Multi-region power system adaptive elastic control method considering new energy disturbance

Abstract

The invention relates to the technical field of electric power systems, and provides a multi-region electric power system adaptive elastic control method considering new energy disturbance, which comprises the following steps: establishing an initial multi-region electric power system load frequency control model considering new energy and load disturbance; carrying out modeling on uncertain quantities in input parameters of the model to obtain a middle multi-region power system load frequency control model; establishing an actuator fault model; constructing a target multi-region power system load frequency control model based on the model and a middle multi-region power system load frequency control model; and designing a self-adaptive elastic control strategy, and applying the strategy to the target multi-region power system load frequency control model to realize self-adaptive elastic control of the multi-region power system considering new energy disturbance. According to the invention, the overcompensation phenomenon of the conventional adaptive control strategy is made up through the improved adaptive control strategy, the system can reach asymptotic stability, and the stability and reliability of the multi-region power system are improved.

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-regional power system taking into account new energy disturbances. Background Art

[0002] As global energy demand grows, the scale and structure of power grids become increasingly complex, and power systems are moving towards multi-regional interconnection. This change not only expands the scope of power supply, but also makes the management objectives and requirements of power systems more complex. In order to ensure the reliability and stability of power systems under disturbances, frequency load control has become an important dispatching method. Its main goal is to maintain the frequency of each control area at a predetermined value by adjusting the power set point when a disturbance occurs, thereby ensuring the stable operation of multi-regional power systems and preventing frequency deviations from affecting system safety and power quality.

[0003] Although frequency load control methods have made some progress, existing technologies face some challenges as the proportion of renewable energy generation increases. First, the volatility of renewable energy (such as wind power and solar energy) is large, which increases the difficulty of stabilizing the frequency of the power grid, and existing studies have not fully considered the impact of renewable energy on multi-regional power systems, which limits the effect of frequency load control when the proportion of renewable energy is high. Secondly, there is a significant coupling relationship between the regions of the multi-regional power system, but many studies ignore this or simplify the coupling as noise. This processing method cannot accurately reflect the actual situation, resulting in inaccurate control strategies. Finally, for the processing of actuator faults in multi-regional power systems, sliding film control and traditional adaptive control are currently mainly used, but sliding film control has a jitter phenomenon and is difficult to cope with complex fault scenarios; while traditional adaptive control is prone to overcompensation, which cannot effectively achieve the progressive stability of the system and cannot meet the high reliability requirements of multi-regional power systems. Summary of the invention

[0004] The disclosed embodiments 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 embodiment of the present disclosure provides a multi-regional power system adaptive elastic control method considering new energy disturbance, including: Establishing an initial multi-regional power system load frequency control model considering new 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-regional power system load frequency control model based on the intermediate multi-regional power system load frequency control model and the actuator fault model; 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.

[0006] In some possible embodiments, the intermediate multi-regional 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 area g; It is represented as the state of the power region g; It represents the state of power area j; N represents the total number of power areas; It is represented as the coupling matrix between power area g and power area j; It is represented as the control input matrix of power area g; It is represented as the control input of the power area g; It is represented as the disturbance input matrix of the power area g; It is represented by the load and renewable resource disturbances in power region g; It is expressed as the uncertainty of the system transfer matrix of power area g; It is expressed as the uncertainty of the coupling matrix between power area g and power area j; It is represented as the uncertainty of the control input matrix of the power area g; It is represented as the uncertainty of the disturbance input matrix of the power area g.

[0007] In some possible embodiments, the actuator fault model is expressed as: ; ; in, Denoted as the damaged control input of power region g; It is expressed as the actuator failure coefficient of power area g; It is represented as the minimum threshold of actuator failure coefficient in power area g; It is expressed as the maximum threshold of the actuator failure coefficient of the power area g.

[0008] In some possible embodiments, 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 area g; Denoted as the first adaptive parameter; is represented as the second adaptive parameter; Denoted as the third adaptive parameter; It is represented as the fourth adaptive parameter; All are normal numbers.

[0009] In some possible embodiments, applying the adaptive elastic control strategy to the target multi-regional power system load frequency control model includes: Constructing an initial gain matrix solution equation, and rearranging 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-regional power system load frequency control model is controlled based on the gain matrix and the adaptive elastic control strategy.

[0010] In some possible embodiments, the initial gain matrix solution equation is expressed as: ; Where c represents any positive constant; and is a positive constant that satisfies Young's inequality; Expressed as the component coupling matrix The first vector of Expressed as the component coupling matrix The second vector of is a symmetric matrix; I is the identity matrix.

[0011] In some possible embodiments, the target gain matrix solution equation is expressed as: ; in, , , , .

[0012] The embodiment of the present disclosure provides a multi-regional power system adaptive elastic control device considering new energy disturbance, including: An initial model building 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 building module, used to establish an actuator fault model; and to build 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; 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.

[0013] In some possible embodiments, the intermediate multi-regional 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 area g; It is represented as the state of the power region g; It represents the state of power area j; N represents the total number of power areas; It is represented as the coupling matrix between power area g and power area j; It is represented as the control input matrix of power area g; It is represented as the control input of power area g; It is represented as the disturbance input matrix of the power area g; It is represented by the load and renewable resource disturbances in power region g; It is expressed as the uncertainty of the system transfer matrix of power area g; It is expressed as the uncertainty of the coupling matrix between power area g and power area j; It is represented as the uncertainty of the control input matrix of the power area g; It is represented as the uncertainty of the disturbance input matrix of the power area g.

[0014] In some possible embodiments, the actuator fault model is expressed as: ; ; in, Denoted as the damaged control input of power region g; It is expressed as the actuator failure coefficient of power area g; It is represented as the minimum threshold of actuator failure coefficient in power area g; It is expressed as the maximum threshold of the actuator failure coefficient of the power area g.

[0015] In some possible embodiments, 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 area 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 area g; Denoted as the first adaptive parameter; is represented as the second adaptive parameter; Denoted as the third adaptive parameter; It is represented as the fourth adaptive parameter; All are normal numbers.

[0016] In some possible embodiments, the control strategy application module is specifically used to: Constructing an initial gain matrix solution equation, and rearranging 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-regional power system load frequency control model is controlled based on the gain matrix and the adaptive elastic control strategy.

[0017] In some possible embodiments, 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 the component coupling matrix The first vector of Expressed as the component coupling matrix The second vector of is a symmetric matrix; I is the identity matrix.

[0018] In some possible embodiments, the target gain matrix solution equation is expressed as: ; in, , , , .

[0019] An embodiment of the present disclosure provides a computer device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and 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, an adaptive elastic control method for a multi-regional power system taking into account new energy disturbances as described in any possible implementation manner described above is performed.

[0020] 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 possible implementation manner described above is implemented.

[0021] 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.

[0022] 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.

[0023] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required to be cited in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification 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 ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A flow chart of a multi-regional power system adaptive elastic control method considering new energy disturbances provided by an embodiment of the present disclosure is shown; Figure 2 A flow chart of a gain matrix solving method provided by an embodiment of the present disclosure is shown; Figure 3 A schematic diagram of the structure of a multi-regional power system adaptive elastic control device considering new energy disturbances provided by an embodiment of the present disclosure is shown; Figure 4 A schematic diagram of the structure of a computer device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the technical scheme 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, rather than all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings here 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 present disclosure for protection, but merely represents the 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 belong to the scope of protection of the present disclosure.

[0027] 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, further definition and explanation thereof is not required in subsequent drawings.

[0028] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0029] As the scale of power grids expands and the complexity of their structures deepens, power systems are developing towards multi-regional interconnection and need to manage more and more targets. In order to achieve reliable and stable operation of multi-regional power systems, researchers are committed to studying frequency load control methods. The main goal of frequency load control is to maintain the frequency of each control area at a predetermined value by adjusting the power set point when a disturbance occurs.

[0030] In recent years, the proportion of renewable energy in the power system has been increasing, and the volatility of renewable energy generation is large, which will have an adverse effect on the quality of power. Therefore, the impact of renewable energy disturbances needs to be considered when controlling multi-regional power systems. Studies have found that actuators execute a large number of control signals and play a vital role in multi-regional power systems. Their failures may damage system performance and have an adverse effect on system stability and safety. However, actuator failures are inevitable due to certain external factors. In addition, there is coupling between the various regions of a multi-regional power system. However, many scholars currently ignore the coupling between regions or directly regard the coupling as noise when studying multi-regional power systems, which is not conducive to analyzing practical problems.

[0031] Based on the above research, an adaptive elastic control method for a multi-regional power system taking into account new energy disturbances is provided in an embodiment of the present disclosure. 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 asymptotic stability, effectively improving the stability and reliability of the power system.

[0032] To facilitate understanding of this embodiment, the execution subject of the adaptive elastic control method for a multi-regional power system considering new energy disturbances provided in the embodiment of the present disclosure is first introduced in detail. The execution subject of the adaptive elastic control method for a multi-regional power system considering new energy disturbances provided in the embodiment of the present disclosure is a computer device. The computer device may be a server. Among them, the server may be an independent physical server, or 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.

[0033] The following is a detailed description of the multi-regional power system adaptive elastic control method considering new energy disturbances provided by the embodiment of the present application in conjunction with the accompanying drawings. Figure 1 As shown, it is a flow chart of a method for adaptive elastic control of a multi-regional power system considering new energy disturbance provided by an embodiment of the present disclosure, and the method includes the following S101-S103: 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.

[0034] It is understandable that new energy refers to renewable energy such as wind energy, solar energy, hydropower, and biomass energy. In the power system, due to the unpredictability and volatility of new energy generation, they often bring disturbances to the power system; while load disturbance refers to the changes in load demand in the power system, including periodic changes (such as day and night changes, seasonal changes) and random changes (such as load surges caused by emergencies). A multi-regional power system refers to a system composed of multiple geographically dispersed power networks interconnected by transmission lines, with power exchange between regions.

[0035] Here, the present disclosure first constructs a model that can effectively describe these disturbances in each regional power system (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: ; In the formula, ; ; ; ; ; in, It is expressed as the load frequency of power area g; It is represented as the system transfer matrix of power area g; It is represented as the state of the power region g; It represents the state of power area j; N represents the total number of power areas; It is represented as the coupling matrix between power area g and power area j; It is represented as the control input matrix of power area g; It is represented as the control input of the power area g; It is represented as the disturbance input matrix of the power area g; It is represented by the load and renewable resource disturbances in power region g; It is expressed as the frequency deviation coefficient of power area 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 power region g; Expressed as the governor time constant for power region g; It is expressed as the frequency variation of power area 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; It is expressed as the integral control variation of the power region g; It is expressed as the rotor angle derivative variation in the power area g; Expressed as the speed adjustment coefficient of the power area g.

[0036] Furthermore, since renewable energy generation (such as wind and solar energy) is volatile and unpredictable, and its power generation is greatly affected by factors such as weather, season, and geographical location, these uncertainties may cause large load frequency fluctuations in the power system. Therefore, in order to cope with the impact of the uncertainty of renewable energy on the load frequency, the present disclosure models the uncertainty in the model, that is, models the uncertainty 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: ; in, It is expressed as the uncertainty of the system transfer matrix of power area g; It is expressed as the uncertainty of the coupling matrix between power area g and power area j; It is represented as the uncertainty of the control input matrix of the power area g; It is represented as the uncertainty of the disturbance input matrix of the power area g.

[0037] For the above formula, we can make ; At this point, the above model can be integrated into: ; in, can be written as: ; In the formula, , is a normal number; Expressed as the component coupling matrix The first vector of Expressed as the component coupling matrix The second vector of .

[0038] 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.

[0039] It is understandable that in the power system, an actuator refers to a physical device that can respond to a control signal and change the system state, such as a speed regulator of a generator set, a load adjustment device, etc. If an actuator fails, it may cause the control system to be unable to adjust the power generation in time, thereby affecting the system frequency stability. In this regard, the present disclosure proposes to establish an actuator fault model to describe the behavior of the actuator under different fault modes (such as complete failure, partial failure, delayed response, etc.).

[0040] Here, the actuator fault model can be expressed as: ; ; in, Denoted as the damaged control input of power region g; It is expressed as the actuator failure coefficient of power area g; It is represented as the minimum threshold of actuator failure coefficient in power area g; It is expressed as the maximum threshold of the actuator failure coefficient of the power area g.

[0041] 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 new energy and load disturbances, but also incorporates the uncertainty of actuator faults. In this way, the target model can more accurately reflect the operating conditions of the actual power system, especially in the face of various sudden faults and disturbances.

[0042] 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 achieve adaptive elastic control of the multi-regional power system considering new energy disturbances.

[0043] Here, the core idea of ​​the adaptive elastic control strategy is to adjust the parameters of the control strategy in real time so that the system can flexibly respond to the changing operating environment, 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.

[0044] Specifically, the adaptive elastic control strategy proposed in the present disclosure can be expressed as: ; in, Expressed as the gain matrix of 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 represented as the symmetric matrix to be solved for the power area g, which is determined as follows: ; ; in, are all normal numbers; and The existence of is to eliminate the transition compensation phenomenon, which can be expressed as: ; ; in, is represented as the second adaptive parameter; It is represented as the fourth adaptive parameter; are all positive numbers. In addition, ; .

[0045] Here, the elastic control strategy proposed in this disclosure has a smaller control input energy and is easier to satisfy in actual systems. and The high-frequency content inherent in the control system is removed while maintaining the asymptotic convergence of the problem under consideration. In addition, the elastic control strategy proposed in the present disclosure can also cope with multiple scenarios at the same time, including actuator failures, attacks, and matching and mismatching uncertainties in the system. This comprehensive control strategy makes the system more adaptable and robust, and can maintain efficient and stable operation in the face of complex actual situations.

[0046] Specifically, the parameters mentioned in step S101 above are represents uncertainty, assuming it consists of matching uncertainty and mismatch uncertainty Composition, that is Here, the matching uncertainty and mismatching uncertainty are determined 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 in the strategy proposed in this application: When handling matching uncertainty, and Replace with , and then deal with the problem of result matching uncertainty.

[0047] For example, refer to Figure 2As shown, after the adaptive elastic control strategy is designed, when it is applied to the target multi-regional power system load frequency control model, the following steps S201-S203 may be included: 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.

[0048] It can be understood that in the load frequency control of multi-region power systems, the gain matrix is ​​a matrix used to describe the relationship between input and output, which contains the control gain values ​​for each region of the system. The function of the gain matrix is ​​to transmit the control signal to each region and adjust the control output of the system according to its dynamic characteristics. In a multi-region power system, each region may have different control requirements, so the design of the gain matrix needs to comprehensively consider the specific requirements of each region and the operation of the system.

[0049] Here, the initial gain matrix solution equation can be expressed as: ; Where c is any positive constant; and is a positive constant that satisfies Young's inequality; Expressed as the component coupling matrix The first vector of Expressed as the component coupling matrix The second vector of is a symmetric matrix; I is the identity matrix.

[0050] It is understandable that Schur complement is an important matrix decomposition method in linear algebra, which is mainly used to decompose complex matrix solution problems into simpler sub-problems. In system design, the Schur complement method is usually used to deal with coupling problems in distributed systems (multi-regional power systems in this application) to help simplify the solution process of the gain matrix. Through this method, the computational complexity can be effectively reduced and the solution efficiency can be improved, so that the target gain matrix solution equation can be obtained more efficiently.

[0051] 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: ; in, , , , .

[0052] S202, solving the target gain matrix solution equation based on a trial and error method to obtain a gain matrix.

[0053] It is understandable that after rearranging the initial gain matrix solution equation based on the Schur complement method, the original formula is only and Specifically, when solving the gain matrix, the present 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 control system, and then these parameters are gradually adjusted by comparing with the actual performance 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 may not rely on a complete system model or precise mathematical description, but gradually approach the optimal solution through feedback and adjustment, and is suitable for processing some complex or incompletely predictable systems.

[0054] In some other embodiments, the gain matrix may also be solved using machine learning, optimization algorithms, and other methods, which are not specifically limited here.

[0055] S203: Control the target multi-region power system load frequency control model based on the gain matrix and the adaptive elastic control strategy.

[0056] 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.

[0057] In order to verify the control performance of the adaptive elastic control strategy proposed in the present disclosure on the multi-regional power system considering the new energy disturbance, the present disclosure also proposes to establish a Lyapunov function to verify the stability of the multi-regional power system containing new energy disturbances and actuator failures. The Lyapunov stability theory is a theory used to analyze the stability of the system. The theory can be applied to the stability analysis of linear systems 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 the "energy function", namely the Lyapunov function. If the Lyapunov function of the system meets certain conditions, such as being positive and having negative derivatives, then it can be judged that the system is stable or asymptotically stable at the equilibrium point.

[0058] Specifically, the Lyapunov function of the g-th power region can be expressed as: ; ; ; exist middle, ; ; right The derivation shows that, ; right The derivation shows that, ; Combination and The derivative of shows that: ; There must be a parameter satisfy: ; Then we can get: ; From this we can conclude that: ; According to the above steps S201 to S203, it can be obtained that: .

[0059] Furthermore, according to Lyapunov stability theory, the elastic control strategy proposed in the present invention can make the multi-regional power system reach asymptotic stability.

[0060] 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 asymptotic stability, effectively improving the stability and reliability of the power system.

[0061] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0062] 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 multi-regional power system adaptive elastic control method considering renewable energy disturbances mentioned above 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.

[0063] 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: The initial model building module 301 is used to build an initial multi-regional power system load frequency control model considering 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 building module 302 is used to establish an actuator fault model; and to build 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; 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.

[0064] In some possible embodiments, the intermediate multi-regional 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 area g; It is represented as the state of the power region g; It represents the state of power area j; N represents the total number of power areas; It is represented as the coupling matrix between power area g and power area j; It is represented as the control input matrix of power area g; It is represented as the control input of the power area g; It is represented as the disturbance input matrix of the power area g; It is represented by the load and renewable resource disturbances in power region g; It is expressed as the uncertainty of the system transfer matrix of power area g; It is expressed as the uncertainty of the coupling matrix between power area g and power area j; It is represented as the uncertainty of the control input matrix of the power area g; It is represented as the uncertainty of the disturbance input matrix of the power area g.

[0065] In some possible embodiments, the actuator fault model is expressed as: ; ; in, Denoted as the damaged control input of power region g; It is expressed as the actuator failure coefficient of power area g; It is represented as the minimum threshold of actuator failure coefficient in power area g; It is expressed as the maximum threshold of the actuator failure coefficient of the power area g.

[0066] In some possible embodiments, 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 area 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 area g; Denoted as the first adaptive parameter; is represented as the second adaptive parameter; Denoted as the third adaptive parameter; It is represented as the fourth adaptive parameter; All are normal numbers.

[0067] In some possible embodiments, the control strategy application module 303 is specifically used to: Constructing an initial gain matrix solution equation, and rearranging 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-regional power system load frequency control model is controlled based on the gain matrix and the adaptive elastic control strategy.

[0068] In some possible embodiments, 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 the component coupling matrix The first vector of Expressed as the component coupling matrix The second vector of is a symmetric matrix; I is the identity matrix.

[0069] In some possible embodiments, the target gain matrix solution equation is expressed as: ; in, , , , .

[0070] Based on the same technical concept, the embodiment of the present disclosure also provides a computer device. Figure 4 4 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, including a memory 4021 and an external memory 4022; the memory 4021 is also called an internal memory, which is used to temporarily store the operation data in the processor 401 and the data exchanged with the external memory 4022 such as a hard disk. The processor 401 exchanges data with the external memory 4022 through the memory 4021.

[0071] In the embodiment of the present application, the memory 402 is specifically used to store the 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 through the bus 403, so that the processor 401 executes the application code stored in the memory 402, and then executes the method described in any of the above embodiments.

[0072] Among them, the memory 402 can be, but is not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

[0073] Processor 401 may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0074] It is to be understood that the structure illustrated in the embodiment 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 in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0075] The embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the multi-regional power system adaptive elastic control method considering new energy disturbances described in the above method embodiment are executed. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0076] The embodiments of the present disclosure also provide a computer program product, which carries a 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, which will not be repeated here.

[0077] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0078] Those skilled in the art can clearly understand that, for the convenience and simplicity 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.

[0079] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] 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.

[0081] 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, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform 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 codes, 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.

[0082] 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 protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope 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 considering new 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-regional power system load frequency control model based on the intermediate multi-regional power system load frequency control model and the actuator fault model; 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.

2. The method according to claim 1, characterized in that The intermediate multi-regional 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 area g; It is represented as the state of the power region g; It represents the state of power area j; N represents the total number of power areas; It is represented as the coupling matrix between power area g and power area j; It is represented as the control input matrix of power area g; It is represented as the control input of the power area g; It is represented as the disturbance input matrix of the power area g; It is represented by the load and renewable resource disturbances in power region g; It is expressed as the uncertainty of the system transfer matrix of power area g; It is expressed as the uncertainty of the coupling matrix between power area g and power area j; It is represented as the uncertainty of the control input matrix of the power area g; It is represented as the uncertainty of the disturbance input matrix of the power area g.

3. The method according to claim 2, characterized in that The actuator fault model is expressed as: ； ； in, Denoted as the damaged control input of power region g; It is expressed as the actuator failure coefficient of power area g; It is represented as the minimum threshold of actuator failure coefficient in power area g; It is expressed as the maximum threshold of the actuator failure coefficient of the power area g.

4. The method according to claim 3, 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 area g; Denoted as the first adaptive parameter; is represented as the second adaptive parameter; Denoted as the third adaptive parameter; It is represented as the fourth adaptive parameter; All are normal numbers.

5. The method according to claim 4, characterized in that The applying the adaptive elastic control strategy to the target multi-regional power system load frequency control model comprises: Constructing an initial gain matrix solution equation, and rearranging 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-regional power system load frequency control model is controlled based on the gain matrix and the adaptive elastic control strategy.

6. The method according to claim 5, 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 the component coupling matrix The first vector of Expressed as the component coupling matrix The second vector of is a symmetric matrix; I is the identity matrix.

7. The method according to claim 6, characterized in that The target gain matrix solution equation is expressed as: ； in, , , , 。 8. A multi-regional power system adaptive elastic control device considering new energy disturbances, characterized in that: include: An initial model building 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 building module, used to establish an actuator fault model; and to build 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; 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.

9. 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 7 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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