Power distribution network multi-time scale voltage regulation method adaptive to topology change

Abstract

The application provides a power distribution network multi-time scale voltage regulation method suitable for topology changes, aiming at solving many problems of traditional power distribution network voltage regulation. The method first monitors key parameters of the power distribution network in real time, identifies topology changes through clustering analysis algorithm, and dynamically updates the node-branch model based on graph theory. Then, long-time and short-time scale strategies are designed, long-time based on load forecasting and generation plan adjustment equipment, short-time for quick adjustment of load and distributed power changes. Then, sensitivity analysis is carried out, and the control strategy is optimized according to the weight of the equipment on the voltage influence. A distributed collaborative control framework is constructed, and the regional controller coordinates the photovoltaic inverter and energy storage device according to the selected protocol and mechanism. Finally, the deviation is calculated by collecting real-time data, and the control strategy is adjusted adaptively. The application effectively responds to topology changes, integrates the advantages of multiple strategies, improves the voltage regulation accuracy, enhances the power supply reliability and distributed energy consumption capacity of the power distribution network, and ensures the stable operation of the power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network voltage regulation, and particularly relates to a power distribution network multi-time scale voltage regulation method suitable for topology changes. BACKGROUND

[0002] In traditional power distribution network operation, voltage regulation faces many challenges. With the widespread access of distributed power sources (such as photovoltaic, wind power, etc.) in the power distribution network, the intermittency and uncertainty of their output greatly affect voltage stability. Because 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 power distribution network, which in turn causes voltage deviation. For example, during periods of strong light, photovoltaic power generation power rises sharply, and if it cannot be adjusted in time, it may cause local grid voltage to rise above the allowed range.

[0003] At the same time, the topology structure of the power distribution network is not immutable. In actual operation, due to line faults, equipment maintenance or load migration, etc., the topology structure of the power distribution network often changes. Traditional voltage regulation methods are mostly based on fixed power grid models and are difficult to quickly and accurately adapt to these topology changes. When the topology structure changes, the power flow distribution and voltage distribution of the power grid will also change, but the traditional method cannot respond effectively to these changes in time, which can easily lead to voltage out-of-limit problems, affecting the safe and stable operation of the power system and the power quality of users.

[0004] In addition, existing voltage regulation strategies often lack fine management of voltage fluctuations at different time scales. Long-time scale generation planning and device adjustment often fail to fully consider short-time scale load rapid fluctuations and immediate output changes of distributed power sources, resulting in poor voltage regulation effect and failing to meet the strict requirements of modern power systems for power quality. SUMMARY

[0005] In view of the above deficiencies in the prior art, the purpose of the present application is to

[0006] In order to achieve the above purpose, the technical scheme of the present application is as follows:

[0007] A power distribution network multi-time scale voltage regulation method suitable for topology changes, comprising the following steps:

[0008] S1. Power distribution network topology change detection and power distribution network model updating, real-time monitoring of key parameters of the power distribution network, taking the key parameters as direct feedback of the power distribution network operation state, applying a clustering analysis algorithm to process the key parameters, identifying topology changes in the power distribution network through the processed key parameters, and dynamically updating the power distribution network model according to the topology changes;

[0009] S2. Multi-time scale voltage regulation strategy design, based on the dynamic updating of the distribution network model, design long-time scale and short-time scale voltage regulation strategy; long-time scale strategy focuses on the device adjustment based on load forecasting and power generation plan, short-time scale strategy for real-time load fluctuations and distributed power output changes for rapid adjustment;

[0010] S3. Sensitivity analysis and control strategy optimization, based on the multi-time scale voltage regulation strategy designed in S2, sensitivity analysis is carried out, and the long-time scale and short-time scale voltage regulation strategy is optimized according to the results of sensitivity analysis, and the influence weight of the control device is considered in the optimization process to realize more accurate voltage control;

[0011] S4. Distributed collaborative control framework, based on the optimized voltage regulation strategy in S3, a distributed collaborative control framework is constructed, and through the distributed collaborative control framework in each local control area, the coordination control of photovoltaic inverter and energy storage device is realized;

[0012] S5. Real-time feedback and adaptive adjustment, real-time collection of voltage and power flow data in each control area, adaptive adjustment of the distributed collaborative control strategy in S4 according to the collected voltage and power flow data.

[0013] Further, in S1, the method for detecting the change of the distribution network topology and updating the distribution network model comprises:

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

[0015] S12. Application of clustering analysis algorithm, K-Means clustering algorithm is used to process key parameters, key parameter data is taken as input, calculation is carried out according to the steps of K-Means algorithm, and the distance square sum of key parameter value to its belonging cluster center is minimized through continuous iteration and update of cluster center, and the objective function is:

[0016]

[0017] Wherein, Jtotal distance square sum, that is, the objective function; x i is the value of the key parameter, u k is the center of the kth cluster; K cluster number is the pre-set cluster number;

[0018] S13. Identify the topology change in the power distribution network, in the clustering algorithm running process of S12, the clustering center is updated constantly, so that the data points can be more reasonably divided into different clusters; when the topology of the power distribution network changes, the data distribution of the key parameters will change, and the change of the key parameters will be reflected on the clustering result, so as to identify the topology change through the clustering analysis result;

[0019] S14. Power distribution network model dynamic update, a node-branch model based on graph theory is used to construct the power distribution network model, in which the node has node voltage, injected power and other attributes; the branch has resistance, reactance, line power flow and other attributes; when the topology changes, the 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 state, and the attribute values of the related nodes and branches are updated.

[0020] Further, in the S14, the node-branch model based on graph theory constructs the power distribution network model construction method, which comprises:

[0021] S141. Node-branch association matrix construction, an m*n matrix is constructed to represent the connection relationship between nodes and branches; wherein m is the number of branches, n is the number of nodes, and the matrix element a ij is defined as:

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

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

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

[0025] S142. Node voltage equation establishment, according to Kirchhoff's current law, for each node, the sum of the current flowing into the node is equal to the sum of the current flowing out of the node, and the current is represented by voltage and branch impedance;

[0026] The branch admittance connecting the node and the node is The voltage of node n is V n , the voltage of node j is V j , according to Ohm's law, the branch current I nj = Y nj (V n -V j );

[0027]

[0028] Since the current flowing into node n is equal to the sum of all branches connected to node n , we have nj = Y nj (V n -V j ), where m is the number of branches connected to node n. Substituting the above equation into the KCL equation, we get

[0029] The injected current of node n is calculated from the injected power P n and Q n and the node voltage V n , which gives

[0030] In the steady-state case, according to KCL, we get the node voltage equation:

[0031]

[0032] In actual calculations, for a power distribution network with N nodes, we obtain N node voltage equations, which are solved simultaneously to obtain the voltage values of each node. The solving method uses numerical calculation methods such as Gauss-Seidel iteration, and the iteration formula is:

[0033]

[0034] where k represents the iteration number; V represents the voltage value of node n at the k+1 iteration, represents the voltage value of node j at the k iteration; the iteration process continues until a certain convergence condition is met, such as the absolute value of the difference between the node voltages of the adjacent two iterations being less than a set threshold, at which point the iteration ends, and the obtained node voltage value is the solution;

[0035] S143. Power flow equation establishment, for each branch, according to Ohm's law and the definition of power, the branch power flow equation is obtained:

[0036]

[0037]

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

[0039]

[0040] where R ij is the resistance of branch ij; g ij is the conductance; b ij is the susceptance; V i is the voltage of node i; Vj Vj is the voltage of node j; θ ij is the phase difference of the voltage of node i and node j;

[0041] In actual calculation, for a power distribution network with M branches, M branch power flow equations are obtained; by simultaneously solving the node voltage equation and the branch power flow equation, the values of the node voltage and the branch power flow are updated continuously by calculation until the convergence condition is met, so that the accurate operation state parameters of the power distribution network are obtained.

[0042] Further, in the S2, the step of designing the multi-time scale voltage regulation strategy comprises:

[0043] S21. Design of long-time scale strategy:

[0044] Load prediction: the ARIMA model in the time series analysis method is used for load prediction, and the formula is:

[0045]

[0046] where p is the autoregressive order; q is the moving average order; L is the lag operator; Y t is a time series; ε t is a white noise sequence; is an autoregressive coefficient; θ j′ is a moving average coefficient; θ0 is a constant term in the model; d is the difference order, in time series analysis, when the original time series is not stationary, it becomes stationary through difference operation;

[0047] Generation plan and equipment adjustment: according to the active power and reactive power of the power generation equipment determined by the generation plan, the generation plan is formulated based on the availability of energy resources, the operation requirements of the power grid and the demand of the power market, etc.

[0048] Compare the load demand and the output power of the power generation equipment, if the active load demand is greater than the active power of the power generation equipment, consider increasing the input of adjustable equipment;

[0049] 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 V H , and the low-voltage side voltage is V L Change the low-voltage side voltage by adjusting k, if the voltage is low, appropriately increase the transformer ratio k to improve the low-voltage side voltage;

[0050] ​For the capacitor bank, according to the reactive power demand, the corresponding capacity of the capacitor bank is put into operation to provide reactive power support and improve voltage stability, and the capacity of the capacitor bank is calculated according to the reactive power shortage; the reactive power shortage is ΔQ, and the capacity of a single capacitor is C, so the number of capacitor banks that need to be put into operation is

[0051] Conversely, 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 needs to be reduced or other adjustments are needed;

[0052] S22. Short-time scale strategy, real-time monitoring of load fluctuation and distributed power output change, and the monitoring data collection frequency should be high enough to accurately capture real-time changes;

[0053] When ΔP l (t)+ΔP g (t)>0, wherein ΔP l (t) represents the load power change at time t, and ΔP g (t) represents the power generation power change at time t, the power is supplemented by controlling the energy storage device to discharge quickly, and the voltage is maintained stable;

[0054] When ΔP l (t)+ΔP g (t)<0, control the energy storage device to charge or adjust the output power of the distributed power supply.

[0055] Further, in the S3, the method for sensitivity analysis and control strategy optimization comprises:

[0056] S31. Sensitivity analysis, for the long-time scale strategy, calculate the sensitivity of device adjustment to voltage, obtain the voltage change under different device adjustment amounts through actual measurement and data analysis, and test multiple times under different load levels and operating conditions; The calculation formula of the sensitivity of the device adjustment to the voltage is:

[0057]

[0058] Wherein, S V-dev represents the sensitivity of the device adjustment to the voltage, ΔV represents the change of the voltage; and Δdev represents the device adjustment amount.

[0059] For the short-time scale strategy, calculate the sensitivity of the energy storage device and the distributed power supply control to the voltage, and for the energy storage device, the sensitivity of the energy storage device to the voltage S V-st is:

[0060]

[0061] ΔV' is the voltage variation caused by the charging and discharging operation of the energy storage device; ΔI is the current variation;

[0062] For the distributed power supply, the sensitivity S of the distributed power supply control to the voltage V-DG is represented as:

[0063]

[0064] ΔV'' is the voltage variation caused by the power adjustment, and ΔP' is the power adjustment amount;

[0065] S32. Control strategy optimization. When optimizing the long-time scale strategy, the influence weight of the control device is considered, and the weight is determined according to the importance of the device to voltage regulation, response speed, adjustment range and other factors;

[0066] According to the sensitivity analysis results and the weight, the step size or decision rule of the device adjustment is adjusted. If the sensitivity of the transformer tap adjustment to the voltage is large and the weight of the transformer tap is also large, it indicates that the transformer tap has a large influence on the voltage and is sensitive, so a smaller step size is adopted when adjusting the transformer tap, which can more accurately control the voltage and avoid large voltage fluctuation or instability caused by large adjustment amplitude;

[0067] If the sensitivity of the transformer tap adjustment to the voltage is small and the weight of the transformer tap is also small, a larger step size is adopted to improve the adjustment efficiency; for the adjustment of the capacitor bank, the sensitivity and weight are also adjusted reasonably;

[0068] The calculation formula of the step size is:

[0069]

[0070] Δdev o is the initial step size; Δdev n is the new step size; S V-tap is the sensitivity of the transformer tap adjustment to the voltage; ω tap is the weight of the transformer tap;

[0071] When optimizing the short-time scale strategy, the influence weights ω st and ω DG of the energy storage device and the distributed power supply control are considered, and the charging and discharging strategy of the energy storage device and the control strategy of the distributed power supply are adjusted according to the sensitivity analysis results and the weights;

[0072] If S V-st is large and ω stThe larger, the more sensitive the energy storage device is to voltage, and a more precise current limit is used to better maintain voltage stability and avoid excessive voltage fluctuations due to improper charging and discharging operations.

[0073] The current limit formula is:

[0074]

[0075] I max The current limit is I new The new current limit is I

[0076] If S V-st The larger and ω st The smaller, a more relaxed control strategy is adopted. For distributed power control, the sensitivity and weight are also adjusted reasonably.

[0077] Further, in the S4, the method for constructing a distributed collaborative control framework to realize coordinated control of photovoltaic inverters and energy storage devices in each local control area includes:

[0078] S41. Framework construction, select communication protocol and data sharing mechanism; for communication protocol, it needs to be configured according to its standard specification, including setting network address, port number, data transmission rate and other parameters;

[0079] Install a regional controller in each local control area and connect it with local photovoltaic inverters, energy storage devices and sensors and other equipment; the connection method uses wired Ethernet or wireless communication;

[0080] S42. Data acquisition and transmission, the regional controller collects local voltage and power flow data through sensors, and the data acquisition frequency is set to 1 per second to ensure that the latest power grid operation state information can be obtained in time; at the same time, receive the feedback of the operation parameters of photovoltaic inverters and energy storage devices;

[0081] Then, according to the selected communication protocol, the data is packaged into a standard data frame format and sent to the adjacent regional controller and the upper 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;

[0082] S43. Coordination control strategy implementation, the regional controller receives data shared by other regional controllers, and combines local data and optimized voltage regulation strategy to determine the control requirements of local photovoltaic inverters and energy storage devices;

[0083] For photovoltaic inverters, the power adjustment amount of photovoltaic inverters is calculated according to the voltage deviation and power demand, and the calculation formula is:

[0084] ΔP p = ΔV p × S P-V ;

[0085] The voltage deviation is ΔV p , S P-V The power-voltage sensitivity of the photovoltaic inverter, ΔP p is the power adjustment amount;

[0086] Then generate a control signal, the control signal format needs to meet the communication protocol requirements of the photovoltaic inverter, including device address, function code, data content and other information, and send the control signal to the photovoltaic inverter, to realize the accurate control of its output power;

[0087] For the energy storage device, according to the voltage regulation requirement and the state of the energy storage device, the charging and discharging power and time of the energy storage device are determined; when the voltage is low and the SOC of the energy storage device is in a reasonable discharging range, the calculation formula of the discharging power is:

[0088]

[0089] P dis is the discharging power, P max is the maximum discharging power of the energy storage device; SOC min and SOC max are the minimum and maximum remaining power allowed respectively;

[0090] Then generate the corresponding control instruction and send it to the energy storage device through the communication interface to control its charging and discharging operation;

[0091] In the control process, the running state of the photovoltaic inverter and the energy storage device is monitored in real time, and if abnormal situation occurs, the control strategy is adjusted in time or an alarm is issued to ensure the safe and stable operation of the equipment.

[0092] Further, the method for adaptively adjusting the distributed collaborative control strategy in S4 by real-time collection of voltage and power flow data of each control area in S5 includes:

[0093] S51. Real-time collection of voltage and power flow data of each control area, and transmission of data to the area controller through sensors installed in each control area and communication network;

[0094] S52. Adaptive adjustment, according to the collected voltage and power flow data, calculate the voltage deviation and power deviation, the voltage deviation and power deviation value reflects the difference between the actual running situation and the expected running situation, according to the voltage deviation and power deviation, adaptively adjust the distributed collaborative control strategy;

[0095] The voltage deviation calculation formula is:

[0096] ΔV c (t) = V c (t) - V r (t) ;

[0097] ΔV c (t) is the voltage deviation; V r (t) is the reference voltage value; V c (t) is the actual measured voltage value;

[0098] The power deviation calculation formula is:

[0099] ΔP c (t) = P c (t) - P r (t) ;

[0100] ΔQ c (t) = Q c (t) - Q r (t) ;

[0101] Wherein, 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;

[0102] According to the voltage deviation and the power deviation, the distributed cooperative control strategy is adaptively adjusted, if ΔV c (t) > 0 and ΔP c (t) > 0, it means that the actual voltage and power are higher than the expected value, the output power of the photovoltaic inverter needs to be reduced or the charging capacity of the energy storage device needs to be increased, to maintain the stability of voltage and power; if ΔV c (t) < 0 and ΔP c (t) < 0, it means that the actual voltage and power are lower than the expected value, the output power of the photovoltaic inverter needs to be increased or the charging capacity of the energy storage device needs to be reduced, through continuous adaptive adjustment, to ensure that the power distribution network can maintain stable voltage and power state under different operating conditions.

[0103] In summary, due to the adoption of the above technical scheme, the beneficial technical effects of the invention are:

[0104] The power distribution network multi-time scale voltage regulation method of the application effectively solves the above problems. By real-time monitoring of the key parameters of the power distribution network, and timely identification of topology changes by applying clustering analysis algorithm, the power distribution network model can be quickly updated, so that it can always accurately reflect the actual operation state of the power grid, thereby effectively responding to the dynamic changes of the topology structure.

[0105] In terms of multi-time scale voltage regulation strategy, the long-time scale load prediction and generation plan equipment adjustment are combined with the short-time scale real-time adjustment, and the voltage influencing factors under different time scales are fully considered. The long-time scale strategy formulates a reasonable generation plan based on energy resources, power grid operation and power market demand, and adjusts the equipment according to the power comparison of load and generation equipment, such as accurately adjusting the transformer tap changer and capacitor bank input capacity, to ensure that the voltage is stable in a reasonable range for a long time. The short-time scale strategy effectively suppresses short-time voltage fluctuations by monitoring the load and distributed power output changes at high frequency, and timely controls the charge and discharge of energy storage devices or adjusts the output power of distributed power sources.

[0106] Sensitivity analysis and control strategy optimization further improve the regulation accuracy. Through sensitivity analysis of different equipment and control methods, and combining with the influence weight to optimize the control strategy, the equipment and control parameters can be more reasonably adjusted according to the actual situation, avoiding voltage fluctuations caused by improper adjustment, and achieving more accurate voltage control.

[0107] The distributed collaborative control framework realizes efficient coordination of photovoltaic inverters and energy storage devices. Through reasonable configuration of communication protocols and data sharing mechanisms between regional controllers, information is quickly exchanged and collaborative decisions are made, ensuring that the global situation can be fully considered in the voltage regulation process in different regions, improving the voltage stability and power quality of the entire power distribution network.

[0108] Real-time feedback and adaptive adjustment mechanism enables the regulation system to continuously optimize the control strategy according to actual operation data. By continuously monitoring voltage and power flow data, calculating deviations and adaptively adjusting the control strategy, a closed-loop control is formed, further enhancing the system's adaptability to various complex operating conditions, ensuring that the power distribution network can maintain stable voltage and power state under different operating conditions, effectively improving the power supply reliability and consumption capacity of distributed energy of the power distribution network, and improving the overall operation efficiency and stability of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0109] Figure 1 A logic diagram of a power distribution network multi-time scale voltage regulation method suitable for topology changes;

[0110] Figure 2 A logic diagram of a method for detecting topology changes of a power distribution network and updating a power distribution network model;

[0111] Figure 3 Method logic diagram for multi-time scale voltage regulation strategy;

[0112] Figure 4 Method logic diagram for sensitivity analysis and control strategy optimization;

[0113] Figure 5 Method logic diagram for coordinated control of photovoltaic inverters and energy storage devices;

[0114] Figure 6 Method logic diagram for adaptive adjustment of distributed collaborative control strategy. DETAILED DESCRIPTION

[0115] In order to make the purpose, technical scheme and advantages of the invention clearer, the invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the invention and do not limit the invention.

[0116] As shown in Figure 1 A multi-time scale voltage regulation method for power distribution networks adapting to topology changes, comprising the following steps:

[0117] S1. Power distribution network topology change detection and power distribution network model updating, real-time monitoring of key parameters of the power distribution network, taking the key parameters as the direct feedback of the power distribution network operation state, applying clustering analysis algorithm to process the key parameters, identifying the topology change in the power distribution network through the processed key parameters, and dynamically updating the power distribution network model according to the topology change;

[0118] S2. Multi-time scale voltage regulation strategy design, based on the dynamically updated power distribution network model, designing long-time scale and short-time scale voltage regulation strategies; the long-time scale strategy focuses on device adjustment based on load prediction and generation plan, and the short-time scale strategy is for rapid adjustment to real-time load fluctuation and distributed power output change;

[0119] S3. Sensitivity analysis and control strategy optimization, based on the multi-time scale voltage regulation strategy designed in S2, performing sensitivity analysis, and optimizing the long-time scale and short-time scale voltage regulation strategies according to the results of the sensitivity analysis, the optimization process considering the influence weight of the control device to achieve more accurate voltage control;

[0120] S4. Distributed collaborative control framework, based on the optimized voltage regulation strategy in S3, constructing a distributed collaborative control framework, and realizing coordinated control of photovoltaic inverters and energy storage devices in each local control area through the distributed collaborative control framework;

[0121] S5. Real-time feedback and adaptive adjustment, real-time collection of voltage and power flow data of each control area, adaptive adjustment of distributed cooperative control strategy in S4 according to collected voltage and power flow data.

[0122] As shown in Figure 2 , the S1, the power distribution network topology change detection and updating method of power distribution network model includes:

[0123] S11. Key parameter monitoring, key parameters including node voltage, line power flow, etc. of power grid, key parameters obtained by installing high-precision sensors on each key node and line of power distribution network;

[0124] S12. Application of clustering analysis algorithm, K-Means clustering algorithm is used to process key parameters, key parameter data is taken as input, calculation is carried out according to steps of K-Means algorithm, clustering center is updated by iteration, so that distance square sum of data points (key parameter values) to its belonging clustering center is minimum, and its objective function is:

[0125]

[0126] Wherein, Jtotal distance square sum, i.e. objective function; x i is data point (i.e. key parameter value), u k is the center of the kth cluster; K cluster number is pre-set cluster number, K is reasonably selected according to scale and structure characteristics of power distribution network;

[0127] S13. Identify topology change in power distribution network, in the running process of clustering analysis algorithm in S12, data points can be more reasonably divided into different clusters by continuously updating clustering center; when topology structure of power distribution network changes, data distribution of key parameters will change, change of key parameters will be reflected on clustering result, so as to identify topology change through clustering analysis result; in actual use process, if a line is disconnected, node voltage and line power flow data related to the line will abnormally change, in clustering result, some data points originally belonging to the same cluster will be divided into different clusters, or position and value of clustering center will greatly change, topology change condition is timely and accurately identified through dynamic monitoring and analysis of clustering result;

[0128] S14. Dynamic updating of the power distribution network model, a node-branch model based on graph theory is used to construct the power distribution network model, in which the nodes have attributes such as node voltage and injected power; the branches have attributes 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, i.e., 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; for example, the injected power of the nodes connected to the disconnected branch will change, and its value needs to be recalculated; for the node voltage, if a node loses part of the connection due to the topology change, its voltage value will be recalculated and updated according to Kirchhoff's law and circuit equivalence principle to ensure that the power distribution network model can accurately reflect the actual power grid topology structure and.

[0129] In S14, the node-branch model based on graph theory includes the following steps:

[0130] S141. Node-branch association matrix construction, an m*n matrix is constructed 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 a ij is defined as:

[0131] If branch j is connected to node i and the current direction is away from the node, then a ij = 1;

[0132] If branch j is connected to node i and the current direction is directed to the node, then a ij = -1;

[0133] If branch j is not connected to node i, then a ij = 0;

[0134] S142. Node voltage equation establishment, 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, and the current is represented by voltage and branch impedance;

[0135] The branch admittance connecting the node and the node is The voltage of node n is V n , the voltage of node j is V j , and according to Ohm's law, the branch current is

[0136]

[0137] Since the current flowing into node n is equal to all the branch currents connected to node n , then I nj = Y nj (V n -V j), m is the number of branches connected to node n, substituting we have:

[0138]

[0139] The injection current of node n The injection power P n and Q n and the node voltage V n are calculated as follows,

[0140] In the steady state, according to KCL, The node voltage equation is obtained:

[0141]

[0142] In actual calculation, for a power distribution network with N nodes, N node voltage equations are obtained, and the equations are solved to obtain the voltage values of each node. The solving method uses numerical calculation methods such as Gauss-Seidel iteration method, and the iteration formula is:

[0143]

[0144] Where k represents the iteration number; Vn(k+1) represents the voltage value of node n in the k+1 iteration, Vj(k) represents the voltage value of node j in the k iteration; the iteration process is continued until a certain convergence condition is met, such as the absolute value of the node voltage difference between adjacent two iterations is less than a set threshold (10 -4 ), the iteration is ended, and the obtained node voltage value is the solution;

[0145] S143. 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:

[0146] The active power flow of the branch is:

[0147]

[0148] The reactive power flow of the branch is:

[0149]

[0150] Where, R ij is the resistance of branch ij; g ij is the conductance; b ij is the susceptance; V i is the voltage of node i; V j is the voltage of node j; θ ij is the phase difference of the voltages of node i and node j;

[0151] In actual calculation, for a power distribution network with M branches, M branch power flow equations are obtained; by simultaneously solving the node voltage equations and the branch power flow equations, the values of the node voltage and the branch power flow are constantly updated by calculation until the convergence condition (such as the power deviation being less than a set threshold) is met, so that the accurate power distribution network operating state parameters are obtained.

[0152] As shown in Figure 3 , in S2, the steps of designing the multi-time scale voltage regulation strategy include:

[0153] S21. Long-time scale strategy design:

[0154] S211. Load forecasting, ARIMA model in time series analysis method is used for load forecasting, the formula is:

[0155]

[0156] Where, p is the autoregressive order; q is the moving average order; L is the lag operator; Y t is a time series; ε t is a white noise sequence; is an autoregressive coefficient; θ j′ is a 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 stationary, it becomes stationary through difference operation;

[0157] According to the characteristics of historical load data, select appropriate model parameters; for load data with obvious daily periodicity and weekly periodicity, set d as 1 (handle daily periodicity) and 7 (handle weekly periodicity) respectively. Through analysis of historical load data and estimation of model parameters, the active load demand and reactive load demand in the future period (such as the next 24 hours) are predicted;

[0158] S212. Generation plan and equipment adjustment, according to the generation plan, the active power and reactive power of the power generation equipment are determined, the generation plan is based on the availability of energy resources (such as coal reserves of thermal power generation, reservoir water level of hydropower generation, etc.), operation requirements of power grid (such as total load demand of system, reserve capacity requirement, etc.) and demand of power market (such as price fluctuation, peak and valley change of electricity demand, etc.) and other factors;

[0159] Compare the load demand and the output power of the power generation equipment, if the active load demand is greater than the active power of the power generation equipment, consider increasing the input of adjustable equipment;

[0160] For transformer taps, adjust the tap position reasonably according to the voltage deviation, the transformer ratio is k, the high-voltage side voltage is V H , and the low-voltage side voltage is VL ,but The low-voltage side voltage can be changed by adjusting k. If the voltage is too low, the transformer turns ratio k can be increased appropriately to improve the low-voltage side voltage.

[0161] For capacitor banks, capacitor banks of appropriate capacity are deployed according to the reactive power demand to provide reactive power support and improve voltage stability. The deployed capacity of the capacitor banks 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 calculations, factors such as the rated voltage of the capacitor and the system voltage also need to be considered for correction.)

[0162] Conversely, if the active load demand is less than the active power generation of the generating equipment, the opposite operation needs to be performed, which requires reducing equipment input or making other adjustments, such as adjusting the transformer tap position to reduce voltage, or reducing the input capacity of the capacitor bank.

[0163] S22. Short-term scale strategy: Real-time monitoring of load fluctuations and distributed power output changes (including distributed power sources such as photovoltaic and wind power). The data collection frequency should be high enough (e.g., data is collected once every 5 minutes) to accurately capture real-time changes.

[0164] When ΔP l (t)+ΔP g (t)>0 (indicating a power deficit in the system), where ΔP l (t) represents the change in load power at time t, ΔP g (t) represents the change in power generation at time t. Power is replenished by controlling the rapid discharge of the energy storage device to maintain voltage stability.

[0165] When ΔP l (t)+ΔP g (t) < 0 (indicating excess system power), control the charging of energy storage devices or adjust the output power of distributed power sources (such as by controlling the power factor of photovoltaic inverters).

[0166] like Figure 4 As shown, in step S3, the method for sensitivity analysis and control strategy optimization includes:

[0167] S31. Sensitivity Analysis: For long-term strategies, calculate the sensitivity of equipment adjustments to voltage. Obtain voltage changes under different equipment adjustment amounts through actual measurements and data analysis. Conduct multiple tests under different load levels (e.g., light load, full load) and operating conditions (e.g., different distributed power supply output conditions). The formula for calculating the sensitivity of equipment adjustments to voltage is:

[0168]

[0169] wherein S V-dev represents the sensitivity of the device adjustment to the voltage, AV represents the change of the voltage; and Adev represents the adjustment amount of the device; for example, when adjusting the transformer tap, Adev is the adjustment amount of the tap, and by measuring and analyzing the voltage change under different adjustment amounts, the sensitivity of the transformer tap to the voltage is obtained;

[0170] For the short-time scale strategy, the sensitivity of the energy storage device and the distributed power supply control to the voltage is calculated, for the energy storage device, the sensitivity of the energy storage device to the voltage S V-st is represented as:

[0171]

[0172] AV' is the voltage change caused by the charging and discharging operation of the energy storage device; and AI is the current change;

[0173] For the distributed power supply, the sensitivity of the distributed power supply control to the voltage S V-DG is represented as:

[0174]

[0175] AV" is the voltage change caused by the power adjustment, and AP' is the power adjustment amount;

[0176] S32. Control strategy optimization, when optimizing the long-time scale strategy, the influence weight of the control device (for example, the weight of the transformer tap, the weight of the capacitor bank, etc.) is considered, and the weight is determined according to the importance of the device to the voltage regulation, the response speed, the adjustment range and other factors; for example, the transformer tap adjustment has a greater impact on the voltage and the response speed is relatively slow, so a higher weight is given; the capacitor bank regulation has an important role in the voltage stability, but its adjustment capacity is limited, so a moderate weight can be given;

[0177] According to the sensitivity analysis results and the weight, the step size or decision rule of the device adjustment is adjusted, if the sensitivity of the transformer tap adjustment to the voltage is large and the weight of the transformer tap is also large, it is indicated that the transformer tap has a greater impact on the voltage and is more sensitive, so a smaller step size is used when adjusting the transformer tap, so that the voltage can be more accurately controlled, and the voltage fluctuation or instability caused by the too large adjustment amplitude can be avoided;

[0178] If the sensitivity of the transformer tap adjustment to the voltage is small 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, the sensitivity and weight are also reasonably adjusted;

[0179] wherein the calculation formula of the step size is:

[0180]

[0181] Where, Δdev o Δdev is the initial step size; n For the new step size; S V-tap Adjusting the voltage sensitivity of the transformer tap changer; ω tap The weight of the transformer tap;

[0182] When optimizing short-term strategies, the influence weights ω of energy storage devices and distributed power source control should be considered. st and ω DG Based on the sensitivity analysis results and weights, adjust the charging and discharging strategies of the energy storage device and the control strategies of the distributed power source.

[0183] If S V-st Larger and ω st The larger the voltage, the greater the impact on the energy storage device and the more sensitive it is to voltage. Therefore, more refined control strategies should be adopted when controlling the charging and discharging of the energy storage device, such as using more precise current limiting, to better maintain voltage stability and avoid excessive voltage fluctuations caused by improper charging and discharging operations.

[0184] The current limiting formula is as follows:

[0185]

[0186] I max For current limiting, I new New current limit;

[0187] If S V-st Larger and ω st For smaller systems, a more lenient control strategy is adopted; for distributed power source control, reasonable adjustments are also made based on its sensitivity and weight.

[0188] like Figure 5 As shown, in step S4, the method for constructing a distributed collaborative control framework to achieve coordinated control of the photovoltaic inverter and energy storage device within each local control area includes:

[0189] S41. Framework construction: Select communication protocol (such as IEC61850 protocol) and data sharing mechanism; For communication protocol, it is necessary to 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 area controller in the same subnet to ensure that they can communicate with each other, and set the data transmission rate to 100Mbps to meet the needs of fast data exchange;

[0190] Install a regional controller in each local control area and connect it with local photovoltaic inverters, energy storage devices, sensors and other equipment; the connection method can use wired Ethernet or wireless communication (such as Wi-Fi or ZigBee, according to actual environment and device compatibility); for example, for photovoltaic inverters, connect with the regional controller through the RS485 serial port to Ethernet module to realize data transmission and control instruction reception; for energy storage devices, if they support CAN bus communication, use a CAN to Ethernet gateway to connect to the regional controller;

[0191] S42. Data collection and transmission, the regional controller collects local voltage and power flow data through sensors, with a data collection frequency of 1 per second to ensure timely access to the latest grid operating status information; at the same time, receive the operating parameters feedback from photovoltaic inverters and energy storage devices, such as photovoltaic inverter output power, efficiency, operating temperature, energy storage device remaining capacity (SOC), charge and discharge current, voltage and other information;

[0192] Subsequently, according to the selected communication protocol, encapsulate the data into a standard data frame format and send it to the adjacent regional controller and the upper 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;

[0193] S43. Coordinate control strategy implementation, the regional controller receives data shared from other regional controllers, combines local data and optimized voltage regulation strategies, and determines the control requirements of local photovoltaic inverters and energy storage devices;

[0194] For photovoltaic inverters, calculate the power adjustment amount of the photovoltaic inverter according to the voltage deviation and power demand, the calculation formula is:

[0195] ΔP p =ΔV p ×S P-V ;

[0196] The voltage deviation is ΔV p , S P-V is the power-voltage sensitivity of the photovoltaic inverter, and ΔP p is the power adjustment amount;

[0197] Subsequently, generate a control signal, the control signal format needs to meet the communication protocol requirements of the photovoltaic inverter, including device address, function code, data content (such as power adjustment value) and other information, and send the control signal to the photovoltaic inverter to realize accurate control of its output power;

[0198] For the energy storage device, the charging and discharging power and time of the energy storage device are determined according to the voltage regulation requirement and the state of the energy storage device; when the voltage is low and the SOC of the energy storage device is in a reasonable discharging range, the calculation formula of the discharging power is:

[0199]

[0200] P dis is the discharging power, P max is the maximum discharging power of the energy storage device; SOC min and SOC max are the minimum and maximum allowed residual power, respectively;

[0201] Subsequently, the corresponding control instructions are generated and sent to the energy storage device through the communication interface of the energy storage device to control the charging and discharging operation of the energy storage device;

[0202] In the control process, the operating states of the photovoltaic inverter and the energy storage device are monitored in real time, and if abnormal conditions (such as sudden change of photovoltaic inverter output power, high temperature of the energy storage device) occur, the control strategy is adjusted in time or an alarm is issued to ensure safe and stable operation of the equipment.

[0203] As shown in Figure 6 , the method of adaptively adjusting the distributed collaborative control strategy in S4 in S5 includes:

[0204] S51. Real-time collection of voltage and power flow data of each control area, and transmission of the data to the area controller through sensors installed in each control area and a communication network;

[0205] S52. Adaptive adjustment, according to the collected voltage and power flow data, the voltage deviation and the power deviation are calculated, the voltage deviation and the power deviation values reflect the difference between the actual operation and the expected operation, and the distributed collaborative control strategy is adaptively adjusted according to the voltage deviation and the power deviation;

[0206] The calculation formula of the voltage deviation is:

[0207] ΔV c (t)=V c (t)-V r (t);

[0208] ΔV c (t) is the voltage deviation; V r (t) is the reference voltage value; V c (t) is the actual measured voltage value;

[0209] The calculation formula of the power deviation is:

[0210] ΔP c (t)=P c(t) - P r (t) ;

[0211] ΔQ c (t) = Q c (t) - Q r (t) ;

[0212] wherein, 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;

[0213] According to the voltage deviation and the power deviation, the distributed cooperative control strategy is adaptively adjusted. If ΔV c (t) > 0 and ΔP c (t) > 0, it means that the actual voltage and power are both higher than the expected values, and the output power of the photovoltaic inverter needs to be reduced or the charging amount of the energy storage device needs to be increased to maintain the stability of the voltage and power. If ΔV c (t) < 0 and ΔP c (t) < 0, it means that the actual voltage and power are both lower than the expected values, and the output power of the photovoltaic inverter needs to be increased or the charging amount of the energy storage device needs to be reduced. Through continuous adaptive adjustment, the power grid can maintain stable voltage and power state under different operating conditions.

[0214] The above is a preferred embodiment of the invention content and does not limit the invention content. Any modification, equivalent replacement and improvement made within the spirit and principle of the invention content shall be included in the protection scope of the invention content.

Claims

1. A method for multi-time scale voltage regulation of a power distribution network adapted to topology changes, characterized in that, The method comprises the following steps: S1. Power grid topology change detection and power grid model updating, real-time monitoring of key parameters of the power grid, the key parameters as direct feedback of the power grid operation state, application of clustering analysis algorithm to process the key parameters, identification of topology changes in the power grid through the processed key parameters, dynamic updating of the power grid model according to the topology changes; S2. Multi-time scale voltage regulation strategy design, based on the dynamically updated power grid model, design of long-time scale and short-time scale voltage regulation strategies; The long-time scale strategy focuses on equipment adjustment based on load prediction and power generation plan, and the short-time scale strategy is used for rapid adjustment in response to real-time load fluctuations and distributed power output changes; S3. Sensitivity analysis and control strategy optimization, based on the multi-time scale voltage regulation strategy designed in S2, sensitivity analysis is performed, and the long-time scale and short-time scale voltage regulation strategies are optimized according to the results of the sensitivity analysis, and the influence weight of the control equipment is considered in the optimization process to achieve more accurate voltage control; S4. Distributed collaborative control framework, based on the optimized voltage regulation strategy in S3, a distributed collaborative control framework is constructed, and through the distributed collaborative control framework, coordinated control of photovoltaic inverters and energy storage devices is realized in each local control area; S5. Real-time feedback and adaptive adjustment, real-time collection of voltage and power flow data in each control area, and adaptive adjustment of the distributed collaborative control strategy in S4 according to the collected voltage and power flow data.

2. The multi-time scale voltage regulation method for power distribution network adaptive to topology changes according to claim 1, characterized in that, In S1, the method for detecting topology changes in the power grid and updating the power grid model comprises: S11. Key parameter monitoring, the key parameters including node voltage and line power flow of the power grid, and the key parameters being obtained by installing high-precision sensors at each key node and line of the power grid; S12. Application of clustering analysis algorithm, K-Means clustering algorithm is used to process the key parameters, the key parameter data is taken as input, calculation is performed according to the steps of K-Means algorithm, and the distance square sum of the key parameter value to the cluster center to which it belongs is minimized through continuous iteration and update of the cluster center, and the objective function is: Wherein, Jtotal distance square sum, namely the objective function; x i is the value of the key parameter, u k is the center of the kth cluster; K cluster number is the pre-set cluster number, K is reasonably selected according to the scale and structure characteristics of the power distribution network; S13. Identification of topology changes in the power grid, in the running process of the clustering analysis algorithm in S12, the data points can be more reasonably divided into different clusters through continuous iteration and update of the cluster center; when the topology structure of the power grid changes, the data distribution of the key parameters will change, and the change of the key parameters will be reflected on the clustering result, so that the topology change can be identified through the clustering analysis result; S14. Dynamic updating of the power grid model, a node-branch model based on graph theory is used to construct the power grid model, in which the nodes have node voltage and injected power attributes; the branches have resistance, reactance and line power flow attributes; when the topology changes, the branch is disconnected, and the connection between the nodes related to the branch needs to be marked as disconnected in the model, and the attribute values of the related nodes and branches are updated.

3. The power distribution grid multi-time scale voltage regulation method adaptive to topology changes according to claim 2, characterized in that, In S14, the method for constructing the power grid model based on the node-branch model based on graph theory comprises: S141. Node-branch association matrix construction, an m x n matrix is constructed 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 ai j is defined as: If branch j is connected to node i and the current direction is away from the node, then a, = 1 j = 1; If branch j is connected to node i and the current direction points towards the node, then a, = -1 j = -1; If branch j is not connected to node i, then ai j = 0; S142. Node voltage equation establishment, according to Kirchhoff's current law, for each node, the sum of the current flowing into the node is equal to the sum of the current flowing out of the node, the current is expressed by voltage and branch impedance; The branch admittance of the connection node and the node is The voltage of the node n is V n , the voltage of the node j is V j , according to Ohm's law, the branch current I nj = Y nj (V n -V j ); the total current of the node is: Since the current flowing into node n is equal to the sum of all the branch currents connected to node n nj = Y nj (V n -V j ), m is the number of branches connected to node n, and substituting gives: Injection current of node n By injection power P n and Q n and node voltage V n are calculated, In the steady state case, according to KCL, The node voltage equation is obtained as In actual calculation, for a power distribution network with N nodes, N node voltage equations are obtained, and the voltage values of each node are obtained by solving the equations, and the solving method adopts the Gauss-Seidel iteration method numerical calculation method, and the iteration formula is: where k denotes the iteration number; Vn(k+1) denotes the voltage value of node n at the k+1 iteration, Vj(k) denotes the voltage value of node j at the k iteration; S143. Power flow equation establishment, 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: wherein Ri j is the resistance of branch ij; gi j is the conductance; bi j is the susceptance; Vi is the voltage at node i; V j is the voltage at node j; θi j is the phase difference between the voltages at node i and node j; In actual calculation, for a power distribution network with M branches, M branch power flow equations are obtained; by solving the node voltage equation and the branch power flow equation, the values of the node voltage and the branch power flow are updated constantly, until the convergence condition is met, so that the accurate power distribution network operating state parameters are obtained.

4. The power distribution grid multi-time scale voltage regulation method adaptive to topology changes according to claim 1, characterized in that, In S2, the steps of the multi-time scale voltage regulation strategy design include: S21. Long-time scale strategy design: Load prediction, ARIMA model in time series analysis method is used for load prediction, and the formula is: where p is the autoregressive order; q is the moving average order; L is the lag operator; Y t is the time series; ε t is the white noise sequence; is the autoregressive coefficient; θ j′ is the moving average coefficient; θ0is the constant term in the model; d is the differencing order, which makes the original time series stationary through differencing when it is not stationary in time series analysis; Generation plan and equipment adjustment, the active power and the reactive power of the power generation equipment are determined according to the generation plan, and the generation plan is based on the availability of energy resources, the operation requirements of the power grid and the demand factors of the power market; Compare the load demand and the output power of the generation equipment, if the active load demand is greater than the active power of the generation equipment, consider increasing the input of adjustable equipment; For the transformer tap, according to the voltage deviation, the tap position is reasonably adjusted, the transformer ratio is k, the high-voltage side voltage is V H , and the low-voltage side voltage is V L . The low-voltage side voltage is changed by adjusting k, and if the voltage is low, the transformer ratio k is appropriately increased to increase the low-voltage side voltage. For the capacitor bank, according to the reactive power demand situation, the corresponding capacity of the capacitor bank is put into operation to provide reactive power support and improve voltage stability. The capacity of the capacitor bank is calculated according to the reactive power shortage. The reactive power shortage is ΔQ, and the capacity of a single capacitor is C. Therefore, the number of capacitor banks that need to be put into operation is On the contrary, if the active load demand is less than the active power of the generation equipment, the opposite operation is performed, and the equipment input needs to be reduced or other adjustments are needed; S22. Short-time scale strategy, real-time monitoring of load fluctuation and distributed power output change, the monitoring data collection frequency should be high enough to accurately capture real-time changes; When ΔP l (t) + ΔP g (t) > 0, where ΔP l (t) represents the load power variation at time t, and ΔP g (t) represents the power generation variation at time t, the power is supplemented by controlling the energy storage device to discharge rapidly, and the voltage is maintained stable. When ΔP l (t) + ΔP g (t) < 0, the energy storage device is controlled to charge or the output power of the distributed power source is adjusted.

5. The power distribution grid multi-time scale voltage regulation method adaptive to topology changes according to claim 1, characterized in that, In S3, the method of sensitivity analysis and control strategy optimization includes: S31. Sensitivity analysis, for the long-time scale strategy, the sensitivity of equipment adjustment to voltage is calculated, the voltage change under different equipment adjustment amounts is obtained through actual measurement and data analysis, and multiple tests are performed under different load levels and operating conditions; the calculation formula of the sensitivity of equipment adjustment to voltage is: where S V-dev represents the sensitivity of the device adjustment to the voltage, AV represents the amount of change in the voltage; and Adev represents the amount of device adjustment. For short-time scale strategy, the sensitivity of energy storage device and distributed power supply control to voltage is calculated, for energy storage device, the sensitivity of energy storage device to voltage S V-st is expressed as: ΔV' is the voltage change caused by the charging and discharging operation of the energy storage device; ΔI is the current change; For a distributed power source, the sensitivity S of the distributed power source control to the voltage V-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-time scale strategy, the influence weight of the control equipment is considered, and the weight is determined according to the importance of the equipment to voltage regulation, response speed and adjustment range factors; According to the sensitivity analysis results and the weight, the step or decision rule of the equipment adjustment is adjusted. If the sensitivity of the transformer tap adjustment to the voltage is large and the weight of the transformer tap is also large, it is indicated that the transformer tap has a large influence on the voltage and is sensitive. Therefore, a small step is adopted when adjusting the transformer tap, so that the voltage can be controlled more accurately, and the voltage fluctuation or instability caused by the large adjustment amplitude can be avoided. If the sensitivity of the transformer tap adjustment to the voltage is small and the weight of the transformer tap is also small, a large step is adopted to improve the adjustment efficiency. For the adjustment of the capacitor bank, the sensitivity and the weight are also reasonably adjusted. The calculation formula of the step is: where Δdev o is the initial step; Δdev n is the new step; S V-tap is the sensitivity of the transformer tap adjustment to the voltage; ω tap is the weight of the transformer tap. In optimizing the short-time scale strategy, the influence weight ω of the energy storage device and the distributed power supply control is considered st and ω DG According to the sensitivity analysis result and the weight, the charge-discharge strategy of the energy storage device and the control strategy of the distributed power supply are adjusted. If S V-st Larger and ω st Larger, it means that the energy storage device has a greater impact on the voltage and is more sensitive, so a more refined control strategy is adopted when controlling the charging and discharging of the energy storage device, and a more accurate current limit is adopted to better maintain voltage stability and avoid excessive voltage fluctuations due to improper charging and discharging operations. The current limiting formula is: I max for current limit, I new new current limit; If S V-st is larger and ω st is smaller, a more relaxed control strategy is adopted; for distributed power control, the sensitivity and weight are also adjusted reasonably.

6. The power distribution grid multi-time scale voltage regulation method adaptive to topology changes according to claim 1, characterized in that, In the S4, the method for constructing a distributed collaborative control framework in each local control area to realize the coordinated control of the photovoltaic inverter and the energy storage device comprises: S41. Framework construction, select communication protocol and data sharing mechanism; for the communication protocol, it needs to be configured according to its standard specification, including setting the network address, port number, data transmission rate parameter; Install a regional controller in each local control area and connect it with the local photovoltaic inverter, energy storage device and sensor equipment; the connection mode adopts wired Ethernet or wireless communication; S42. Data acquisition and transmission, the regional controller collects the local voltage and power flow data through the sensor, and the data acquisition frequency is set to 1 time per second to ensure that the latest grid operation state information can be obtained in time; at the same time, the operation parameters fed back by the photovoltaic inverter and the energy storage device are received; Then, according to the selected communication protocol, the data is packaged into a standard data frame format, and is sent to the adjacent regional controller and the upper control center; the data frame should contain the data source, timestamp, data type key information, so that the receiver can accurately parse and use the data; S43. Implementation of coordinated control strategy, the regional controller receives the data shared by other regional controllers, and combines the local data and the optimized voltage regulation strategy to judge the control demand of the local photovoltaic inverter and the energy storage device; For the photovoltaic inverter, according to the voltage deviation and the power demand, the power adjustment amount of the photovoltaic inverter is calculated, and the calculation formula is: ΔP p = ΔV p × S P-V ; The voltage deviation is ΔV p , S P-V The power-voltage sensitivity of the photovoltaic inverter, ΔP p is the power adjustment amount; Then, the control signal is generated, the control signal format needs to meet the communication protocol requirements of the photovoltaic inverter, including device address, function code, data content information, and the control signal is sent to the photovoltaic inverter to realize the accurate control of the output power of the photovoltaic inverter; For the energy storage device, according to the voltage regulation demand and the state of the energy storage device, the charge and discharge power and time of the energy storage device are determined; when the voltage is low and the SOC of the energy storage device is in a reasonable discharge range, the calculation formula of the discharge power is: P dis is the discharge power, P max is the maximum discharge power of the energy storage device; SOC min and SOC max are the minimum and maximum residual charge allowed, respectively; Then, the corresponding control instruction is generated and sent to the energy storage device through the communication interface of the energy storage device to control the charge and discharge operation of the energy storage device; In the control process, the running state of the photovoltaic inverter and the energy storage device is monitored in real time, and if an abnormal condition occurs, the control strategy is adjusted in time or an alarm is issued to ensure the safe and stable operation of the equipment.

7. The power distribution grid multi-time scale voltage regulation method adaptive to topology changes according to claim 1, characterized in that, The method for adaptively adjusting the distributed cooperative control strategy in S4 by real-time collection of voltage and power flow data of each control area in S5 comprises: S51. Real-time collection of voltage and power flow data of each control area, and transmission of the data to the area controller through sensors installed in each control area and a communication network; S52. Adaptive adjustment, calculation of voltage deviation and power deviation according to the collected voltage and power flow data, the voltage deviation and power deviation values reflecting the difference between the actual operation and the expected operation, and adaptive adjustment of the distributed cooperative control strategy according to the voltage deviation and power deviation; The voltage deviation calculation formula is: AV c (t) = V c (t) - V r (t); ΔV c (t) is the voltage deviation; V r (t) is the reference voltage value; V c (t) is the actual measured voltage value; The power deviation calculation formula is: ΔP c (t) = P c (t) - P r (t); ΔQ c (t) = Q c (t) - Q r (t); where 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 the power deviation, the distributed cooperative control strategy is adaptively adjusted. If ΔV c (t) > 0 and ΔP c (t) > 0, it means that the actual voltage and power are both higher than the expected values, and the output power of the photovoltaic inverter needs to be reduced or the charging amount of the energy storage device needs to be increased to maintain the stability of the voltage and the power. If ΔV c (t) < 0 and ΔP c (t) < 0, it means that both actual voltage and power are lower than the expected values, and the output power of the photovoltaic inverter needs to be increased or the charging amount of the energy storage device needs to be reduced. Through continuous adaptive adjustment, it ensures that the power grid can maintain stable voltage and power state under different operating conditions.

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