A power system transient power angle stability and adequacy coordinated control decision method, system, medium and product

Abstract

The application discloses a power system transient power angle stability and adequacy coordination control decision method, system, medium and product, relates to the power system control decision field, and the method comprises the following steps: constructing a fault scene set based on an element failure rate model according to typhoon data and a power grid topology of the power system; decoupling system adequacy optimization and power angle stability control decision, constructing a system adequacy optimization model by considering source, load and storage adjustable resources on the basis of the fault scene set; quantifying 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 the control decision under the fault scene in the fault scene set according to the double-layer optimization model. The application can meet the system adequacy demand while guaranteeing the power angle stability of the system, and effectively reduce the load reduction under the typhoon scene.

Description

Technical Field

[0001] This 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 in a power system. Background Technology

[0002] In recent years, typhoon disasters have occurred frequently, posing a significant challenge to the safe and stable operation of power systems. Typhoon landfall can cause a rapid and concentrated failure of transmission and transformation equipment, severely weakening the power grid's supply capacity. Dispatching adjustable resources based on fault scenario prediction, such as source-load-storage, can help reduce load shedding during typhoons. However, current research focuses solely on improving system operational adequacy to reduce load shedding, neglecting the potential power angle safety issues caused by typhoons, thus posing a risk of dispatch scheme failure. Since system adequacy optimization and transient power angle stability control involve coupled decision variables that influence each other during optimization, coordinating their decision-making is a challenging problem.

[0003] Based on the above problems, in order to reduce the scale of power outages caused by typhoons by coordinating system adequacy optimization and transient power angle stability control decisions under typhoon scenarios, it is urgent to provide a control decision method or system that coordinates 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-making method, system, medium, and product for coordinating transient power angle stability and adequacy in a 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 solution:

[0006] Firstly, this application provides a control decision method for coordinating transient power angle stability and adequacy in a power system, the control decision method for coordinating transient power angle stability and adequacy in a power system includes:

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

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

[0009] The system adequacy optimization and power angle stability control decisions are decoupled. Based on the set of fault scenarios, a system adequacy optimization model is constructed with the goal of minimizing adequacy load shedding, taking into account the adjustable resources of source load storage.

[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. Then, a power angle stability control decision model for prevention-emergency control coordination is constructed. The power angle stability control decision model for prevention-emergency control coordination aims to maximize the expected value of transient stability margin in the prevention control stage and minimize the safety load shedding in the emergency control stage.

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

[0012] The control decisions for each fault scenario in the fault scenario set are determined based on the two-level optimization model.

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

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

[0015] Using formula Determine the probability that scenario k will occur at time t;

[0016] in, Let be the failure rate of component l at time t. For the design wind speed of component l, a f v is the parameter for the component failure rate model. t Let be the wind speed at the observation point at time t, and let the wind direction be the direction perpendicular to the line connecting the observation point and the center of the cyclone. r is the distance from the observation point to the center of the cyclone; V max R represents the wind speed at the location of the strongest wind belt in a typhoon. max The distance between the strongest wind belt of the typhoon and the center of the cyclone is denoted by , and the radius of the maximum wind speed is denoted by . Let be the probability that scenario k occurs at time t. Let k be the probability of scenario k occurring when there are impossible scenarios in the scenario set. N T ΔT represents the number of time periods considered during rolling optimization, ΔT represents the step size of rolling optimization, and b represents the optimization time period number. This is an intermediate variable, representing the probability of a failure occurring at time t. Let Ω be the failure rate of transmission line i at time t. T To form the collection of towers for a transmission line, Ω L For the collection of lines on a power transmission line, Ω u This represents the set of impossible scenarios, where m is the tower number and n is the line number. Let be the failure rate of the nth line at time t. Let be the failure rate of the m-th tower at time t.

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

[0018]

[0019] in, Let t be the load shedding amount for the j-th node in scenario k at time t, where K is the set of fault scenarios, B is the set of load nodes, Δt is the time step, and t0 is the start time of the optimization decision.

[0020] Optionally, the step of 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 trajectory sensitivity analysis, and then constructing a work angle stability control decision model for prevention-emergency control coordination, specifically includes:

[0021] The multi-machine trajectory is obtained through time-domain simulation. Based on the simulation results, the generator group is divided into two mutually exclusive clusters. 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 transient stability margin was determined by trajectory sensitivity analysis.

[0024] During the prevention and control phase, the optimization objective is to maximize the expected value of transient stability margin.

[0025] During the emergency control phase, the optimization objective is to minimize the load shedding for safety.

[0026] Optionally, using the formula Determine the optimal objective f for the prevention and control phase 21 ;

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

[0028] Optionally, using the formula Determine the optimization objective f for the emergency control phase. 22 ;

[0029] in, Let be the safety load shedding amount at time t of the j-th node in scenario k, and be the decision variable for transient power angle stability emergency control.

[0030] Optionally, the constraints of the two-level optimization model include: thermal power unit output constraints, thermal power unit ramping constraints, pumped storage unit output constraints, pumped storage unit state constraints, pumped storage unit energy storage constraints, demand-side reserve resource scheduling constraints, adequacy load shedding constraints, power flow constraints, load shedding amount constraints, and transient power angle stability constraints.

[0031] Secondly, this application provides a control decision system for coordinating transient power angle stability and adequacy in a power system, the control decision system for coordinating transient power angle stability and adequacy in a power system comprising:

[0032] The data acquisition module is used to acquire typhoon data and the power grid topology of the power system;

[0033] The fault scenario set construction module is used to construct a fault scenario set based on typhoon data and the power grid topology of the power system, and on the component failure rate model.

[0034] The system adequacy optimization model construction module is used to decouple system adequacy optimization and power angle stability control decisions. Based on the set of fault scenarios, it considers the adjustable resources of source load storage to construct a system adequacy optimization model with the minimum adequacy load shedding as the optimization objective.

[0035] The power angle stability control decision model construction module is used to quantify transient stability margin based on the extended equal area criterion, determine the sensitivity of each control measure to transient stability margin through trajectory sensitivity analysis, and then construct a power angle stability control decision model for prevention-emergency control coordination. The power angle stability control decision model for prevention-emergency control coordination aims to maximize the expected value of transient stability margin in the prevention control stage and minimize the safety load shedding in the emergency control stage.

[0036] The two-layer optimization model construction module is used to construct a two-layer optimization model with a system adequacy optimization model as the upper-layer model and a power angle stability control decision model that coordinates prevention and emergency control as the lower-layer model.

[0037] The control decision module is used to determine the control decisions for fault scenarios in the fault scenario set based on the two-level optimization model.

[0038] Thirdly, this 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] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the control decision method for coordinating transient power angle stability and adequacy in a power system.

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

[0041] This application provides a control decision-making method, system, medium, and product for coordinating transient power angle stability and adequacy in power systems. Based on typhoon data and grid topology, a fault scenario set is constructed. Compared to direct faults on each line at the initial disturbance moment, using a fault scenario set can consider the optimization capabilities between successive faults, effectively reducing load reduction during typhoons. Based on the idea of ​​"decoupling optimization and aggregation coordination," a decoupling method for system adequacy optimization and power angle stability control decision-making is proposed. For adequacy optimization, a system adequacy optimization model is established considering source-load-storage resources. For power angle stability control, a prevention-emergency control coordinated power angle stability control decision-making model is established based on the Extended Equal Area Criteria (EEAC). On this basis, a two-layer optimization decision-making model is constructed and iteratively solved. This application can ensure system power angle stability while meeting system adequacy requirements, and effectively reduce load reduction during typhoons, minimizing the scale of power outages caused by typhoons. It has significant application value for regional power grids frequently affected by typhoons. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic flowchart of a control decision-making method for coordinating transient power angle stability and adequacy in a power system according to an embodiment of this application. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] In one exemplary embodiment, such as Figure 1 As shown, a control decision-making method for coordinating transient power angle stability and adequacy in a power system is provided. This method includes the following steps S101 to S106: Wherein:

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

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

[0049] The wind speed and direction data of the typhoon were collected, and a typhoon simulation was performed based on the Batts model. The resulting wind speed was:

[0050]

[0051] In the formula: v t Let be the wind speed at the observation point at time t, and let the wind direction be the perpendicular direction of the line connecting the observation point and the cyclone center; r is the distance from the observation point to the cyclone center; V max R represents the wind speed at the location of the strongest wind belt in a typhoon. max It is the distance between the strongest wind belt of the typhoon and the center of the cyclone, i.e., the radius of maximum wind speed.

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

[0053]

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

[0055] The prerequisite for the normal operation of a transmission line is that both the line and the towers are functioning normally. Treating the transmission line as a series model, the failure rate of the transmission line is calculated based on the failure rates of its constituent components. The failure rate of the transmission line is:

[0056]

[0057] In the formula: Let Ω be the failure rate of transmission line i at time t. T To form the collection of towers for a transmission line, Ω L Let m be the set of transmission lines, where m is the tower number and n is the line number. Let be the failure rate of the nth line at time t. Let be the failure rate of the m-th tower at time t.

[0058] During a typhoon, multiple power lines may be at risk of failure simultaneously. Simply assuming these lines will fail at the initial stage of disruption is overly conservative and ignores the optimization potential between successive failures. Furthermore, compared to the total length of transmission lines, the typhoon's movement speed is limited, meaning that potentially failing lines may occur at the same timeframe. Therefore, setting an appropriate decision timeframe ensures that potentially failing lines fall within different time periods of rolling optimization. This transforms the Nk failures within the decision timeframe into multiple successive N-1 failures, listing all possible failure scenarios to form a set of failure scenarios for a single optimization iteration.

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

[0060]

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

[0062] S103 decouples system adequacy optimization and power angle stability control decisions. Based on the set of fault scenarios, it considers adjustable resources of source, load and storage to construct a system adequacy optimization model with the goal of minimizing adequacy load shedding.

[0063] Based on the concept of "decoupling optimization and aggregation coordination", S103 adjusts the output of thermal power units and pumped storage units, while configuring a certain amount of demand-side reserve resources to improve system adequacy and reduce load reduction during typhoons.

[0064] Specifically, the output of thermal power units is decomposed into the initial output value of the unit in the adequacy optimization stage and the output adjustment amount of the unit in the power angle stabilization 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 stabilization control stage.

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

[0066] The adequacy optimization model takes minimizing the adequacy load shedding as its optimization objective, and the objective function is shown in equation (7):

[0067]

[0068] In the formula: Let t be the load shedding amount at time t for the j-th node in the k-th scenario, where K is the set of fault scenarios, B is the set of load nodes, Δt is the time step, and t0 is the start time of the optimization decision.

[0069] The constraints that exist during the operation of thermal power units 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 ramp constraint of the thermal power unit.

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

[0071]

[0072] In the formula: P i,t Let ΔP be the initial output of the i-th thermal power unit at time t. i,t P represents the output adjustment of the thermal power unit and the decision variable for transient power angle stability prevention and control. i,max and P i,min These are the upper and lower limits of the output of the i-th thermal power unit, respectively; and These are the upper and lower limits of the ramp rate for the i-th thermal power unit, respectively.

[0073] The pumped storage unit is equivalent to an energy storage unit, and the constraints it has during operation 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 constraints of the pumped storage unit, and equations (13) to (14) are the energy storage constraints of the pumped storage unit.

[0074]

[0075] In the formula: and These represent the charging and discharging power of the l-th pumped storage unit at time t; and These are the upper and lower limits of the discharge power of the lth pumped storage unit, respectively. and These are the upper and lower limits of the charging power of the lth pumped storage unit, respectively. and The variables are 0-1, representing the pumped storage unit in charging and discharging states, respectively; E pss,l,t Let t be the electrical charge of the l-th pumped storage unit at time t; and These are the upper and lower limits of the power output of the l-th pumped storage unit.

[0076] Other constraints on the existence of adjustable resources are shown in equations (15) to (16). Equation (15) is the constraint on the amount of reserve resources to be scheduled on the demand side, and equation (16) is the constraint on the adequacy load shedding.

[0077]

[0078] In the formula: Let μ be the reserve capacity of the nth reserve resource of node j at time t. j,n,t It is a 0-1 variable representing the configuration status of the backup resource; 1 indicates that the backup resource is configured, and 0 indicates that the backup resource is not configured. L represents the scheduling amount of the nth backup resource of node j in the kth scenario; j,t Let be the load at time t of the j-th node.

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

[0080]

[0081] In the formula: Let be the safe load shedding amount at time t of the j-th node in the k-th scenario, and be the decision variable for transient power angle stability emergency control; Let L be the power flowing on line l; L(i) be the set of lines connected to node i; B l For the susceptance of the line, θ h,t The phase of node h; Line l Let be the set of nodes at both ends of line l; R is the maximum power carried on transmission line l, which equals 0 when l is faulted; R(i) is the set of demand-side reserve resources possessed by load node i.

[0082] S104, Based on the extended equal area criterion, the transient stability margin is quantified, and the sensitivity of each control measure to the transient stability margin is determined by the trajectory sensitivity analysis method. Then, a power angle stability control decision model for prevention-emergency control coordination is constructed. The power angle stability control decision model for prevention-emergency control coordination aims to maximize the expected value of transient stability margin in the prevention control stage and minimize the safety load shedding in the emergency control stage.

[0083] EEAC first obtains the multi-machine trajectory through time-domain simulation. Based on the simulation results, the generator group is divided into two mutually exclusive clusters: the leading group CMs consists of the critical generators that cause instability, and the remaining group NMs consists of other non-critical generators. Then, the multi-machine system is equivalent to a single-machine system through the following formulas, 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] In the formula: C is the set of generator sets in CMs, N is the set of generator sets in NMs; M C and M N Let be 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) represents the mechanical power and electromagnetic power of the single-machine infinite system at time t, respectively; M represents 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] In the formula: A dec and Aacc These are the deceleration area and acceleration area of ​​the equivalent single-machine system, respectively; η is the transient stability margin, which is greater than 0 to indicate that the system is transiently stable in terms of power angle, and vice versa.

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

[0094]

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

[0096] In the prevention and control phase, the optimization objective is to maximize the expected value of the transient stability margin, and the objective function is shown in equation (31):

[0097]

[0098] Preventive control involves adjusting the unit output for each anticipated fault scenario, before the actual fault occurs. It must satisfy the thermal power unit operation constraints shown in equations (8) to (9) and the power flow constraints shown in equations (17) to (19).

[0099] During the emergency control phase, the optimization objective is to minimize the load shedding for safety, and the objective function is shown in equation (32):

[0100]

[0101] After emergency control is implemented, pumped storage units with rapid adjustment capabilities need to be dispatched to achieve power balance of the system. The operation constraints of pumped storage units shown in equations (10) to (14) and the power flow constraints shown in equations (17) to (19) need to be met. The constraints that also need to 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 the transient power angle stability constraints.

[0102]

[0103] In the formula: Δη represents the transient stability margin of the system under the k-th fault scenario after preventive control measures are implemented. k The change in the transient stability margin of the system after emergency control is implemented for the k-th fault scenario.

[0104] S105, Construct a two-layer optimization model with the system adequacy optimization model as the upper-layer model and the power angle stability control decision model of prevention-emergency control coordination as the lower-layer model;

[0105] S106, Determine the control decision under the fault scenario in the fault scenario set based on the two-level optimization model.

[0106] Assuming the initial safety decision value obtained from the power angle stability control decision model is 0, adequacy optimization is performed. Then, the adequacy optimization result is used as input for power angle stability control decision-making. Iterative solutions are performed until the convergence condition is met, and the control strategy for each fault scenario is output.

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

[0108]

[0109] In the formula: f (h) ε represents the total load reduction of the system during the h-th iteration of optimization; ε is the convergence threshold.

[0110] Based on the same inventive concept, this application also provides a control decision system for coordinating transient power angle stability and adequacy in a power system, used to implement the control decision method for coordinating transient power angle stability and adequacy in a power system as described above. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the control decision system for coordinating transient power angle stability and adequacy in a power system provided below can be found in the limitations of the control decision method for coordinating transient power angle stability and adequacy in a power system described above, and will not be repeated here.

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

[0112] The data acquisition module is used to acquire typhoon data and the power grid topology of the power system;

[0113] The fault scenario set construction module is used to construct a fault scenario set based on typhoon data and the power grid topology of the power system, and on the component failure rate model.

[0114] The system adequacy optimization model construction module is used to decouple system adequacy optimization and power angle stability control decisions. Based on the set of fault scenarios, it considers the adjustable resources of source load storage to construct a system adequacy optimization model with the minimum adequacy load shedding as the optimization objective.

[0115] The power angle stability control decision model construction module is used to quantify transient stability margin based on the extended equal area criterion, determine the sensitivity of each control measure to transient stability margin through trajectory sensitivity analysis, and then construct a power angle stability control decision model for prevention-emergency control coordination. The power angle stability control decision model for prevention-emergency control coordination aims to maximize the expected value of transient stability margin in the prevention control stage and minimize the safety load shedding in the emergency control stage.

[0116] The two-layer optimization model construction module is used to construct a two-layer optimization model with a system adequacy optimization model as the upper-layer model and a power angle stability control decision model that coordinates prevention and emergency control as the lower-layer model.

[0117] The control decision module is used to determine the control decisions for fault scenarios in the fault scenario set based on the two-level optimization model.

[0118] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0119] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[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, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0121] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this 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 can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0122] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0123] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.

[0124] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A control decision-making method for coordinating transient power angle stability and adequacy in a power system, characterized in that, The control decision-making method for coordinating transient power angle stability and adequacy in the power system includes: Acquire typhoon data and the power grid topology of the power system; Based on typhoon data and the power grid topology of the power system, a set of failure scenarios is constructed based on the component failure rate model. The system adequacy optimization and power angle stability control decisions are decoupled. Based on the set of fault scenarios, a system adequacy optimization model is constructed with the goal of minimizing adequacy load shedding, taking into account the adjustable resources of source load storage. 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. Then, a power angle stability control decision model for prevention-emergency control coordination is constructed. The power angle stability control decision model for prevention-emergency control coordination aims to maximize the expected value of transient stability margin in the prevention control stage and minimize the safety load shedding in the emergency control stage. A two-layer optimization model is constructed, with the system adequacy optimization model as the upper-layer model and the power angle stability control decision model of prevention-emergency control coordination as the lower-layer model. The control decisions for each fault scenario in the fault scenario set are determined based on a two-level optimization model. Based on typhoon data and the power grid topology, a set of failure scenarios is constructed using a component failure rate model, specifically including: Using formula Determine the component failure rate model; Using formula Determine the probability that scenario k will occur at time t; in, Let be the failure rate of component l at time t. For the design wind speed of component l, a f v is the parameter for the component failure rate model. t Let be the wind speed at the observation point at time t, and let the wind direction be the direction perpendicular to the line connecting the observation point and the center of the cyclone. r is the distance from the observation point to the center of the cyclone; V max R represents the wind speed at the location of the strongest wind belt in a typhoon. max The distance between the strongest wind belt of the typhoon and the center of the cyclone is denoted by , and the radius of the maximum wind speed is denoted by . Let be the probability that scenario k occurs at time t. Let k be the probability of scenario k occurring when there are impossible scenarios in the scenario set. N T ΔT represents the number of time periods considered during rolling optimization, ΔT represents the step size of rolling optimization, and b represents the optimization time period number. This is an intermediate variable, representing the probability of a failure occurring at time t. Let Ω be the failure rate of transmission line i at time t. T To form the collection of towers for a transmission line, Ω L For the collection of lines on a power transmission line, Ω u This represents the set of impossible scenarios, where m is the tower number and n is the line number. Let be the failure rate of the nth line at time t. Let be the failure rate of the m-th tower at time t.

2. The control decision-making method for coordinating transient power angle stability and adequacy in a power system according to claim 1, characterized in that, The objective function f1 of the system adequacy optimization model includes the following formula: in, Let t be the load shedding amount for the j-th node in scenario k at time t, where K is the set of fault scenarios, B is the set of load nodes, Δt is the time step, and t0 is the start time of the optimization decision.

3. The control decision-making method for coordinating transient power angle stability and adequacy in a power system according to claim 2, 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 through trajectory sensitivity analysis. This leads to the construction of a coordinated prevention-emergency control decision model for power angle stability control, specifically including: The multi-machine trajectory is obtained through time-domain simulation. Based on the simulation results, the generator group is divided into two mutually exclusive clusters. 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 transient stability margin was determined by trajectory sensitivity analysis. During the prevention and control phase, the optimization objective is to maximize the expected value of transient stability margin. During the emergency control phase, the optimization objective is to minimize the load shedding for safety.

4. The control decision-making method for coordinating transient power angle stability and adequacy in a power system according to claim 3, characterized in that, Using formula Determine the optimal objective f for the prevention and control phase 21 ; Where, ΔP i,t η is the output adjustment of the thermal power unit, P is the decision variable for transient power angle stability prevention and control, η is the transient stability margin, and P is the transient stability margin. i,t Let Φ be the initial output of the i-th thermal power unit at time t. k (η,P i,t ) represents P under the k-th fault scenario i,t Sensitivity to η.

5. The control decision-making method for coordinating transient power angle stability and adequacy in a power system according to claim 4, characterized in that, Using formula Determine the optimization objective f for the emergency control phase. 22 ; in, Let be the safety load shedding amount at time t of the j-th node in scenario k, and be the decision variable for transient power angle stability emergency control.

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

7. A control decision system for coordinating transient power angle stability and adequacy in a power system, used to implement the control decision method for coordinating transient power angle stability and adequacy in a power system as described in any one of claims 1-6, characterized in that, The control and decision-making system for coordinating transient power angle stability and adequacy in the power system includes: The data acquisition module is used to acquire typhoon data and the power grid topology of the power system; The fault scenario set construction module is used to construct a fault scenario set based on typhoon data and the power grid topology of the power system, and on the component failure rate model. The system adequacy optimization model construction module is used to decouple system adequacy optimization and power angle stability control decisions. Based on the set of fault scenarios, it considers the adjustable resources of source load storage to construct a system adequacy optimization model with the minimum adequacy load shedding as the optimization objective. The power angle stability control decision model construction module is used to quantify transient stability margin based on the extended equal area criterion, determine the sensitivity of each control measure to transient stability margin through trajectory sensitivity analysis, and then construct a power angle stability control decision model for prevention-emergency control coordination. The power angle stability control decision model for prevention-emergency control coordination aims to maximize the expected value of transient stability margin in the prevention control stage and minimize the safety load shedding in the emergency control stage. The two-layer optimization model construction module is used to construct a two-layer optimization model with a system adequacy optimization model as the upper-layer model and a power angle stability control decision model that coordinates prevention and emergency control as the lower-layer model. The control decision module is used to determine the control decisions for fault scenarios in the fault scenario set based on the two-level optimization model.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the control decision method for coordinating transient power angle stability and adequacy of a power system as described in any one of claims 1-6.

9. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the control decision method for coordinating transient power angle stability and adequacy of a power system as described in any one of claims 1-6.

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