n, we get: The injected current at node n Calculated from the injected power Pn and Qn and the node voltage Vn, In steady-state conditions, according to KCL, The node voltage equation is obtained: In actual calculation, for a distribution network with nodes, N node voltage equations are obtained. The voltage value of each node can be obtained by solving the equations simultaneously. The solution method adopts the Gauss-Seidel iteration numerical calculation method, and its iteration formula is: Where k represents the number of iterations; represents the voltage value of node n at the k+1th iteration, represents the voltage value of node j at the kth iteration; the iteration process ends until a certain convergence condition is met, such as when the absolute value of the node voltage difference between two adjacent iterations is less than the set threshold, and the node voltage value obtained at this time is the desired value; S143. The power flow equation is established. For each branch, according to Ohm's law and the definition of power, the branch power flow equation is obtained: The active power flow of the branch is: The reactive power flow of the branch is: Among them, Rij is the resistance of branch ij; gij is the conductance; bij is the susceptance; Vi is the voltage of node i; Vj is the voltage at node j; θij is the phase difference between the voltages at node i and node j; In actual calculations, for a distribution network with M branches, M branch power flow equations are obtained; by combining the node voltage equation and the branch power flow equation, the values ​​of the node voltage and branch power flow are continuously updated by calculation until the convergence conditions are met, thereby obtaining accurate distribution network operation status parameters.

4. The method for multi-time-scale voltage control in a distribution network adapting to topology changes according to claim 1 is characterized in that: In S2, the steps of designing a multi-time scale voltage regulation strategy include: S21. Long-term strategy design: Load forecasting uses the ARIMA model in the time series analysis method to forecast load. The formula is: Among them, p is the autoregressive order; q is the moving average order; q is the difference order; L is the lag operator; Yt is the time series; εt is the white noise series; is the autoregressive coefficient; θj is the moving average coefficient; θ0 is the constant term in the model; d is the difference order. In time series analysis, when the original time series is not stable, it is made stable through the difference operation; Power generation plan and equipment adjustment, determining the active and reactive power of power generation equipment according to the power generation plan. The power generation plan is formulated based on the availability of energy resources, the operating requirements of the power grid and the demand factors of the power market; Compare the load demand and the output power of the power generation equipment. If the active load demand is greater than the active power generation of the power generation equipment, consider increasing the investment in adjustable equipment. For the transformer tap, adjust the tap position reasonably according to the voltage deviation. The transformer ratio is k, the high-voltage side voltage is VH, and the low-voltage side voltage is VL. Change the voltage on the low-voltage side by adjusting k. If the voltage is too low, increase the transformer ratio k appropriately to increase the voltage on the low-voltage side. For capacitor banks, capacitor banks of corresponding capacity are put into use according to the reactive power demand to provide reactive power support and improve voltage stability. The input capacity of the capacitor bank is calculated based on the reactive power deficit. If the reactive power deficit is ΔQ and the capacity of a single capacitor is C, then the number of capacitor banks required is On the contrary, if the active load demand is less than the active power generation of the power generation equipment, the opposite operation is performed, and the equipment investment needs to be reduced or other adjustments need to be made; S22. Short-term strategy: real-time monitoring of load fluctuations and changes in distributed generation output. The frequency of monitoring data collection should be high enough to accurately capture real-time changes. When ΔPl(t)+ΔPg(t)>0, where ΔPl(t) represents the load power change at time t, and ΔPg(t) represents the power generation change at time t, the power is supplemented by controlling the rapid discharge of the energy storage device to maintain voltage stability; When ΔPl(t)+ΔPg(t)<0, the energy storage device is controlled to charge or the output power of the distributed power source is adjusted.

5. The method for multi-time-scale voltage control in a distribution network adapting to topology changes according to claim 1 is characterized in that: In S3, the method of sensitivity analysis and control strategy optimization includes: S31. Sensitivity analysis: For long-term strategies, calculate the sensitivity of equipment adjustment to voltage. Obtain the voltage change under different equipment adjustment amounts through actual measurement and data analysis. Perform multiple tests under different load levels and operating conditions. The calculation formula for the sensitivity of equipment adjustment to voltage is: Among them, SV-dev represents the sensitivity of device adjustment to voltage, ΔV represents the change in voltage; Δdev represents the device adjustment amount; For the short-term strategy, the sensitivity of the energy storage device and distributed generation control to the voltage is calculated. For the energy storage device, the sensitivity of the energy storage device to the voltage SV-st is expressed as: ΔV′ is the voltage change caused by the charge and discharge operation of the energy storage device; ΔI is the current change; For distributed power sources, the sensitivity of distributed power control to voltage SV-DG is expressed as: ΔV″ is the voltage change caused by power adjustment, and ΔP′ is the power adjustment amount; S32. Control strategy optimization. When optimizing the long-term strategy, the influence weight of the control device is considered. The weight is determined based on the importance of the device to voltage regulation, response speed, and adjustment range. According to the sensitivity analysis results and weights, adjust the step size or decision rule of equipment adjustment. If the transformer tap adjustment is more sensitive to voltage and the transformer tap weight is also larger, it means that the transformer tap has a greater impact on voltage and is more sensitive. In this case, a smaller step size is used when adjusting the transformer tap. This can control the voltage more accurately and avoid excessive voltage fluctuations or instability caused by excessive adjustment. If the transformer tap adjustment is less sensitive to voltage and the weight of the transformer tap is also small, a larger step size is used to improve the adjustment efficiency; for the adjustment of the capacitor bank, reasonable adjustments are also made according to its sensitivity and weight; The calculation formula of step length is: Wherein, Δdevo is the initial step size; Δdevn is the new step size; SV-tap is the sensitivity of the transformer tap adjustment to voltage; ωtap is the weight of the transformer tap; When optimizing the short-term strategy, the influence weights ωst and ωt of energy storage devices and distributed generation control are considered. DG , adjust the charging and discharging strategy of the energy storage device and the control strategy of the distributed power source according to the sensitivity analysis results and weights; If SV-st is large and ωst is large, it means that the energy storage device has a greater impact on the voltage and is more sensitive. In this case, a more sophisticated control strategy and more precise current limit should be used when controlling the charging and discharging of the energy storage device to better maintain voltage stability and avoid excessive voltage fluctuations due to improper charging and discharging operations. The current limiting formula is: Imax is the current limit, Inew is the new current limit; If SV-st is large and ωst is small, a more relaxed control strategy is adopted; for distributed power supply control, reasonable adjustments are also made according to its sensitivity and weight.

6. The multi-time-scale voltage control method for distribution network adapting to topology changes according to claim 1 is characterized in that: In S4, the method of constructing a distributed collaborative control framework to achieve coordinated control of the photovoltaic inverter and the energy storage device in each local control area includes: S41. Build the framework, select the communication protocol and data sharing mechanism; for the communication protocol, configure it according to its standard specifications, including setting the network address, port number, and data transmission rate parameters; Install a regional controller in each local control area and connect it to the local photovoltaic inverter, energy storage device and sensor equipment; the connection method adopts wired Ethernet or wireless communication; S42. Data collection and transmission. The regional controller collects local voltage and power flow data through sensors. The data collection frequency is set to once per second to ensure that the latest grid operation status information can be obtained in a timely manner. At the same time, it receives the operating parameters fed back by the photovoltaic inverter and energy storage device. Then, according to the selected communication protocol, the data is encapsulated into a standard data frame format and sent to the adjacent regional controller and the superior control center; the data frame should contain key information such as data source, timestamp, and data type so that the receiver can accurately parse and use the data; S43. Coordinate the implementation of the control strategy. The regional controller receives data shared from other regional controllers and determines the control requirements of the local photovoltaic inverter and energy storage device by combining local data and optimized voltage regulation strategy; For photovoltaic inverters, the power adjustment of photovoltaic inverters is calculated according to the voltage deviation and power demand. The calculation formula is: ΔPp=ΔVp×SP-V; The voltage deviation is ΔVp, the power-voltage sensitivity of the SP-V photovoltaic inverter, and ΔPp is the power adjustment amount; Then a control signal is generated. The control signal format must comply with the communication protocol requirements of the photovoltaic inverter, including device address, function code, and data content information. The control signal is then sent to the photovoltaic inverter to achieve precise control of its output power. For the energy storage device, the charging and discharging power and time of the energy storage device are determined according to the voltage control requirements and the state of the energy storage device; when the voltage is low and the SOC of the energy storage device is within a reasonable discharge range, the calculation formula for the discharge power is: Pdis is the discharge power, Pmax is the maximum discharge power of the energy storage device; SOCmin and SOCmax are the minimum and maximum remaining power allowed, respectively; Then, corresponding control instructions are generated and sent to the energy storage device through the communication interface with the energy storage device to control its charging and discharging operations; During the control process, the operating status of the photovoltaic inverter and energy storage device is monitored in real time. If any abnormal situation occurs, the control strategy is adjusted or an alarm is issued in time to ensure the safe and stable operation of the equipment.

7. The method for multi-time-scale voltage control in a distribution network adapting to topology changes according to claim 1 is characterized in that: The method of collecting voltage and power flow data of each control area in real time in S5 and adaptively adjusting the distributed collaborative control strategy in S4 includes: S51. Collect voltage and power flow data of each control area in real time, and transmit the data to the regional controller through sensors and communication networks installed in each control area; S52. Adaptive adjustment: Calculate voltage deviation and power deviation based on the collected voltage and power flow data. The voltage deviation and power deviation values ​​reflect the difference between the actual operation and the expected operation. Adaptively adjust the distributed collaborative control strategy based on the voltage deviation and power deviation. The voltage deviation calculation formula is: ΔVc(t)=Vc(t)-Vr(t); ΔVc(t) is the voltage deviation; Vr(t) is the reference voltage value; Vc(t) is the actual measured voltage value; The power deviation calculation formula is: ΔPc(t)=Pc(t)-Pr(t); ΔQc(t)=Qc(t)-Qr(t); Among them, ΔP c (t) is the active power deviation, P r (t) is the reference active power value; P c (t) is the actual measured active power value; ΔQ c (t) is the reactive power deviation, Q r (t) is the reference reactive power value; Q c (t) is the actual measured reactive power value; According to the voltage deviation and power deviation, the distributed cooperative control strategy is adaptively adjusted. If ΔV c (t)>0 and ΔP c (t)>0, which means that the actual voltage and power are higher than the expected values. It is necessary to reduce the output power of the photovoltaic inverter or increase the charging capacity of the energy storage device to maintain the stability of voltage and power. c (t)<0 and ΔPc(t)<0, indicating that the actual voltage and power are lower than the expected values. It is necessary to increase the output power of the photovoltaic inverter or reduce the charging amount of the energy storage device. Through continuous adaptive adjustment, it is ensured that the distribution network can maintain a stable voltage and power state under different operating conditions.

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