Adaptive elastic control method for multi-regional power systems considering renewable energy disturbances

Abstract

The present disclosure relates to the technical field of power systems, and provides a method for adaptive elastic control of a multi-regional power system considering disturbances caused by renewable energy, including: establishing an initial multi-regional power system load frequency control model considering renewable energy and load disturbances; modeling the uncertainties in the input parameters of the model to obtain an intermediate multi-regional power system load frequency control model; establishing an actuator fault model; constructing a target multi-regional power system load frequency control model based on the model and the intermediate multi-regional power system load frequency control model; designing an adaptive elastic control strategy, applying the strategy to the target multi-regional power system load frequency control model, and realizing adaptive elastic control of a multi-regional power system considering disturbances caused by renewable energy. The present disclosure compensates for the over-compensation phenomenon of conventional adaptive control strategies through an improved adaptive control strategy, can enable the system to achieve asymptotic stability, and improve the stability and reliability of the multi-regional power system.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power systems, and in particular to a method for adaptive elastic control of a multi-region power system considering new energy disturbances. Background Art

[0002] As global energy demand grows and the scale and structure of power grids become increasingly complex, power systems are evolving toward multi-regional interconnection. This shift not only expands the scope of power supply but also complicates the management objectives and requirements of power systems. To ensure the reliability and stability of power systems under disturbances, frequency load control has become a key scheduling method. Its primary goal is to maintain the frequency of each control area at a predetermined value during disturbances by adjusting the power setpoint, thereby ensuring the stable operation of multi-region power systems and preventing frequency deviations from impacting system safety and power quality.

[0003] Despite progress in frequency load control methods, existing technologies face challenges as the proportion of renewable energy generation increases. First, the high volatility of renewable energy sources (such as wind and solar power) increases the difficulty of stabilizing grid frequency. Existing research has not fully considered the impact of renewable energy on multi-regional power systems, limiting the effectiveness of frequency load control when the proportion of renewable energy is high. Second, multi-regional power systems exhibit significant coupling relationships between regions, but many studies have overlooked this or simplified the coupling as noise. This approach fails to accurately reflect the actual situation and results in inaccurate control strategies. Finally, sliding film control and traditional adaptive control are currently the main methods for handling actuator faults in multi-regional power systems. However, sliding film control suffers from chattering, making it difficult to cope with complex fault scenarios. Traditional adaptive control is prone to overcompensation, failing to effectively achieve progressive stability and meeting the high reliability requirements of multi-regional power systems. Summary of the Invention

[0004] The embodiments of the present disclosure at least provide a method for adaptive elastic control of a multi-regional power system taking into account new energy disturbances. The improved adaptive control strategy compensates for the over-compensation phenomenon of the conventional adaptive control strategy, enables the system to achieve asymptotic stability, and improves the stability and reliability of the multi-regional power system.

[0005] The present disclosure provides a method for adaptive elastic control of a multi-regional power system considering renewable energy disturbances, including:

[0006] Establishing an initial multi-regional power system load frequency control model that considers renewable energy and load disturbances; and modeling uncertainties in input parameters of the initial multi-regional power system load frequency control model to obtain an intermediate multi-regional power system load frequency control model;

[0007] Establishing an actuator fault model; and constructing a target multi-region power system load frequency control model based on the intermediate multi-region power system load frequency control model and the actuator fault model;

[0008] An adaptive elastic control strategy is designed and applied to the target multi-regional power system load frequency control model to achieve adaptive elastic control of the multi-regional power system considering new energy disturbances.

[0009] In some possible embodiments, the intermediate multi-region power system load frequency control model is expressed as:

[0010] ;

[0011] in, It is expressed as the load frequency of power area g; It is represented as the system transfer matrix of power region g; It is represented as the state of the power region g; It represents the status of power area j; N represents the total number of power areas; It is represented as the coupling matrix between power region g and power region j; It is represented as the control input matrix of the power region g; It is represented as the control input of the power region g; It is represented as the disturbance input matrix of the power area g; It is represented by the load and renewable resource disturbances of power region g; It is expressed as the uncertainty of the system transfer matrix of the power area g; It is expressed as the uncertainty of the coupling matrix between power area g and power area j; It is expressed as the uncertainty of the control input matrix of the power area g; It is expressed as the uncertainty of the disturbance input matrix of the power area g.

[0012] In some possible embodiments, the actuator fault model is expressed as:

[0013] ;

[0014] ;

[0015] in, Denote the damaged control input of power region g; It is expressed as the actuator failure coefficient of the power area g; It is expressed as the minimum threshold of actuator failure coefficient in power region g; It is expressed as the maximum threshold of the actuator failure coefficient of the power area g.

[0016] In some possible embodiments, the adaptive elastic control strategy is expressed as:

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] in, It is represented as the gain matrix of the power region g; It is represented as the symmetric matrix to be solved for the power region g; Denoted as the first adaptive parameter; Denoted as the second adaptive parameter; Denoted as the third adaptive parameter; It is expressed as the fourth adaptive parameter; All are normal numbers.

[0024] In some possible embodiments, applying the adaptive elastic control strategy to the target multi-region power system load frequency control model includes:

[0025] Constructing an initial gain matrix solution equation, and sorting the initial gain matrix solution equation based on the Schur complement method to obtain a target gain matrix solution equation;

[0026] Solving the target gain matrix solution equation based on a trial-and-error method to obtain a gain matrix;

[0027] The target multi-region power system load frequency control model is controlled based on the gain matrix and the adaptive elastic control strategy.

[0028] In some possible embodiments, the initial gain matrix solution equation is expressed as:

[0029] ;

[0030] Where c represents any positive constant; and is a positive constant that satisfies Young's inequality; Expressed as a coupling matrix The first vector of Expressed as a coupling matrix The second vector of is a symmetric matrix; I is the identity matrix.

[0031] In some possible embodiments, the target gain matrix solution equation is expressed as:

[0032] ;

[0033] in, , , , .

[0034] The present disclosure provides a multi-region power system adaptive elastic control device considering new energy disturbances, including:

[0035] An initial model establishment module is used to establish an initial multi-regional power system load frequency control model that takes into account new energy and load disturbances; and to model the uncertainties in the input parameters of the initial multi-regional power system load frequency control model to obtain an intermediate multi-regional power system load frequency control model;

[0036] a target model construction module, configured to establish an actuator fault model; and construct a target multi-region power system load frequency control model based on the intermediate multi-region power system load frequency control model and the actuator fault model;

[0037] The control strategy application module is used to design an adaptive elastic control strategy and apply the adaptive elastic control strategy to the target multi-regional power system load frequency control model to achieve adaptive elastic control of the multi-regional power system considering new energy disturbances.

[0038] In some possible embodiments, the intermediate multi-region power system load frequency control model is expressed as:

[0039] ;

[0040] in, It is expressed as the load frequency of power area g; It is represented as the system transfer matrix of power region g; It is represented as the state of the power region g; It represents the status of power area j; N represents the total number of power areas; It is represented as the coupling matrix between power region g and power region j; It is represented as the control input matrix of the power region g; It is represented as the control input of the power region g; It is represented as the disturbance input matrix of the power area g; It is represented by the load and renewable resource disturbances of power region g; It is expressed as the uncertainty of the system transfer matrix of the power area g; It is expressed as the uncertainty of the coupling matrix between power area g and power area j; It is expressed as the uncertainty of the control input matrix of the power area g; It is expressed as the uncertainty of the disturbance input matrix of the power area g.

[0041] In some possible embodiments, the actuator fault model is expressed as:

[0042] ;

[0043] ;

[0044] in, Denote the damaged control input of power region g; It is expressed as the actuator failure coefficient of the power area g; It is expressed as the minimum threshold of actuator failure coefficient in power region g; It is expressed as the maximum threshold of the actuator failure coefficient of the power area g.

[0045] In some possible embodiments, the adaptive elastic control strategy is expressed as:

[0046] ;

[0047] ;

[0048] ;

[0049] ;

[0050] ;

[0051] ;

[0052] in, It is represented as the gain matrix of the power region g; is represented as the symmetric matrix to be solved for the power region g; where It is represented as the gain matrix of the power region g; It is represented as the symmetric matrix to be solved for the power region g; Denoted as the first adaptive parameter; Denoted as the second adaptive parameter; Denoted as the third adaptive parameter; It is expressed as the fourth adaptive parameter; All are normal numbers.

[0053] In some possible embodiments, the control strategy application module is specifically used to:

[0054] Constructing an initial gain matrix solution equation, and sorting the initial gain matrix solution equation based on the Schur complement method to obtain a target gain matrix solution equation;

[0055] Solving the target gain matrix solution equation based on a trial-and-error method to obtain a gain matrix;

[0056] The target multi-region power system load frequency control model is controlled based on the gain matrix and the adaptive elastic control strategy.

[0057] In some possible embodiments, the initial gain matrix solution equation is expressed as:

[0058] ;

[0059] Where c represents any positive constant; and is a positive constant that satisfies Young's inequality; Expressed as a coupling matrix The first vector of Expressed as a coupling matrix The second vector of is a symmetric matrix; I is the identity matrix.

[0060] In some possible embodiments, the target gain matrix solution equation is expressed as:

[0061] ;

[0062] in, , , , .

[0063] An embodiment of the present disclosure provides a computer device, comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the method for adaptive elastic control of a multi-regional power system taking into account new energy disturbances as described in any possible embodiment described above is performed.

[0064] An embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for adaptive elastic control of a multi-regional power system taking into account new energy disturbances as described in any of the above possible implementation modes is implemented.

[0065] The method for adaptive elastic control of a multi-regional power system considering renewable energy disturbances provided in the embodiments of the present disclosure first establishes an initial multi-regional power system load frequency control model considering renewable energy and load disturbances; and models the uncertainties in the input parameters of the initial multi-regional power system load frequency control model to obtain an intermediate multi-regional power system load frequency control model; then, establishes an actuator fault model; and constructs a target multi-regional power system load frequency control model based on the intermediate multi-regional power system load frequency control model and the actuator fault model; finally, designs an adaptive elastic control strategy, and applies the adaptive elastic control strategy to the target multi-regional power system load frequency control model to achieve adaptive elastic control of the multi-regional power system considering renewable energy disturbances.

[0066] In this way, the embodiment of the present disclosure constructs a target control model that is closer to the actual operating conditions by modeling the load frequency control model of the multi-regional power system and the uncertainties in its input parameters, and combining it with the actuator fault model; at the same time, the core design of the adaptive elastic control strategy enables it to automatically adjust the control parameters according to the real-time operating status of the system, external disturbances and fault information, ensuring precise control while paying more attention to the dynamic response and stability of the system, effectively avoiding overcompensation, and enabling the system to achieve gradual stability, effectively improving the stability and reliability of the power system.

[0067] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings that need to be cited in the embodiments. The drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without inventive effort.

[0069] Figure 1 A flowchart of a multi-regional power system adaptive elastic control method considering new energy disturbances provided by an embodiment of the present disclosure is shown;

[0070] Figure 2 A flow chart of a gain matrix solving method provided by an embodiment of the present disclosure is shown;

[0071] Figure 3 A schematic structural diagram of a multi-regional power system adaptive elastic control device considering new energy disturbances provided by an embodiment of the present disclosure is shown;

[0072] Figure 4 A schematic structural diagram of a computer device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the disclosure for which protection is sought, but merely represents selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present disclosure.

[0074] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0075] The term "and / or" herein simply describes an association relationship, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, and the existence of B alone. In addition, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0076] As power grids expand in size and complexity, power systems are becoming interconnected across multiple regions, requiring the management of an increasing number of objectives. To achieve reliable and stable operation of multi-region power systems, researchers are working on frequency load control methods. The primary goal of frequency load control is to maintain the frequency of each control region at a predetermined value by adjusting the power set point in the event of a disturbance.

[0077] In recent years, renewable energy has accounted for an increasing proportion of power systems. However, the high volatility of renewable energy generation can adversely affect power quality. Therefore, the impact of renewable energy disturbances needs to be considered when controlling multi-regional power systems. Research has found that actuators, which execute a large number of control signals, play a vital role in multi-regional power systems. Their failures can disrupt system performance and adversely affect system stability and security. However, actuator failures are inevitable due to certain external factors. Furthermore, multi-regional power systems are coupled between regions. However, many current researchers have ignored this coupling or simply treated it as noise when studying multi-regional power systems, which hinders the analysis of practical problems.

[0078] Based on the above research, an embodiment of the present disclosure provides an adaptive elastic control method for a multi-regional power system taking into account renewable energy disturbances. By modeling the load frequency control model of the multi-regional power system and the uncertainties in its input parameters, and combining the actuator fault model, a target control model that is closer to the actual operating conditions is constructed; at the same time, the core design of the adaptive elastic control strategy enables it to automatically adjust the control parameters according to the real-time operating status of the system, external disturbances and fault information, ensuring precise control while paying more attention to the dynamic response and stability of the system, effectively avoiding overcompensation, and enabling the system to achieve gradual stability, effectively improving the stability and reliability of the power system.

[0079] To facilitate understanding of this embodiment, the execution entity of the adaptive elastic control method for a multi-regional power system considering renewable energy disturbances provided in the embodiment of the present disclosure is first introduced in detail. The execution entity of the adaptive elastic control method for a multi-regional power system considering renewable energy disturbances provided in the embodiment of the present disclosure is a computer device. The computer device can be a server. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms.

[0080] The following describes in detail the adaptive elastic control method for a multi-region power system considering new energy disturbances provided by the embodiment of the present application with reference to the accompanying drawings. Figure 1 FIG. 1 is a flow chart of a method for adaptive elastic control of a multi-regional power system considering new energy disturbances provided by an embodiment of the present disclosure. The method includes the following steps S101 to S103:

[0081] S101, establishing an initial multi-regional power system load frequency control model considering new energy and load disturbances; and modeling the uncertainties in the input parameters of the initial multi-regional power system load frequency control model to obtain an intermediate multi-regional power system load frequency control model.

[0082] It's understandable that renewable energy refers to renewable energy sources such as wind, solar, hydro, and biomass. In power systems, the unpredictability and volatility of renewable energy generation often cause disturbances. Load disturbances, on the other hand, refer to changes in load demand within a power system, including cyclical variations (such as diurnal and seasonal variations) and random variations (such as load surges caused by emergencies). A multi-regional power system is comprised of multiple geographically dispersed power networks interconnected by transmission lines, with power exchanged between regions.

[0083] Here, the present disclosure first constructs a model that can effectively describe these disturbances in the power systems of various regions (i.e., the initial multi-regional power system load frequency control model). Load frequency control (LFC) is one of the most basic control methods in the power system. Its purpose is to balance the power generation and demand of the power system by adjusting the output power of the generators in each region, thereby controlling the frequency of the system. This model needs to take into account the power generation capacity and load changes of each region in the multi-regional power system. It is a mathematical model used to describe how the system frequency changes when the load and power generation power of the power system change, and how to maintain frequency stability through control measures. Specifically, in the early stage of model establishment, the initial multi-regional power system load frequency control model considering new energy and load disturbances is expressed as follows:

[0084] ;

[0085] Where, ; ;

[0086] ;

[0087] ; ;

[0088] in, It is expressed as the load frequency of power area g; It is represented as the system transfer matrix of power region g; It is represented as the state of the power region g; It represents the status of power area j; N represents the total number of power areas; It is represented as the coupling matrix between power region g and power region j; It is represented as the control input matrix of the power region g; It is represented as the control input of the power region g; It is represented as the disturbance input matrix of the power area g; It is represented by the load and renewable resource disturbances of power region g; It is expressed as the frequency deviation coefficient of power region g; It is expressed as the integral control gain of the power region g; It is expressed as the interconnection gain between power area g and power area j; It is expressed as the power system gain between power area g and power area j; The engine time constant expressed as the power region g; The prime mover time constant is expressed as the power region g; Expressed as the governor time constant in power region g; It is expressed as the frequency variation of power region g; It is expressed as the power output change of power area g; The position change of the regulating valve in the power area g is shown; Expressed as the integral control variation of the power region g; It is expressed as the rotor angle derivative change in the power area g; Expressed as the speed adjustment coefficient of the power area g.

[0089] Furthermore, because renewable energy generation (such as wind and solar energy) is volatile and unpredictable, and its power generation is significantly affected by factors such as weather, season, and geographic location, these uncertainties may lead to large load frequency fluctuations in the power system. Therefore, in order to address the impact of renewable energy uncertainty on load frequency, the present disclosure models the uncertainties in the model, that is, models the uncertainties in the input parameters of the initial multi-regional power system load frequency control model to obtain an intermediate multi-regional power system load frequency control model, which can be expressed as:

[0090] ;

[0091] in, It is expressed as the uncertainty of the system transfer matrix of the power area g; It is expressed as the uncertainty of the coupling matrix between power area g and power area j; It is expressed as the uncertainty of the control input matrix of the power area g; It is expressed as the uncertainty of the disturbance input matrix of the power area g.

[0092] For the above formula, we can make

[0093] ;

[0094] At this point, the above model can be integrated into:

[0095] ;

[0096] in, can be written as:

[0097] ;

[0098] Where, , is a positive constant; Expressed as a coupling matrix The first vector of Expressed as a coupling matrix The second vector of .

[0099] S102 , establishing an actuator fault model; and constructing a target multi-regional power system load frequency control model based on the intermediate multi-regional power system load frequency control model and the actuator fault model.

[0100] It's understood that in power systems, actuators are physical devices that respond to control signals and change system states, such as generator speed regulators and load regulators. A failure in an actuator can prevent the control system from adjusting power generation in a timely manner, thereby impacting system frequency stability. To address this, this disclosure proposes establishing an actuator fault model to describe the behavior of actuators under different failure modes, such as complete failure, partial failure, and delayed response.

[0101] Here, the actuator fault model can be expressed as:

[0102] ;

[0103] ;

[0104] in, Denote the damaged control input of power region g; It is expressed as the actuator failure coefficient of the power area g; It is expressed as the minimum threshold of actuator failure coefficient in power region g; It is expressed as the maximum threshold of the actuator failure coefficient of the power area g.

[0105] Specifically, after establishing the actuator fault model, a target multi-regional power system load frequency control model can be constructed based on the intermediate multi-regional power system load frequency control model and the actuator fault model. This target model not only considers the impact of renewable energy and load disturbances, but also incorporates the uncertainty of actuator faults. This allows the target model to more accurately reflect the actual operating conditions of the power system, especially in the face of various sudden faults and disturbances.

[0106] S103 , designing an adaptive elastic control strategy, and applying the adaptive elastic control strategy to the target multi-regional power system load frequency control model to implement adaptive elastic control of the multi-regional power system considering new energy disturbances.

[0107] Here, the core idea of the adaptive elastic control strategy is to enable the system to flexibly respond to the ever-changing operating environment by adjusting the parameters of the control strategy in real time, especially when facing the uncertainty of renewable energy power generation and possible failures of system actuators, while still maintaining good system stability and load frequency control performance.

[0108] Specifically, the adaptive elastic control strategy proposed in this disclosure can be expressed as:

[0109] ;

[0110] in, Expressed as the gain matrix of the power region g, ; Denoted as the first adaptive parameter; Denoted as the third adaptive parameter; and is the time-varying gain / adaptive gain; , It is expressed as the symmetric matrix to be solved for the power area g, which is determined as follows:

[0111] ;

[0112] ;

[0113] in, are all positive numbers; and The existence of is to eliminate the transition compensation phenomenon, which can be expressed as:

[0114] ;

[0115] ;

[0116] in, Denoted as the second adaptive parameter; It is expressed as the fourth adaptive parameter; are all positive numbers. In addition, ; .

[0117] Here, the elastic control strategy proposed in this disclosure has a smaller control input energy and is easier to meet in actual systems. and The proposed resilient control strategy can simultaneously address a variety of scenarios, including actuator failures, attacks, and the presence of both matching and mismatching uncertainties in the system. This comprehensive control strategy makes the system more adaptable and robust, enabling efficient and stable operation in the face of complex real-world situations.

[0118] Specifically, the parameters mentioned in step S101 above are represents uncertainty, assuming it consists of matching uncertainty and mismatch uncertainty Composition, that is Here, matching uncertainty and mismatch uncertainty are defined according to their The adaptive elastic control strategy proposed in this disclosure can solve the uncertainty of matching and mismatching at the same time. The following is an example of the process of handling matching uncertainty by the strategy proposed in this application: When handling matching uncertainty, and Replace with , and then deal with the problem of result matching uncertainty.

[0119] For example, referring to Figure 2 As shown, after the adaptive elastic control strategy is designed, when applying it to the target multi-region power system load frequency control model, the following steps S201 to S203 may be included:

[0120] S201, constructing an initial gain matrix solution equation, and sorting the initial gain matrix solution equation based on the Schur complement method to obtain a target gain matrix solution equation.

[0121] It's understandable that in load frequency control of a multi-area power system, the gain matrix describes the relationship between input and output. It contains the control gain values for each area of the system. The gain matrix's function is to transmit control signals to each area and adjust the system's control output based on its dynamic characteristics. In a multi-area power system, each area may have different control requirements. Therefore, the design of the gain matrix needs to comprehensively consider the specific requirements of each area and the system's operating conditions.

[0122] Here, the initial gain matrix solution equation can be expressed as:

[0123] ;

[0124] Where c represents any positive constant; and is a positive constant that satisfies Young's inequality; Expressed as a coupling matrix The first vector of Expressed as a coupling matrix The second vector of is a symmetric matrix; I is the identity matrix.

[0125] As you can understand, the Schur complement is an important matrix decomposition method in linear algebra, primarily used to decompose complex matrix-solving problems into simpler subproblems. In system design, the Schur complement method is often used to address coupling problems in distributed systems (multi-region power systems in this application), helping to simplify the gain matrix solution process. This method can effectively reduce computational complexity and improve solution efficiency, resulting in a more efficient solution to the target gain matrix.

[0126] Here, the initial gain matrix solution equation is sorted out by the Schur complement method, and the target gain matrix solution equation can be obtained as follows:

[0127] ;

[0128] in, , , , .

[0129] S202: Solve the target gain matrix solution equation based on a trial-and-error method to obtain a gain matrix.

[0130] It is understandable that after sorting out the initial gain matrix solution equation based on the Schur complement method, the original formula is only and Specifically, when solving the gain matrix, this application adopts a trial-and-error method. First, according to the parameters The control system is solved by using the initial feasible values of the parameters, and then by comparing them with the actual performance, these parameters are gradually adjusted until the system reaches the expected stable state or performance. The value of , and then solve the gain matrix In this way, the trial-and-error method proposed in the present disclosure does not rely on a complete system model or precise mathematical description, but gradually approaches the optimal solution through feedback and adjustment, and is suitable for processing some complex or incompletely predictable systems.

[0131] In some other embodiments, the gain matrix may be solved using machine learning, optimization algorithms, and other methods, which are not specifically limited here.

[0132] S203 : Controlling the target multi-region power system load frequency control model based on the gain matrix and the adaptive elastic control strategy.

[0133] Specifically, after obtaining the gain matrix, it can be used in an adaptive elastic control strategy to cope with the challenges of power system load frequency fluctuations and uncertainties, and to adjust the load frequency of multi-regional power systems, thereby improving the reliability and economy of the system.

[0134] In order to verify the control performance of the adaptive elastic control strategy proposed in this disclosure for a multi-regional power system considering new energy disturbances, this disclosure also proposes to establish a Lyapunov function to verify the stability of a multi-regional power system containing new energy disturbances and actuator failures. Lyapunov stability theory is a theory used to analyze system stability. This theory can be applied to the stability analysis of linear and nonlinear systems, steady-state systems and time-varying systems at the same time. It is a more general stability analysis method. It mainly studies the stability of the system by constructing a scalar function similar to an "energy function", namely the Lyapunov function. If the Lyapunov function of the system meets certain conditions, such as being positive and having a negative derivative, then it can be judged that the system is stable or asymptotically stable at the equilibrium point.

[0135] Specifically, the Lyapunov function of the g-th power region can be expressed as:

[0136] ;

[0137] ;

[0138] ;

[0139] exist middle,

[0140] ; ;

[0141] right The derivation shows that,

[0142] ;

[0143] right The derivation shows that,

[0144] ;

[0145] Combine and The derivative of :

[0146] ;

[0147] Parameters must exist satisfy:

[0148] ;

[0149] Then we can get:

[0150] ;

[0151] From this we can conclude that:

[0152] ;

[0153] According to the above steps S201 to S203, it can be obtained:

[0154] .

[0155] Furthermore, according to Lyapunov stability theory, the elastic control strategy proposed in the present invention can enable the multi-region power system to achieve asymptotic stability.

[0156] The adaptive elastic control method for a multi-regional power system taking into account new energy disturbances provided in the embodiments of the present disclosure constructs a target control model that is closer to the actual operating conditions by modeling the load frequency control model of the multi-regional power system and the uncertainties in its input parameters, and combining it with the actuator fault model; at the same time, the core design of the adaptive elastic control strategy enables it to automatically adjust the control parameters according to the real-time operating status of the system, external disturbances and fault information, ensuring precise control while paying more attention to the dynamic response and stability of the system, effectively avoiding overcompensation, and enabling the system to achieve gradual stability, thereby effectively improving the stability and reliability of the power system.

[0157] Those skilled in the art will understand that in the above-mentioned method of the specific implementation method, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0158] Based on the same inventive concept, the embodiment of the present disclosure also provides a multi-regional power system adaptive elastic control device considering renewable energy disturbances, which corresponds to the multi-regional power system adaptive elastic control method considering renewable energy disturbances. Since the principle of solving the problem by the device in the embodiment of the present disclosure is similar to the above-mentioned multi-regional power system adaptive elastic control method considering renewable energy disturbances in the embodiment of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0159] Reference Figure 3 FIG. 1 is a schematic diagram of a multi-regional power system adaptive elastic control device 300 considering new energy disturbances provided by an embodiment of the present disclosure, the device comprising:

[0160] An initial model building module 301 is used to establish an initial multi-regional power system load frequency control model that takes into account new energy and load disturbances; and to model the uncertainties in the input parameters of the initial multi-regional power system load frequency control model to obtain an intermediate multi-regional power system load frequency control model;

[0161] A target model building module 302 is configured to establish an actuator fault model; and to build a target multi-region power system load frequency control model based on the intermediate multi-region power system load frequency control model and the actuator fault model;

[0162] The control strategy application module 303 is used to design an adaptive elastic control strategy and apply the adaptive elastic control strategy to the target multi-regional power system load frequency control model to achieve adaptive elastic control of the multi-regional power system considering new energy disturbances.

[0163] In some possible embodiments, the intermediate multi-region power system load frequency control model is expressed as:

[0164] ;

[0165] in, It is expressed as the load frequency of power area g; It is represented as the system transfer matrix of power region g; It is represented as the state of the power region g; It represents the status of power area j; N represents the total number of power areas; It is represented as the coupling matrix between power region g and power region j; It is represented as the control input matrix of the power region g; It is represented as the control input of the power region g; It is represented as the disturbance input matrix of the power area g; It is represented by the load and renewable resource disturbances of power region g; It is expressed as the uncertainty of the system transfer matrix of the power area g; It is expressed as the uncertainty of the coupling matrix between power area g and power area j; It is expressed as the uncertainty of the control input matrix of the power area g; It is expressed as the uncertainty of the disturbance input matrix of the power area g.

[0166] In some possible embodiments, the actuator fault model is expressed as:

[0167] ;

[0168] ;

[0169] in, Denote the damaged control input of power region g; It is expressed as the actuator failure coefficient of the power area g; It is expressed as the minimum threshold of actuator failure coefficient in power region g; It is expressed as the maximum threshold of the actuator failure coefficient of the power area g.

[0170] In some possible embodiments, the adaptive elastic control strategy is expressed as:

[0171] ;

[0172] ;

[0173] ;

[0174] ;

[0175] ;

[0176] ;

[0177] in, It is represented as the gain matrix of the power region g; is represented as the symmetric matrix to be solved for the power region g; where It is represented as the gain matrix of the power region g; It is represented as the symmetric matrix to be solved for the power region g; Denoted as the first adaptive parameter; Denoted as the second adaptive parameter; Denoted as the third adaptive parameter; It is expressed as the fourth adaptive parameter; All are normal numbers.

[0178] In some possible embodiments, the control strategy application module 303 is specifically configured to:

[0179] Constructing an initial gain matrix solution equation, and sorting the initial gain matrix solution equation based on the Schur complement method to obtain a target gain matrix solution equation;

[0180] Solving the target gain matrix solution equation based on a trial-and-error method to obtain a gain matrix;

[0181] The target multi-region power system load frequency control model is controlled based on the gain matrix and the adaptive elastic control strategy.

[0182] In some possible embodiments, the initial gain matrix solution equation is expressed as:

[0183] ;

[0184] Where c represents any positive constant; and is a positive constant that satisfies Young's inequality; Expressed as a coupling matrix The first vector of Expressed as a coupling matrix The second vector of is a symmetric matrix; I is the identity matrix.

[0185] In some possible embodiments, the target gain matrix solution equation is expressed as:

[0186] ;

[0187] in, , , , .

[0188] Based on the same technical concept, the embodiment of the present disclosure also provides a computer device. Figure 44 is a schematic diagram of the structure of a computer device 400 provided in an embodiment of the present disclosure, including a processor 401, a memory 402, and a bus 403. The memory 402 is used to store execution instructions and includes a memory 4021 and an external memory 4022. The memory 4021 is also referred to as internal memory and is used to temporarily store operation data in the processor 401 and data exchanged with an external memory 4022 such as a hard disk. The processor 401 exchanges data with the external memory 4022 through the memory 4021.

[0189] In the embodiment of the present application, the memory 402 is specifically used to store application code for executing the solution of the present application, and the execution is controlled by the processor 401. That is, when the computer device 400 is running, the processor 401 communicates with the memory 402 via the bus 403, so that the processor 401 executes the application code stored in the memory 402, thereby performing the method described in any of the aforementioned embodiments.

[0190] The memory 402 may be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc.

[0191] Processor 401 may be an integrated circuit chip with signal processing capabilities. Such processors may be general-purpose processors, including central processing units (CPUs) and network processors (NPs). They may also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. These processors may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor.

[0192] It should be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the computer device 400. In other embodiments of the present application, the computer device 400 may include more or fewer components than shown, or may combine or separate certain components, or arrange the components differently. The components shown in the illustrations may be implemented in hardware, software, or a combination of software and hardware.

[0193] The present disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, executes the steps of the method for adaptive resilient control of a multi-regional power system considering renewable energy disturbances described in the above method embodiment. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0194] An embodiment of the present disclosure also provides a computer program product, which carries program code. The instructions included in the program code can be used to execute the steps of the adaptive elastic control method of a multi-regional power system considering new energy disturbances described in the above method embodiment. For details, please refer to the above method embodiment and will not be repeated here.

[0195] The computer program product may be implemented in hardware, software, or a combination thereof. In one embodiment, the computer program product is implemented as a computer storage medium. In another embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0196] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed system and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0197] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0198] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0199] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0200] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The scope of protection of the present disclosure is not limited thereto. Although the present disclosure has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed in the present disclosure, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure shall be subject to the scope of protection of the claims.

Claims

1. A multi-regional power system adaptive elastic control method considering renewable energy disturbances, characterized in that: include: Establishing an initial multi-regional power system load frequency control model that considers renewable energy and load disturbances; and modeling uncertainties in input parameters of the initial multi-regional power system load frequency control model to obtain an intermediate multi-regional power system load frequency control model; Establishing an actuator fault model; and constructing a target multi-region power system load frequency control model based on the intermediate multi-region power system load frequency control model and the actuator fault model; Designing an adaptive elastic control strategy and applying the adaptive elastic control strategy to the target multi-regional power system load frequency control model to achieve adaptive elastic control of the multi-regional power system considering renewable energy disturbances; The intermediate multi-region power system load frequency control model is expressed as: ； in, It is expressed as the load frequency of power area g; It is represented as the system transfer matrix of power region g; It is represented as the state of the power region g; It represents the status of power area j; N represents the total number of power areas; It is represented as the coupling matrix between power region g and power region j; It is represented as the control input matrix of the power region g; It is represented as the control input of the power region g; It is represented as the disturbance input matrix of the power area g; It is represented by the load and renewable resource disturbances of power region g; It is expressed as the uncertainty of the system transfer matrix of the power area g; It is expressed as the uncertainty of the coupling matrix between power area g and power area j; It is expressed as the uncertainty of the control input matrix of the power area g; The uncertainty of the disturbance input matrix of the power area g is expressed as; The actuator fault model is expressed as: ； ； in, Denote the damaged control input of power region g; It is expressed as the actuator failure coefficient of the power area g; It is expressed as the minimum threshold of actuator failure coefficient in power region g; It is expressed as the maximum threshold of the actuator failure coefficient of the power area g.

2. The method according to claim 1, characterized in that The adaptive elastic control strategy is expressed as: ； ； ； ； ； ； in, It is represented as the gain matrix of the power region g; It is represented as the symmetric matrix to be solved for the power region g; Denoted as the first adaptive parameter; Denoted as the second adaptive parameter; Denoted as the third adaptive parameter; It is expressed as the fourth adaptive parameter; All are normal numbers.

3. The method according to claim 2, characterized in that The applying the adaptive elastic control strategy to the target multi-region power system load frequency control model includes: Constructing an initial gain matrix solution equation, and sorting the initial gain matrix solution equation based on the Schur complement method to obtain a target gain matrix solution equation; Solving the target gain matrix solution equation based on a trial-and-error method to obtain a gain matrix; The target multi-region power system load frequency control model is controlled based on the gain matrix and the adaptive elastic control strategy.

4. The method according to claim 3, characterized in that The initial gain matrix solution equation is expressed as: ； Where c is any positive constant; and is a positive constant that satisfies Young's inequality; Expressed as a coupling matrix The first vector of Expressed as a coupling matrix The second vector of is a symmetric matrix; I is the identity matrix.

5. The method according to claim 4, characterized in that The target gain matrix solution equation is expressed as: ； in, , , , .

6. A multi-regional power system adaptive elastic control device considering new energy disturbances, characterized in that: include: An initial model establishment module is used to establish an initial multi-regional power system load frequency control model that takes into account new energy and load disturbances; and to model the uncertainties in the input parameters of the initial multi-regional power system load frequency control model to obtain an intermediate multi-regional power system load frequency control model; a target model construction module, configured to establish an actuator fault model; and construct a target multi-region power system load frequency control model based on the intermediate multi-region power system load frequency control model and the actuator fault model; a control strategy application module, configured to design an adaptive elastic control strategy and apply the adaptive elastic control strategy to the target multi-regional power system load frequency control model to implement adaptive elastic control of the multi-regional power system considering renewable energy disturbances; The intermediate multi-region power system load frequency control model is expressed as: ； in, It is expressed as the load frequency of power area g; It is represented as the system transfer matrix of power region g; It is represented as the state of the power region g; It represents the status of power area j; N represents the total number of power areas; It is represented as the coupling matrix between power region g and power region j; It is represented as the control input matrix of the power region g; It is represented as the control input of the power region g; It is represented as the disturbance input matrix of the power area g; It is represented by the load and renewable resource disturbances of power region g; It is expressed as the uncertainty of the system transfer matrix of the power area g; It is expressed as the uncertainty of the coupling matrix between power area g and power area j; It is expressed as the uncertainty of the control input matrix of the power area g; The uncertainty of the disturbance input matrix of the power area g is expressed as; The actuator fault model is expressed as: ； ； in, Denote the damaged control input of power region g; It is expressed as the actuator failure coefficient of the power area g; It is expressed as the minimum threshold of actuator failure coefficient in power region g; It is expressed as the maximum threshold of the actuator failure coefficient of the power area g.

7. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

8. A computer device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

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