New energy power distribution system active defense method and device considering uncertainty of typhoon rainstorm evolution path

Abstract

The invention discloses a new energy power distribution system active defense method and device considering uncertainty of a typhoon rainstorm evolution path. The method comprises the following steps: obtaining a vulnerable element at each moment according to a time sequence scene; calculating load loss caused by disconnection of the vulnerable element of the system at each moment through a multi-objective optimization model; considering each number of vulnerability elements at the moment, and combining the vulnerability elements at each moment with vulnerability load loss by considering all selection conditions when any number of vulnerability elements are selected; calculating a combined survival probability; sequentially running the vulnerable element combinations under each number at each moment according to survival probabilities from maximum to minimum in a sequence from the minimum number to the maximum number; rescheduling the system when each fragile element combination runs; and judging whether the system has load loss or not when each fragile element combination is put into operation according to the put-into-operation sequence, and if not, performing active defense at the moment according to the fragile element combination running this time and active scheduling.

Description

Technical Field

[0001] The present invention relates to the field of power emergency technology, and in particular to an active defense method and device for a new energy power distribution system taking into account the uncertainty of the evolution path of a typhoon or rainstorm. Background Art

[0002] The new energy distribution system has the characteristics of strong weather correlation and weak disaster bearing capacity. With the intensification of climate change, extreme natural weather events such as typhoons and rainstorms occur frequently, which often cause serious damage to the new energy distribution system and even cause large-scale power outages. Therefore, the research on pre-disaster prevention measures for new energy distribution systems to deal with extreme natural events is crucial, which is also an effective way to enhance the ability of power grids to resist extreme natural disasters.

[0003] Pre-disaster prevention measures for distribution systems generally include long-term resilience investment and short-term advance preparation. Long-term resilience investment is a measure oriented towards hardening. Among them, the reinforcement strategy of distribution infrastructure is the most direct means to deal with typhoons. In addition, installing underground cables, improving the location of substations, and carrying out vegetation management are also long-term resilience investment methods. However, although long-term resilience investment can improve the system's ability to cope with typhoons to a certain extent, it still cannot completely eliminate the threat posed by typhoons, and it is usually costly. Short-term advance preparation is operation-oriented, aiming to enable the system to actively cater to configuration adjustments when facing extreme natural disasters, rather than directly collapse. Common short-term advance preparations mainly include two types: 1) pre-deployment of flexible resources, such as mobile power supplies, remote control switches, soft switches, and maintenance personnel; 2) active dispatch management. In recent years, active dispatch management has gradually received more and more attention. As an important means of short-term advance preparation, active dispatch management can not only enhance the risk resistance of the distribution system before the disaster, but also effectively improve the recovery efficiency after the disaster, providing strong support for resisting extreme natural disasters such as typhoons and rainstorms. Some studies have proposed active management methods for distribution systems that take into account the worst-case scenario, ensuring the normal operation of important loads in the system before a storm occurs and reducing the vulnerability of the system when a disaster occurs. However, on the one hand, the operating state of the system is significantly affected by the intensity of typhoon and rainstorm disasters, but existing studies often ignore the impact of the uncertainty of the intensity of typhoon and rainstorm disasters on the accuracy of the operating state assessment of system components, resulting in poor results. On the other hand, existing active scheduling schemes are usually designed based on the worst-case scenario of the maximum wind speed of the typhoon, and do not fully consider the temporal impact of the gradual destruction of system components by typhoon and rainstorm disasters, resulting in poor flexibility and insufficient economy of the scheme.

[0004] Therefore, a new technical solution is urgently needed to solve the technical problem of how to design an active prevention solution for a new energy distribution system that takes into account the uncertainty of the evolution path of typhoons and rainstorms. Summary of the invention

[0005] The present invention provides an active defense method and device for a new energy distribution system taking into account the uncertainty of the evolution path of typhoons and rainstorms, so as to solve the technical problem of how to design an active prevention scheme for a new energy distribution system taking into account the uncertainty of the evolution path of typhoons and rainstorms.

[0006] To achieve the above-mentioned purpose, the present invention provides an active defense method for a new energy distribution system considering the uncertainty of the evolution path of typhoons and rainstorms, comprising: Obtain the dual uncertainty time series scenario of the typhoon and rainstorm evolution path and the coupling of system component states; obtain the vulnerable components at each moment during the typhoon and rainstorm duration based on the time series scenario.

[0007] The system is switched to island operation while maintaining droop control for the dispatchable distributed power sources in the system. The vulnerable load losses caused by disconnecting all vulnerable components of the system at each moment are calculated separately through a multi-objective optimization model.

[0008] Considering each number of the total number of fragile elements from 0 to the current moment, each fragile element with fragile load loss moment is combined when any number is selected, considering all the selected situations; calculate the survival probability of the fragile element combination.

[0009] In order from the smallest to the largest quantity, each combination of fragile elements with each quantity at each moment of fragile load loss is put into operation in sequence from the largest to the smallest survival probability, and the fragile elements outside the operating fragile element combination are disconnected.

[0010] The system is rescheduled when each combination of vulnerable components is put into operation through active scheduling and multi-objective optimization models. Active scheduling includes power generation re-scheduling, pre-disaster network reconstruction, droop control parameter optimization setting and protective voltage regulation.

[0011] Determine in turn in the order of commissioning whether there is a vulnerable load loss in the system when each vulnerable component combination is put into operation. If not, active defense is performed at this moment based on the vulnerable component combination put into operation this time and active scheduling.

[0012] Preferably, calculating the vulnerable load loss caused by disconnecting all vulnerable components of the system at each moment by means of a multi-objective optimization model includes: Case 1: If there is no load loss in the distribution system itself, the vulnerable load loss caused by disconnecting all vulnerable components of the system at each moment is directly calculated through the multi-objective optimization model.

[0013] Scenario 2: The distribution system has its own load loss. When calculating the vulnerable load loss caused by disconnecting all vulnerable components of the system at each moment through the multi-objective optimization model, it is necessary to first calculate the total load loss of the distribution system, and then subtract the inherent load loss from the total loss of the distribution system to obtain the vulnerable load loss.

[0014] The inherent load loss includes the load loss of the distribution system excluding the vulnerable load loss caused by disconnecting the vulnerable components; the total load loss of the distribution system includes the inherent load loss and the vulnerable load loss.

[0015] Preferably, the dual uncertainty time series scenario of obtaining the typhoon rainstorm evolution path and the coupling of the system component state includes: Construct an uncertainty model of typhoon and rainstorm disasters and a system disaster dynamics model under typhoon and rainstorm.

[0016] Based on the uncertainty model of typhoon and rainstorm disasters and the system disaster dynamics model under typhoon and rainstorm, a dual uncertainty time series scenario of typhoon and rainstorm evolution path and system component state coupling is generated.

[0017] Preferably, the typhoon and rainstorm disaster uncertainty model includes two parts: a typhoon and rainstorm evolution uncertainty path model and a typhoon and rainstorm wind field model: The uncertainty path model of typhoon rainstorm evolution includes: In order to simulate the possible movement path of typhoon and rainstorm after landing, the typhoon and rainstorm empirical path mechanism model is used to calculate the moving wind speed and direction angle : (1) (2) (3) (4) in, It represents the logarithmic deviation of the moving wind speed of the typhoon rainstorm center; Indicates the moving direction angle of the typhoon rainstorm center; It represents the logarithm of the moving wind speed of the typhoon rainstorm center; and Respectively Moving wind speed and moving direction angle of the typhoon and rainstorm center at the moment; and Respectively The latitude and longitude of the typhoon and rainstorm center at the moment; and are the prediction errors that follow Gaussian distribution; and is the fitting coefficient obtained based on the historical data of different regions; is the time interval; Based on the typhoon rainstorm empirical path mechanism model combined with the data-driven method, the prediction error is processed through historical data, including: It is assumed that the typhoon rainstorm path revolves around the predicted path, and the prediction error obeys a Gaussian distribution with a mean of zero; the uncertainty of the typhoon rainstorm path is quantified by the deviation between the empirical path and the historical true path, including the first calculation.

[0018] The first calculation includes: selecting two adjacent typhoon rainstorm locations A1 and A2 from the historical real path; calculating the empirical path from A1 to A3 by equations (1) to (4); there is a prediction error of the moving direction angle between the historical real path A1 to A2 and the empirical path A1 to A3 ; The predicted moving speed and moving direction angle of the empirical path A1 to A3 can be calculated according to the coordinates of A1 and A3; the historical real moving speed and moving direction angle of the historical real path A1 to A2 are obtained; the error between the empirical path A1 to A3 and the historical real path A1 to A2, that is, the central moving wind speed prediction error, can be obtained according to the predicted moving speed and moving direction angle and the historical real moving speed and moving direction angle and the prediction error of the moving direction angle .

[0019] The first calculation is performed on each historical real path to obtain the corresponding and , and then get the central moving wind speed prediction error set and the moving direction angle prediction error set .

[0020] Will and The prediction error in is the same as the prediction value calculated by equations (1) and (2) and Add them together to obtain various typhoon and rainstorm path scenarios.

[0021] Typhoon rainstorm wind field model includes: For the typhoon rainstorm path scenario moving over the ocean surface, the first model can be used to estimate the time series wind speed at different locations , the first model includes: (5) (6) (7) (8) (9) (10) (11) (12) in, Indicates the circulation wind speed; and Respectively time Typhoon rainstorm wind speed and rainfall rate; , and is the correction factor; and are the distance between the power distribution system and the typhoon and rainstorm center and the maximum wind speed radius respectively; and is the central pressure difference and its rate of change; and They are the coordinates of the power distribution system and the typhoon rainstorm center respectively; The duration of the typhoon and rainstorm disaster; for time Typhoon wind speed based on the Batts model at ; express Maximum circulation wind speed at the moment; Indicates the moving wind speed in the center of a typhoon or rainstorm.

[0022] For the typhoon rainstorm path scenario moving on land, the wind speed decreases due to friction after the typhoon rainstorm lands. Therefore, the attenuation coefficient is introduced on the basis of the first model. It is assumed that the time series wind speed of the typhoon rainstorm moving on the ocean surface calculated by the first model is , and assume that the time series wind speed of the typhoon rainstorm moving over land is , then the attenuation coefficient can be expressed as: (13) in, and represent the roughness length and retardation factor respectively.

[0023] According to the geographical location of the typhoon and rainstorm, select or Calculate the time series wind speed.

[0024] Preferably, the system catastrophe dynamics model under typhoon and rainstorm includes two parts: system component failure probability model and photovoltaic and wind turbine output model: System component failure probability models include: Assuming that the system component failure is mainly caused by wind speed, the system component failure includes damage to distribution towers, failure of overhead distribution wires, and failure of photovoltaic components; assuming that different components of the distribution line are independent of each other, the failure probability of the distribution line can be calculated through the series model, which can be expressed as: Preferably, the system catastrophe dynamics model under typhoon and rainstorm includes two parts: system component failure probability model and photovoltaic and wind turbine output model: (14) (15) (16) in, and represent the failure probabilities of distribution lines, towers and conductors respectively; and Respectively represent the number of towers and conductor segments of the line; and Represents the towers of the line and wire segments probability of failure.

[0025] The standard log-normal distribution function is used to model the failure probability of photovoltaic components, which can be expressed as: (17) in, represents the standard lognormal distribution function; represents the failure probability of the PV element; and represent the standard deviation and the design wind speed of PV, respectively.

[0026] The photovoltaic and wind turbine output models include: In typhoons and rainstorms, the photovoltaic output is intact and available It can be expressed as: (18) (19) in, It represents the impact of typhoon and rainstorm disasters on light irradiance in logarithmic space; is the light irradiance under normal conditions; and They are typhoon intensity, normalized distance, photovoltaic power generation efficiency and area; Represents a collection of photovoltaic nodes.

[0027] Since wind turbines are usually resistant to typhoons and rainstorms, it is assumed that the wind turbines are not damaged or malfunction during the typhoon and rainstorm disaster. The wind field changes caused by typhoons and rainstorms directly affect the wind power output. Specifically, the available wind power output can be calculated through the wind speed-power curve of the wind turbine. , which can be expressed as: (20) in, and They represent the cut-in wind speed, rated wind speed and cut-out wind speed of the fan, and the values ​​are 3m / s, 12m / s and 25m / s respectively; and They represent air density, power coefficient, fan blade swept area and rated power respectively; Represents a collection of wind turbine nodes.

[0028] Preferably, generating a dual uncertainty time series scenario of typhoon and rainstorm evolution path and system component state coupling based on the typhoon and rainstorm disaster uncertainty model and the system disaster dynamics model under typhoon and rainstorm includes: Get the coordinates of the power distribution system; The coordinates, moving wind speed and moving direction angle of the typhoon rainstorm center at this moment are obtained, and the moving wind speed and moving direction angle of the typhoon rainstorm center at the next moment are calculated through the typhoon rainstorm empirical path mechanism model, i.e., equations (1) to (4).

[0029] Take the prediction error of the moving wind speed and moving direction angle of the typhoon rainstorm center and , the moving wind speed and moving direction angle of the typhoon rainstorm center at the next moment are compared with the prediction error and The corresponding addition is performed to obtain the coordinates of the typhoon rainstorm center at the next moment; the moving wind speed, moving direction angle and coordinates of the typhoon rainstorm center at subsequent moments are continuously calculated in time sequence until the maximum number of scenes of the typhoon rainstorm path samples reaches S trackmax。

[0030] The scene number is reduced by using the scene backward reduction algorithm. S trackmax Reduced to S reduced , and record each typical scene s ∈ S reduced The corresponding probability of occurrence .

[0031] Performing a second calculation at each moment during the typhoon and rainstorm duration period, the second calculation comprising: According to formula (10) and the coordinates of the distribution system, the distance from the typhoon and rainstorm center to the distribution system in each typical scenario is calculated. ,in Take 1, 2… S reduced ; Then, considering all typical scenarios, the expected time series distance can be expressed as .

[0032] According to equations (7) to (9) and (11) to (13), the expected predicted wind speed at the distribution system is obtained: .

[0033] According to equations (18) and (20), the expected photovoltaic output is calculated as Expected output of wind power .

[0034] According to equations (14) and (17), the failure probability of photovoltaic is calculated respectively: and the failure probability of the distribution line .

[0035] Using vulnerability thresholds As a criterion for determining vulnerable elements.

[0036] Distribution lines Vulnerability status , 0 represents a vulnerable line, and 1 represents a normal line.

[0037] Photovoltaic components Vulnerability status , 0 represents a fragile photovoltaic element, and 1 represents a good photovoltaic element.

[0038] The uncertainty vulnerability status of all components in the distribution system at this moment, as well as the uncertainty output of photovoltaic and wind power are obtained.

[0039] After completing the second calculation for each moment in the typhoon and rainstorm duration period, the time series scene is obtained.

[0040] Preferably, considering each number of the total number of vulnerable elements from 0 to the current moment, each vulnerable element with a vulnerable load loss moment is combined by considering all selected situations when selecting any number; calculating the survival probability of the vulnerable element combination includes: Assume that there is a vulnerable load loss moment including A vulnerable component, is a positive integer; Choose from a number of vulnerable components components, is an integer, ;get Combinations of vulnerable components; calculation The survival probabilities of the combinations of vulnerable components are sorted from the highest to the lowest survival probabilities.

[0041] The calculation of survival probability includes: (twenty one) in, represents the probability of survival; Indicates the combination of vulnerable components; Indicates the distribution line number; represents the probability of failure of the distribution line; Indicates the photovoltaic equipment number; represents the probability of PV failure; Indicates the moment; Indicates the duration of the typhoon and heavy rain.

[0042] All possible selection combinations are performed at each moment when there is a fragile load loss, and the survival probabilities of all fragile element combinations are calculated.

[0043] Preferably, the multi-objective optimization model includes an objective function and constraints; The multi-objective optimization model aims to minimize the overall operating cost, including minimizing the penalty cost caused by load reduction and reducing the operating cost of dispatchable distributed power sources, photovoltaic and wind power. The objective function of the multi-objective optimization model can be expressed as: (twenty two) (twenty three) (twenty four) (25) (26) (27) (28) in, Indicates the time when the typhoon and rainstorm begin; Indicates the time when the typhoon and rainstorm end; and They represent the load reduction penalty cost and the power generation operation cost, respectively, where the power generation operation cost includes the photovoltaic operation cost , Wind power operation costs , Dispatchy Distributed Generation Operating Cost and mobile energy storage operating costs ; and They represent the unit penalty cost of load reduction, the unit operating cost of photovoltaic power generation, the unit operating cost of wind power generation, the unit operating cost of mobile energy storage, and the unit operating cost of dispatchable distributed power generation respectively; express Active power of load at the moment; is a binary decision variable, where “1” represents load restoration, otherwise it represents load shedding; and Respectively The power generation of photovoltaic, wind power, distributed power and mobile energy storage at each moment are all continuous decision variables; and They represent the node set, photovoltaic node set, wind turbine node set, distributed power source node set and mobile energy storage node set respectively; and Respectively represent the indexes of the nodes where the load, photovoltaic, wind turbine, distributed power source and mobile energy storage are located; and is the weight coefficient, and .

[0044] The constraints of the multi-objective optimization model include power flow and safe operation constraints, photovoltaic and wind turbine operation constraints, load demand model based on static voltage, dispatchable distributed power sources and mobile energy storage operation constraints based on droop control characteristics, and radial topology constraints based on improved spanning tree.

[0045] Power flow and safe operation constraints include: The optimal power flow equation based on Disflow is used for modeling. The AC power flow equation is given by equations (29), (30) and (31), the line capacity constraint is expressed by equation (32), and the operation safety constraint is given by equations (33) and (34): (29) (30) (31) (32) (33) (34) in, express Time Node Injected active power; express Time Node Injected reactive power; and Respectively indicate lines and Active power on and Respectively indicate lines and Reactive power on and Respectively indicate lines and The current on and Respectively indicate lines and Reactance; and Respectively indicate lines and resistance; express The reactive power of the load at the moment; and Respectively Time Node Active output power of distributed power sources, photovoltaics, wind turbines and mobile energy storage; express Time Node Reactive output power of distributed power sources; is a binary variable, when the line The value is 1 when the power is on, otherwise it is 0; and Respectively represent nodes The voltage amplitude and system frequency at Representation Node The voltage amplitude at ; is a large positive number; and They are the maximum values ​​of node voltage, system frequency, branch current, branch active power and reactive power respectively; and are the minimum values ​​of node voltage, system frequency, branch current, branch active power and reactive power respectively; A collection of lines.

[0046] PV and wind turbine operation constraints include: Photovoltaic and wind turbines are modeled as PQ control resources with unity power factor. It is assumed that the photovoltaic units and wind turbines have strong anti-typhoon and rainstorm characteristics, that is, the design wind speed is higher than the maximum wind speed of the typhoon and rainstorm, and according to the vulnerability theory, the photovoltaic units and wind turbines will not fail during the typhoon and rainstorm. At the same time, it is assumed that both photovoltaic and wind turbines are equipped with energy storage and converters, and each photovoltaic unit and wind turbine is regarded as an independent entity, ignoring the detailed model of energy storage and converter. The actual available output of photovoltaic and wind turbines during typhoon and rainstorm is considered. Therefore, the operation constraints of photovoltaic and wind turbines can be expressed as follows: (35) (36) in, and Represent the expected output of photovoltaic and wind power respectively; and Respectively The power generation of photovoltaic and wind power at each moment.

[0047] The load demand model based on static voltage includes: To describe the relationship between load and voltage amplitude, a polynomial is used: Load Model: (37) (38) in, and are the participation factors of the constant impedance, current and power terms in the active power load, respectively; , and are the participation factors of the constant impedance, current and power terms in the reactive power load, respectively; and They represent the rated active power and reactive power of the load respectively.

[0048] The operating constraints of dispatchable distributed generation and mobile energy storage based on droop control characteristics include: The dispatchable distributed generation using droop control needs to meet the capacity and power constraints, active-frequency and reactive-voltage droop control characteristics, including: (39) (40) (41) (42) (43) (44) (45) (46) (47) in, and They represent the maximum active output power, maximum reactive output power and total output capacity of the dispatchable distributed generation respectively; and Respectively Time Node Droop control decision variables of distributed generation; and denote the reference voltage amplitude and reference frequency decision variables respectively; and Respectively represent the maximum values ​​of reference frequency, reference voltage, active power droop coefficient and reactive power droop coefficient; and Respectively represent the minimum values ​​of reference frequency, reference voltage, active power droop coefficient and reactive power droop coefficient; Represents the angular frequency of the system.

[0049] In addition to the controllable distributed power supply with droop control function, the mobile energy storage is also equipped with a droop controller; assuming that the mobile energy storage provides as much active output as possible, the mobile energy storage is charged or discharged according to the droop characteristics, which can be expressed as: (48) The charging or discharging action of mobile energy storage is subject to the rated capacity The restriction can be expressed as: (49) in, is the rated capacity of mobile energy storage; express The amount of electricity generated by mobile energy storage at all times.

[0050] The radial topology constraints based on the improved spanning tree include: During typhoon and rainstorm disasters, the distribution system may form multiple islands. Improved spanning tree constraints are used to meet the radial topology requirements of each island, including: (50) (51) in, and is a binary variable; if node is a substation node, then The value is 1, otherwise it is 0; if the node is a distributed power generation node, then The value is 1, otherwise it is 0; is a binary variable, indicating the node Whether it is the root bus in the island, if so, the value is 1, otherwise it is 0; and Busbar The parent node and child node collection of ; It is a binary variable, and its value is 1, indicating that the branch Power on, a value of 0 indicates a branch No electricity; and is a binary variable. If the node Is a node The parent node of , or busbar It is a busbar The parent node of , then select the branch Power on, take , and Cannot be 1 at the same time; represents a binary variable, represents a node Is it a node? The parent node of Time Node For Node The parent node of .

[0051] Preferably, the multi-objective optimization model also includes: Convex optimization solution of multi-objective optimization model: There are nonlinear equations in the multi-objective optimization model, including those in (29), (31), (37) and (38). , , in formula (42) , in formula (43) , in formula (48) , and equations (32) and (41); the nonlinear equations in the multi-objective optimization model can be divided into three types: the first type is the square of continuous variables, the second type is quadratic equations or quadratic inequalities, and the third type is the product of continuous variables; these three types of nonlinear equations are subjected to convex optimization processing: Convex optimization of nonlinear equations of the first type: Since equations (29), (31), (37) and (38) contain square terms of branch current and node voltage and , using the variable and To replace the two quadratic terms separately, we get: (52) (53) (54) (55) in, and Node Voltage and branches The square term of the current; and Node Voltage and branches The square of the current.

[0052] Since equations (54) and (55) contain square terms , so it is still a nonlinear expression. Therefore, Taylor series expansion is used to linearize equations (54) and (55), including: Assume that the initial value is , through Taylor series expansion we can get: (56) in, Represents an infinitesimal quantity; For Node Initial value of the voltage amplitude.

[0053] By ignoring the second-order and higher-order terms, equation (56) can be approximately expressed as: (57) The node voltage amplitude is usually about 1.0 pu, so assuming , formula (57) can be further approximated as: (58) The distance function definition of the Taylor series approximation includes: (59) in, is the distance function approximated by the Taylor series.

[0054] By substituting equation (58) into equations (54) and (55), an approximate load demand model is obtained, which can be expressed as: (60) (61) Convex optimization of nonlinear equations of the second type: Equations (32) and (41) can be transformed into second-order cone constraints and rotational second-order cone constraints, respectively, which can be expressed as: (62) (63) Convex optimization of the third type of nonlinear equations: Since equations (42), (43) and (48) all contain the product of two bounded continuous variables, the McCormick envelope approximation is used to process them and the auxiliary variable and Respectively and , and approximated using twelve linear constraints, can be expressed as: (64) (65) (66) (67) (68) (69) (70) (71) (72) (73) (74) (75) The present invention also provides an active defense device for a new energy power distribution system taking into account the uncertainty of the evolution path of typhoons and rainstorms, which is used in the method of the present invention. The device includes a first module, a second module, a third module, a fourth module, a fifth module and a sixth module.

[0055] The first module is used to obtain the dual uncertainty time series scenario of the typhoon and rainstorm evolution path and the coupling of system component states; based on the time series scenario, the vulnerable components at each moment during the typhoon and rainstorm duration are obtained.

[0056] The second module is used to switch the system to island operation and keep the dispatchable distributed power sources in the system using droop control; the vulnerable load loss caused by disconnecting all vulnerable elements of the system at each moment is calculated separately through a multi-objective optimization model.

[0057] The third module is used to consider each number of the total number of fragile elements from 0 to the current moment, and to combine each fragile element at the moment of fragile load loss when selecting any number of them, taking all the selected situations into consideration; and to calculate the survival probability of the fragile element combination.

[0058] The fourth module is used to put into operation, in order from the smallest to the largest number, each combination of fragile elements in each quantity at each moment of fragile load loss according to the highest to the lowest survival probability, and disconnect the fragile elements outside the operating fragile element combination.

[0059] The fifth module is used to reschedule the system when each combination of vulnerable components is put into operation through active scheduling and multi-objective optimization models. Active scheduling includes power generation re-scheduling, pre-disaster network reconstruction, droop control parameter optimization setting and protective voltage regulation.

[0060] The sixth module is used to determine whether there is a vulnerable load loss in the system when each vulnerable component combination is put into operation in the order of operation. If not, active defense is performed at this moment based on the vulnerable component combination put into operation this time and active scheduling.

[0061] The present invention has the following beneficial effects: The active defense method of the new energy distribution system considering the uncertainty of the evolution path of typhoon and rainstorm of the present invention forms a robust scheduling strategy by integrating dual uncertainty scenarios in advance, aiming to ensure that the system maximizes the number of vulnerable components that are out of service under full load operation conditions, thereby effectively reducing the overall vulnerability of the system in typhoon and rainstorm disasters. Switching the system to island operation not only helps to reduce the load loss of the distribution system during disasters and improve the safety of the system, but also reduces the protection coordination requirements with the main grid and alleviates other technical problems that may be caused during disasters. The vulnerable load loss caused by disconnecting the vulnerable components at each moment is calculated through a multi-objective model, and the vulnerable components at each moment of vulnerable load loss are combined considering all possible combinations, and the survival probability of each combination is calculated. The trial operation is started from the combination with the least number of vulnerable components, and then the vulnerable component combination with the least number of vulnerable components at each moment of vulnerable load loss is found and put into operation, thereby improving the safety and robustness of the system. The method of the present invention combines mechanism analysis and data-driven models to adopt a robust strategy for optimization. Compared with the active management method that does not consider the evolution path of typhoon and rainstorm, the method of the present invention shows the best comprehensive benefits. In the worst case, compared with the post-disaster dispatch results of the active management method and the passive strategy method that do not consider the evolution path of the typhoon and rainstorm, the method of the present invention effectively reduces the load loss and improves the satisfaction rate of the load demand. The active defense scheme obtained by the method of the present invention has good effect, flexibility and economy.

[0062] The active defense device of the new energy distribution system taking into account the uncertainty of the typhoon and rainstorm evolution path of the present invention is used in the method of the present invention and has the same beneficial effects as the method of the present invention.

[0063] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings: Figure 1 It is a flow chart of a preferred embodiment of the present invention.

[0065] Figure 2 It is a schematic diagram of prediction error of simulating typhoon and rainstorm path according to a preferred embodiment of the present invention.

[0066] Figure 3 Schematic diagram of mobile wind speed prediction error according to a preferred embodiment of the present invention.

[0067] Figure 4 It is a schematic diagram of the moving direction angle prediction error of the preferred embodiment of the present invention.

[0068] Figure 5 4 is a Taylor series approximation error curve diagram of a preferred embodiment of the present invention.

[0069] Figure 6 It is a schematic diagram of the test system of the preferred embodiment of the present invention and the simulated typical typhoon and rainstorm movement path.

[0070] Figure 7 It is a schematic diagram of a modified IEEE 33-node test system according to a preferred embodiment of the present invention.

[0071] Figure 8 It is a wind-induced vulnerability curve diagram of overhead distribution lines and photovoltaics according to a preferred embodiment of the present invention.

[0072] Fig. 9 It is a schematic diagram of the network topology structure of the active defense of the power distribution system at time t5 of the preferred embodiment of the present invention.

[0073] Fig.10 It is a schematic diagram of the network topology structure of the active defense of the power distribution system at time t6 of the preferred embodiment of the present invention.

[0074] Fig.11 It is a schematic diagram of the network topology structure of the active defense of the power distribution system at time t7 of the preferred embodiment of the present invention.

[0075] Fig.12 It is a schematic diagram of active power output of a distributed power source according to a preferred embodiment of the present invention.

[0076] Fig.13 It is a schematic diagram of active output of a fan according to a preferred embodiment of the present invention.

[0077] Fig.14 It is a schematic diagram of photovoltaic active output according to a preferred embodiment of the present invention.

[0078] Fig.15 It is a schematic diagram of active output of mobile energy storage according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0079] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0080] In a preferred embodiment of the present invention, the distribution system maintains an island operation mode during active defense; since the range of change of the steady-state frequency is usually very small (about ±0.005pu), the influence of the frequency change on the bus admittance is negligible; the dispatchable distributed power source adopts traditional active-frequency and reactive-voltage droop control, mainly due to the influence of the coupled inductance used at the output end of the distributed power interface converter, the network feeder impedance ratio and the virtual inductive output impedance, to ensure the frequency and voltage stability of the island system; the droop control and damping device of the distribution system can effectively control the transient stability and dynamic oscillation of the system.

[0081] See also Figure 1 In a preferred embodiment of the present invention, a method for active defense of a new energy distribution system considering the uncertainty of the evolution path of typhoons and rainstorms is provided, comprising: F1. Obtain a dual uncertainty time series scenario of the coupling of the typhoon and rainstorm evolution path and the system component status; and obtain the vulnerable components at each moment during the typhoon and rainstorm duration period according to the time series scenario.

[0082] In a preferred embodiment of the present invention, the dual uncertainty time series scenario of obtaining the typhoon and rainstorm evolution path and the coupling of the system component status includes: Construct an uncertainty model of typhoon and rainstorm disasters and a system disaster dynamics model under typhoon and rainstorm.

[0083] Based on the uncertainty model of typhoon and rainstorm disasters and the system disaster dynamics model under typhoon and rainstorm, a dual uncertainty time series scenario of typhoon and rainstorm evolution path and system component state coupling is generated.

[0084] In a preferred embodiment of the present invention, the typhoon and rainstorm disaster uncertainty model includes two parts: a typhoon and rainstorm evolution uncertainty path model and a typhoon and rainstorm wind field model: The uncertainty path model of typhoon rainstorm evolution includes: In the active defense of the system, the dispatcher usually receives early warning information of typhoon and rainstorm activities, and the meteorological department also provides typhoon path forecast data. The dispatcher can use this information to evaluate the possible impact of typhoon and rainstorm disasters on the distribution system and formulate corresponding active defense plans and strategies. In order to simulate the possible movement path of typhoon and rainstorm after landing, the typhoon and rainstorm empirical path mechanism model is used to calculate the moving wind speed and direction angle : (1) (2) (3) (4) in, It represents the logarithmic deviation of the moving wind speed of the typhoon rainstorm center; Indicates the moving direction angle of the typhoon rainstorm center; It represents the logarithm of the moving wind speed of the typhoon rainstorm center; and Respectively Moving wind speed and moving direction angle of the typhoon and rainstorm center at the moment; and Respectively The latitude and longitude of the typhoon and rainstorm center at the moment; and are the prediction errors that follow Gaussian distribution; and is the fitting coefficient obtained based on the historical data of different regions; is the time interval, usually 1 hour.

[0085] When using the above model to predict the typhoon rainstorm path, prediction errors will inevitably occur. Over time, these errors that affect the moving speed and moving direction angle of the typhoon rainstorm center gradually accumulate, resulting in increased uncertainty in the typhoon rainstorm path. These uncertainties cause deviations between the actual state of the components in the distribution system and the predicted values, which may cause the existing active defense methods to fail.

[0086] In order to solve the uncertainty problem of typhoon and rainstorm paths, the prediction error is processed through historical data based on the typhoon and rainstorm empirical path mechanism model combined with the data-driven method, including: It is assumed that the typhoon rainstorm path revolves around the predicted path, and the prediction error obeys a Gaussian distribution with a mean of zero; the uncertainty of the typhoon rainstorm path is quantified by the deviation between the empirical path and the historical true path, including the first calculation.

[0087] The first calculation includes: selecting two adjacent typhoon rainstorm locations A1 and A2 from the historical real paths; calculating the empirical path from A1 to A3 by equations (1) to (4); Figure 2 There is a prediction error of the moving direction angle between the historical real path (actual historical trajectory) A1 to A2 and the empirical path A1 to A3 , there is also displacement deviation ; According to the coordinates of A1 and A3, the predicted moving speed and moving direction angle of the empirical path A1 to A3 can be calculated; the historical real moving speed and moving direction angle of the historical real path A1 to A2 can be obtained; according to the predicted moving speed and moving direction angle and the historical real moving speed and moving direction angle, the error between the empirical path A1 to A3 and the historical real path A1 to A2, that is, the central moving wind speed prediction error and the prediction error of the moving direction angle .

[0088] Perform the first calculation on each historical real path to obtain the corresponding and , and then get the central moving wind speed prediction error set and the moving direction angle prediction error set , see Figure 3 and Figure 4 .

[0089] Will and The prediction error in is the same as the prediction value calculated by equations (1) and (2) and Add them together to obtain various typhoon and rainstorm path scenarios.

[0090] Typhoon rainstorm wind field model includes: For the typhoon rainstorm path scenario moving over the ocean surface, the first model can be used to estimate the time series wind speed at different locations , the first model includes: (5) (6) (7) (8) (9) (10) (11) (12) in, Indicates the circulation wind speed; and Respectively time Typhoon rainstorm wind speed and rainfall rate; , and is the correction factor; and are the distance between the power distribution system and the typhoon and rainstorm center and the maximum wind speed radius respectively; and is the central pressure difference and its rate of change; and They are the coordinates of the power distribution system and the typhoon rainstorm center respectively; The duration of the typhoon and rainstorm disaster; for time Typhoon wind speed based on the Batts model at ; express Maximum circulation wind speed at the moment; Indicates the moving wind speed in the center of a typhoon or rainstorm.

[0091] For the typhoon rainstorm path scenario moving on land, the wind speed decreases due to friction after the typhoon rainstorm lands. Therefore, the attenuation coefficient is introduced on the basis of the first model. It is assumed that the time series wind speed of the typhoon rainstorm moving on the ocean surface calculated by the first model is , and assume that the time series wind speed of the typhoon rainstorm moving over land is , then the attenuation coefficient can be expressed as: (13) in, and They represent the roughness length and the retardation factor, which are usually 0.005m and 0.85 respectively. .

[0092] According to the geographical location of the typhoon and rainstorm, select or Calculate the time series wind speed.

[0093] In the preferred embodiment of the present invention, the system catastrophe dynamics model under typhoon and rainstorm includes two parts: system component failure probability model and photovoltaic and wind turbine output model: System component failure probability models include: Assuming that the system component failure is mainly caused by wind speed, the system component failure includes damage to distribution towers, failure of overhead distribution wires, and failure of photovoltaic components; assuming that different components of the distribution line are independent of each other, the failure probability of the distribution line can be calculated through the series model, which can be expressed as: (14) (15) (16) in, and represent the failure probabilities of distribution lines, towers and conductors respectively; and Respectively represent the number of towers and conductor segments of the line; and Respectively represent the towers of the line and wire segments probability of failure.

[0094] The standard log-normal distribution function is used to model the failure probability of photovoltaic components, which can be expressed as: (17) in, represents the standard lognormal distribution function; represents the failure probability of the PV element; and represent the standard deviation and the design wind speed of PV, respectively.

[0095] The photovoltaic and wind turbine output models include: In typhoons and rainstorms, the photovoltaic output is intact and available It can be expressed as: (18) (19) in, It represents the impact of typhoon and rainstorm disasters on light irradiance in logarithmic space; is the light irradiance under normal conditions; and They are typhoon intensity, normalized distance, photovoltaic power generation efficiency and area; Represents a collection of photovoltaic nodes.

[0096] Since wind turbines are usually resistant to typhoons and rainstorms, it is assumed that the wind turbines are not damaged or malfunction during the typhoon and rainstorm disaster. The wind field changes caused by typhoons and rainstorms directly affect the wind power output. Specifically, the available wind power output can be calculated through the wind speed-power curve of the wind turbine. , which can be expressed as: (20) in, and They represent the cut-in wind speed, rated wind speed and cut-out wind speed of the fan, and the values ​​are 3m / s, 12m / s and 25m / s respectively; and They represent air density, power coefficient, fan blade swept area and rated power respectively; Represents a collection of wind turbine nodes.

[0097] In a preferred embodiment of the present invention, generating a dual uncertainty time series scenario of coupling the typhoon and rainstorm evolution path and the system component state according to the typhoon and rainstorm disaster uncertainty model and the system disaster dynamics model under the typhoon and rainstorm includes: Get the coordinates of the power distribution system.

[0098] The coordinates, moving wind speed and moving direction angle of the typhoon rainstorm center at this moment are obtained, and the moving wind speed and moving direction angle of the typhoon rainstorm center at the next moment are calculated through the typhoon rainstorm empirical path mechanism model, i.e., equations (1) to (4).

[0099] Take the prediction error of the moving wind speed and moving direction angle of the typhoon rainstorm center and , the moving wind speed and moving direction angle of the typhoon rainstorm center at the next moment are compared with the prediction error and The corresponding addition is used to obtain the coordinates of the typhoon rainstorm center at the next moment; the moving wind speed, moving direction angle and coordinates of the typhoon rainstorm center at subsequent moments are calculated in time sequence until the maximum number of scenes of the typhoon rainstorm path samples reaches S trackmax .

[0100] The scene number is reduced by using the scene backward reduction algorithm. S trackmax Reduced to S reduced , and record each typical scene s ∈ S reduced The corresponding probability of occurrence .

[0101] The second calculation is performed at each moment during the typhoon and rainstorm duration period, and the second calculation includes: According to formula (10) and the coordinates of the distribution system, the distance from the typhoon and rainstorm center to the distribution system in each typical scenario is calculated. ,in Take 1, 2… S reduced ; Then, considering all typical scenarios, the expected time series distance can be expressed as .

[0102] According to equations (7) to (9) and (11) to (13), the expected predicted wind speed at the distribution system is obtained: .

[0103] According to equations (18) and (20), the expected photovoltaic output is calculated as Expected output of wind power .

[0104] According to equations (14) and (17), the failure probability of photovoltaic is calculated respectively: and the failure probability of the distribution line .

[0105] Using vulnerability thresholds As a criterion for determining vulnerable elements.

[0106] Distribution lines Vulnerability status , 0 represents a vulnerable line, and 1 represents a normal line.

[0107] Photovoltaic components Vulnerability status , 0 represents a fragile photovoltaic element, and 1 represents a good photovoltaic element.

[0108] Obtain the uncertainty vulnerability status of all components of the distribution system at this moment, as well as the uncertainty output of photovoltaic and wind power; After completing the second calculation for each moment during the typhoon and rainstorm duration period, a time series scene is obtained.

[0109] F2. Switch the system to island operation and keep the dispatchable distributed power sources in the system using droop control; calculate the vulnerable load loss caused by disconnecting all vulnerable components of the system at each moment through a multi-objective optimization model.

[0110] In a preferred embodiment of the present invention, the vulnerable load loss caused by disconnecting all vulnerable components of the system at each moment is calculated by a multi-objective optimization model, including: Case 1: If there is no load loss in the distribution system itself, the vulnerable load loss caused by disconnecting all vulnerable components of the system at each moment is directly calculated through the multi-objective optimization model.

[0111] Scenario 2: The distribution system has its own load loss. When calculating the vulnerable load loss caused by disconnecting all vulnerable components of the system at each moment through the multi-objective optimization model, it is necessary to first calculate the total load loss of the distribution system, and then subtract the inherent load loss from the total loss of the distribution system to obtain the vulnerable load loss.

[0112] The inherent load loss includes the load loss of the distribution system excluding the vulnerable load loss caused by disconnecting the vulnerable components; the total load loss of the distribution system includes the inherent load loss and the vulnerable load loss.

[0113] In addition, when the vulnerable load loss caused by disconnecting all vulnerable components of the system at each moment is calculated by the multi-objective optimization model, if there is no load loss at a certain moment, all vulnerable components are directly disconnected at that moment.

[0114] If there is no vulnerable component at a certain moment, the system operates normally at that moment.

[0115] F3. Considering each number of the total number of fragile elements from 0 to the current moment, each fragile element with fragile load loss moment is combined when any number is selected, considering all the selected situations; calculate the survival probability of the fragile element combination.

[0116] In a preferred embodiment of the present invention, S3 specifically includes: Assume that there is a vulnerable load loss moment including A vulnerable component, is a positive integer; Choose from a number of vulnerable components components, is an integer, ;get A combination of vulnerable components. It should be noted that There is only one possible component configuration, that is, all vulnerable components are shut down. Therefore, in the first subsequent iteration, active scheduling is applied to this unique configuration.

[0117] calculate The survival probabilities of the combinations of vulnerable components are sorted from the highest to the lowest survival probabilities.

[0118] The calculation of survival probability includes: (twenty one) in, represents the probability of survival; Indicates the combination of vulnerable components; Indicates the distribution line number; represents the probability of failure of the distribution line; Indicates the photovoltaic equipment number; represents the probability of PV failure; Indicates the moment; Indicates the duration of the typhoon and heavy rain.

[0119] For each moment of vulnerable load loss, all possible selection combinations are performed and the survival probability of all vulnerable component combinations is calculated.

[0120] F4. In order from the smallest to the largest quantity, put the fragile element combinations with each quantity at each moment of fragile load loss into operation in order from the largest to the smallest survival probability, and disconnect the fragile elements outside the operating fragile element combinations.

[0121] F5. Rescheduling the system when each combination of vulnerable components is put into operation through active scheduling and multi-objective optimization models. Active scheduling includes power generation rescheduling, pre-disaster network reconstruction, droop control parameter optimization setting and protective voltage regulation.

[0122] F6. Determine in sequence in the order in which each vulnerable component combination is put into operation whether there is a vulnerable load loss in the system. If not, perform active defense at this moment based on the vulnerable component combination put into operation and active scheduling.

[0123] In a preferred embodiment of the present invention, the multi-objective optimization model includes an objective function and constraints.

[0124] The multi-objective optimization model aims to minimize the overall operating cost, including minimizing the penalty cost caused by load reduction and reducing the operating cost of dispatchable distributed power sources, photovoltaic and wind power. The objective function of the multi-objective optimization model can be expressed as: (twenty two) (twenty three) (twenty four) (25) (26) (27) (28) in, Indicates the time when the typhoon and rainstorm begin; Indicates the time when the typhoon and rainstorm end; and They represent the load reduction penalty cost and the power generation operation cost, respectively, where the power generation operation cost includes the photovoltaic operation cost , Wind power operation costs , Dispatchy Distributed Generation Operating Cost and mobile energy storage operating costs ; and They represent the unit penalty cost of load reduction, the unit operating cost of photovoltaic power generation, the unit operating cost of wind power generation, the unit operating cost of mobile energy storage, and the unit operating cost of dispatchable distributed power generation respectively; express Active power of load at the moment; is a binary decision variable, where “1” represents load restoration, otherwise it represents load shedding; and Respectively The power generation of photovoltaic, wind power, distributed power and mobile energy storage at each moment are all continuous decision variables; and They represent the node set, photovoltaic node set, wind turbine node set, distributed power source node set and mobile energy storage node set respectively; and Respectively represent the indexes of the nodes where the load, photovoltaic, wind turbine, distributed power source and mobile energy storage are located; and is the weight coefficient, and .

[0125] The weight coefficient can be determined by Pareto analysis. In the preferred embodiment of the present invention, and , to prioritize load reduction over generation operating costs.

[0126] The constraints of the multi-objective optimization model include power flow and safe operation constraints, photovoltaic and wind turbine operation constraints, load demand model based on static voltage, dispatchable distributed power sources and mobile energy storage operation constraints based on droop control characteristics, and radial topology constraints based on improved spanning tree.

[0127] Power flow and safe operation constraints include: The optimal power flow equation based on Disflow is used for modeling. The AC power flow equation is given by equations (29), (30) and (31), the line capacity constraint is expressed by equation (32), and the operation safety constraint is given by equations (33) and (34): (29) (30) (31) (32) (33) (34) in, express Time Node Injected active power; express Time Node Injected reactive power; and Respectively indicate lines and Active power on and Respectively indicate lines and Reactive power on and Respectively indicate lines and The current on and Respectively indicate lines and Reactance; and Respectively indicate lines and resistance; express The reactive power of the load at the moment; and Respectively Time Node Active output power of distributed power sources, photovoltaics, wind turbines and mobile energy storage; express Time Node Reactive output power of distributed power sources; is a binary variable, when the line The value is 1 when the power is on, otherwise it is 0; and Respectively represent nodes The voltage amplitude and system frequency at Representation Node The voltage amplitude at ; is a large positive number; and They are the maximum values ​​of node voltage, system frequency, branch current, branch active power and reactive power respectively; and are the minimum values ​​of node voltage, system frequency, branch current, branch active power and reactive power respectively; A collection of lines.

[0128] AC power flow constraints ensure the feasibility of distribution system operation through equations (29)–(31), avoiding widespread load shedding or even complete blackouts due to mismatches in active power, reactive power, and voltage constraints. In general, thermal and stability considerations may limit the transmission capacity of distribution lines. Loads on long transmission lines are usually subject to stability constraints. However, distribution lines may be affected by their thermal limits, which are reflected in equations (32) and (33) because the line power flow limitations are determined by the thermal capacity of the line. Equation (34) ensures that global frequency and local voltage are maintained within reasonable ranges.

[0129] PV and wind turbine operation constraints include: Photovoltaic and wind turbines are modeled as PQ control resources with unity power factor. It is assumed that the photovoltaic units and wind turbines have strong anti-typhoon and rainstorm characteristics, that is, the design wind speed is higher than the maximum wind speed of the typhoon and rainstorm, and according to the vulnerability theory, the photovoltaic units and wind turbines will not fail during the typhoon and rainstorm. At the same time, it is assumed that both photovoltaic and wind turbines are equipped with energy storage and converters, and each photovoltaic unit and wind turbine is regarded as an independent entity, ignoring the detailed model of energy storage and converter. The actual available output of photovoltaic and wind turbines during typhoon and rainstorm is considered. Therefore, the operation constraints of photovoltaic and wind turbines can be expressed as follows: (35) (36) in, and Represent the expected output of photovoltaic and wind power respectively; and Respectively The power generation of photovoltaic and wind power at each moment.

[0130] The load demand model based on static voltage includes: Protective voltage regulation controls the system voltage to the lower limit allowed to reduce peak demand, especially in extreme natural disaster events. Therefore, protective voltage regulation helps improve the resilience of the system during island operation. To describe the relationship between load and voltage amplitude, the polynomial ZIP load model is used: (37) (38) in, and are the participation factors of the constant impedance, current and power terms in the active power load, respectively; and are the participation factors of the constant impedance, current and power terms in the reactive power load, respectively; and Respectively represent the rated active power and reactive power of the load; the polynomial ZIP load model considers a more accurate system load forecast. If the forecast error is considered, the load demand can be expressed as the forecast value plus the error obtained according to the probability distribution of historical data.

[0131] The operating constraints of dispatchable distributed generation and mobile energy storage based on droop control characteristics include: Dispatched distributed power sources such as diesel generators are usually equipped with droop controllers to provide voltage and frequency support during island operation to ensure safe and reliable operation of the system. Dispatched distributed power sources using droop control must meet capacity and power constraints, active-frequency and reactive-voltage droop control characteristics, including: (39) (40) (41) (42) (43) (44) (45) (46) (47) in, and They represent the maximum active output power, maximum reactive output power and total output capacity of the dispatchable distributed generation respectively; and Respectively Time Node Droop control decision variables of distributed generation; and denote the reference voltage amplitude and reference frequency decision variables respectively; and Respectively represent the maximum values ​​of reference frequency, reference voltage, active power droop coefficient and reactive power droop coefficient; and Respectively represent the minimum values ​​of reference frequency, reference voltage, active power droop coefficient and reactive power droop coefficient; Represents the angular frequency of the system.

[0132] It should be noted that compared with the transmission system, the droop-controlled dispatchable distributed power sources in the distribution system are usually smaller in size, have higher ramp rates, and shorter reaction times. Therefore, the start-up time of this type of power source is not considered in the formula, which is also a common practice in distribution system research.

[0133] In addition to the controllable distributed power supply with droop control function, the mobile energy storage is also equipped with a droop controller; assuming that the mobile energy storage provides as much active output as possible, the mobile energy storage is charged or discharged according to the droop characteristics, which can be expressed as: (48) The charging or discharging action of mobile energy storage is subject to the rated capacity The restriction can be expressed as: (49) in, is the rated capacity of mobile energy storage; express The amount of electricity generated by mobile energy storage at all times.

[0134] The radial topology constraints based on the improved spanning tree include: During typhoon and rainstorm disasters, the distribution system may form multiple islands. Improved spanning tree constraints are used to meet the radial topology requirements of each island, including: (50) (51) in, and is a binary variable; if node is a substation node, then The value is 1, otherwise it is 0; if the node is a distributed power generation node, then The value is 1, otherwise it is 0; is a binary variable, indicating the node Whether it is the root bus in the island, if so, the value is 1, otherwise it is 0; and Busbar The parent node and child node collection of ; It is a binary variable, and its value is 1, indicating that the branch Power on, a value of 0 indicates a branch No electricity; and is a binary variable. If the node Is a node The parent node of , or busbar It is a busbar The parent node of , then select the branch Power on, take , and Cannot be 1 at the same time; represents a binary variable, represents a node Is it a node? The parent node of Time Node For Node The parent node of .

[0135] In a preferred embodiment of the present invention, the multi-objective optimization model further includes: Convex optimization solution of multi-objective optimization model: There are nonlinear equations in the multi-objective optimization model, including those in (29), (31), (37) and (38). , , in formula (42) , in formula (43) , in formula (48) , as well as equations (32) and (41); the nonlinear equations in the multi-objective optimization model can be divided into three types: the first type is the square of continuous variables, the second type is quadratic equations or quadratic inequalities, and the third type is the product of continuous variables; these three types of nonlinear equations are subjected to convex optimization: Convex optimization of nonlinear equations of the first type: Since equations (29), (31), (37) and (38) contain square terms of branch current and node voltage and , using the variable and To replace the two quadratic terms separately, we get: (52) (53) (54) (55) in, and Node Voltage and branches The square term of the current; and Node Voltage and branches The square of the current.

[0136] Since equations (54) and (55) contain square terms , so it is still a nonlinear expression. Therefore, Taylor series expansion is used to linearize equations (54) and (55), including: Assume that the initial value is , through Taylor series expansion we can get: (56) in, Represents an infinitesimal quantity; For Node Initial value of the voltage amplitude.

[0137] By ignoring the second-order and higher-order terms, equation (56) can be approximately expressed as: (57) The node voltage amplitude is usually about 1.0 pu, so assuming , formula (57) can be further approximated as: (58) The distance function definition of the Taylor series approximation includes: (59) in, is the distance function approximated by the Taylor series; See also Figure 5 From the error curve of Taylor series approximation, it can be seen that the calculation error is The magnitude meets the approximate accuracy requirements within the given voltage amplitude range [0.95, 1.05]. By substituting equation (58) into equations (54) and (55), the approximate load demand model is obtained, which can be expressed as: (60) (61) Convex optimization of nonlinear equations of the second type: Equations (32) and (41) can be transformed into second-order cone constraints and rotational second-order cone constraints, respectively, which can be expressed as: (62) (63) Convex optimization of the third type of nonlinear equations: Since equations (42), (43) and (48) all contain the product of two bounded continuous variables, the McCormick envelope approximation is used to process them and the auxiliary variable and Respectively and , and approximated using twelve linear constraints, can be expressed as: (64) (65) (66) (67) (68) (69) (70) (71) (72) (73) (74) (75) The active defense method of the new energy distribution system considering the uncertainty of the evolution path of typhoon and rainstorm of the present invention forms a robust scheduling strategy by integrating dual uncertainty scenarios in advance, aiming to ensure that the system maximizes the number of vulnerable components that are out of service under full load operation conditions, thereby effectively reducing the overall vulnerability of the system in typhoon and rainstorm disasters. Switching the system to island operation not only helps to reduce the load loss of the distribution system during disasters and improve the safety of the system, but also reduces the protection coordination requirements with the main grid and alleviates other technical problems that may be caused during disasters. The vulnerable load loss caused by disconnecting the vulnerable components at each moment is calculated through a multi-objective model, and the vulnerable components at each moment of vulnerable load loss are combined considering all possible combinations, and the survival probability of each combination is calculated. The trial operation is started from the combination with the least number of vulnerable components, and then the vulnerable component combination with the least number of vulnerable components at each moment of vulnerable load loss is found and put into operation, thereby improving the safety and robustness of the system. The method of the present invention combines mechanism analysis and data-driven models to adopt a robust strategy for optimization. Compared with the active management method that does not consider the evolution path of typhoon and rainstorm, the method of the present invention shows the best comprehensive benefits. In the worst case, compared with the post-disaster dispatch results of the active management method and the passive strategy method that do not consider the evolution path of the typhoon and rainstorm, the method of the present invention effectively reduces the load loss and improves the satisfaction rate of the load demand. The active defense scheme obtained by the method of the present invention has good effect, flexibility and economy.

[0138] In a preferred embodiment of the present invention, there is also provided an active defense device for a new energy power distribution system taking into account the uncertainty of the evolution path of typhoons and rainstorms, which is used in the method of the present invention. The device includes a first module, a second module, a third module, a fourth module, a fifth module and a sixth module.

[0139] The first module is used to obtain the dual uncertainty time series scenario of the typhoon and rainstorm evolution path and the coupling of system component states; based on the time series scenario, the vulnerable components at each moment during the typhoon and rainstorm duration are obtained.

[0140] The second module is used to switch the system to island operation and keep the dispatchable distributed power sources in the system using droop control; the vulnerable load loss caused by disconnecting all vulnerable elements of the system at each moment is calculated separately through a multi-objective optimization model.

[0141] The third module is used to consider each number of the total number of fragile elements from 0 to the current moment, and to combine each fragile element at the moment of fragile load loss when selecting any number of them, taking all the selected situations into consideration; and to calculate the survival probability of the fragile element combination.

[0142] The fourth module is used to put into operation, in order from the smallest to the largest number, each combination of fragile elements in each quantity at each moment of fragile load loss according to the highest to the lowest survival probability, and disconnect the fragile elements outside the operating fragile element combination.

[0143] The fifth module is used to reschedule the system when each combination of vulnerable components is put into operation through active scheduling and multi-objective optimization models. Active scheduling includes power generation re-scheduling, pre-disaster network reconstruction, droop control parameter optimization setting and protective voltage regulation.

[0144] The sixth module is used to determine whether there is a vulnerable load loss in the system when each vulnerable component combination is put into operation in the order of operation. If not, active defense is performed at this moment based on the vulnerable component combination put into operation this time and active scheduling.

[0145] The active defense device of the new energy distribution system taking into account the uncertainty of the typhoon and rainstorm evolution path of the present invention is used in the method of the present invention and has the same beneficial effects as the method of the present invention.

[0146] Verification part: In order to evaluate the effectiveness of the method of the present invention, verification is carried out from two aspects: 1) verification of active defense plan and effectiveness; 2) verification of the superiority of the active defense method.

[0147] See also Figure 6 , the modified IEEE 33-node test system is located at (110.19°E, 21.22°N), represented by a hexagon. The typhoon rainstorm moved northwest from the initial position (117.9°E, 17.1°N). The typhoon rainstorm path and the operating status of the components introduced two layers of uncertainty, long-term and short-term. For each typhoon rainstorm path, the wind field in the area where the system is located is first determined, and the vulnerability status of the components and the uncertainty output of photovoltaic and wind power are evaluated. In the preferred embodiment of the present invention, 50 typhoon rainstorm path scenario samples are generated, and 5 typical path scenarios (i.e. Figure 6 The typical paths 1 to 5 in the figure have corresponding occurrence probabilities of 0.12, 0.37, 0.11, 0.12 and 0.28 respectively. Figure 6 In the figure, typical paths 1 to 5 are represented by purple, light blue, dark green, pink and dark blue solid lines respectively. In order to study the impact of typhoon and rainstorm on the distribution system, 12 moments under the typical path are selected (respectively t 1 to t 12 Table 1 shows the expected time series distances and their corresponding expected time series predicted wind speeds in all typical scenarios.

[0148] Table 1 Considering all typical scenarios, the expected time series distance and its corresponding expected time series predicted wind speed .

[0149] The tested IEEE 33-bus system includes 1 substation, 5 dispatchable distributed generation (DER), 5 identical photovoltaic (PV), 2 identical wind turbines (WT), 5 sets of mobile energy storage (ESS), 5 tie switch branches and 32 segment switch branches (a total of 37 switch branches, represented by S1 to S37). Its main parameters and topological results are shown in Tables 2 to 4 and Figure 7 As shown. The voltage level of the system is 12.66kV, the base power is 100MVA, and the rated active load and reactive load are 3.715MW and 2.300Mvar respectively. In addition, it is assumed that before the typhoon and rainstorm arrive, all interconnection switch branches are in a disconnected state and all mobile energy storages are fully charged. The test system includes commercial users, residential users and industrial users. The unit penalty cost of load reduction, the unit operating cost of PV, the unit operating cost of WT, the unit operating cost of ESS and the unit operating cost of DER are 20$ / kW, 10$ / kW, 20$ / kW, 20$ / kW and 100$ / kW respectively. Assume that the power supply load demand is constant , and the maximum load in a day is regarded as a constant load of the distribution system during the typhoon rainstorm path. This is equivalent to considering the most unfavorable situation under the condition of load uncertainty. Figure 8 The wind-induced vulnerability curves of distribution system components are shown. The vulnerability threshold of distribution system components is assumed to be YUZ=5%.

[0150] Table 2 Main parameters of dispatchable distributed generation .

[0151] Table 3 Droop control parameters of dispatchable distributed generation and mobile energy storage .

[0152] Table 4 Main parameters of wind turbines, photovoltaics and mobile energy storage .

[0153] 1) Active defense plan and effectiveness verification According to the method of the present invention, the time series vulnerable components under the typhoon and rainstorm disaster are identified, and the specific information is shown in Table 5. It can be seen from the table that t 1~ t 4 and t 8~ t 12 During this period, because the center of the typhoon and rainstorm was far away from the distribution system and the wind speed at the location of the system was low, the failure probability of the components was lower than the set vulnerability threshold, and no vulnerable components appeared at this time. t 5~ t During the period of 7, as the center of the typhoon and rainstorm gradually approached the distribution system, the wind speed at the location of the system gradually increased, causing the number of components with failure probability exceeding the vulnerability threshold to increase rapidly. t At time 5, the number of vulnerable branches increases to 5, namely S2, S10, S14, S24 and S30; as the wind speed further increases, the number of vulnerable components increases. t 6 and t 7 moments increased to 7 and 9 respectively. It can be seen that the impact of typhoon and rainstorm disasters on distribution system components shows obvious temporal changes, especially in a specific time period, the impact rapidly intensifies and then rapidly weakens.

[0154] Table 5 Time series vulnerability components under typhoon and rainstorm disasters .

[0155] Table 6 lists the test cases in detail. t 1~ t 12 Active defense plan during the period. t 1 to t In period 4, since no vulnerable components appeared during this period, the distribution system was divided into 2, 2, 3 and 2 independent islands through optimal scheduling and reconstruction of network resources, and each island could meet its corresponding load demand. t At time 5, vulnerable branches S2, S10, S14, S24 and S30 appear, as shown in Fig. 9 As shown in Figure 2, by actively shutting down all vulnerable components, the system is able to meet the power supply needs of all loads, and the average calculation time is 7.01 seconds. The load demand at this time is 0.9962pu, mainly because the protective voltage regulation reduces the node voltage, thereby reducing the voltage-related load demand.

[0156] exist t At 6 moments, the system situation changes, such as Fig.10As shown. By adopting the proposed active defense strategy, the total load demand of the system is met by shutting down other vulnerable components except components S5 and S31, and the combination C (7, 2) corresponding to this configuration has the maximum survival probability. At this time, all the interconnecting switches have been closed, and the distribution system is reconstructed into three independent islands, each of which can meet its load demand. The average time for the whole process is 55.51 seconds.

[0157] exist t At time 7, the branches S5, S20 and S31 with the smallest vulnerability are kept in operation, such as Fig.11 As shown, this configuration becomes the combination with the highest survival probability in combination C (9, 3). At the same time, S2, S7, S10, S14, S24 and S30 are shut down to meet the total power supply load demand of the system. At this time, the total load demand drops to the minimum value of 0.9884pu, and the system is reconstructed into three independent islands. The average time taken for the calculation process is only 330.66 seconds, which meets the response time requirements of the pre-disaster defense strategy.

[0158] Table 6 t 1~ t 12 Active defense dispatch of power distribution system during the period .

[0159] Later, in t 8~ t 12 During this period, since there are no vulnerable components, the system is divided into 3, 4, 2, 4, and 3 independent islands through optimal scheduling and reconstruction, and each island can meet its internal load requirements. Figures 12 to 15 Showing typhoon rainstorm t 1~ t 12 Active dispatch of distributed power sources, wind turbines, photovoltaics and mobile energy storage during the period. The changes in the total output power of distributed power sources, wind turbines, photovoltaics and mobile energy storage are shown in Table 7.

[0160] Table 7 Sequential total output power of distributed power sources, wind turbines, photovoltaics and mobile energy storage .

[0161] The active defense scheme obtained by this method can be applied to the distribution system before the disaster. As the typhoon and rainstorm disaster develops, the vulnerable components (such as those in Table 5) t 6) may fail and stop operating. As shown in Table 6, in the active defense scheduling t 5~ t7, 0, 2, and 3 vulnerable components remain operational, respectively. However, in the worst case, the vulnerable components may t 5. t 6 and t At 7 o'clock, all of them were shut down due to faults. Therefore, the distribution system needs to switch to the disaster dispatch strategy. This strategy has been completed in the pre-disaster active defense framework, and the dispatch scheme is provided through the pre-stored lookup table, thus realizing the rapid switching of the disaster dispatch, as shown in Tables 8 and 9.

[0162] From Table 8, we can see that t At time 5, since all five vulnerable components are allowed to shut down, the system can still meet the power supply needs of all loads. t 6 and t At time 7, the total power supply load of the system is 0.7461pu and 0.6703pu respectively, and the corresponding power supply rates are 74.43% and 67.1% respectively. As can be seen from Table 9, t In the worst case at time 5, the output of distributed power sources, wind turbines, photovoltaics, and mobile energy storage is consistent with the set values ​​of the pre-disaster active defense scheduling strategy; t 6 and t At 7 o'clock, due to the emergence of more power outage islands, some loads were forced to be reduced, resulting in the output of distributed power sources, wind turbines, photovoltaics and mobile energy storage being lower than the set values ​​of the pre-disaster active defense scheduling strategy. t 6 and t The optimal droop parameters of the distributed power supply at 7 moments are shown in Table 10. These droop parameter settings can enable the power supply island to share the load with minimum load loss. Therefore, the effectiveness of the method of the present invention is verified.

[0163] Table 8 Post-disaster dispatch of the method of the present invention under the worst case .

[0164] Table 9 Total output power of distributed power generation, wind turbines, photovoltaics and mobile energy storage in the worst case .

[0165] Table 10 Optimal droop parameters of distributed power generation .

[0166] 2) Verification of the superiority of active defense methods The superiority of the method of the present invention will be verified from two aspects: 1. t 5~ t 7 is the comparison of the overall operating costs during the period; ② is the comparison of disaster dispatch under the worst case scenario.

[0167] ① t5~ t Comparison of overall operating costs during the 7 periods The method of the present invention is compared with the active management method that does not consider the evolution path of the typhoon and rainstorm, and the comparison results are shown in Table 11.

[0168] Table 11 t 5~ t 7 Period Overall Operating Cost Comparison .

[0169] From Table 11, we can see that the active management method without considering the evolution path of typhoon and rainstorm is adopted. t 5~ t The overall operating cost for the 7-period period is 109.4915×10 4 $, and the overall operating cost of the method of the present invention is only 108.6554×10 4 In contrast, the operating cost of the method of the present invention is significantly reduced, reducing by 0.8361×10 4 As the unit operating cost increases, the advantages of the method of the present invention in overall operating cost will become more obvious.

[0170] ② Comparison of post-disaster dispatch under the worst case scenario In the worst case, the method of the present invention, the active management method without considering the evolution path of typhoon and rainstorm, and the passive strategy are compared and analyzed. The passive strategy refers to the operation that only relies on the protection system to shut down the components. t 7 (i.e. the moment with the largest number of fragile components).

[0171] Table 12 Comparison of post-disaster scheduling of different methods under the worst case scenario .

[0172] Table 12 shows the situation of post-disaster dispatch of the distribution system using a passive strategy in the worst case. The results show that the distribution system is divided into 10 islands and the number of power-off nodes is 16, mainly because some islands have no power supply or some large-capacity distributed power sources are restricted to islands with lower loads. Under the passive strategy, only 0.4569pu of load demand is met, and the corresponding power supply rate is 45.71%. In contrast, the method of the present invention reduces the number of islands to 9 in the worst case, significantly reduces the number of power-off nodes, and effectively improves the load demand satisfaction rate, and the power supply rate is increased by 21.39%. It is worth noting that although the active management method that does not consider the evolution path of typhoons and rainstorms is the same as the method of the present invention in post-disaster dispatch, its overall operating cost is higher. Therefore, through the above analysis, the superiority of the method of the present invention in post-disaster dispatch and cost-effectiveness is verified.

[0173] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An active defense method for a new energy distribution system considering the uncertainty of the evolution path of typhoons and rainstorms, characterized in that: include: Obtain dual uncertainty time series scenarios of typhoon and rainstorm evolution path and system component status coupling; According to the time series scenario, the vulnerable components at each moment during the typhoon and rainstorm duration are obtained; Switch the system to island operation and keep the dispatchable distributed power sources in the system in droop control; calculate the vulnerable load loss caused by disconnecting all vulnerable components of the system at each moment through a multi-objective optimization model; Considering each number of the total number of vulnerable elements from 0 to the current moment, each vulnerable element with a vulnerable load loss moment is combined by considering all selected situations when any number is selected; calculating the survival probability of the combination of vulnerable elements; In order from the smallest to the largest number, the fragile element combinations under each number at each moment of fragile load loss are put into operation in order from the largest to the smallest survival probability, and the fragile elements outside the operating fragile element combinations are disconnected; rescheduling the system for each combination of vulnerable components to be put into operation through active scheduling and multi-objective optimization models, wherein the active scheduling includes generation rescheduling, pre-disaster network reconstruction, droop control parameter optimization setting and protective voltage regulation; It is determined in turn in the order of being put into operation whether there is a vulnerable load loss in the system when each vulnerable component combination is put into operation. If not, active defense is performed at this moment based on the vulnerable component combination put into operation this time and the active scheduling.

2. The active defense method for a new energy power distribution system considering the uncertainty of the evolution path of typhoons and rainstorms according to claim 1 is characterized in that: The method of calculating the vulnerable load loss caused by disconnecting all vulnerable components of the system at each moment by using the multi-objective optimization model includes: Case 1: If the distribution system does not have any load loss, the vulnerable load loss caused by disconnecting all vulnerable components of the system at each moment is directly calculated by the multi-objective optimization model. Scenario 2: The power distribution system has its own load loss. When calculating the vulnerable load loss caused by disconnecting all vulnerable components of the system at each moment through the multi-objective optimization model, it is necessary to first calculate the total load loss of the power distribution system, and then subtract the inherent load loss from the total loss of the power distribution system to obtain the vulnerable load loss; The inherent load loss includes the load loss of the power distribution system excluding the fragile load loss caused by disconnecting the fragile element; the total load loss of the power distribution system includes the inherent load loss and the fragile load loss.

3. The active defense method for a new energy power distribution system considering the uncertainty of the evolution path of typhoons and rainstorms according to claim 2 is characterized in that: The dual uncertainty time series scenario of obtaining the typhoon rainstorm evolution path and the coupling of system component states includes: Construct uncertainty model of typhoon and rainstorm disaster and system disaster dynamics model under typhoon and rainstorm; A dual uncertainty time series scenario of the coupling of the typhoon and rainstorm evolution path and the system component state is generated according to the typhoon and rainstorm disaster uncertainty model and the system disaster dynamics model under the typhoon and rainstorm.

4. The active defense method for a new energy power distribution system considering the uncertainty of the evolution path of typhoons and rainstorms according to claim 3 is characterized in that: The typhoon and rainstorm disaster uncertainty model includes two parts: the typhoon and rainstorm evolution uncertainty path model and the typhoon and rainstorm wind field model: The uncertainty path model of typhoon rainstorm evolution includes: In order to simulate the possible movement path of typhoon and rainstorm after landing, the typhoon and rainstorm empirical path mechanism model is used to calculate the moving wind speed and direction angle : (1) (2) (3) (4) in, It represents the logarithmic deviation of the moving wind speed of the typhoon rainstorm center; Indicates the moving direction angle of the typhoon rainstorm center; It represents the logarithm of the moving wind speed of the typhoon rainstorm center; and Respectively Moving wind speed and moving direction angle of the typhoon and rainstorm center at the moment; and Respectively The latitude and longitude of the typhoon and rainstorm center at the moment; and are the prediction errors that follow Gaussian distribution; and is the fitting coefficient obtained based on the historical data of different regions; is the time interval; Based on the typhoon rainstorm empirical path mechanism model combined with the data-driven method, the prediction error is processed through historical data, including: It is assumed that the typhoon rainstorm path is developed around the predicted path, and the prediction error follows a Gaussian distribution with a mean of zero; the uncertainty of the typhoon rainstorm path is quantified by the deviation between the empirical path and the historical true path, including the first calculation; The first calculation includes: selecting two adjacent typhoon rainstorm locations A1 and A2 from the historical real path; calculating the empirical path from A1 to A3 by equations (1) to (4); there is a prediction error of the moving direction angle between the historical real path A1 to A2 and the empirical path A1 to A3 ; The predicted moving speed and moving direction angle of the empirical path A1 to A3 can be calculated according to the coordinates of A1 and A3; the historical real moving speed and moving direction angle of the historical real path A1 to A2 are obtained; the error between the empirical path A1 to A3 and the historical real path A1 to A2, that is, the central moving wind speed prediction error, can be obtained according to the predicted moving speed and moving direction angle and the historical real moving speed and moving direction angle and the prediction error of the moving direction angle ; The first calculation is performed on each historical real path to obtain the corresponding and , and then get the central moving wind speed prediction error set and the moving direction angle prediction error set ; Will and The prediction error in is the same as the prediction value calculated by equations (1) and (2) and Add them together to get various typhoon and rainstorm path scenarios; The typhoon rainstorm wind field model includes: For the typhoon rainstorm path scenario moving over the ocean surface, the first model can be used to estimate the time series wind speed at different locations , the first model includes: (5) (6) (7) (8) (9) (10) (11) (12) in, Indicates the circulation wind speed; and Respectively time Typhoon rainstorm wind speed and rainfall rate; , and is the correction factor; and are the distance between the power distribution system and the typhoon and rainstorm center and the maximum wind speed radius respectively; and is the central pressure difference and rate of change; and They are the coordinates of the power distribution system and the typhoon rainstorm center respectively; The duration of the typhoon and rainstorm disaster; for time Typhoon wind speed based on the Batts model at ; express Maximum circulation wind speed at the moment; Indicates the moving wind speed of the typhoon rainstorm center; For the typhoon rainstorm path scenario moving on land, the wind speed decreases due to friction after the typhoon rainstorm lands. Therefore, the attenuation coefficient is introduced on the basis of the first model. It is assumed that the time series wind speed of the typhoon rainstorm moving on the ocean surface calculated by the first model is , and assume that the time series wind speed of the typhoon rainstorm moving over land is , then the attenuation coefficient can be expressed as: (13) in, and represent the roughness length and retardation factor respectively; According to the geographical location of the typhoon and rainstorm, select or Calculate the time series wind speed.

5. The active defense method for a new energy power distribution system considering the uncertainty of the evolution path of typhoons and rainstorms according to claim 4 is characterized in that: The system disaster dynamics model under typhoon and rainstorm includes two parts: system component failure probability model and photovoltaic and wind turbine output model: The system component failure probability model includes: Assuming that the system component failure is mainly caused by wind speed, the system component failure includes damage to distribution towers, failure of overhead distribution wires, and failure of photovoltaic components; assuming that different components of the distribution line are independent of each other, the failure probability of the distribution line can be calculated through the series model, which can be expressed as: (14) (15) (16) in, and represent the failure probabilities of distribution lines, towers and conductors respectively; and Respectively represent the number of towers and conductor segments of the line; and Respectively represent the towers of the line and wire segments The probability of failure; The standard log-normal distribution function is used to model the failure probability of photovoltaic components, which can be expressed as: (17) in, represents the standard lognormal distribution function; represents the failure probability of the PV element; and denote the standard deviation and the design wind speed of PV, respectively; The photovoltaic and wind turbine output models include: In typhoons and rainstorms, the photovoltaic output is intact and available It can be expressed as: (18) (19) in, It represents the impact of typhoon and rainstorm disasters on light irradiance in logarithmic space; is the light irradiance under normal conditions; and They are typhoon intensity, normalized distance, photovoltaic power generation efficiency and area; represents a set of photovoltaic nodes; Since wind turbines are usually resistant to typhoons and rainstorms, it is assumed that the wind turbines will not be damaged or fail during the typhoon and rainstorm disaster. The wind field changes caused by typhoons and rainstorms directly affect the wind power output. Specifically, the available wind power output can be calculated through the wind speed-power curve of the wind turbine. , which can be expressed as: (20) in, and They represent the cut-in wind speed, rated wind speed and cut-out wind speed of the fan, and the values ​​are 3m / s, 12m / s and 25m / s respectively; and They represent air density, power coefficient, fan blade swept area and rated power respectively; Represents a collection of wind turbine nodes.

6. The active defense method for a new energy power distribution system considering the uncertainty of the typhoon and rainstorm evolution path according to claim 5 is characterized in that: The dual uncertainty time series scenario of coupling the typhoon and rainstorm evolution path and the system component state is generated according to the typhoon and rainstorm disaster uncertainty model and the system disaster dynamics model under the typhoon and rainstorm, including: Get the coordinates of the power distribution system; Obtain the coordinates, moving wind speed and moving direction angle of the typhoon rainstorm center at this moment, and calculate the moving wind speed and moving direction angle of the typhoon rainstorm center at the next moment through the typhoon rainstorm empirical path mechanism model, i.e., equations (1) to (4); Take the prediction error of the moving wind speed and moving direction angle of the typhoon rainstorm center and , the moving wind speed and moving direction angle of the typhoon rainstorm center at the next moment are compared with the prediction error and The corresponding addition is performed to obtain the coordinates of the typhoon rainstorm center at the next moment; the moving wind speed, moving direction angle and coordinates of the typhoon rainstorm center at subsequent moments are continuously calculated in time sequence until the maximum number of scenes of the typhoon rainstorm path samples reaches S trackmax ; The scene number is reduced by using the scene backward reduction algorithm. S trackmax Reduced to S reduced , and record each typical scene s ∈ S reduced The corresponding probability of occurrence π s ; Performing a second calculation at each moment during the typhoon and rainstorm duration period, the second calculation comprising: According to formula (10) and the coordinates of the distribution system, the distance from the typhoon and rainstorm center to the distribution system in each typical scenario is calculated. ,in Take 1, 2… S reduced ; Then, considering all typical scenarios, the expected time series distance can be expressed as ; According to equations (7) to (9) and (11) to (13), the expected predicted wind speed at the distribution system is obtained: ; According to equations (18) and (20), the expected photovoltaic output is calculated as Expected output of wind power ; According to equations (14) and (17), the failure probability of photovoltaic is calculated respectively: and the failure probability of the distribution line ; Using vulnerability thresholds As a criterion for determining vulnerable elements; Distribution lines Vulnerability status , 0 represents a vulnerable line, and 1 represents a normal line; Photovoltaic components Vulnerability status , 0 represents a fragile photovoltaic element, and 1 represents a sound photovoltaic element; Obtain the uncertainty vulnerability status of all components of the distribution system at this moment, as well as the uncertainty output of photovoltaic and wind power; After completing the second calculation for each moment in the typhoon and rainstorm duration period, the time series scene is obtained.

7. The active defense method for a new energy power distribution system considering the uncertainty of the evolution path of typhoons and rainstorms according to claim 6 is characterized in that: The said considering each number of the total number of vulnerable elements from 0 to the current moment, combining each vulnerable element at the moment of vulnerable load loss when selecting any number of them, taking into account all the selected situations; Calculating the survival probability of a combination of vulnerable components involves: Assume that there is a vulnerable load loss moment including A vulnerable component, is a positive integer; Choose from a number of vulnerable components components, is an integer, ;get Combinations of vulnerable components; Calculate the The survival probabilities of the combinations of vulnerable components are sorted from the highest to the lowest survival probabilities; The calculation of the survival probability includes: (21) in, represents the probability of survival; Indicates the combination of vulnerable components; Indicates the distribution line number; represents the probability of failure of the distribution line; Indicates the photovoltaic equipment number; represents the probability of PV failure; Indicates the moment; Indicates the duration of the typhoon and rainstorm; All possible selection combinations are performed at each moment when there is a fragile load loss, and the survival probabilities of all fragile element combinations are calculated.

8. The active defense method for a new energy power distribution system considering the uncertainty of the typhoon and rainstorm evolution path according to claim 7 is characterized in that: The multi-objective optimization model includes an objective function and constraints; The multi-objective optimization model aims to minimize the overall operating cost, including minimizing the penalty cost caused by load reduction and reducing the operating costs of dispatchable distributed power sources, photovoltaic and wind power. The objective function of the multi-objective optimization model can be expressed as: (22) (23) (24) (25) (26) (27) (28) in, Indicates the time when the typhoon and rainstorm begin; Indicates the time when the typhoon and rainstorm end; and They represent the load reduction penalty cost and the power generation operation cost, respectively, where the power generation operation cost includes the photovoltaic operation cost , Wind power operation costs , Dispatchy Distributed Generation Operating Cost and mobile energy storage operating costs ; and They represent the unit penalty cost of load reduction, the unit operating cost of photovoltaic power generation, the unit operating cost of wind power generation, the unit operating cost of mobile energy storage, and the unit operating cost of dispatchable distributed power generation respectively; express Active power of load at the moment; is a binary decision variable, where "1" represents load restoration, otherwise it represents load shedding; and Respectively The power generation of photovoltaic, wind power, distributed power and mobile energy storage at each moment are all continuous decision variables; and They represent the node set, photovoltaic node set, wind turbine node set, distributed power source node set and mobile energy storage node set respectively; and Respectively represent the indexes of the nodes where the load, photovoltaic, wind turbine, distributed power source and mobile energy storage are located; and is the weight coefficient, and ; The constraints of the multi-objective optimization model include power flow and safe operation constraints, photovoltaic and wind turbine operation constraints, a load demand model based on static voltage, dispatchable distributed power supply and mobile energy storage operation constraints based on droop control characteristics, and radial topology constraints based on improved spanning tree; The power flow and safe operation constraints include: The optimal power flow equation based on Disflow is used for modeling. The AC power flow equation is given by equations (29), (30) and (31), the line capacity constraint is expressed by equation (32), and the operation safety constraint is given by equations (33) and (34): (29) (30) (31) (32) (33) (34) in, express Time Node Injected active power; express Time Node Injected reactive power; and Respectively indicate lines and Active power on and Respectively indicate lines and Reactive power on and Respectively indicate lines and The current on and Respectively indicate lines and Reactance; and Respectively indicate lines and resistance; express The reactive power of the load at the moment; and Respectively Time Node Active output power of distributed power sources, photovoltaics, wind turbines and mobile energy storage; express Time Node Reactive output power of distributed power sources; is a binary variable, when the line The value is 1 when the power is on, otherwise it is 0; and Respectively represent nodes The voltage amplitude and system frequency at Representation Node The voltage amplitude at ; is a large positive number; and They are the maximum values ​​of node voltage, system frequency, branch current, branch active power and reactive power respectively; and are the minimum values ​​of node voltage, system frequency, branch current, branch active power and reactive power respectively; is a collection of lines; The photovoltaic and wind turbine operation constraints include: Photovoltaic and wind turbines are modeled as PQ control resources with unity power factor. It is assumed that the photovoltaic units and wind turbines have strong anti-typhoon and rainstorm characteristics, that is, the design wind speed is higher than the maximum wind speed of the typhoon and rainstorm, and according to the fragility theory, the photovoltaic units and wind turbines will not fail during the typhoon and rainstorm. At the same time, it is assumed that both photovoltaic and wind turbines are equipped with energy storage and converters, and each photovoltaic unit and wind turbine is regarded as an independent entity, ignoring the detailed model of energy storage and converter. The actual available output of photovoltaic and wind turbines during typhoon and rainstorm is considered. Therefore, the operation constraints of photovoltaic and wind turbines can be expressed as follows: (35) (36) in, and Represent the expected output of photovoltaic and wind power respectively; and Respectively The power generation of photovoltaic and wind power at each moment; The load demand model based on static voltage includes: To describe the relationship between load and voltage amplitude, a polynomial is used: Load Model: (37) (38) in, and are the participation factors of the constant impedance, current and power terms in the active power load, respectively; , and are the participation factors of the constant impedance, current and power terms in the reactive power load, respectively; and Respectively represent the rated active power and reactive power of the load; The dispatchable distributed power supply and mobile energy storage operation constraints based on the droop control characteristics include: The dispatchable distributed generation using droop control needs to meet the capacity and power constraints, active-frequency and reactive-voltage droop control characteristics, including: (39) (40) (41) (42) (43) (44) (45) (46) (47) in, and They represent the maximum active output power, maximum reactive output power and total output capacity of the dispatchable distributed generation respectively; and Respectively Time Node Droop control decision variables of distributed generation; and denote the reference voltage amplitude and reference frequency decision variables respectively; and Respectively represent the maximum values ​​of reference frequency, reference voltage, active power droop coefficient and reactive power droop coefficient; and Respectively represent the minimum values ​​of reference frequency, reference voltage, active power droop coefficient and reactive power droop coefficient; represents the angular frequency of the system; In addition to the controllable distributed power supply with droop control function, the mobile energy storage is also equipped with a droop controller; assuming that the mobile energy storage provides as much active output as possible, the mobile energy storage is charged or discharged according to the droop characteristics, which can be expressed as: (48) The charging or discharging action of mobile energy storage is subject to the rated capacity The restriction can be expressed as: (49) in, is the rated capacity of mobile energy storage; express The amount of electricity generated by mobile energy storage at all times; The radial topology constraint based on the improved spanning tree includes: During typhoon and rainstorm disasters, the distribution system may form multiple islands. Improved spanning tree constraints are used to meet the radial topology requirements of each island, including: (50) (51) in, and is a binary variable; if node is a substation node, then The value is 1, otherwise it is 0; if the node is a distributed power generation node, then The value is 1, otherwise it is 0; is a binary variable, indicating the node Whether it is the root bus in the island, if so, the value is 1, otherwise it is 0; and Busbar The parent node and child node collection of ; It is a binary variable, and its value is 1, indicating that the branch Power on, a value of 0 indicates a branch No electricity; and is a binary variable. If the node Is a node The parent node of , or busbar It is a busbar The parent node of , then select the branch Power on, take , and Cannot be 1 at the same time; represents a binary variable, represents a node Is it a node? The parent node of Time Node For Node The parent node of .

9. The active defense method for a new energy power distribution system considering the uncertainty of the typhoon and rainstorm evolution path according to claim 8 is characterized in that: The multi-objective optimization model also includes: Convex optimization solution of the multi-objective optimization model: There are nonlinear equations in the multi-objective optimization model, including equations (29), (31), (37) and (38). , , in formula (42) , in formula (43) , in formula (48) , and equations (32) and (41); the nonlinear equations in the multi-objective optimization model can be divided into three types: the first type is the square of continuous variables, the second type is quadratic equations or quadratic inequalities, and the third type is the product of continuous variables; these three types of nonlinear equations are subjected to convex optimization processing: Convex optimization of nonlinear equations of the first type: Since equations (29), (31), (37) and (38) contain square terms of branch current and node voltage and , using the variable and To replace the two quadratic terms separately, we get: (52) (53) (54) (55) in, and Node Voltage and branches The square term of the current; and Node Voltage and branches The square term of the current; Since equations (54) and (55) contain square terms , so it is still a nonlinear expression. Therefore, Taylor series expansion is used to linearize equations (54) and (55), including: Assume that the initial value is , through Taylor series expansion we can get: (56) in, Represents an infinitesimal quantity; For Node The initial value of the voltage amplitude; By ignoring the second-order and higher-order terms, equation (56) can be approximately expressed as: (57) The node voltage amplitude is usually about 1.0 pu, so assuming , formula (57) can be further approximated as: (58) The distance function definition of the Taylor series approximation includes: (59) in, is the distance function approximated by the Taylor series; By substituting equation (58) into equations (54) and (55), an approximate load demand model is obtained, which can be expressed as: (60) (61) Convex optimization of nonlinear equations of the second type: Equations (32) and (41) can be transformed into second-order cone constraints and rotational second-order cone constraints, respectively, which can be expressed as: (62) (63) Convex optimization of the third type of nonlinear equations: Since equations (42), (43) and (48) all contain the product of two bounded continuous variables, the McCormick envelope approximation is used to process them and the auxiliary variable and Respectively and , and approximated using twelve linear constraints, can be expressed as: (64) (65) (66) (67) (68) (69) (70) (71) (72) (73) (74) (75)。 10. An active defense device for a new energy power distribution system taking into account the uncertainty of the evolution path of a typhoon or rainstorm, used in the method according to any one of claims 1 to 9, characterized in that: The device comprises a first module, a second module, a third module, a fourth module, a fifth module and a sixth module; The first module is used to obtain a dual uncertainty time series scenario of the typhoon and rainstorm evolution path and the coupling of the system component state; and obtain the vulnerable components at each moment during the typhoon and rainstorm duration period according to the time series scenario; The second module is used to switch the system to island operation and keep the dispatchable distributed power sources in the system in droop control; calculate the vulnerable load loss caused by disconnecting all vulnerable elements of the system at each moment through a multi-objective optimization model; The third module is used to consider each number of the total number of vulnerable elements from 0 to the current moment, and to combine each vulnerable element at the moment of vulnerable load loss when selecting any number of them, taking into account all the selected situations; and to calculate the survival probability of the combination of vulnerable elements; The fourth module is used to put into operation the vulnerable element combinations of each quantity at each vulnerable load loss moment in order from the smallest quantity to the largest quantity according to the survival probability from the largest to the smallest, and disconnect the vulnerable elements outside the running vulnerable element combinations; The fifth module is used to reschedule the system in which each vulnerable component combination is put into operation through active scheduling and a multi-objective optimization model, wherein the active scheduling includes power generation rescheduling, pre-disaster network reconstruction, droop control parameter optimization setting and protective voltage regulation; The sixth module is used to determine whether there is a vulnerable load loss in the system when each vulnerable component combination is put into operation in the order of operation. If not, active defense is performed at this moment based on the vulnerable component combination put into operation this time and the active scheduling.

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