Power system power angle stability control method and system considering participation of energy storage

Abstract

The invention discloses an electric power system power angle stability control method considering energy storage participation. The method comprises the steps that data information of a target electric power system is acquired; constructing a power angle stability analysis model containing energy storage dynamic characteristics of the target power system; simulating a transient process of the target power system under a set fault through a transient stability calculation scheme, and calculating a corresponding power angle stability margin; taking the stability margin meeting the set requirement as a target, considering the service life and the control cost of the energy storage system, and performing multi-target optimization based on a particle swarm algorithm to obtain an optimal control scheme; and according to the obtained optimal control scheme, based on the real-time state of the target power system, performing power angle stability control of the power system. The invention also discloses a system for realizing the power angle stability control method of the power system considering the participation of the energy storage. According to the method, the power angle stability control of the power system considering the participation of energy storage is realized, the reliability is higher, and the accuracy is better.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of electrical automation, and particularly relates to a power system power angle stability control method and system considering energy storage participation. BACKGROUND

[0002] With the development of economy and technology and the improvement of people's living standards, electric energy has become an essential secondary energy in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring the stable and reliable supply of electric energy has become one of the most important tasks of the power system.

[0003] Power angle stability of the power system is the core problem of ensuring safe and stable operation of the power system, and its essence is the ability of synchronous generators to maintain synchronous operation after the power system is subjected to a large disturbance. At present, more and more new energy power generation systems begin to be integrated into the power system and generate power. The output fluctuation and randomness of the new energy power generation system bring unprecedented challenges to the power angle stability analysis and control of the power system: on the one hand, the replacement of synchronous units by new energy units leads to a significant decrease in the total inertia and damping level of the power system, making the power system more prone to instability after disturbance; on the other hand, the randomness and fluctuation of new energy output make the operation mode of the power system complex and changeable, and the traditional pre-control strategy based on fixed operation mode is not adaptive enough. Therefore, the power system begins to widely use energy storage systems to improve its stability and reliability.

[0004] At present, the power angle stability pre-control method widely used in the power system mainly relies on the classic transient stability analysis theory such as extended equal area criterion (EEAC) and time domain simulation scheme. These schemes assess the stability margin of the system and take pre-control measures such as adjusting the output of traditional generators and cutting off loads when the margin is insufficient. However, this traditional scheme has the following limitations when facing the current power system: 1) the existing scheme relies heavily on synchronous generators and load-side resources; the frequency modulation response speed of the generator is slow (seconds to minutes), and the load shedding measure is economically costly and affects power supply reliability; therefore, the existing scheme is difficult to cope with the fast and variable stability problem of high proportion of new energy systems; 2) the power angle stability analysis model of the existing scheme is mainly modeled for synchronous generators, but it does not organically integrate the dynamic characteristics of the energy storage system such as state of charge (SOC) constraint, charging and discharging efficiency, power ramp rate and life loss into the stability analysis framework, but only regards the energy storage as a simple power source, ignoring the influence of its state change on the sustainability of the control effect, resulting in distorted evaluation results and decreased decision accuracy; 3) the existing pre-control decision optimization mostly takes improving the stability margin as a single target, and the accuracy of the scheme is relatively poor. SUMMARY

[0005] One of the purposes of the present application is to provide a power system angle stability control method considering energy storage participation with high reliability and good accuracy.

[0006] The second purpose of the present application is to provide a system for implementing the power system angle stability control method considering energy storage participation.

[0007] The power system angle stability control method considering energy storage participation provided by the present application comprises the following steps:

[0008] S1. Obtain data information of the target power system;

[0009] S2. According to the data information obtained in step S1, construct a power angle stability analysis model of the target power system with energy storage dynamic characteristics;

[0010] S3. Through a transient stability calculation scheme, simulate the transient process of the target power system under a set fault, and calculate the corresponding power angle stability margin;

[0011] S4. With the stability margin meeting the set requirements as the target, while considering the service life and control cost of the energy storage system, perform multi-objective optimization based on a particle swarm algorithm to obtain an optimal control scheme;

[0012] S5. According to the optimal control scheme obtained in step S4, based on the real-time state of the target power system, perform power system angle stability control.

[0013] The step S1 of obtaining data information of the target power system comprises the following steps:

[0014] According to a set sampling frequency, obtain data information of the target power system;

[0015] The data information includes generator internal potential power angle , generator rotor speed , node voltage amplitude , node voltage phase angle , current maximum chargeable power of the energy storage system, current maximum dischargeable power of the energy storage system, state of charge of the energy storage system , and health state of the energy storage system .

[0016] The step S2 of constructing a power angle stability analysis model of the target power system with energy storage dynamic characteristics according to the data information obtained in step S1 comprises the following steps:

[0017] The multi-machine system model of the target power system is represented as:

[0018] In the formula is the state variable vector of the system; is the function vector of the system dynamic differential equations; is the state variable, including the generator internal voltage power angle and the generator rotor speed; is the agent variable vector, including the node voltage amplitude and the node voltage phase angle ; is the generator control variable; is the generator system parameter; is the function vector of the algebraic equation set describing the system network power flow;

[0019] The dynamic differential equation of the state of charge SOC is expressed as:

[0020] In the formula, is the state of charge of the i th energy storage system at time t; is the efficiency sign function; is the output active power of the i th energy storage system at time t; is the rated capacity of the i th energy storage system; is the rated DC voltage of the i th energy storage system;

[0021] The first order inertia link of the energy storage converter is ignored, and the energy storage system is equivalent to an ideal power source with amplitude limiting and rate limiting. The state of charge SOC of the energy storage system is taken as a new state variable, and the multi-machine system model of the target power system is introduced, which is expressed as:

[0022] In the formula, is the state variable vector composed of the state of charge of all energy storage systems; is the control input vector of the energy storage system; is the function vector of the extended system dynamic differential equation set; is the function vector of the extended system algebraic equation set.

[0023] The transient stability calculation scheme in step S3 simulates the transient process of the target power system under the set fault and calculates the corresponding power angle stability margin, which specifically includes the following steps:

[0024] In the transient stability calculation module of the power system analysis software, the multi-machine system model constructed in the import step S2 is imported, and the energy storage system is modeled in a controllable power form with charge and discharge power constraints, SOC dynamic characteristics and SOH dynamic characteristics, and a predetermined fault is set.

[0025] The transient stability calculation module of the power system analysis software is used to simulate the predetermined fault: the dynamic process of the potential power angle of each generator in the target power system changing with time after the occurrence, development and clearing of the predetermined fault is simulated.

[0026] The corresponding power angle stability margin is calculated by the following formula :

[0027] In the formula is the critical time of the target power system from transient stability to instability; is the actual clearing time of the predetermined fault;

[0028] The power angle stability margin is determined:

[0029] If , it is determined that the target power system has a stability margin;

[0030] If , it is determined that the target power system is transiently unstable, and power system power angle stability control is needed.

[0031] Step S4: Based on the particle swarm algorithm, the optimal control scheme is obtained by multi-objective optimization considering the stability margin meeting the set requirements, the service life of the energy storage system and the control cost, specifically including the following steps:

[0032] Construct the constraint condition:

[0033] Energy storage power constraint:

[0034] In the formula is the negative value of the maximum charge power of the i-th energy storage; is the maximum discharge power of the i-th energy storage;

[0035] Energy storage state of charge constraint:

[0036] In the formula is the SOC value of the i-th energy storage after executing the domain control strategy; is the minimum value of the SOC of the i th energy storage; is the maximum value of the SOC of the i th energy storage;

[0037] Energy storage health state constraint:

[0038] wherein is the SOH degradation value of the i th energy storage caused by single control;

[0039] Power balance constraint:

[0040] wherein represents the power adjustment amount of all energy storages; is the power adjustment amount of all conventional units; is the power shortage;

[0041] Objective function construction:

[0042] wherein is the objective function value; is the set power angle stability margin weight value; is the power angle stability margin improvement amount, and , is the power angle stability margin of the target power system after the power angle stability control is performed, is the power angle stability margin safety threshold value; is the set energy storage power station life consumption weight value; is the total life consumption of all energy storage power stations after single power angle stability control is performed, and n is the total number of energy storage power stations; is the set single control cost weight value; is the total control cost of performing single power angle stability control, and , is the energy storage charging and discharging cost of performing single power angle stability control, is the conventional unit adjustment cost of performing single power angle stability control; is the penalty term, and , is the set penalty weight coefficient, represents the total penalty value of violating the constructed constraint conditions, is the parameter value, is the boundary value of the constraint condition corresponding to the parameter value;

[0043] With the minimum of the constructed objective function as the goal, combining the constraint conditions, a particle swarm algorithm is used for multi-objective optimization to obtain an optimal control scheme for the power angle stability control.

[0044] The optimal control scheme obtained according to step S4 is used to perform the power angle stability control of the target power system based on the real-time state of the target power system, and specifically includes the following steps:

[0045] Real-time operation state data information of the target power system is acquired.

[0046] The power angle stability control of the target power system is performed according to the following rules:

[0047] If the power angle of the target power system is converted from divergent oscillation to convergent oscillation, and the power angle stability margin of the target power system is kept unchanged, it is determined that the target power system has a risk of instability, and the optimal control scheme obtained in step S4 is used to perform the power angle stability control.

[0048] If the power angle of the target power system is converted from divergent oscillation to convergent oscillation, and the power angle stability margin of the target power system is kept unchanged, it is determined that the target power system has a risk of instability, and the optimal control scheme obtained in step S4 is used to perform the power angle stability control.

[0049] If the power angle of the target power system is divergent and the continuous divergence time reaches a set value, it is determined that the target power system has a risk of instability, and the emergency control of the target power system is triggered and an alarm is given.

[0050] The application further provides a system for realizing the power system power angle stability control method considering energy storage participation, comprising a data acquisition module, a model construction module, a margin calculation module, a scheme optimization module and a stability control module; the data acquisition module, the model construction module, the margin calculation module, the scheme optimization module and the stability control module are connected in series; the data acquisition module is used for acquiring data information of a target power system and uploading the data information to the model construction module; the model construction module is used for constructing a power angle stability analysis model of the target power system with energy storage dynamic characteristics according to the received data information and the acquired data information, and uploading the data information to the margin calculation module; the margin calculation module is used for simulating a transient process of the target power system under a set fault through a transient stability calculation scheme, calculating a corresponding power angle stability margin, and uploading the data information to the scheme optimization module; the scheme optimization module is used for performing multi-objective optimization based on a particle swarm algorithm to obtain an optimal control scheme, with the stability margin meeting a set requirement as the target and the service life and control cost of the energy storage system being considered, and uploading the data information to the stability control module; and the stability control module is used for performing power system power angle stability control based on the real-time state of the target power system according to the obtained optimal control scheme and the received data information.

[0051] The power system power angle stability control method and system considering energy storage participation provided by the application comprehensively consider the service life and control cost of the energy storage system according to real-time data information of the target power system and real-time state information of the energy storage system, realize power angle stability control of the power system considering energy storage participation, and have higher reliability and better accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 The application provides a method flowchart of the method.

[0053] Figure 2 The application provides a function module schematic diagram of the system. DETAILED DESCRIPTION

[0054] As shown in the method flowchart of the method, the power system power angle stability control method considering energy storage participation disclosed by the application comprises the following steps: Figure 1

[0055] S1. Acquire data information of a target power system; specifically comprising the following steps:

[0056] According to a set sampling frequency, acquire data information of the target power system;

[0057] The data information comprises generator internal potential power angle , generator rotor speed ​Node voltage amplitude Node voltage phase angle Current maximum chargeable power of energy storage system, current maximum dischargeable power of energy storage system, state of charge of energy storage system And health status of energy storage system ;

[0058] S2. According to the data information obtained in step S1, a power angle stability analysis model of the target power system containing dynamic characteristics of energy storage is constructed; specifically, the following steps are included:

[0059] The multi-machine system model of the target power system is represented as:

[0060] In the formula, is the state variable vector of the system; is the control input vector of the energy storage system; is the state variable, including the generator internal potential power angle and the generator rotor speed; is the agent variable vector, including the node voltage amplitude and the node voltage phase angle ; is the generator control variable; is the generator system parameter; is the function vector describing the algebraic equation set of the system network power flow;

[0061] The dynamic differential equation of the state of charge SOC is represented as:

[0062] In the formula, is the state of charge of the i th energy storage system at time t; is the efficiency symbol function; is the output active power of the i th energy storage system at time t; is the rated capacity of the i th energy storage system; is the rated DC voltage of the i th energy storage system;

[0063] The present application regards the energy storage system as a controllable current source or power source that can provide fast power support, and explicitly embeds its dynamic characteristics in the above-mentioned model. Considering that the response speed of the energy storage converter (PCS) is extremely fast, its dynamic process is much faster than the generator power angle dynamic; therefore, in the power angle stability analysis, the first-order inertia link of the energy storage converter is ignored, and the energy storage system is equivalent to an ideal power source with amplitude limiting and rate limiting; the state of charge SOC of the energy storage system is taken as a new state variable, and is introduced into the multi-machine system model of the target power system, represented as:

[0064] wherein is a state variable vector composed of all the state of charge of the energy storage system; is a control input vector of the energy storage system; is a function vector of the extended system dynamic differential equations; is a function vector of the extended system algebraic equations;

[0065] The model explicitly shows the direct influence of the energy storage power instruction and the energy storage state on the system dynamics, providing support for subsequent accurate stability margin evaluation and multi-objective optimization;

[0066] S3. Through the transient stability calculation scheme, the transient process of the target power system under the set fault is simulated, and the corresponding power angle stability margin is calculated; specifically including the following steps:

[0067] In the transient stability calculation module of the power system analysis software, the multi-machine system model constructed in step S2 is imported, and the energy storage system is modeled in the form of controllable power with charge and discharge power constraints, SOC dynamic characteristics, and SOH dynamic characteristics, and a predetermined fault is set. The predetermined fault is defined as a fault that occurs in the target power system after which stability control measures must be taken to maintain the stability of the target power system, such as a severe three-phase short circuit fault, multiple circuit faults, sudden switching of key power sources or loads, etc.

[0068] The transient stability calculation module of the power system analysis software is used to simulate the predetermined fault: simulate the dynamic process of the potential angle of each generator in the target power system changing with time after the occurrence, development, and clearing of the predetermined fault;

[0069] The corresponding power angle stability margin is calculated using the following formula :

[0070] wherein is the critical time for the target power system to transition from transient stability to instability; is the actual clearing time of the predetermined fault;

[0071] The power angle stability margin is determined:

[0072] If , it is determined that the target power system has a stability margin;

[0073] If , it is determined that the target power system is transiently unstable, and power angle stability control of the power system is needed;

[0074] S4. With the set requirement as the target and considering the life of the energy storage system and the control cost, multi-objective optimization is performed based on a particle swarm algorithm to obtain an optimal control scheme; the specific steps include the following:

[0075] Constraint conditions are constructed:

[0076] Energy storage power constraint:

[0077] In the formula, is the negative value of the maximum charging power of the i th energy storage; is the maximum discharging power of the i th energy storage;

[0078] Energy storage state of charge constraint:

[0079] In the formula, is the SOC value of the i th energy storage after the domain control strategy is executed; is the minimum SOC value of the i th energy storage; is the maximum SOC value of the i th energy storage;

[0080] Energy storage health state constraint:

[0081] In the formula, is the SOH attenuation value of the i th energy storage caused by single control;

[0082] Power balance constraint:

[0083] In the formula, represents the power adjustment amount of all energy storages; is the power adjustment amount of all conventional units; is the power shortage;

[0084] Objective function is constructed:

[0085] In the formula, is the objective function value; is the set power angle stability margin weight value; is the power angle stability margin improvement amount, and , is the power angle stability margin of the target power system after the power angle stability control is executed, is the power angle stability margin safety threshold value; is the set energy storage power station life loss weight value; is the total life loss sum of all energy storage power stations after single power angle stability control, and n is the total number of energy storage power stations; is a set single control cost weight value; is the total control cost of performing single power angle stability control, and , is the energy storage charge and discharge cost of performing single power angle stability control, is the conventional unit regulation cost of performing single power angle stability control; is a penalty term, and , is a set penalty weight coefficient, represents the total penalty value of violating the constructed constraint condition, is a parameter value, is the boundary value of the constraint condition corresponding to the parameter value;

[0086] With the constructed objective function minimization as the goal, combined with the constraint condition, a particle swarm algorithm is used for multi-objective optimization to obtain the optimal control scheme of power angle stability control;

[0087] S5. According to the optimal control scheme obtained in step S4, based on the real-time state of the target power system, the power angle stability control of the power system is performed; specifically including the following steps:

[0088] Real-time acquisition of the operating state data information of the target power system;

[0089] The power angle stability control of the target power system is performed by using the following rules:

[0090] If the target power system is converted from divergent oscillation to convergent oscillation, the target power system is power angle stability margin improved but the improvement speed is lower than the set value, and the target power system is not unstable, the current control scheme is maintained;

[0091] If the target power system is converted from divergent oscillation to convergent oscillation, and the target power system is power angle stability margin unchanged, it is determined that the target power system has instability risk, at this time the optimal control scheme obtained in step S4 is used for power angle stability control;

[0092] If the target power system is divergent and the continuous divergence time reaches the set value, it is determined that the target power system has instability risk, at this time the emergency control of the target power system is triggered, and an alarm is given.

[0093] As Figure 2The system of the application is shown in the functional module diagram: the system for realizing the power system angle stability control method considering energy storage participation disclosed in the application comprises a data acquisition module, a model construction module, a margin calculation module, a scheme optimization module and a stability control module; the data acquisition module, the model construction module, the margin calculation module, the scheme optimization module and the stability control module are connected in series; the data acquisition module is used for acquiring data information of a target power system and uploading the data information to the model construction module; the model construction module is used for constructing a power angle stability analysis model of the target power system containing dynamic characteristics of energy storage according to the received data information and the acquired data information and uploading the data information to the margin calculation module; the margin calculation module is used for simulating a transient process of the target power system under a set fault through a transient stability calculation scheme, calculating corresponding power angle stability margins and uploading the data information to the scheme optimization module; the scheme optimization module is used for performing multi-objective optimization based on a particle swarm algorithm to obtain an optimal control scheme while considering the stability margin meeting a set requirement as a target and the service life and control cost of the energy storage system and uploading the data information to the stability control module; and the stability control module is used for performing power system angle stability control based on a real-time state of the target power system according to the received data information and the obtained optimal control scheme.

Claims

1. A power system power angle stability control method considering energy storage participation, comprising the following steps: S1. Obtain data information about the target power system; S2. Based on the data obtained in step S1, construct a power angle stability analysis model of the target power system including the dynamic characteristics of energy storage; S3. Using a transient stability calculation scheme, simulate the transient process of the target power system under a set fault and calculate the corresponding power angle stability margin; S4. With the goal of meeting the set requirements for stability margin, and taking into account the lifespan of the energy storage system and control costs, multi-objective optimization is performed based on the particle swarm optimization algorithm to obtain the optimal control scheme; S5. Based on the optimal control scheme obtained in step S4, perform power angle stability control of the power system according to the real-time state of the target power system.

2. The power system power angle stability control method considering energy storage participation according to claim 1, characterized in that... Step S1, which involves acquiring data information about the target power system, specifically includes the following steps: According to the set sampling frequency, acquire data information of the target power system; The data information includes the generator's internal electromotive force and power angle. Generator rotor speed Node voltage amplitude Node voltage phase angle The maximum rechargeable power of the energy storage system, the maximum dischargeable power of the energy storage system, and the state of charge of the energy storage system. and the health status of energy storage systems .

3. The power system power angle stability control method considering energy storage participation according to claim 2, characterized in that... Step S2, which involves constructing a power angle stability analysis model of the target power system including the dynamic characteristics of energy storage based on the data information obtained in step S1, specifically includes the following steps: The multi-machine system model of the target power system is represented as follows: In the formula The vector of state variables of the system; The function vector of the system's dynamic differential equations; These are state variables, including the generator's internal electromotive force and power angle. and generator rotor speed; A vector of surrogate variables, including node voltage magnitudes. Phase angle of node voltage ; For generator control variables; These are the parameters of the generator system; Let be the function vector describing the system of algebraic equations for the power flow of the network. The dynamic differential equation of the state of charge (SOC) is expressed as: In the formula Let be the state of charge of the i-th energy storage system at time t; For efficiency sign function; Let be the output active power of the i-th energy storage system at time t; Let be the rated capacity of the i-th energy storage system; The rated DC voltage of the i-th energy storage system; Ignoring the first-order inertial element of the energy storage converter, and equating the energy storage system to an ideal power source with limiting amplitude and rate; the state of charge (SOC) of the energy storage system is taken as a new state variable and introduced into the multi-machine system model of the target power system, expressed as: In the formula The state variable vector consisting of the charged states of all energy storage systems; This is the control input vector for the energy storage system; For the function vectors of the extended system dynamic differential equations; is the function vector of the extended system of algebraic equations.

4. The power system power angle stability control method considering energy storage participation according to claim 3, characterized in that... Step S3, which involves simulating the transient process of the target power system under a set fault using a transient stability calculation scheme and calculating the corresponding power angle stability margin, specifically includes the following steps: In the transient stability calculation module of the power system analysis software, the multi-machine system model constructed in step S2 is imported, and the energy storage system is modeled in a controllable power form with charging and discharging power constraints, SOC dynamic characteristics, and SOH dynamic characteristics, and a predetermined fault is set; the predetermined fault is defined as a fault in which stability control measures must be taken to maintain the stability of the target power system after a fault occurs in the target power system. The transient stability calculation module of the power system analysis software is used to simulate a predetermined fault: the dynamic process of the change of electromotive force angle of each generator in the target power system with time during the occurrence, development and clearance of the predetermined fault. The corresponding power angle stability margin is calculated using the following formula. : In the formula The critical time for the target power system to transition from transient stability to instability; The actual time to clear the scheduled fault; stability margin of work angle Make a judgment: like If so, it is determined that the target power system has a stability margin; like If the target power system is found to be transiently unstable, then power angle stability control of the power system is required.

5. The power system power angle stability control method considering energy storage participation according to claim 4, characterized in that... Step S4, which aims to meet the set requirements for stability margin while considering the lifespan of the energy storage system and control costs, involves multi-objective optimization based on the particle swarm optimization algorithm to obtain the optimal control scheme. Specifically, it includes the following steps: Construct constraints: Energy storage power constraints: In the formula It is the negative value of the maximum charging power of the i-th energy storage; The maximum discharge power of the i-th energy storage unit; Energy storage state of charge constraints: In the formula The SOC value of the i-th energy storage after executing the domain control policy; Let SOC be the minimum value of the i-th energy storage; Let SOC be the maximum value of the i-th energy storage; Energy storage health status constraints: In the formula The SOH decay value of the i-th energy storage caused by a single control operation; Power balance constraints: In the formula This indicates the power regulation of all energy storage devices. This refers to the power regulation of all conventional generating units; This is due to a power deficit. Construct the objective function: In the formula The objective function value; The set power angle stability margin weight value; This is to increase the stability margin of the power angle, and , To determine the power angle stability margin of the target power system after implementing power angle stability control. This is the safety threshold for the stability margin of the power angle; The set weight value for the lifespan loss of the energy storage power station; This represents the total lifespan loss of all energy storage power stations after a single power angle stabilization control operation, and , where n is the total number of energy storage power stations; The set weight value for single control cost; The total control cost for performing single-cycle power angle stabilization control, and , To reduce the cost of energy storage charging and discharging for single-cycle power angle stabilization control, The regulation cost of conventional units performing single-cycle power angle stabilization control; As a penalty item, and , The penalty weighting coefficient is set. This represents the total penalty value for violating the constraints of the construction. For parameter values, These are the boundary values ​​of the constraints corresponding to the parameter values; With the objective function being minimized and combined with constraints, a particle swarm optimization algorithm is used for multi-objective optimization to obtain the optimal control scheme for power angle stability control.

6. The power system power angle stability control method considering energy storage participation according to claim 5, characterized in that... Step S5, which describes the power angle stability control of the power system based on the optimal control scheme obtained in step S4 and the real-time state of the target power system, specifically includes the following steps: Real-time acquisition of operational status data of the target power system; The following rules are used for power angle stability control of the target power system: If the target power system changes from divergent oscillation to convergent oscillation, the target power system has an increased power angle stability margin but the increase rate is lower than the set value, and the target power system is not unstable, then the current control scheme is maintained. If the target power system changes from divergent oscillation to convergent oscillation in terms of power angle, and the power angle stability margin of the target power system remains unchanged, then it is determined that the target power system has the risk of instability. In this case, the optimal control scheme obtained in step S4 is used for power angle stability control. If the target power system exhibits power angle divergence and the continuous divergence time reaches a set value, it is determined that the target power system is at risk of instability. At this time, the emergency control of the target power system is triggered, and an alarm is triggered.

7. A system for implementing the power system power angle stability control method considering energy storage participation as described in any one of claims 1 to 6, characterized in that... The system comprises a data acquisition module, a model building module, a margin calculation module, a scheme optimization module, and a stability control module, which are connected in series. The data acquisition module acquires data information about the target power system and uploads it to the model building module. The model building module constructs a power angle stability analysis model of the target power system, incorporating the dynamic characteristics of energy storage, based on the received and acquired data, and uploads this model to the margin calculation module. The margin calculation module simulates the transient process of the target power system under a set fault using a transient stability calculation scheme, calculates the corresponding power angle stability margin, and uploads this data to the scheme optimization module. The scheme optimization module performs multi-objective optimization based on a particle swarm optimization algorithm, taking into account the stability margin meeting set requirements while also considering the energy storage system's lifetime and control costs, to obtain the optimal control scheme, and uploads this data to the stability control module. The stability control module is used to perform power angle stability control of the power system based on the received data information, the obtained optimal control scheme, and the real-time state of the target power system.

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