Power system transient power angle stability and adequacy coordination control decision method, system, medium and product

Abstract

The invention discloses a power system transient power angle stability and adequacy coordination control decision method and system, a medium and a product, and relates to the field of power system control decision, and the method comprises the steps: building a fault scene set based on an element fault rate model according to typhoon data and the power grid topology of a power system; decoupling system adequacy optimization and a power angle stability control decision, and constructing a system adequacy optimization model by considering source-load-storage adjustable resources on the basis of a fault scene set; quantifying the transient stability margin based on an extended equal-area criterion, determining the sensitivity of each control measure to the transient stability margin through a trajectory sensitivity analysis method, and constructing a power angle stability control decision model; constructing a double-layer optimization model; and determining a control decision under the fault scene in the fault scene set according to the double-layer optimization model. The power angle stability of the system can be guaranteed while the adequacy requirement of the system is met, and meanwhile load reduction in a typhoon scene is effectively reduced.

Description

Technical Field

[0001] The present application relates to the field of power system control decision-making, and in particular to a control decision-making method, system, medium and product for coordinating transient power angle stability and adequacy of a power system. Background Art

[0002] In recent years, typhoon disasters have occurred frequently, posing great challenges to the safe and stable operation of the power system. The landing of a typhoon will cause intensive failures of power transmission and transformation equipment in a short period of time, and the power supply capacity of the power grid will be seriously weakened. Dispatching adjustable resources of source, load and storage based on the prediction of fault scenarios is conducive to reducing load reduction in typhoon scenarios. However, current research only reduces load reduction by improving the system's operational adequacy, ignoring the power angle safety issues that may be caused by typhoon scenarios, and the dispatching scheme has the risk of failure. Since there are coupled decision variables in the system adequacy optimization and transient power angle stability control, they will affect each other during the optimization process, so the coordinated decision-making of the two is a difficult problem.

[0003] Based on the above problems, in order to reduce the scale of power outages caused by typhoon scenarios by coordinating system adequacy optimization and transient power angle stability control decisions in typhoon scenarios, it is urgently necessary to provide a control decision method or system for coordinating transient power angle stability and adequacy of the power system. Summary of the invention

[0004] The purpose of this application is to provide a control decision method, system, medium and product for coordinating the transient power angle stability and adequacy of an electric power system, which can ensure the power angle stability of the system while meeting the system adequacy requirements, and effectively reduce load reduction in typhoon scenarios.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a control decision method for coordinating transient power angle stability and adequacy of an electric power system, the control decision method for coordinating transient power angle stability and adequacy of an electric power system comprising:

[0007] Obtain typhoon data and power grid topology of the power system;

[0008] According to typhoon data and the power grid topology of the power system, a set of fault scenarios is constructed based on the component failure rate model;

[0009] Decouple the system adequacy optimization and power angle stability control decision, and consider the adjustable resources of source, load and storage based on the set of fault scenarios to build a system adequacy optimization model with the minimum adequacy load shedding as the optimization goal;

[0010] The transient stability margin is quantified based on the extended equal area criterion, and the sensitivity of each control measure to the transient stability margin is determined by the trajectory sensitivity analysis method, and then a power angle stability control decision model of the prevention-emergency control coordination is constructed; the power angle stability control decision model of the prevention-emergency control coordination takes the maximum expected value of the transient stability margin as the optimization target in the prevention control stage; and takes the minimum safety load shedding as the optimization target in the emergency control stage;

[0011] A two-layer optimization model is constructed with the system adequacy optimization model as the upper model and the power angle stability control decision model coordinated by prevention and emergency control as the lower model.

[0012] The control decision under the fault scenario in the fault scenario set is determined according to the two-layer optimization model.

[0013] Optionally, according to the typhoon data and the grid topology of the power system, a set of fault scenarios is constructed based on a component failure rate model, specifically including:

[0014] Using the formula Determine component failure rate model;

[0015] Using the formula Determine the probability of scenario k occurring at time t;

[0016] in, is the failure rate of component l at time t, is the design wind speed of element l, a f is the component failure rate model parameter, v t is the wind speed at the observation point at time t, and the wind direction is the direction of the vertical line connecting the observation point and the cyclone center. r is the distance between the observation point and the cyclone center; V max is the wind speed at the strongest wind belt of the typhoon, R max is the distance between the strongest wind belt of the typhoon and the center of the cyclone, is the maximum wind speed radius, is the probability of scenario k occurring at time t, is the probability of scenario k occurring when there is an impossible scenario in the scenario set, N T is the number of time periods considered in rolling optimization, ΔT is the step length of rolling optimization, b is the optimization time period sequence number, is an intermediate variable, representing the probability of failure at time t, is the failure rate of transmission line i at time t, Ω T is the set of towers that make up the transmission line, Ω L is the set of lines on the transmission line, Ω u is the impossible scenario set, m is the tower number, n is the line number, is the failure rate of the nth line at time t, is the failure rate of the mth tower at time t.

[0017] Optionally, the objective function f1 of the system adequacy optimization model includes the following formula:

[0018]

[0019] in, is the sufficient load shedding amount of the jth node at time t under scenario k, K is the set of fault scenarios, B is the set of load nodes, Δt is the time step, and t0 is the starting time of the optimization decision.

[0020] Optionally, the transient stability margin is quantified based on the extended equal area criterion, and the sensitivity of each control measure to the transient stability margin is determined by a trajectory sensitivity analysis method, and then a power angle stability control decision model of preventive-emergency control coordination is constructed, which specifically includes:

[0021] The multi-machine trajectories are obtained through time domain simulation, and the generator set is divided into two mutually exclusive clusters according to the simulation results; and the multi-machine system corresponding to the power system is equivalent to a single-machine system;

[0022] The transient stability margin is quantified by the difference between the deceleration area and the acceleration area;

[0023] The sensitivity of each control measure to the transient stability margin is determined by trajectory sensitivity analysis method;

[0024] In the preventive control stage, the optimization goal is to maximize the expected value of transient stability margin;

[0025] In the emergency control stage, the optimization goal is to minimize the amount of safe load shedding.

[0026] Alternatively, using the formula Determine the optimization target f in the prevention and control stage 21 ;

[0027] Where ΔP i,t is the output adjustment of the thermal power unit, is the decision variable of transient power angle stability prevention control, η is the transient stability margin, P i,t is the initial output of the i-th thermal power unit at time t, Φ k (η,P i,t ) is P under the kth fault scenario i,t Sensitivity to η.

[0028] Alternatively, using the formula Determine the optimization target f in the emergency control stage 22 ;

[0029] in, is the safe load shedding amount at the jth node at time t under scenario k, and is the decision variable for the emergency control of transient power angle stability.

[0030] Optionally, the constraints of the two-layer optimization model include: thermal power unit output constraints, thermal power unit climbing constraints, pumped storage unit output constraints, pumped storage unit state constraints, pumped storage unit energy storage constraints, demand-side backup resource scheduling constraints, abundance load shedding constraints, flow constraints, load shedding constraints and transient power angle stability constraints.

[0031] In a second aspect, the present application provides a control decision system for coordinating transient power angle stability and adequacy of a power system, wherein the control decision system for coordinating transient power angle stability and adequacy of a power system comprises:

[0032] Data acquisition module, used to obtain typhoon data and power grid topology of the power system;

[0033] A fault scenario set building module is used to build a fault scenario set based on typhoon data and the power grid topology of the power system and the component failure rate model;

[0034] The system adequacy optimization model building module is used to decouple the system adequacy optimization and power angle stability control decisions, and consider the adjustable resources of source, load and storage based on the set of fault scenarios to build a system adequacy optimization model with the minimum adequacy load shedding as the optimization goal;

[0035] A power angle stability control decision model construction module is used to quantify the transient stability margin based on the extended equal area criterion, determine the sensitivity of each control measure to the transient stability margin through the trajectory sensitivity analysis method, and then construct a preventive-emergency control coordinated power angle stability control decision model; the preventive-emergency control coordinated power angle stability control decision model takes the maximum expected value of the transient stability margin as the optimization target in the preventive control stage; and takes the minimum safety load shedding as the optimization target in the emergency control stage;

[0036] A two-layer optimization model building module is used to build a two-layer optimization model with a system adequacy optimization model as the upper model and a preventive-emergency control coordinated power angle stability control decision model as the lower model;

[0037] The control decision module is used to determine the control decision under the fault scenario in the fault scenario set according to the two-layer optimization model.

[0038] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the control decision method for coordinating transient power angle stability and adequacy of the power system.

[0039] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the control decision method for coordinating transient power angle stability and adequacy of the power system.

[0040] According to the specific embodiments provided in this application, this application has the following technical effects:

[0041] The present application provides a control decision method, system, medium and product for the coordination of transient power angle stability and adequacy of an electric power system. A fault scenario set is constructed based on typhoon data and power grid topology. Compared with the direct failure of each line at the beginning of the disturbance, the use of a fault scenario set can take into account the optimization capabilities between successive faults and effectively reduce the load reduction in the typhoon scenario. Based on the idea of ​​"decoupling optimization, aggregation and coordination", a decoupling method for system adequacy optimization and power angle stability control decision is proposed. In terms of adequacy optimization, a system adequacy optimization model is established by considering source, load and storage resources; in terms of power angle stability control, a prevention-emergency control coordinated power angle stability control decision model is established based on the Extended EqualArea Criteria (EEAC). On this basis, a two-layer optimization decision model is constructed and iteratively solved. The present application can ensure the power angle stability of the system while meeting the system adequacy requirements, and effectively reduce the load reduction in the typhoon scenario, and reduce the scale of power outages caused by the typhoon scenario. It has important application value for regional power grids where typhoon disasters occur frequently. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is a flowchart of a control decision method for coordinating transient power angle stability and adequacy of a power system in one embodiment of the present application. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0046] In an exemplary embodiment, Figure 1 As shown, a control decision method for coordinating transient power angle stability and adequacy of a power system is provided, and the method includes the following S101 to S106. Among them:

[0047] S101, obtain typhoon data and power grid topology of the power system;

[0048] S102, constructing a set of fault scenarios based on the typhoon data and the power grid topology of the power system and the component failure rate model;

[0049] The wind speed and direction data of the typhoon are collected, and the typhoon simulation is performed based on the Batts model, and the wind speed obtained is:

[0050]

[0051] Where: v t is the wind speed at the observation point at time t, and the wind direction is the direction of the vertical line connecting the observation point and the cyclone center; r is the distance between the observation point and the cyclone center; V max is the wind speed at the strongest wind belt of the typhoon, R max It is the distance between the typhoon's strongest wind belt and the center of the cyclone, that is, the maximum wind speed radius.

[0052] Based on the determination of wind speed, a component failure rate model is established:

[0053]

[0054] is the failure rate of component l at time t, is the design wind speed of element l, model parameter a f This can be obtained through statistical analysis of historical data of components of the same type.

[0055] The premise of normal operation of the transmission line is that the line and the tower are operating normally. The transmission line is equivalent to a series model, and the failure rate of the transmission line is calculated by the failure rate of the components. The failure rate of the transmission line is:

[0056]

[0057] Where: is the failure rate of transmission line i at time t, Ω T is the set of towers that make up the transmission line, Ω L is the line set on the transmission line, m is the tower number, n is the line number, is the failure rate of the nth line at time t, is the failure rate of the mth tower at time t.

[0058] During the typhoon, there may be multiple lines at risk of failure at the same time. It is too conservative to simply assume that these lines fail at the beginning of the disturbance, ignoring the optimization ability between successive failures. At the same time, compared with the total length of the transmission line, the typhoon movement speed is limited, and the lines that may fail within a certain period of time are the same. Therefore, set a suitable decision time so that the lines that may fail are located in different periods of rolling optimization, convert the Nk failures within the decision time into multiple successive N-1 failures, list all possible failure scenarios, and form a single optimized failure scenario set.

[0059] A transmission line consists of many towers and conductors, and any component failure will cause the entire transmission line to fail. Therefore, a component failure rate model based on the fragility curve is established to characterize the line and tower failure rate. Among them, the probability of occurrence of a single scenario is calculated by equations (4) to (6).

[0060]

[0061] in, is the probability of scenario k occurring at time t, is the probability of scenario k occurring when there is an impossible scenario in the scenario set; and is an intermediate variable, representing the probability of failure at time t; Ω u is a set of impossible scenarios; N T is the number of time periods considered during rolling optimization, and ΔT is the step size of rolling optimization.

[0062] S103, decoupling the system adequacy optimization and the power angle stability control decision, and building a system adequacy optimization model with minimum adequacy load shedding as the optimization goal based on the set of fault scenarios and taking into account the adjustable resources of source, load and storage;

[0063] Based on the idea of ​​"decoupling optimization, aggregation and coordination", S103 adjusts the output of thermal power units and pumped storage units, and at the same time configures certain demand-side backup resources to improve system redundancy and reduce load reduction in typhoon scenarios.

[0064] Specifically, the output of the thermal power unit is decomposed into the initial value of the unit output in the adequacy optimization stage and the unit output adjustment amount in the power angle stability control decision stage; the load shedding is decomposed into the adequacy load shedding in the adequacy optimization stage and the safety load shedding in the power angle stability control stage.

[0065] In terms of adequacy optimization decision-making, various possible failure scenarios are taken into consideration, and the output of thermal power units and pumped storage units is adjusted. At the same time, some demand-side backup resources are allocated "timely and appropriate" to reduce load reduction after a failure occurs.

[0066] The adequacy optimization model takes the minimum adequacy load shedding as the optimization goal, and the objective function is shown in formula (7):

[0067]

[0068] Where: is the sufficient load shedding amount of the jth node at time t under the kth scenario, K is the set of fault scenarios, B is the set of load nodes, Δt is the time step, and t0 is the starting time of the optimization decision.

[0069] The constraints of thermal power units during operation are shown in equations (8) to (9). Among them, equation (8) is the output constraint of the thermal power unit, and equation (9) is the climbing constraint of the thermal power unit.

[0070] P i,min ≤P i,t +ΔP i,t ≤P i,max (8)

[0071]

[0072] Where: P i,t is the initial output of the i-th thermal power unit at time t, ΔP i,t is the output adjustment of the thermal power unit and the decision variable of the transient power angle stability preventive control; P i,max and P i,min are the upper and lower limits of the output of the i-th thermal power unit respectively; and are the upper and lower limits of the ramp rate of the i-th thermal power unit respectively.

[0073] The pumped storage unit is equivalent to an energy storage unit, and the constraints existing in its operation process are shown in equations (10) to (14). Among them, equations (10) to (11) are the output constraints of the pumped storage unit, equation (12) is the state constraint of the pumped storage unit, and equations (13) to (14) are the energy storage constraints of the pumped storage unit.

[0074]

[0075] Where: and are the charge and discharge power of the lth pumped storage unit at time t respectively; and are the upper and lower limits of the discharge power of the first pumped storage unit respectively; and They are the upper and lower limits of the charging power of the first pumped storage unit respectively; and is a 0-1 variable, representing the pumped storage unit in the charging and discharging states; E pss,l,t is the power of the lth pumped storage unit at time t; and They are respectively the upper and lower limits of the power of the lth pumped storage unit.

[0076] The constraints on other adjustable resources are shown in equations (15) and (16). Equation (15) is the demand side reserve resource scheduling constraint, and equation (16) is the abundance load shedding constraint.

[0077]

[0078] Where: is the spare capacity of the nth spare resource of node j at time t, μ j,n,t It is a 0-1 variable representing the configuration of the backup resource. 1 means that the backup resource is configured, and 0 means that the backup resource is not configured. is the scheduling amount of the nth backup resource of node j in the kth scenario; L j,t is the load of the j-th node at time t.

[0079] In addition, the system should satisfy the power flow constraints during operation, as shown in Equations (17) to (19).

[0080]

[0081] Where: is the safe load shedding amount at the jth node at time t under the kth scenario, and is the decision variable for the emergency control of transient power angle stability; is the power flowing on line l; L(i) is the set of lines connected to node i; B l is the line susceptance, θ h,t is the phase of node h; Line l is the set of nodes at both ends of line l; is the maximum flow power on transmission line l, which is equal to 0 when l fails; R(i) is the set of demand-side backup resources of load node i.

[0082] S104, quantifying the transient stability margin based on the extended equal area criterion, determining the sensitivity of each control measure to the transient stability margin through a trajectory sensitivity analysis method, and then constructing a prevention-emergency control coordinated power angle stability control decision model; the prevention-emergency control coordinated power angle stability control decision model takes the maximum expected value of the transient stability margin as the optimization target in the prevention control stage; and takes the minimum safety load shedding amount as the optimization target in the emergency control stage;

[0083] EEAC first obtains multi-machine trajectories through time domain simulation, and divides the generator set into two mutually exclusive clusters according to the simulation results: the leading group CMs is composed of the key units that cause instability, and the remaining group NMs is composed of other non-critical units. Then, the multi-machine system is equivalent to a single-machine system through the following formula, as shown in Equations (20) to (28).

[0084]

[0085] δ(t)=δ C (t)-δ N (t) (24)

[0086] ω(t)=ω C (t)-ω N (t) (25)

[0087]

[0088] M=(M C ·M N )·(M C +M N ) -1 (28)

[0089] Where: C is the set of generator sets in CMs, N is the set of generator sets in NMs; M C and M N are the inertia of the single-machine system formed by CMs and NMs respectively; δ(t) and ω(t) are the power angle and angular velocity of the single-machine infinite system at time t respectively; P m (t) and P e (t) are the mechanical power and electromagnetic power of the single-machine infinite system at time t; M is the inertia of the single-machine infinite system.

[0090] After forming an equivalent single-machine system, the transient stability margin is quantified by the difference between the deceleration area and the acceleration area:

[0091] η=A dec -A acc (29)

[0092] Where: A dec and Aacc are the deceleration area and acceleration area of ​​the equivalent single-machine system respectively; η is the transient stability margin, and its value greater than 0 indicates that the transient power angle of the system is stable, otherwise it indicates that the transient power angle is unstable.

[0093] The sensitivity of each control measure to the transient stability margin is determined by trajectory sensitivity analysis method.

[0094]

[0095] Where: η0 is the initial transient stability margin of the system; Φ(η,γ) is the first-order trajectory sensitivity of the regulation measure γ to the transient stability margin.

[0096] The optimization objective of the preventive control stage is to maximize the expected value of the transient stability margin. The objective function is shown in formula (31):

[0097]

[0098] Preventive control is to adjust the unit output according to each expected fault scenario, and the actual fault has not occurred yet. It needs to meet the thermal power unit operation constraints shown in equations (8) to (9) and the power flow constraints shown in equations (17) to (19).

[0099] In the emergency control stage, the optimization goal is to minimize the safety load shedding amount. The objective function is shown in formula (32):

[0100]

[0101] After emergency control, it is necessary to dispatch pumped storage units with rapid adjustment capabilities to achieve system power balance, which must meet the pumped storage unit operation constraints shown in equations (10) to (14) and the flow constraints shown in equations (17) to (19). The constraints that must be met during the emergency control phase are shown in equations (33) to (36), where equation (33) is the load shedding constraint and equations (34) to (36) are transient power angle stability constraints.

[0102]

[0103] Where: is the transient stability margin of the system under the kth fault scenario after preventive control; Δη k is the change in system transient stability margin after emergency control for the kth fault scenario.

[0104] S105, constructing a double-layer optimization model with a system adequacy optimization model as an upper model and a preventive-emergency control coordinated power angle stability control decision model as a lower model;

[0105] S106, determining a control decision under a fault scenario in the fault scenario set according to the two-layer optimization model.

[0106] Assuming that the initial value of the safety decision obtained by the power angle stability control decision model is 0, the adequacy optimization is performed. After that, the adequacy optimization result is used as input for the power angle stability control decision. The iterative solution is performed until the convergence condition is met, and the control strategy for each fault scenario is output.

[0107] Taking the total load reduction of the system in the first optimization period as the model convergence index, the condition for terminating the iteration of the model is shown in formula (37):

[0108]

[0109] Where: f (h) is the total load reduction of the system during the hth iteration optimization; ε is the convergence threshold.

[0110] Based on the same inventive concept, the embodiment of the present application also provides a control decision system for coordination of transient power angle stability and adequacy of power system for realizing the control decision method for coordination of transient power angle stability and adequacy of power system involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the control decision system for coordination of transient power angle stability and adequacy of power system provided below can be referred to the limitations of the control decision method for coordination of transient power angle stability and adequacy of power system in the above text, and will not be repeated here.

[0111] In an exemplary embodiment, a control decision system for coordinating transient power angle stability and adequacy of a power system is provided, comprising:

[0112] Data acquisition module, used to obtain typhoon data and power grid topology of the power system;

[0113] A fault scenario set building module is used to build a fault scenario set based on typhoon data and the power grid topology of the power system and the component failure rate model;

[0114] The system adequacy optimization model building module is used to decouple the system adequacy optimization and power angle stability control decisions, and consider the adjustable resources of source, load and storage based on the set of fault scenarios to build a system adequacy optimization model with the minimum adequacy load shedding as the optimization goal;

[0115] A power angle stability control decision model construction module is used to quantify the transient stability margin based on the extended equal area criterion, determine the sensitivity of each control measure to the transient stability margin through the trajectory sensitivity analysis method, and then construct a preventive-emergency control coordinated power angle stability control decision model; the preventive-emergency control coordinated power angle stability control decision model takes the maximum expected value of the transient stability margin as the optimization target in the preventive control stage; and takes the minimum safety load shedding as the optimization target in the emergency control stage;

[0116] A two-layer optimization model building module is used to build a two-layer optimization model with a system adequacy optimization model as the upper model and a preventive-emergency control coordinated power angle stability control decision model as the lower model;

[0117] The control decision module is used to determine the control decision under the fault scenario in the fault scenario set according to the two-layer optimization model.

[0118] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0119] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0120] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0121] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0122] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0123] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0124] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0125] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A control decision method for coordinating transient power angle stability and adequacy of a power system, characterized in that: The control decision method for coordinating transient power angle stability and adequacy of the power system includes: Obtain typhoon data and power grid topology of the power system; According to typhoon data and the power grid topology of the power system, a set of fault scenarios is constructed based on the component failure rate model; Decouple the system adequacy optimization and power angle stability control decision, and consider the adjustable resources of source, load and storage based on the set of fault scenarios to build a system adequacy optimization model with the minimum adequacy load shedding as the optimization goal; The transient stability margin is quantified based on the extended equal area criterion, and the sensitivity of each control measure to the transient stability margin is determined by the trajectory sensitivity analysis method, and then a power angle stability control decision model of the prevention-emergency control coordination is constructed; the power angle stability control decision model of the prevention-emergency control coordination takes the maximum expected value of the transient stability margin as the optimization target in the prevention control stage; and takes the minimum safety load shedding as the optimization target in the emergency control stage; A two-layer optimization model is constructed with the system adequacy optimization model as the upper model and the power angle stability control decision model coordinated by prevention and emergency control as the lower model. The control decision under the fault scenario in the fault scenario set is determined according to the two-layer optimization model.

2. The control decision method for coordinating transient power angle stability and adequacy of a power system according to claim 1, characterized in that: According to typhoon data and the power grid topology of the power system, a set of fault scenarios is constructed based on the component failure rate model, including: Using the formula Determine component failure rate model; Using the formula Determine the probability of scenario k occurring at time t; in, is the failure rate of component l at time t, is the design wind speed of element l, a f is the component failure rate model parameter, v t is the wind speed at the observation point at time t, and the wind direction is the direction of the vertical line connecting the observation point and the cyclone center. r is the distance between the observation point and the cyclone center; V max is the wind speed at the strongest wind belt of the typhoon, R max is the distance between the strongest wind belt of the typhoon and the center of the cyclone, is the maximum wind speed radius, is the probability of scenario k occurring at time t, is the probability of scenario k occurring when there is an impossible scenario in the scenario set, N T is the number of time periods considered in rolling optimization, ΔT is the step length of rolling optimization, b is the optimization time period sequence number, is an intermediate variable, representing the probability of failure at time t, is the failure rate of transmission line i at time t, Ω T is the set of towers that make up the transmission line, Ω L is the set of lines on the transmission line, Ω u is the impossible scenario set, m is the tower number, n is the line number, is the failure rate of the nth line at time t, is the failure rate of the mth tower at time t.

3. The control decision method for coordinating transient power angle stability and adequacy of a power system according to claim 2, characterized in that: The objective function f1 of the system adequacy optimization model includes the following formula: in, is the sufficient load shedding amount of the jth node at time t under scenario k, K is the set of fault scenarios, B is the set of load nodes, Δt is the time step, and t0 is the starting time of the optimization decision.

4. The control decision method for coordinating transient power angle stability and adequacy of a power system according to claim 3 is characterized in that: The transient stability margin is quantified based on the extended equal area criterion, and the sensitivity of each control measure to the transient stability margin is determined by the trajectory sensitivity analysis method, and then a power angle stability control decision model of prevention-emergency control coordination is constructed, which specifically includes: The multi-machine trajectories are obtained through time domain simulation, and the generator set is divided into two mutually exclusive clusters according to the simulation results; and the multi-machine system corresponding to the power system is equivalent to a single-machine system; The transient stability margin is quantified by the difference between the deceleration area and the acceleration area; The sensitivity of each control measure to the transient stability margin is determined by trajectory sensitivity analysis method; In the preventive control stage, the optimization goal is to maximize the expected value of transient stability margin; In the emergency control stage, the optimization goal is to minimize the amount of safe load shedding.

5. The control decision method for coordinating transient power angle stability and adequacy of a power system according to claim 4, characterized in that: Using the formula Determine the optimization target f in the prevention and control stage 21 ; Where ΔP i,t is the output adjustment of the thermal power unit, is the decision variable of transient power angle stability prevention control, η is the transient stability margin, P i,t is the initial output of the i-th thermal power unit at time t, Φ k (η,P i,t ) is P under the kth fault scenario i,t Sensitivity to η.

6. The control decision method for coordinating transient power angle stability and adequacy of a power system according to claim 5, characterized in that: Using the formula Determine the optimization target f in the emergency control stage 22 ; in, is the safe load shedding amount at the jth node at time t under scenario k, and is the decision variable for the emergency control of transient power angle stability.

7. The control decision method for coordinating transient power angle stability and adequacy of a power system according to claim 1, characterized in that: The constraints of the two-layer optimization model include: thermal power unit output constraints, thermal power unit climbing constraints, pumped storage unit output constraints, pumped storage unit state constraints, pumped storage unit energy storage constraints, demand-side backup resource scheduling constraints, abundance load shedding constraints, flow constraints, load shedding constraints and transient power angle stability constraints.

8. A control decision system for coordination of transient power angle stability and adequacy of power system, characterized in that: The control decision system for coordinating transient power angle stability and adequacy of the power system includes: Data acquisition module, used to obtain typhoon data and power grid topology of the power system; A fault scenario set building module is used to build a fault scenario set based on typhoon data and the power grid topology of the power system and the component failure rate model; The system adequacy optimization model building module is used to decouple the system adequacy optimization and power angle stability control decisions, and consider the adjustable resources of source, load and storage based on the set of fault scenarios to build a system adequacy optimization model with the minimum adequacy load shedding as the optimization goal; A power angle stability control decision model construction module is used to quantify the transient stability margin based on the extended equal area criterion, determine the sensitivity of each control measure to the transient stability margin through the trajectory sensitivity analysis method, and then construct a preventive-emergency control coordinated power angle stability control decision model; the preventive-emergency control coordinated power angle stability control decision model takes the maximum expected value of the transient stability margin as the optimization target in the preventive control stage; and takes the minimum safety load shedding as the optimization target in the emergency control stage; A two-layer optimization model building module is used to build a two-layer optimization model with a system adequacy optimization model as the upper model and a preventive-emergency control coordinated power angle stability control decision model as the lower model; The control decision module is used to determine the control decision under the fault scenario in the fault scenario set according to the two-layer optimization model.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the control decision method for coordinating transient power angle stability and adequacy of a power system described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the control decision method for coordinating transient power angle stability and adequacy of a power system described in any one of claims 1 to 7 is implemented.

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