Energy storage cooperative emergency scheduling method based on DRO under strong uncertainty of typhoon

Abstract

The invention provides an energy storage cooperative emergency scheduling method based on DRO under strong uncertainty of typhoon. The method comprises the following steps: S1, constructing a typhoon wind field model by considering uncertainty of typhoon path and strength; s2, depicting the fault rates of overhead lines and towers of the power transmission line of the power distribution network under typhoon disasters, constructing a power transmission line fault model of the power distribution network, depicting the uncertainty of line faults by using a moment fuzzy set, and constructing a power transmission line fault fuzzy set of the power distribution network; s3, constructing a two-stage energy storage cooperative emergency scheduling model based on DRO, optimizing the pre-disaster layout position of the MES in the first stage of the model, and optimizing the power output scheme of each SES and the MES in the typhoon disaster period in the second stage of the model; s4, obtaining typhoon forecast information and power distribution network information; step S5, inputting the acquired information into a two-stage energy storage collaborative emergency scheduling model based on CG and Camp; and solving the CG to obtain an energy storage cooperative emergency scheduling strategy under the strong uncertainty of the typhoon.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power distribution network control, and particularly discloses an energy storage collaborative emergency scheduling method based on DRO under strong uncertainty of typhoon. BACKGROUND

[0002] Under the background of global ocean warming, extreme weather events such as typhoons and hurricanes are increasingly frequent, which seriously threatens the safe and reliable operation of power distribution systems in coastal areas. The development of energy storage technology provides an effective solution for disaster emergency response. By reasonably scheduling the energy storage system, the economic loss caused by power gaps during disasters can be maximally alleviated. According to the different scheduling modes, the energy storage system is usually divided into two categories: stationary energy storage (SES) and mobile energy storage (MES). SES has the advantages of long-time and large-scale storage of electric energy, and MES has the characteristics of flexible scheduling and on-demand adjustment of deployment location. However, due to the high uncertainty of typhoon disasters, the path prediction still has an error of tens of kilometers, which makes it difficult to accurately estimate the wind speed intensity and line fault conditions of the power distribution network. This uncertainty significantly increases the complexity of energy storage system scheduling, especially for MES, if the deployment location is not appropriate, its emergency value will decrease sharply.

[0003] In order to cope with the strong uncertainty of disasters, the existing research mainly adopts three types of modeling ideas: one is scenario-based stochastic programming (SP), which generates a large number of scenarios to describe uncertainty through sampling simulation, and guides the scheduling scheme by assigning weights to the scenarios. It needs a large number of scenarios to ensure representativeness, and its problem size is large, the solution time is long, and it is insufficient for low-probability high-loss scenarios; two is robust optimization (RO), which models each uncertain quantity as a variety of uncertainty sets, and determines the scheduling scheme based on the worst-case scenario. Although robust optimization can effectively improve the safety of the scheduling scheme, its defect is that it only focuses on the most unfavorable situation, resulting in a conservative scheme with high redundancy and significant economic loss, so it often lacks appeal in the practical application of power enterprises; three is distributionally robust optimization (DRO), which takes into account randomness and robustness, and becomes a compromise between the first two methods. It identifies the most unfavorable probability distribution within the fuzzy set and optimizes the scheduling scheme based on the distribution.

[0004] In the research of distribution robust optimization, Sadra proposed a fuzzy set of moments to characterize the line failure characteristics under extreme events, and accordingly constructed a distribution robust optimization model for network reconfiguration, but did not involve the energy storage scheduling problem. Yujia Li established a distribution robust optimization model for the pre-disaster line reinforcement situation, and fitted the corresponding line vulnerability curve according to different reinforcement strategies. Yuan Yang constructed a distribution robust optimization model based on the scene probability fuzzy set by extracting typical experience scenes and their probability distribution, and allowing a certain deviation of scene probability, and solved it by using Benders decomposition and C&CG collaborative algorithm. The modeling and solving complexity of distribution robust optimization is high, the calculation time is significant, and the most unfavorable scene or distribution is usually difficult to output explicitly, which limits its explanatory and guiding value in practical application. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a DRO-based energy storage collaborative emergency scheduling method under strong uncertainty of typhoon, which can realize the collaborative scheduling of energy storage system under strong uncertainty of typhoon disaster, effectively reduce the economic loss during typhoon, and improve the emergency response capability and operation resilience of the power distribution system.

[0006] To solve the above technical problems, the present application adopts the following technical method: a DRO-based energy storage collaborative emergency scheduling method under strong uncertainty of typhoon, comprising: Step S1, considering the uncertainty of typhoon path and intensity, a typhoon wind field model is constructed; Step S2, the failure probability of overhead lines and towers of power transmission lines under typhoon disaster is described, a power transmission line failure model is constructed, and then the line failure uncertainty of the power distribution network is described by using the method of fuzzy set of moments, and a fuzzy set of power transmission line failure of the power distribution network is constructed; Step S3, a two-stage energy storage collaborative emergency scheduling model based on DRO is constructed, which takes minimizing the loss of load loss and system economic loss caused by typhoon disaster as the target, the first stage optimizes the pre-disaster layout position of MES, and the second stage optimizes the power output scheme of each SES and MES during typhoon disaster; Step S4, obtain typhoon forecast information and power distribution network information; Step S5, input the information obtained in step S4 into the two-stage energy storage collaborative emergency scheduling model, solve the model, and obtain the energy storage collaborative emergency scheduling strategy under strong uncertainty of typhoon.

[0007] Further, in step S1, the instantaneous typhoon wind field attacking the power distribution network is decoupled into translation speed and circulation wind speed, the typhoon moving behavior is described by using the Miyazaki Masayoshi model, and the Rankine circulation wind speed model is used to analyze the near-ground wind field distribution, and the obtained typhoon wind field model is as follows: (1) (2) (3) (4) (5) wherein, , is a set of typhoon paths; is the distance between the node and the typhoon center under the th typhoon path, which is calculated by the real-time typhoon center coordinate and the power grid coordinate ; is the moving speed of the typhoon wind field in the power grid with the distance of from the typhoon center; is the earth radius, which is 6371km; is the moving speed of the typhoon center; is the maximum circulation wind speed of the typhoon, is the corresponding maximum wind speed radius; is the maximum wind speed near the typhoon center; and respectively represent the wind speed and the wind circle radius corresponding to the Beaufort wind scale Lev.

[0008] Further, in the step S2, the failure rate of the overhead line between the adjacent nodes , of the power grid is represented by the following model: (6) wherein, is the failure rate of the overhead line between the nodes , at the time under the th typhoon path; is the circulation wind speed of the overhead line between the nodes , at the time under the th typhoon path; and are model coefficients, take [11, 13], take [-20, -16]; is the designed wind speed of the overhead line, which sharply increases the failure rate of the overhead line when exceeding the wind speed; is the distance between the nodes , The length of the overhead line between them; adjacent nodes of distribution network , The failure rate of the towers for the overhead lines between them is represented by the following model: (7) In the formula, For the first Under the typhoon path, nodes , Between Failure rate of the tower; These are model coefficients, set to 0.3; This is the design wind speed for the tower; exceeding this wind speed will cause the tower's failure rate to rise sharply. Construct adjacent nodes of the distribution network based on the reliability formula of a series system. , The fault model for the transmission line is as follows: (8) In the formula, Adjacent nodes of the distribution network , Transmission lines between Failure rate at any given time; Adjacent nodes of the distribution network , The total number of towers between; The sign for product.

[0009] Furthermore, in step S2, the uncertainty of distribution network line faults is characterized using the moment fuzzy set method, as shown in the following equation: (9) (10) In the formula, In order to target the The fuzzy set of fault moments for power distribution lines constructed from the typhoon path contains the set of all possible line fault distributions. This represents a specific probability distribution that satisfies the definition. For the state space of line faults, given by equation Definition, constructed based on the idea of ​​budget uncertainty sets; In state space The domain of all distributions; Indicates in The set of all probability distributions in the above; It is the collection of all lines; Index for scheduling periods; The set of dispatch periods is determined by the duration of typhoon coverage of the power distribution network area by typhoons with winds of at least level 7. This is achieved by solving... This equation yields two roots; the absolute value of the difference between the two roots is... ,in, Indicates the first Typhoon path under the typhoon The distance from the distribution network at any given time is defined as the typhoon's coordinates at the initial time. , For the first The angle of movement of the typhoon under the given typhoon path, then ; Indicates the first Under the typhoon path, the line exist The status at any given moment: a value of 1 indicates that the line is normal, and a value of 0 indicates that the line is faulty. Indicates distribution Down Expectations Indicates distribution Below, the line exist The expected failure rate at any given time should be less than the failure rate. ; Budget for line faults.

[0010] Furthermore, in step S3, the objective function of the constructed two-stage energy storage coordinated emergency dispatch model based on DRO is: (11) In the formula, For the first Fixed costs for vehicle MES dispatch; It means the first Does the vehicle's MES connect to the node? A binary variable, where 1 represents access and 0 represents no access; A set of nodes; A collection of MES; For the first The weights of each typhoon path represent the probability of its occurrence; Node weights; This is the unit loss coefficient for load shedding; For the first Under the typhoon path, nodes exist The amount of load loss at any given moment; , The unit discharge cost for SES and MES are respectively; , SES and MES respectively direct to distribution network nodes The active power generated.

[0011] Furthermore, in step S3, the constructed two-stage energy storage coordinated emergency dispatch model based on DRO includes the following constraints: 1) MES pre-disaster deployment constraints: It restricts that at most one MES can be connected to each distribution network node and each MES can be assigned to at most one distribution network node; 2) Power balance constraints of the power distribution system: These are established based on the linearized DistFlow power flow model; 3) Node voltage constraints; 4) Power constraints on transmission lines; 5) Node load loss constraints; 6) Energy storage dispatch constraints: These include energy constraints during emergency dispatch of SES and MES, as well as power constraints during emergency dispatch of SES and MES.

[0012] Going further: The constraints for the pre-disaster deployment of the MES are: (18) (19) The power balance constraint of the power distribution system is: (20) (twenty one) In the formula, , For node indexing; , For the first Under the typhoon path, the line exist The constant ebb and flow of merit and demerit; , This indicates the active and reactive power that the distribution network obtains from the upstream power grid; For nodes exist The reactive power gap at any given moment; , It is a node exist The active and reactive power requirements at any given time; , Indicates SES from distribution network node The absorbed active and reactive power, i.e., charging; , This indicates that SES is directed to the distribution network nodes. The active and reactive power generated; , represents the active and reactive power absorbed by the first MES from the distribution grid node ; and represents the active and reactive power emitted by the first MES to the distribution grid node , i.e. the discharge; The node voltage constraint is: (22) (23) wherein, , are the voltage of the node under the first typhoon path at time , is the voltage of the node at time ; is the reference voltage, taken as 1.0 p.u.; , are the resistance and reactance of the line ; , are the minimum and maximum voltages allowed for the node ; M is the maximum value; The transmission line power constraint is: (24) (25) wherein, , represent the upper limit of the active and reactive power transmitted by the line ; The node loss-of-load constraint is: (26) (27) The energy storage dispatching constraint is: (28) (29) (30) (31) (32) (33) ​​wherein, formula (28)-(31) are energy constraints of energy storage scheduling, and formula (32)-(33) are power constraints of energy storage scheduling; 、 are the lower limits of the SES and MES electric quantity respectively, are the lower limits of the SES and MES electric quantity respectively, are the upper limits of the SES and MES electric quantity respectively; is the energy storage charging and discharging efficiency; is the scheduling time interval; , are the rated active power and reactive power of the SES on the node , are the rated active power and reactive power of the MES. are the rated active power and reactive power of the MES. are the rated active power and reactive power of the MES. are the rated active power and reactive power of the MES.

[0013] Preferably, the step S5 comprises: Step S501, expressing the two-stage energy storage cooperative emergency scheduling model into the following compact form: (12) wherein, represents the decision variable of the first stage, is a vector composed of ;is the coefficient in the first stage objective function; represents the decision variable of the second stage for the th typhoon path, , , , , , , , , , , , , , , , , , are composed of , , , , , , , ,​​ 、 、 、 、 、 、 、 、 is the coefficient in the second-stage objective function; superscript T denotes transpose; denotes the transmission line fault variable; denotes the constraint of the inner min model on ; G, h, E, M are model coefficients; is the surrogate model of the inner min model ; Step S502, according to the C&CG algorithm, the two-stage energy storage collaborative emergency dispatching model as shown in formula (12) is decomposed into the following C&CG master problem and C&CG sub-problem, and according to the CG algorithm, the C&CG sub-problem is decomposed into the following CG master problem and CG sub-problem: ① CG master problem The part of in formula (12) is equivalent to: (13) In the formula, denotes the index of the transmission line fault scenario, is the line fault condition under the th typhoon path in the th scenario, is the occurrence probability of the th scenario, is the total number of scenarios; (14) In the formula, is the optimal value of the CG master problem; is the worst scenario set of the th typhoon path, obtained by searching the CG sub-problem; denotes the transmission line fault rate vector; 、 is the value of the dual variable of the constraint condition; ② CG sub-problem (15) In the formula, denotes the Hadamard product; is the optimal value of the CG sub-problem; The inner min model of formula (15) is subjected to dual transformation, and formula (15) is reconstructed as: ​ (16) In the formula, For the dual variables of the inner min model constraint of equation (15); ③C&CG main problem (17) In the formula, The optimal value for the C&CG principal problem; These are variables associated with the target value in the second stage; and It is an index related to the number of iterations. Indicates the first Scenario during the next iteration The probability of; ④C&CG subproblem The C&CG subproblem is the problem of solving the second stage of the two-stage energy storage coordinated emergency dispatch model. ; Step S503: Input the information obtained in step S4 into the two-stage energy storage collaborative emergency dispatch model as shown in equation (12); Step S504: Use the CG algorithm to iteratively solve the CG main problem and subproblems, and obtain the worst-case scenario set. and its distribution Add the variables to the main C&CG problem, and then use the C&CG algorithm to solve the main C&CG problem. Iterate in this way until the optimal decision variables of the two-stage energy storage coordinated emergency dispatch model are obtained by gradually converging.

[0014] As another aspect of the present invention, an energy storage collaborative emergency dispatch device based on DRO under strong typhoon uncertainty includes a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of the aforementioned energy storage collaborative emergency dispatch method based on DRO under strong typhoon uncertainty.

[0015] As another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the aforementioned energy storage collaborative emergency dispatch method based on DRO under strong uncertainty of typhoons.

[0016] As another aspect of the present invention, a computer program product includes a computer program that, when executed by a processor, implements the steps of the aforementioned energy storage collaborative emergency dispatch method based on DRO under strong uncertainty of typhoons.

[0017] In summary, the energy storage collaborative emergency dispatch method based on DRO under strong typhoon uncertainty proposed in this invention mainly consists of three parts. The first part constructs a wind field model and a fuzzy set of transmission line faults under strong typhoon uncertainty. The second part constructs a two-stage energy storage collaborative emergency dispatch model based on DRO. The third part solves the model using algorithms based on CG and C&CG. The first part of this method uses the moment fuzzy set method to characterize the probability of distribution line faults under typhoon disasters, considering the uncertainty of typhoon path and intensity, so that the line fault rate can be more accurately described at the probabilistic level, providing reliable input parameters for the subsequent dispatch model. The second part of this method, based on the aforementioned fuzzy set of transmission line faults, constructs a two-stage energy storage collaborative emergency dispatch model under the DRO framework. The first stage determines the spatial deployment scheme of MES (Mechanical Storage Execution System), and the second stage optimizes the power output strategy of each SES (System-Specific Energy Execution System) and MES during the disaster to minimize the system's economic losses caused by the disaster. The third part of this method addresses the high complexity and difficulty in solving the two-stage energy storage collaborative emergency dispatch model proposed in the second part. It designs a hybrid solution framework combining column generation CG and C&CG algorithms. By iteratively generating key scenarios and constraints, it effectively improves the model's computational efficiency and convergence, thus ensuring its applicability in real-world scenarios. These three parts respectively cover uncertainty modeling, optimization model construction, and algorithm solving. Their sequential connection and coordinated operation enable collaborative dispatch of energy storage systems under conditions of high uncertainty in disasters, effectively reducing economic losses during typhoons and enhancing the emergency response capability and operational resilience of the power distribution system. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the solution process for the DRO-based energy storage collaborative emergency dispatch model involved in this invention. Figure 2 This is a test distribution network topology diagram in an embodiment of the present invention; Figure 3 This is a schematic diagram of a representative typhoon prediction path and corresponding circulation wind speed in an embodiment of the present invention. Figure 4 This is a comparison chart of the expected load loss at different nodes in different embodiments of the present invention. Detailed Implementation

[0019] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0020] DRO can balance between the conservativeness and the economy, but the research on the energy storage collaborative scheduling under disasters based on DRO is still to be improved. Therefore, the application provides a DRO-based energy storage collaborative emergency scheduling method under strong uncertainty of typhoon, which constructs a two-stage optimization framework based on DRO, and adopts column generation (CG) and C&CG to collaboratively solve the model, so that the calculation time is significantly shortened, and the applicability of the model is enhanced. Through the method, decision guidance can be provided for emergency scheduling of a distribution system containing SES and MES, economic loss caused by disasters can be effectively reduced, and the resilience and reliability of the distribution system can be improved.

[0021] Specifically, the DRO-based energy storage collaborative emergency scheduling method under strong uncertainty of typhoon provided by the application comprises the following steps. Step S1, considering the uncertainty of the typhoon path and intensity, a typhoon wind field model is constructed.

[0022] The instantaneous typhoon wind field attacking the distribution network can be decoupled into two parts: "translation speed" and "circulation wind speed". The former determines the duration of regional disaster, and the latter determines the fault condition of overhead lines and towers. Since the research area is located in the coastal area of China, which belongs to the main path of the western Pacific typhoon, the application adopts the Miyasaka model to describe the typhoon movement behavior, and the Rankine circulation wind speed model to analyze the near-ground wind field distribution. For a typhoon path , the typhoon wind field model is as follows: (1) (2) (3) (4) (5) In the formula, , is a set of typhoon paths; is the distance from the center of the typhoon to the distribution network region under the th typhoon path, which is calculated by the real-time typhoon center coordinates and the distribution network coordinates ; is the moving speed of the typhoon wind field in the distribution network with a distance of from the center of the typhoon; is the circulation wind speed of the distribution network wind field; is the radius of the earth, which is 6371km; is the moving speed of the typhoon center; is the maximum circulation wind speed of the typhoon, is the corresponding maximum wind speed radius; This is the maximum wind speed near the center of the typhoon; and These represent the wind speed and the radius of the wind circle corresponding to the Beaufort scale (Lev), for example... This indicates the radius of the 7-level wind circle.

[0023] Although the aforementioned wind field model can accurately analyze instantaneous wind speeds under a single typhoon path, the meteorological department's forecasts of the typhoon's center location and intensity still suffer from significant uncertainty due to the combined effects of initial field bias and model errors, posing difficulties for subsequent emergency energy storage dispatch. Therefore, this invention introduces multiple possible typhoon path scenarios during the modeling process, namely… ( This involves creating a set of typhoon paths and deriving the potential wind speed levels in the distribution network area under each scenario based on wind field calculation formulas. Furthermore, by incorporating historical data showing the deviation patterns between predicted and actual paths, different path scenarios are assigned corresponding weights to more accurately characterize the impact of typhoon uncertainties on distribution network operation.

[0024] Step S2: Characterize the fault probability of overhead lines and towers of power distribution network transmission lines under typhoon disaster, construct a fault model of power distribution network transmission lines, and then use the moment fuzzy set method to characterize the uncertainty of power distribution network line faults and construct a fuzzy set of power distribution network transmission line faults.

[0025] During typhoons, power outages in the distribution network are mainly caused by damage to overhead lines and towers between nodes. Given the extremely low failure rate of underground cables, this invention only establishes a fault model for the "overhead line-tower" structure. Since the line length between adjacent nodes is relatively short compared to the entire typhoon wind field, it can be reasonably assumed that they experience similar levels of wind damage. Therefore, adjacent nodes in the distribution network... , Faults in multiple sections of overhead lines can be uniformly represented using the following probabilistic model: (6) In the formula, For the first Under the typhoon path, nodes , The overhead lines between Failure rate at any given time; For the first Under the typhoon path, nodes , The overhead lines between The constant circulating wind speed; and These are the model coefficients. Take [11,13], Take [-20, -16]; The design wind speed for overhead lines is set; if the wind speed is exceeded, the failure rate of overhead lines increases sharply. It is a node , The length of the overhead line between them.

[0026] adjacent nodes of distribution network , The overhead lines between them are usually supported by multiple towers, and the failure of each tower can be considered an independent event. Their failure rates are as follows: (7) In the formula, For the first Under the typhoon path, nodes , Between Failure rate of the tower; These are model coefficients, set to 0.3; This refers to the design wind speed of the tower. If the wind speed exceeds this, the failure rate of the tower will increase sharply.

[0027] The reliability formula for a series system can be used to construct the adjacent nodes of a distribution network. , The fault model for the transmission line is as follows: (8) In the formula, Adjacent nodes of the distribution network , Transmission lines between Failure rate at any given time; Adjacent nodes of the distribution network , The total number of towers between; The sign for product.

[0028] To overcome the conservatism of robust optimization, which only focuses on the worst-case scenario, this invention introduces the moment fuzzy set method to characterize the uncertainty of distribution network line faults, as shown in the following equation: (9) (10) In the formula, In order to target the The fuzzy set of fault moments for power distribution lines constructed from the typhoon path contains the set of all possible line fault distributions. This represents a specific probability distribution that satisfies the definition. The state space for line faults is defined by equation (10) and constructed based on the idea of ​​budget uncertainty sets. In state space The domain of all distributions; Indicates in The set of all probability distributions is the complete set of all possible line fault distributions; It is the collection of all lines; Index for scheduling periods; This is a set of scheduling periods, the range of which is determined by the effective impact time of the typhoon on the power distribution network. Considering that outdoor operations cannot be carried out when the wind speed exceeds level 7, this invention assumes that the duration of the typhoon's level 7 wind circle covering the power distribution network area is the typhoon's impact time. This is achieved by solving... This equation yields two roots; the absolute value of the difference between the two roots is... ,in, Indicates the first Typhoon path under the typhoon The distance from the distribution network at any given time is defined as the typhoon's coordinates at the initial time. , For the first The angle of movement of the typhoon under the given typhoon path, then ; Indicates the first Under the typhoon path, the line exist The status at any given moment: a value of 1 indicates that the line is normal, and a value of 0 indicates that the line is faulty. Indicates distribution Down Expectations Indicates distribution Below, the line exist The expected failure rate at any given time should be less than the failure rate. ; Budget for line faults.

[0029] As can be seen from the above formula, the fuzzy set constructed in this invention introduces line failure rate information when characterizing uncertainty, and its conservatism is effectively reduced compared with the traditional robust optimization method that only relies on boundary constraints.

[0030] Step S3: Construct a two-stage energy storage collaborative emergency dispatch model based on DRO.

[0031] 1. Objective function The objective function of the two-stage energy storage coordinated emergency dispatch model based on DRO in this invention is: (11) In the formula, For the first Fixed costs for vehicle MES dispatch; It means the first Does the vehicle's MES connect to the node? binary variable, 1 represents access, and 0 represents no access; is a node set; is a set of MESs; is the first typhoon path weight, representing the occurrence probability; is a node weight; is a unit load loss coefficient; is the first typhoon path, the load loss of node at time; , are unit discharge costs of SES and MES, respectively; , are active power outputs of SES and MES to the distribution network node , respectively.

[0032] From the objective function, a two-stage optimization model is constructed: the first stage optimizes the pre-disaster layout position of the MES, and the second stage optimizes the power output scheme of each SES and MES during the typhoon disaster. The objective function of the collaborative scheduling model not only focuses on minimizing the load loss, but also ensures the reasonable use of MES to improve the overall economy of the system.

[0033] 2. Constraint conditions 1) MES pre-disaster layout constraint, which is: (18) (19) Here, constraint (18) limits that each distribution network node can access at most one MES, and constraint (19) indicates that each MES is assigned to at most one distribution network node.

[0034] 2) Power balance constraint of the distribution system Based on the linearized DistFlow power flow model, the power balance constraint of the distribution system is as follows: (20) (21) In the formula, , is a node index; , is the first typhoon path, the active and reactive power flow of line at time; , P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid; P, Q represent the active and reactive power acquired by the distribution network from the upper grid;

[0035] 3) Node voltage constraint, which is: (22) (23) ,

[0036] 4) Transmission line power constraint, which is: (24) (25)​​​​​​​​​​​​​​​ wherein, , respectively represent the upper limit of the line transmitting active power and reactive power.

[0037] 5) Node loss-of-load constraint, which is: (26) (27) 6) Energy storage scheduling constraint The energy constraints of SES and MES in emergency scheduling are shown in equations (28)-(31), and the power constraints are shown in equations (32)-(33): (28) (29) (30) (31) (32) (33) wherein, , are the lower limits of the energy storage of the SES and the MES in the th typhoon path at the th time, , are the lower limits of the energy storage of the SES and the MES, , are the upper limits of the energy storage of the SES and the MES; is the charging and discharging efficiency of the energy storage; is the scheduling time interval; , are the rated active power and the rated reactive power of the SES at the node ; , are the rated active power and the rated reactive power of the th MES.

[0038] Step S4: Obtain typhoon forecast information through the typhoon network of the Central Meteorological Observatory, and obtain power distribution network information through the power distribution geographic information system and the power distribution automation system.

[0039] Step S5: Input the information obtained in step S4 into the two-stage energy storage collaborative emergency scheduling model, solve the model, and obtain the energy storage collaborative emergency scheduling strategy under the strong uncertainty of typhoons.

[0040] Step S501, the two-stage energy storage collaborative emergency dispatching model is expressed in the following compact form: (12) In the formula, X1 represents the decision variable of the first stage, is a vector composed of ; is a coefficient in the first stage objective function; X2 represents the decision variable of the second stage for the first typhoon path, , , , , , , , , , , , , , , , , are vectors composed of , , , , , , , , , , , , , , , , ; is a coefficient in the second stage objective function; the superscript T represents transposition; X3 represents a transmission line fault variable; X4 represents the related constraint of the inner min model ,G, h, E and M are model coefficients; in order to facilitate the reconstruction of the subsequent algorithm part, the inner min model of the second stage is replaced by .

[0041] Step S502, a CG and C&CG-based energy storage collaborative emergency dispatching model solving algorithm.

[0042] The two-stage energy storage coordination emergency dispatching model constructed by the application cannot be directly solved, and needs to be reconstructed. First, the second stage of the model Part can be equivalent to: (13) In the formula, is expressed as the index of the transmission line fault scenario, is the line fault condition of the first typhoon path in the first scenario, is the occurrence probability of the first scenario, is the total number of scenarios. It is worth mentioning that the line fault scenario refers to the line fault condition, for example, if there are 5 lines in the system, only line 1 fails at 12 o'clock can be regarded as a scenario, and lines 2 and 3 both fail at 13 o'clock are also regarded as a scenario. The difference between different scenarios is that the lines that fail in the system are different, and the time when the failure occurs is different.

[0043] As can be seen from the above, the meaning of formula (13) is that a series of discrete scenarios can be used to equivalently calculate the expectation.

[0044] However, enumerating scenarios will greatly increase the size of the problem, resulting in an increase in solving time. In order to speed up the solution, the application introduces the CG algorithm. The CG algorithm decomposes the original large-scale problem into a main problem and a sub-problem, and iteratively solves between the two. The main problem is responsible for optimizing under a given candidate scenario set, thereby obtaining a temporary solution; while the sub-problem searches and generates new scenarios that may improve the current solution according to the dual information of the main problem. When the candidate scenario found by the sub-problem can reduce the objective function value, the scenario will be added to the main problem, expanding the feasible region of the solution. Through this way of continuous iteration and updating, the CG algorithm can avoid enumerating all scenarios in the initial stage, greatly reducing the calculation scale, while gradually approaching the optimal solution. Finally, when the sub-problem can no longer generate scenarios with improvement, the iteration process terminates, and the solution of the main problem converges to the global optimal or approximate optimal solution.

[0045] For the second stage , for any typhoon path , according to the CG algorithm, the following CG main problem and CG sub-problem can be written: ① CG main problem (14) In the formula, is the optimal value of the CG main problem; is the worst scenario set of the first typhoon path, which is searched by the CG sub-problem; represents the transmission line failure rate vector; , is the dual variable value of the constraint.

[0046] ② CG sub-problem (15) wherein, represents the Hadamard product; is the optimal value of the CG sub-problem. This formula (15) is still a double-layer model and cannot be directly solved, but at this time and involved objective function and constraints are linear, therefore, the inner min model meets the strong duality theorem. By dual transformation of the inner min model, formula (15) can be reconstructed as: (16) wherein, is the dual variable of the constraint of the inner min model of formula (15).

[0047] The reconstructed CG sub-problem can be directly solved by calling a commercial solver. On the basis of the above reconstruction, the process of solving the second stage based on the CG algorithm is expressed as: Step 1: initialize the scene set in the CG algorithm and the tolerance , and set the iteration number .

[0048] Step 2: solve the CG main problem shown in formula (14) to obtain the optimal value of the CG main problem in the first iteration and the dual variable corresponding to the constraint condition. , .

[0049] Step 3: solve the CG sub-problem shown in formula (16) to obtain the optimal value of the CG main problem and the extreme scene .

[0050] Step 4: if , at this time is the engineering approximation value of the second stage model , and the iteration is terminated. Otherwise, pass to the scene set of the CG main problem, let , and return to step 2.

[0051] As can be seen from the above model, the present application can derive the worst scene set under any typhoon path and its distribution , thereby providing more information to assist decision-making personnel.

[0052] After solving the solution problem of the second stage of the model, the complete model shown in formula (12) is solved by using the C&CG algorithm. The C&CG algorithm is a commonly used decomposition method, and the core idea is to generate a dual cut or key scenario through the iterative solution of the master problem and the sub-problem, and dynamically add it to the master problem. Specifically, the master problem provides a candidate one-stage solution under the condition of a given limited scene set, while the sub-problem verifies whether there is a situation that violates the constraint under this solution, and generates a new effective inequality or scene accordingly. Through this iterative process, the algorithm can gradually converge to the global optimal solution, thereby effectively reducing the computational complexity while ensuring accuracy.

[0053] According to the idea of the C&CG algorithm, the phase energy storage collaborative emergency dispatching model is reconstructed, and the master problem and the sub-problem are: ③C&CG master problem (17) In the formula, is the optimal value of the C&CG master problem; is a variable associated with the second stage target value; and is an index related to the number of iterations, represents the probability of scenario at the th iteration.

[0054] ④C&CG sub-problem The C&CG sub-problem is the problem of solving the second stage of the two-stage energy storage collaborative emergency dispatching model , which can be solved by using the aforementioned CG algorithm, and will not be described here.

[0055] As shown in Figure 1 , the complete process for solving the two-stage energy storage collaborative emergency dispatching model proposed by the present application based on the CG algorithm and the C&CG algorithm is as follows: Step 1: input the information obtained in step S4 into the two-stage energy storage collaborative emergency dispatching model shown in formula (12), and initialize the lower limit , the upper limit , the number of iterations =0, , and the tolerance =minimum value in the C&CG algorithm.

[0056] Step 2: solve formula (17) to obtain the first-stage decision variable and the optimal value of the C&CG master problem at the th iteration , update LB = LB + (UB - LB) / 2 .

[0057] Step 3: Solve the second stage based on CG algorithm , get the optimal value of CG master problem in the first iteration , the worst scenario set and its distribution , add , and related constraints to the C&CG master problem.

[0058] Step 4: update UB = min{UB, LB + (UB - LB) / 2}.

[0059] Step 5: if UB - LB ≤ , iteration terminates, which is the optimal decision of formula (12). Otherwise, let , return to step 2.

[0060] So far, the solving algorithm of the two-stage energy storage collaborative emergency dispatching model based on DRO has been described.

[0061] On the other hand, based on the same principle as the DRO-based energy storage collaborative emergency dispatching method under strong typhoon uncertainty described in the above embodiment, the present application also provides a DRO-based energy storage collaborative emergency dispatching device under strong typhoon uncertainty, which comprises a memory, a processor and a computer program stored in the memory. The processor executes the computer program to realize the steps of the DRO-based energy storage collaborative emergency dispatching method under strong typhoon uncertainty described in the above embodiment. Specifically, the device can be an electronic computer or a tablet computer, the processor can be a CPU, a GPU, etc., the memory can be a RAM, a ROM, an EEPROM, a CDROM, a magnetic disk storage medium, or any other medium that can be used to carry or store a computer program and can be read by a computer, which is not limited here.

[0062] On the other hand, the present application also provides a computer readable storage medium having a computer program stored thereon, which is executed by a processor to realize the steps of the DRO-based energy storage collaborative emergency dispatching method under strong typhoon uncertainty described in the above embodiment. Specifically, the computer readable storage medium can be a RAM, a ROM, an EEPROM, a SSD, a CDROM, a DVD, a U disk, or any other medium that can be used to carry or store a computer program and can be read by a computer.

[0063] ​​On the other hand, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the energy storage collaborative emergency dispatch method based on DRO under strong uncertainty of typhoons as described in the above embodiments.

[0064] To verify the feasibility and effectiveness of the method proposed in this invention, a case study analysis will be conducted using the 18-node distribution network test system provided by Matpower.

[0065] I. Test System Data exist Figure 2 In the test distribution network shown, node 1 is a substation node with a voltage amplitude per unit value of 1.0 pu. The numbers on the lines indicate the line length in km. Assume that nodes 9 and 18 are both equipped with SES (Self-Discharge Execution System), with capacities of 1MW / 2MWh and 0.4kW / 0.8MWh respectively. The system has two MES (Mechanical Discharge Execution System), with capacities of 0.5MW / 1MWh (MES 1, fixed dispatch cost of 3000 yuan / time) and 0.2MW / 0.5MWh (MES 2, fixed dispatch cost of 2000 yuan / time). The unit discharge cost of both SES and MES is set to 100 yuan / MWh. Important loads are distributed at nodes 5, 9, and 18 (…). Ordinary loads are distributed among the remaining nodes ( The system's load shedding factor is ¥10800 / MWh, and the upper and lower voltage limits are set at 1.05 pu and 0.95 pu, respectively. Line fault budget... Set to 3. Tolerance of the CG algorithm. Set to 0.1, the tolerance of the C&CG algorithm Set to 1. The test was performed on a personal computer with an Intel(R) Core i5-11260H CPU, 16.0GB of memory, MATLAB version R2020b, and CPLEX solver version 12.7.

[0066] Regarding typhoon information, this example selects Typhoon "Mangkut" which occurred in September 2024 as the research object, and assumes that the test power distribution network is located in Zhanjiang, Guangdong, and will be affected by "Talim" for about 8 hours. Based on the meteorological department's prediction of the typhoon's impact range, this scheme selects three representative possible paths for analysis, with their occurrence probabilities set at 40% (path 1), 20% (path 2) and 10% (path 3), respectively. The corresponding circulation wind speed every 0.5 hours is calculated using formula (2), such as... Figure 3The rest of the path is offset to the south, so the circulation wind speed borne by the power distribution network does not exceed the design wind speed of the overhead line (Note: The design wind speed of the power distribution line in the present application refers to the basic wind speed adopted when the line and tower are mechanically designed according to the national standard, that is, the 30-year, 10-meter height, 10-minute average maximum wind speed, which is used to characterize the wind resistance of the line structure.).

[0067] II. Example analysis 1. Verification of effectiveness and superiority of two-stage energy storage collaborative emergency scheduling model In order to verify the effectiveness and superiority of the two-stage energy storage collaborative emergency scheduling model proposed in the present application, five different cases are set for comparative analysis.

[0068] Case 1: Adopt DRO modeling method for SES and MES collaborative scheduling (the present application).

[0069] Case 2: Adopt DRO modeling method for only SES scheduling, and MES does not participate in pre-disaster scheduling.

[0070] Case 3: Adopt DRO modeling method for only MES scheduling, and the operating state of SES is not considered during scheduling.

[0071] Case 4: Adopt SP modeling method for SES and MES collaborative scheduling.

[0072] Case 5: Adopt RO modeling method for SES and MES collaborative scheduling.

[0073] It should be noted that case 4 generates 30 fault scenarios based on Monte Carlo sampling, and then performs collaborative scheduling; case 5 performs collaborative scheduling based on the uncertainty set shown in equation (10). After executing the scheduling model, the MES access position of each case is as shown in Table 1: ; In order to verify the effect of the scheduling of each case, the present test re-samples 100 fault scenarios for each typhoon path, and simulates the operation cost of the system by taking as the objective function, and equations (20) to (33) as the constraints, to obtain the expected load loss of each node (as shown in Figure 4 , the nodes with black triangles are important load nodes), the expected system load loss, and the expected system operation cost (as shown in Table 2) as follows: ; The results of the comparison of cases 1 to 3 can find that: if the MES is not scheduled, the emergency resources of the system cannot be fully utilized, and the fixed scheduling cost saved is not enough to offset the economic loss caused by the loss of load; if the operating state of the SES is not considered when scheduling the MES, it will lead to redundancy of resource allocation. For example, in the case of node 18 having configured SES, one MES will still be allocated to the node, compared with case 1, this repeated allocation makes the expected operation cost of the system increase by 800 yuan. The above comparison results show that the SES and MES collaborative scheduling method proposed in the application can significantly improve the overall utilization efficiency of emergency resources and effectively reduce the potential economic loss under disaster conditions, thereby verifying the rationality and effectiveness of the two-stage energy storage collaborative emergency scheduling model constructed.

[0074] The results of the comparison of cases 1, 4 and 5 can find that: SP takes the scene probability as the basis for decision-making, which leads to the MES being excessively concentrated in the downstream nodes of the high failure rate line, although it performs well in common failures, it systematically ignores the low probability-high loss extreme event, and the expected operation cost is therefore increased; RO completely discards the probability information and only takes the “worst line failure” as the anchor point, for example, it determines that the line failure between nodes 3 and 4 is the most serious, which forces MES1 to be deployed at node 4, which leads to a significant decrease in resource utilization in non-worst scenarios, and the expected cost is higher than DRO. In summary, the DRO modeling of the application incorporates probability and extreme value information through fuzzy sets, which suppresses the excessive reaction to high-frequency scenarios and maintains moderate vigilance to extreme tail risks, achieving an optimal trade-off between economy and conservatism, and verifying the superiority of the application in the strong uncertainty environment of typhoon.

[0075] 2. Algorithm effectiveness and superiority verification For solving the DRO model based on the matrix fuzzy set, the traditional solving algorithm is to dualize the fuzzy set constraint, which is equivalent to transforming into a two-stage optimization model containing a Lagrange multiplier, as shown in the following formula (34): (34) In the formula, is the Lagrange multiplier with respect to the constraint For this model, the C&CG algorithm can be applied for solving. It can also be seen from formula (34) that the traditional algorithm cannot derive the distribution.

[0076] To verify the superiority of the solving algorithm combining CG and C&CG proposed in the application, the solving performance of the algorithm and the traditional solving algorithm is compared in the Matpower 18-node and IEEE 33-node systems, and the results are shown in Table 3: ; The data in Table 3 show that the algorithm of the application has a significant improvement in solving efficiency compared with the traditional method: the main reason is that the application avoids the explicit modeling of the Lagrange multiplier variable in the main problem, thereby effectively reducing the model size and complexity. With the expansion of the system size, this advantage becomes more and more obvious. It is worth pointing out that due to the influence of the internal convergence tolerance of CG , the objective function value obtained by the algorithm is slightly higher than that of the traditional accurate algorithm, but the relative deviation is less than 0.1%, which is within the tolerance range of engineering application. In summary, the algorithm of the application exchanges a negligible loss of accuracy for considerable calculation acceleration, verifying its superiority in actual emergency scheduling of distribution networks.

[0077] In summary, the energy storage collaborative emergency scheduling method based on DRO under strong uncertainty of typhoon proposed by the application can effectively derive the extreme scenarios and the most unfavorable distribution searched by using the matrix fuzzy set to depict the uncertainty of the line fault probability distribution, provide more rich scenario information for decision makers, and enhance the scientificity and reliability of auxiliary decision making. Moreover, the application establishes a two-stage energy storage collaborative emergency scheduling model based on full use of statistical information, determines the spatial deployment scheme of MES in the first stage, optimizes the power output strategy of each SES and MES in the second stage, realizes the collaborative scheduling of SES and MES under strong uncertainty environment through alternating iteration, effectively improves the overall utilization efficiency of emergency resources, significantly reduces the potential economic loss under disaster conditions, and improves the resilience and reliability of the distribution system. In addition, the application can effectively reduce the size of the two-stage energy storage collaborative emergency scheduling model by using the alternating iteration solving method of CG and C&CG algorithm, and improve the calculation efficiency of the model.

[0078] The above embodiments are the preferred implementation schemes of the application, in addition to this, the application can also be implemented in other ways, any obvious substitution without departing from the technical scheme concept of the application is within the protection scope of the application.

[0079] In order for those skilled in the art to more conveniently understand the improvements of the application over the prior art, some drawings and descriptions of the application have been simplified, and some other elements have been omitted from the application file for the sake of clarity, and those skilled in the art should realize that these omitted elements can also constitute the content of the application.

Claims

1. A method for coordinated emergency dispatch of energy storage based on DRO under strong uncertainty of typhoons, characterized in that, include: Step S1: Considering the uncertainties in the typhoon's path and intensity, construct a typhoon wind field model; Step S2: Characterize the fault probability of overhead lines and towers of power distribution network transmission lines under typhoon disaster, construct a fault model of power distribution network transmission lines, and then use the moment fuzzy set method to characterize the uncertainty of power distribution network line faults and construct a fuzzy set of power distribution network transmission line faults. Step S3: Construct a two-stage energy storage collaborative emergency dispatch model based on DRO. The model aims to minimize the load loss and system economic loss caused by typhoon disasters. The first stage optimizes the pre-disaster layout location of MES, and the second stage optimizes the power output scheme of each SES and MES during the typhoon disaster. Step S4: Obtain typhoon forecast information and power distribution network information; Step S5: Input the information obtained in step S4 into the two-stage energy storage collaborative emergency dispatch model, solve the model, and obtain the energy storage collaborative emergency dispatch strategy under the strong uncertainty of typhoon.

2. The energy storage collaborative emergency dispatch method based on DRO under strong typhoon uncertainty as described in claim 1, characterized in that: In step S1, the instantaneous typhoon wind field hitting the power distribution network is decoupled into translational velocity and circulation wind speed. The typhoon movement behavior is characterized by the Masao Miyazaki model, and the near-ground wind field distribution is analyzed by the Rankine circulation wind speed model. The resulting typhoon wind field model is as follows: （1） （2） （3） （4） （5） In the formula, , A collection of typhoon paths; It is the first Under the given typhoon path, the distance between the power distribution network area and the typhoon center is determined by the real-time coordinates of the typhoon center. and distribution network coordinates Calculated; The distance from the center of the typhoon is The speed at which the typhoon wind field moves within the power distribution network; It is the circulating wind speed of the power distribution network wind field; The radius of the Earth is taken as 6371 km; This refers to the speed at which the typhoon's center moves. This represents the maximum circulation wind speed of the typhoon. This corresponds to the radius of the maximum wind speed. This is the maximum wind speed near the center of the typhoon; and These represent the wind speed and the radius of the wind circle corresponding to the Beaufort scale (Lev), respectively.

3. The energy storage collaborative emergency dispatch method based on DRO under strong typhoon uncertainty as described in claim 2, characterized in that: In step S2, adjacent nodes of the distribution network , The failure rate of the overhead lines between them is represented by the following model: （6） In the formula, For the first Under the typhoon path, nodes , The overhead lines between Failure rate at any given time; For the first Under the typhoon path, nodes , The overhead lines between The constant circulating wind speed; and These are the model coefficients. Take [11,13], Take [-20, -16]; Design wind speed for overhead power lines; It is a node , The length of the overhead line between them; adjacent nodes of distribution network , The failure rate of the towers for the overhead lines between them is represented by the following model: （7） In the formula, For the first Under the typhoon path, nodes , Between Failure rate of the tower; These are model coefficients, set to 0.3; It is the design wind speed of the tower; Construct adjacent nodes of the distribution network based on the reliability formula of a series system. , The fault model for the transmission line is as follows: （8） In the formula, Adjacent nodes of the distribution network , Transmission lines between Failure rate at any given time; Adjacent nodes of the distribution network , The total number of towers between; The sign for product.

4. The energy storage collaborative emergency dispatch method based on DRO under strong typhoon uncertainty as described in claim 3, characterized in that: In step S2, the uncertainty of faults in the distribution network lines is characterized using the moment fuzzy set method, as shown in the following equation: （9） （10） In the formula, In order to target the The fuzzy set of fault moments for power distribution lines constructed from the typhoon path contains the set of all possible line fault distributions. This represents a specific probability distribution that satisfies the definition. For the state space of line faults, given by equation Definition, constructed based on the idea of ​​budget uncertainty sets; In state space The domain of all distributions; Indicates in The set of all probability distributions in the above; It is the collection of all lines; Index for scheduling periods; The set of dispatch periods is determined by the duration of typhoon coverage of the power distribution network area by typhoons with winds of at least level 7. This is achieved by solving... This equation yields two roots; the absolute value of the difference between the two roots is... ,in, Indicates the first Typhoon path under the typhoon The distance from the distribution network at any given time is defined as the typhoon's coordinates at the initial time. , For the first The angle of movement of the typhoon under the given typhoon path, then ; Indicates the first Under the typhoon path, the line exist The status at any given moment: a value of 1 indicates that the line is normal, and a value of 0 indicates that the line is faulty. Indicates distribution Down Expectations Indicates distribution Below, the line exist The expected failure rate at any given time should be less than the failure rate. ; Budget for line faults.

5. The energy storage collaborative emergency dispatch method based on DRO under strong typhoon uncertainty as described in claim 4, characterized in that: In step S3, the objective function of the constructed two-stage energy storage coordinated emergency dispatch model based on DRO is: （11） In the formula, For the first Fixed costs for vehicle MES dispatch; It means the first Does the vehicle's MES connect to the node? A binary variable, where 1 represents access and 0 represents no access; A set of nodes; A collection of MES; For the first The weights of the typhoon paths represent the probability of occurrence; Node weights; This is the unit loss coefficient for load shedding; For the first Under the typhoon path, nodes exist The amount of load loss at any given moment; , The unit discharge cost for SES and MES are respectively; , SES and MES respectively direct to distribution network nodes The active power generated.

6. The energy storage collaborative emergency dispatch method based on DRO under strong typhoon uncertainty as described in claim 5, characterized in that: In step S3, the constructed two-stage energy storage coordinated emergency dispatch model based on DRO includes the following constraints: 1) MES pre-disaster deployment constraints: It restricts that at most one MES can be connected to each distribution network node and each MES can be assigned to at most one distribution network node; 2) Power balance constraints of the power distribution system: These are established based on the linearized DistFlow power flow model; 3) Node voltage constraints; 4) Power constraints on transmission lines; 5) Node load loss constraints; 6) Energy storage dispatch constraints: These include energy constraints during emergency dispatch of SES and MES, as well as power constraints during emergency dispatch of SES and MES.

7. The energy storage collaborative emergency dispatch method based on DRO under strong typhoon uncertainty as described in claim 6, characterized in that: Step S5 includes: Step S501, the two-stage energy storage coordinated emergency dispatch model is expressed in the following compact form: （12） In the formula, Indicates the decision variables for the first stage. For the reason The vector formed; These are the coefficients in the objective function of the first stage; This indicates that the second phase is aimed at the first... Decision variables for typhoon paths , , , , , , , , , , , , , , , , They are respectively from , , , , , , , , , , , , , , , , The vector formed; The coefficients in the objective function of the second stage; superscript Indicates transpose; Represents transmission line fault variables; Indicates the inner min model pair The relevant constraints are given by G, h, E, and M, which are model coefficients. inner layer min model Alternative models; Step S502: Based on the C&CG algorithm, the two-stage energy storage collaborative emergency dispatch model shown in equation (12) is decomposed into the following C&CG main problem and C&CG sub-problems, and based on the CG algorithm, the C&CG sub-problems are decomposed into the following CG main problem and CG sub-problems: ① CG main issue In equation (12) Partially equivalent to: （13） In the formula, This represents an index of power transmission line fault scenarios. For the first Typhoon path number Line fault scenarios in various situations. For the first The probability of each scenario occurring. Total number of scenes; （14） In the formula, This represents the optimal value for the principal problem of CG. For the first The worst-case scenario set for each typhoon path is obtained by searching the CG subproblem; Represents the transmission line fault rate vector; , The values ​​of the dual variables of the constraints; ② CG subproblem （15） In the formula, Represents the Hadamard product; The optimal value for the CG subproblem; Performing a dual transformation on the inner min model of equation (15), equation (15) is reconstructed as follows: （16） In the formula, For the dual variables of the inner min model constraint of equation (15); ③C&CG main problem （17） In the formula, The optimal value for the C&CG principal problem; It is a variable associated with the target value of the second stage; and It is an index related to the number of iterations. Indicates the first Scenario during the next iteration The probability of; ④C&CG subproblem The C&CG subproblem is to solve the second stage of the two-stage energy storage coordinated emergency dispatch model. ; Step S503: Input the information obtained in step S4 into the two-stage energy storage collaborative emergency dispatch model as shown in equation (12); Step S504: Use the CG algorithm to iteratively solve the CG main problem and subproblems, and obtain the worst-case scenario set. and its distribution Add the variables to the main C&CG problem, and then use the C&CG algorithm to solve the main C&CG problem. Iterate in this way until the optimal decision variables of the two-stage energy storage coordinated emergency dispatch model are obtained by gradually converging.

8. A DRO-based energy storage collaborative emergency dispatch device under strong uncertainty of typhoons, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the energy storage collaborative emergency dispatch method based on DRO under strong uncertainty of typhoons as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the energy storage collaborative emergency dispatch method based on DRO under typhoon strong uncertainty as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by the processor, it implements the steps of the energy storage collaborative emergency dispatch method based on DRO under typhoon strong uncertainty as described in any one of claims 1-7.

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