Adaptive power system frequency control method

Abstract

The invention discloses a self-adaptive power system frequency control method, which belongs to the field of power system frequency modulation control and comprises the following steps: S1, acquiring historical operation data of a networked power system and preprocessing the historical operation data to obtain a historical data set; s2, constructing an adaptive trigger frequency control model based on the dynamic change characteristics of the networked power system; s3, taking the historical data set as the input of the self-adaptive trigger frequency control model for training; s4, constructing a frequency control model verification mechanism based on a Lyapunov method to perform reliability verification on the trained adaptive trigger frequency control model; s5, applying the adaptive trigger frequency control model with qualified reliability verification to the networked power system, and responding to the real-time operation data to obtain a frequency control scheme; according to the method, the problem that the frequency optimization of the power system is insufficient due to the fact that the dynamic change of the power system is not considered in the prior art is solved, and the disturbance suppression capability of the power system is remarkably enhanced.

Description

Technical Field

[0001] The present invention relates to the field of power system frequency modulation control, and in particular to an adaptive power system frequency control method. Background Art

[0002] In recent years, with the large-scale access of electric vehicles to the power grid and the development of power electronics technology, electric vehicles can participate in the primary and secondary frequency regulation of the power system, thereby quickly suppressing the frequency fluctuations of the power system caused by load disturbances. Therefore, the maintenance of the battery charge state is an important performance of the vehicle-grid interactive system. At the same time, in order to consider the needs of car owners, how to balance the control needs of the power grid and the influence of the car owners' personal behavior is a challenging problem for the frequency regulation of the new power system; in addition, due to the bandwidth and energy constraints of microprocessor electronic units, if the power system is not disturbed, the traditional time-triggered control method will inevitably cause a waste of communication resources, and when facing the frequency stabilization control problem of networked power systems containing vehicle-grid interaction, it is usually based on the premise of maintaining the performance of the power system, and unidirectionally considers how to reduce communication resources for transmission, without considering the dynamic changes of the power system, which leads to insufficient optimization of the power system frequency.

[0003] Chinese patent, publication number: CN104836292A, publication date: August 12, 2015, discloses an electric vehicle charging pile control system and method considering the safety of power grid frequency, including a single-phase power input port of the power grid, a voltage transformer, an AD conversion chip, a first data processor, a second data processor, a real-time data display device, a GPS / Beidou signal receiver and a human-computer interaction device; the single-phase power input port of the power grid, the voltage transformer, the AD conversion chip, the first data processor, and the second data processor are connected in sequence; the first data processor is also connected to the real-time data display device and the GPS / Beidou signal receiver respectively; the second data processor communicates with the electric vehicle charging pile and the electric vehicle battery pack respectively; the second data processor is also connected to the human-computer interaction device; and the electric vehicle charging pile control system and method considering the safety of power grid frequency proposed in the invention, based on the performance of the power system, unidirectionally considers how to adjust the power system frequency by reducing the output power of the electric vehicle charging pile, and does not consider the dynamic changes of the power system, resulting in the power system frequency adjustment performed by it not being fully optimized. Summary of the invention

[0004] The purpose of the present invention is to address the problem of insufficient optimization of the frequency of the power system due to the failure to consider the dynamic changes of the power system in the process of frequency regulation and control of the networked power system containing vehicle-grid interaction in the prior art; an adaptive power system frequency control method is proposed, by constructing an adaptive trigger frequency control model based on the dynamic change characteristics of the networked power system, and training it in combination with historical data to ensure that the adaptive trigger frequency control model fully considers the entire process of power system frequency control, and at the same time constructs a frequency control model verification mechanism based on the Lyapunov method to verify the reliability of the adaptive trigger frequency control model, and applies the adaptive trigger frequency control model with qualified reliability verification to the power system and obtains a frequency control scheme with highly optimized communication performance in response to real-time data, fully considers and optimizes the frequency control of the power system, and significantly enhances the disturbance suppression capability of the power system.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is a method for adaptive power system frequency control, comprising the following steps: S1. Acquire historical operation data of the networked power system and perform preprocessing to obtain a historical data set; S2. Construct an adaptive trigger frequency control model based on the dynamic characteristics of networked power systems; S3, using the historical data set as input of the adaptive trigger frequency control model for training; S4. Construct a frequency control model verification mechanism based on the Lyapunov method to verify the reliability of the trained adaptive trigger frequency control model; S5. Apply the adaptive triggered frequency control model with qualified reliability verification to the networked power system and obtain a frequency control solution in response to the real-time operation data of the networked power system.

[0006] In this scheme, the historical operation data of the networked power system is firstly preprocessed to obtain a historical data set, so as to ensure that the data involved in the subsequent training and use of the adaptive trigger frequency control model are all data in the real operating environment of the power system, and effectively improve the generalization ability and accuracy of the adaptive trigger frequency control model; secondly, an adaptive trigger frequency control model is constructed based on the dynamic change characteristics of the networked power system. Starting from the dynamic change characteristics of the networked power system, the entire process of the frequency control of the power system is fully considered, and the adaptive trigger frequency control model is trained using the historical data set to improve the performance of the adaptive trigger frequency control model, and a frequency control model verification mechanism is constructed based on the Lyapunov method to verify the reliability of the trained adaptive trigger frequency control model, so as to ensure that the adaptive trigger frequency control model meets the needs of the actual operating environment of the power system, and can fully consider the entire process of the frequency control of the power system while achieving the effect of fully optimizing the frequency of the power system; finally, the adaptive trigger frequency control model that has passed the reliability verification is applied to the power system and a frequency control scheme with highly optimized communication performance is obtained in response to real-time data, which significantly enhances the disturbance suppression capability of the power system.

[0007] Preferably, in S1, the historical operation data of the networked power system is obtained and preprocessed to obtain the specific process of the historical data set: S11, cleaning the acquired historical frequency data of the networked power system, and marking the cleaned historical frequency data to obtain a historical frequency data set; Synchronously, the acquired historical power data of the networked power system is cleaned, and the cleaned historical power data is marked to obtain a historical power data set; Synchronously, the acquired historical charge data of the networked power system is cleaned, and the cleaned historical charge data is marked to obtain a historical charge data set; S12, integrating the historical frequency data set, the historical power data set, and the historical charge data set to obtain a historical data set.

[0008] In this solution, the historical frequency data, historical power data, and historical charge data of the networked power system are cleaned separately, and the outliers and missing values ​​in the data are removed and repaired to ensure the authenticity and validity of the historical operation data. At the same time, the cleaned historical frequency data, historical power data, and historical charge data are marked separately to facilitate data storage and retrieval.

[0009] Preferably, in S2, the specific process of constructing the adaptive trigger frequency control model based on the dynamic change characteristics of the networked power system is: An adaptive event triggering mechanism is constructed based on the data transmission characteristics of networked power systems; Synchronously, a vehicle-grid frequency coordinated control mechanism is constructed based on the system frequency regulation characteristics of the networked power system.

[0010] In this scheme, an adaptive event triggering mechanism is constructed based on the data transmission characteristics of the networked power system. The adaptive event triggering mechanism can bidirectionally adjust data transmission and control performance through system state change trend, relative state error and adjustment event trigger threshold parameters; at the same time, a vehicle-grid frequency collaborative control mechanism is constructed based on the system frequency regulation characteristics of the networked power system to achieve collaborative control of vehicle-grid frequency.

[0011] Preferably, the specific process of constructing the adaptive event triggering mechanism based on the data transmission characteristics of the networked power system is as follows: A deterministic event triggering unit is constructed based on the sampling moment, and a deterministic data transmission unit is constructed based on the Bernoulli distribution sequence; At the same time, an adaptive event triggering mechanism is constructed based on the deterministic event triggering unit combined with the deterministic data transmission unit.

[0012] In this scheme, a deterministic event trigger unit is constructed based on the sampling time, which can obtain the general distribution of the corresponding measurement output when the event does not occur, fully explore its distribution space, and fully utilize the implicit information of the deterministic event trigger mechanism; and a deterministic data transmission unit is constructed based on the Bernoulli distribution sequence to describe the probability of data loss during transmission, and improve the corresponding measurement information update method, effectively solving the problem of data packet loss caused by limited network communication.

[0013] Preferably, the specific process of constructing the vehicle-grid frequency coordinated control mechanism based on the system frequency regulation characteristics of the networked power system is as follows: A100. Establish the power plant power output deviation formula and electric vehicle aggregate power output formula based on the system frequency regulation characteristics of the networked power system; A200. Construct a vehicle-grid frequency coordinated control mechanism based on the power plant power output deviation formula and the electric vehicle aggregate power output formula.

[0014] In this scheme, based on the system frequency regulation characteristics of the networked power system, a power plant power output deviation formula and an electric vehicle aggregate power output formula are established to obtain an effective control method for the power of the power plant and the aggregate power of the electric vehicle. Based on the power plant power output deviation formula and the electric vehicle aggregate power output formula, a vehicle-grid frequency collaborative control mechanism is constructed to obtain a vehicle-grid frequency collaborative control mechanism that comprehensively considers the factors affecting the frequency change of the networked power system, thereby achieving the effect of effectively regulating the frequency of the power system based on the actual operating environment of the power system.

[0015] Preferably, there are preparatory steps before the step A100 is performed, specifically: Based on the networked power system, a corresponding network transmission interval time-varying delay is defined.

[0016] In this scheme, defining the network transmission interval time-varying delay of the network power system helps to improve the stability and adaptability of the network power system, and effectively optimizes the frequency control strategy of the power system through the transmission interval time-varying delay.

[0017] Preferably, in A100, the specific process of establishing the power plant power output deviation formula based on the system frequency regulation characteristics of the networked power system is: A111. Construct the primary frequency regulation signal of the power plant based on the system frequency regulation characteristics of the networked power system and the droop characteristic parameters of the power plant; A112. Constructing a secondary frequency modulation signal of the power plant based on the primary frequency modulation signal of the power plant and the time-varying delay of the network transmission interval; A113. Constructing a power output deviation formula of the power plant based on the primary frequency modulation signal of the power plant and the secondary frequency modulation signal of the power plant.

[0018] In this solution, a secondary frequency modulation signal of the power plant is constructed based on the primary frequency modulation signal of the power plant combined with the time-varying delay of the network transmission interval, which effectively improves the signal transmission quality during data transmission and reduces the possibility of data packet loss.

[0019] Preferably, in A100, the specific process of establishing the electric vehicle aggregate power output formula based on the network transmission interval time-varying delay is as follows: A121. Construct the primary frequency modulation signal of electric vehicles based on the system frequency modulation characteristics of the networked power system and the droop characteristic parameters of electric vehicles; A122, constructing a secondary frequency modulation signal for the electric vehicle based on the primary frequency modulation signal of the electric vehicle and combining the time-varying time delay of the network transmission interval; A123. Based on the primary frequency modulation signal of the electric vehicle and the secondary frequency modulation signal of the electric vehicle and the dynamic characteristics of the electric vehicle in the networked power system, a formula for the aggregated power output of the electric vehicle is constructed.

[0020] In this solution, the electric vehicle's secondary frequency modulation signal is constructed based on the electric vehicle's primary frequency modulation signal and combined with the network transmission interval time-varying time delay, which effectively improves the signal transmission quality during data transmission and reduces the possibility of data packet loss; and based on the electric vehicle's primary frequency modulation signal and the electric vehicle's secondary frequency modulation signal combined with the dynamic characteristics of the electric vehicle in the networked power system, an electric vehicle aggregate power output formula is constructed, making the electric vehicle's aggregate power control more accurate.

[0021] Preferably, in A200, the specific process of constructing the vehicle-grid frequency coordinated control mechanism based on the power plant power output deviation formula and the electric vehicle aggregate power output formula is as follows: A211. Based on the power plant power output deviation formula and the electric vehicle aggregate power output formula combined with the regional control deviation, a networked power system state space equation with electric vehicle access is constructed; A212. Construct a state feedback controller, and use the constructed state feedback controller to improve the state space equation of the networked power system with electric vehicles connected to obtain a vehicle-grid frequency coordinated control mechanism.

[0022] Preferably, in S4, the specific process of constructing the frequency control model verification mechanism based on the Lyapunov method is: A frequency control model verification mechanism is constructed based on the Lyapunov method combined with positive definite matrices.

[0023] In this scheme, a frequency control model verification mechanism is constructed based on the Lyapunov method combined with a positive definite matrix, and the frequency control model verification mechanism can prove the stability and uncertainty suppression characteristics of the above-mentioned adaptive trigger frequency control model, effectively ensuring the reliability of the adaptive trigger frequency control model.

[0024] The present invention has at least the following substantial effects: Firstly, the historical operation data of the networked power system is preprocessed to obtain a historical data set, so as to ensure that the data involved in the subsequent training and use of the adaptive trigger frequency control model are all data in the real operating environment of the power system, and effectively improve the generalization ability and accuracy of the adaptive trigger frequency control model; secondly, an adaptive trigger frequency control model is constructed based on the dynamic change characteristics of the networked power system. Starting from the dynamic change characteristics of the networked power system, the entire process of the frequency control of the power system is fully considered, and the adaptive trigger frequency control model is trained using the historical data set to improve the performance of the adaptive trigger frequency control model, and a frequency control model verification mechanism is constructed based on the Lyapunov method to verify the reliability of the trained adaptive trigger frequency control model, and the stability and uncertainty suppression characteristics of the adaptive trigger frequency control model are proved to ensure that the adaptive trigger frequency control model is reliable while meeting the needs of the actual operating environment of the power system, and can fully consider the entire process of the frequency control of the power system to achieve the effect of fully optimizing the frequency of the power system; finally, the adaptive trigger frequency control model with qualified reliability verification is applied to the power system and a frequency control scheme with highly optimized communication performance is obtained in response to real-time data, which fully considers and optimizes the frequency control of the power system and significantly enhances the disturbance suppression capability of the power system.

[0025] The above invention content is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0027] Figure 1 The present invention is a flowchart of an adaptive power system frequency control method. DETAILED DESCRIPTION

[0028] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0030] Embodiment 1: like Figure 1 As shown, this embodiment provides an adaptive power system frequency control method, comprising the following steps: S1. Obtain historical operation data of the networked power system and perform preprocessing to obtain a historical data set.

[0031] In one embodiment, in order to prevent abnormal data and missing data from affecting the frequency control of the power system, this embodiment adopts a preprocessing solution, which specifically includes the following steps: S11, cleaning the acquired historical frequency data of the networked power system, and marking the cleaned historical frequency data to obtain a historical frequency data set; Synchronously, the acquired historical power data of the networked power system is cleaned, and the cleaned historical power data is marked to obtain a historical power data set; Synchronously, the acquired historical charge data of the networked power system is cleaned, and the cleaned historical charge data is marked to obtain a historical charge data set; S12, integrating the historical frequency data set, the historical power data set, and the historical charge data set to obtain a historical data set.

[0032] In this embodiment, the acquired historical operation data of the networked power system is cleaned, including removing outliers in the data, and randomly selecting adjacent data at corresponding positions of the outliers and missing values ​​to fill the outliers or missing values, so as to obtain smooth historical operation data.

[0033] S2. Construct an adaptive trigger frequency control model based on the dynamic change characteristics of the networked power system.

[0034] In one embodiment, in order to construct an adaptive trigger frequency control model, this embodiment adopts a dynamic change characteristic scheme of a networked power system, which specifically includes the following steps: An adaptive event triggering mechanism is constructed based on the data transmission characteristics of networked power systems.

[0035] In one embodiment, in order to construct an adaptive event triggering mechanism, this embodiment adopts a data transmission characteristic scheme of a networked power system, which specifically includes the following steps: A deterministic event triggering unit is constructed based on the sampling moment, and a deterministic data transmission unit is constructed based on the Bernoulli distribution sequence; Synchronously, an adaptive event triggering mechanism is constructed based on the deterministic event triggering unit in combination with the deterministic data transmission unit.

[0036] In this embodiment, the sampling time refers to the event release time sequence related to the sensor. Specifically, a communication channel is set on the sensor node side for communication, and the sensor node is set with a certain event trigger to determine whether the sensor measurement update at time k needs to be transmitted, and then the general distribution of the corresponding measurement output when the event does not occur can be obtained through the event trigger, that is, the implicit information of the deterministic event trigger mechanism can be fully utilized; the setting of the certain event trigger is as follows: Among them, i,kis an event trigger set. When the measurement output of the sensor node k at time k belongs to this set, the sensor measurement update does not need to transmit data to the fusion center. That is, when γ i,k = 1, sensor node i will update the measurement y i,k Sent to the fusion center, on the contrary, when γ i,k =0, no information transmission operation will be performed; the event trigger set is set as follows: In the formula, It represents the measurement information transmitted by sensor node i to the fusion center at the last event triggering time, based on which an event release time sequence is derived It represents the release time sequence of sensor node i, and the release time is part of the sampling time, which can be determined by the following formula: in, is the release time, and t is the sampling time; however, due to limited network communication, packet loss may occur in the process of transmitting measurement updates to the fusion center. In order to solve the problem of packet loss, the measurement update received by the fusion center from sensor node i can be described as: The random variable α is used i,k , i∈V is used to describe the random packet loss, and takes the value of "0" or "1" to indicate whether the fusion center receives the measurement information of the local sensor. When the value is "0", it means that the data is lost during the transmission process; assuming that these variables are independent Bernoulli distribution sequences, their probability description is as follows: in, is a known constant, which can be understood as the success rate of the measurement information of sensor node i reaching the fusion center.

[0037] At the same time, a vehicle-grid frequency coordinated control mechanism is constructed based on the system frequency regulation characteristics of the networked power system.

[0038] In one embodiment, in order to construct a vehicle-grid frequency coordinated control mechanism, this embodiment adopts a system frequency modulation characteristic solution of a networked power system, which specifically includes the following steps: A100. Based on the system frequency regulation characteristics of the networked power system, the power plant power output deviation formula and the electric vehicle aggregate power output formula are established.

[0039] In one embodiment, in order to establish the power plant power output deviation formula and the electric vehicle aggregate power output formula, this embodiment adopts a network transmission interval time-varying delay solution before step A100 starts, specifically including: Based on the networked power system, a corresponding network transmission interval time-varying delay is defined.

[0040] In this embodiment, the network transmission interval time-varying delay is associated with the control center, power plant, and electric vehicle aggregation, and d1(t) is defined as the network transmission interval time-varying delay between the control center and the power plant, and d2(t) is defined as the network transmission interval time-varying delay between the control center and the electric vehicle aggregation, which respectively satisfy

[0041] In one embodiment, the process of establishing the power plant power output deviation formula is as follows: A111. Construct the primary frequency regulation signal of the power plant based on the system frequency regulation characteristics of the networked power system and the droop characteristic parameters of the power plant; A112. Constructing a secondary frequency modulation signal of the power plant based on the primary frequency modulation signal of the power plant and the time-varying delay of the network transmission interval; A113. Constructing a power output deviation formula of the power plant based on the primary frequency modulation signal of the power plant and the secondary frequency modulation signal of the power plant.

[0042] In this embodiment, in order to keep the networked power system frequency f(t) within the allowable range, the power output deviation of the power plant turbine is determined by the primary frequency regulation and the secondary frequency regulation power. Specifically, the governor droop characteristic parameter is defined as R g , then the primary frequency modulation signal received by the power plant is: Through the participation factor α g , and the power set point P sent by the control center c (t) is an incremental change, the power plant receives the control signal P cg (t), then in a networked environment, the secondary frequency modulation signal received by the power plant is: ε cg (t) = α g P c (t-d1(t)); Then, the load frequency control signal received by the power plant can be obtained as: ε g (t) = ε cg (t)-ε p (t); In summary, the power output deviation equation of the power plant is as follows:

[0043] In one embodiment, the process of establishing the electric vehicle aggregate power output formula is as follows: A121. Construct the primary frequency modulation signal of electric vehicles based on the system frequency modulation characteristics of the networked power system and the droop characteristic parameters of electric vehicles; A122, constructing a secondary frequency modulation signal for the electric vehicle based on the primary frequency modulation signal of the electric vehicle and combining the time-varying time delay of the network transmission interval; A123. Based on the primary frequency modulation signal of the electric vehicle and the secondary frequency modulation signal of the electric vehicle and the dynamic characteristics of the electric vehicle in the networked power system, a formula for the aggregated power output of the electric vehicle is constructed.

[0044] In this embodiment, in order to keep the networked power system frequency f(t) within the allowable range, the aggregate power output deviation of the electric vehicle is determined by the primary frequency regulation and the secondary frequency regulation power. Specifically, the speed regulator droop characteristic parameter is defined as ρ e , then the primary frequency modulation signal received by the electric vehicle is: ε d (t) = ρ e f(t); Through the participation factor α e , and the power set point P sent by the control center c (t) is an incremental change, the electric vehicle receives the control signal P ce (t), then in a networked environment, the secondary frequency modulation signal received by the electric vehicle is: ε ce (t) = α e P c (t-d2(t)); Then the load frequency control signal received by the electric vehicle can be obtained as: ε e (t) = ε ce (t)-ε d (t); However, electric vehicles usually exist in clusters and participate in the load frequency control of the networked power system in an aggregated form. The networked power system sends control signals to individual electric vehicles through the control center. For the i-th electric vehicle in the cluster, consider the first-order model and time constant T ei , control gain K ei , let T ei =T e , ε ei (t) = ε e (t) / N, then the aggregate power output deviation of electric vehicles is: in, N is the number of electric vehicles. Based on this, the ith electric vehicle can participate in load frequency control under the condition of considering or not considering the battery state of charge. When the battery state of charge environment is not considered, the ith electric vehicle absorbs or releases electric energy to the grid. When considering the battery state of charge environment, the control gain of the i-th electric vehicle is determined by the state of charge calculation module. Specifically, SOC is defined i , SOC low,i , SOC high,i , SOC min,i , SOC max,i Represent the current, known minimum, maximum, known minimum, and maximum battery charge data respectively. Then, the control gain of the i-th electric vehicle is: in, Assume that at time t, there are N electric vehicles participating in load frequency control, of which N1 electric vehicles do not consider the state of charge, and N-N1 electric vehicles consider the state of charge, then the electric vehicle aggregate control gain is: At this time, the EV aggregation gain can be expressed as in It's K e Nominal value, ΔK e It's K e The perturbation value of Where η2=N2 / N, And define η as the maximum value of η2, then the electric vehicle gain can be obtained as: In summary, the formula for the aggregate power output of electric vehicles is:

[0045] A200. Construct a vehicle-grid frequency coordinated control mechanism based on the power plant power output deviation formula and the electric vehicle aggregate power output formula.

[0046] In one embodiment, the construction process of the vehicle-grid frequency coordinated control mechanism is as follows: A211. Based on the power plant power output deviation formula and the electric vehicle aggregate power output formula combined with the regional control deviation, a networked power system state space equation with electric vehicle access is constructed; A212. Construct a state feedback controller, and use the constructed state feedback controller to improve the state space equation of the networked power system with electric vehicles connected to obtain a vehicle-grid frequency coordinated control mechanism.

[0047] In this embodiment, the regional control deviation ACE(t)=bf(t) is used to eliminate the steady-state frequency deviation, and the system state is defined as At the same time, define the output variable as in Therefore, the state space equation of the networked power system with electric vehicles connected is: Where u(t) = P c (t), w(t) = P l (t), ΔA(t)=Hg(t)E, ΔB2(t)=H2g(t)E g ; B1=[0 β4 0 1×(n-2) ],B2=[0 1×3 β5 0 1×(n-4) ],C=[0 1×4 1],E=[1 0 1×4 ], E g =1, H=[0 1×3 -ηβ3 0],H2=[0 1×3 -ηβ5 0], A state feedback controller is constructed and the state space equation of the networked power system with electric vehicles connected is improved to obtain the vehicle-grid frequency coordinated control mechanism. The state feedback controller is as follows: u(t)=Kx(t); The closed-loop model of the networked power system with electric vehicles connected, that is, the vehicle-grid frequency coordinated control mechanism is as follows: Where A1=B1K, A2=B2K, ΔA2=ΔB2(t)K.

[0048] S3. Using the historical data set as input of the adaptive trigger frequency control model for training.

[0049] S4. Based on the Lyapunov method, a frequency control model verification mechanism is constructed to verify the reliability of the trained adaptive trigger frequency control model.

[0050] In one embodiment, in order to verify whether the adaptive trigger frequency control model is reliable, this embodiment adopts the Lyapunov scheme, which specifically includes the following steps: A frequency control model verification mechanism is constructed based on the Lyapunov method combined with positive definite matrices.

[0051] In this embodiment, based on the state of the networked power system, a Lyapunov functional is constructed based on the Lyapunov method combined with a positive definite matrix. The Lyapunov functional constitutes the frequency control model verification mechanism. Specifically, if there is a positive definite matrix with a suitable dimension and meet And any matrix X n ,S n (n=1,2), and given constants h, μ1 and σ, the following inequality is feasible: It can be judged that the adaptive trigger frequency control model is asymptotically stable. In the above inequality,

[0052] In order to increase the persuasiveness of the above inequality, that is, the frequency control model verification mechanism can verify the reliability of the adaptive trigger frequency control model, the LKF method is used here to prove it, as follows: Build the LKF shown: At this time, the LKF derivative is calculated: in, There exist any matrices X1, X2 with: in, The double integral in the above process can be defined as: For any matrices S1 and S2, we have: in, According to the above analysis, we have: in, Obviously, if holds, for sufficiently small scalar ∈, we have Now use Schur's complement lemma, let Equivalent to It can be concluded that as long as the above inequality holds, the adaptive trigger frequency control model is stable and reliable.

[0053] At the same time, the definition transformation is performed to further ensure the reliability of the adaptive trigger frequency control model, as follows: If there is a positive definite matrix with a suitable dimension and meet And any matrix and the symmetric matrix At the same time, given constants h, μ1, σ, and So that the following inequality holds: in, The corresponding controller gain is

[0054] And prove the result of the definition change as follows: Define the following matrix: Use E and E respectively T By left-multiplying and right-multiplying the inequality in the definition transformation, we get: At the same time, define the following matrix: Where n = 1, 2, and Then there is, So far, through the above definitions and changes, it is effectively proved that the adaptive trigger frequency control model is stable and reliable.

[0055] S5. Apply the adaptive triggered frequency control model with qualified reliability verification to the networked power system and obtain a frequency control solution in response to the real-time operation data of the networked power system.

[0056] This embodiment has at least the following substantial effects: Firstly, the historical operation data and real-time operation data of the networked power system are preprocessed to obtain the historical data set and the real-time data set, so as to ensure that the data involved in the subsequent training and use of the adaptive trigger frequency control model are all data in the real operating environment of the power system, and effectively improve the generalization ability and accuracy of the adaptive trigger frequency control model; secondly, the adaptive trigger frequency control model is constructed based on the dynamic change characteristics of the networked power system. Starting from the dynamic change characteristics of the networked power system, the whole process of the frequency control of the power system is fully considered, and the adaptive trigger frequency control model is trained with the historical data set to improve the performance of the adaptive trigger frequency control model, and the frequency control model verification mechanism is constructed based on the Lyapunov method to verify the reliability of the trained adaptive trigger frequency control model, and the stability and uncertainty suppression characteristics of the adaptive trigger frequency control model are proved to ensure that the adaptive trigger frequency control model is reliable and meets the needs of the actual operating environment of the power system, and can fully consider the whole process of the frequency control of the power system to achieve the effect of fully optimizing the frequency of the power system; finally, the real-time data set is input into the adaptive trigger frequency control model to obtain and execute the frequency control scheme with full optimization of the frequency of the power system.

[0057] The above specific embodiments are preferred embodiments of the present invention, and are not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the present specific embodiments. All equivalent changes made in accordance with the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. An adaptive power system frequency control method, characterized in that: The method comprises the following steps: S1. Acquire historical operation data of the networked power system and perform preprocessing to obtain a historical data set; S2. Construct an adaptive trigger frequency control model based on the dynamic characteristics of networked power systems; S3, using the historical data set as input of the adaptive trigger frequency control model for training; S4. Construct a frequency control model verification mechanism based on the Lyapunov method to verify the reliability of the trained adaptive trigger frequency control model; S5. Apply the adaptive triggered frequency control model with qualified reliability verification to the networked power system and obtain a frequency control solution in response to the real-time operation data of the networked power system.

2. The adaptive power system frequency control method according to claim 1, characterized in that: In S1, the historical operation data of the networked power system is obtained and preprocessed to obtain the specific process of the historical data set: S11, cleaning the acquired historical frequency data of the networked power system, and marking the cleaned historical frequency data to obtain a historical frequency data set; Synchronously, the acquired historical power data of the networked power system is cleaned, and the cleaned historical power data is marked to obtain a historical power data set; Synchronously, the acquired historical charge data of the networked power system is cleaned, and the cleaned historical charge data is marked to obtain a historical charge data set; S12, integrating the historical frequency data set, the historical power data set, and the historical charge data set to obtain a historical data set.

3. The adaptive power system frequency control method according to claim 1, characterized in that: In S2, the specific process of constructing an adaptive trigger frequency control model based on the dynamic change characteristics of the networked power system is as follows: An adaptive event triggering mechanism is constructed based on the data transmission characteristics of networked power systems; Synchronously, a vehicle-grid frequency coordinated control mechanism is constructed based on the system frequency regulation characteristics of the networked power system.

4. The adaptive power system frequency control method according to claim 3, characterized in that: The specific process of constructing the adaptive event triggering mechanism based on the data transmission characteristics of the networked power system is as follows: A deterministic event triggering unit is constructed based on the sampling moment, and a deterministic data transmission unit is constructed based on the Bernoulli distribution sequence; At the same time, an adaptive event triggering mechanism is constructed based on the deterministic event triggering unit combined with the deterministic data transmission unit.

5. The adaptive power system frequency control method according to claim 3, characterized in that: The specific process of constructing the vehicle-grid frequency coordinated control mechanism based on the system frequency regulation characteristics of the networked power system is as follows: A100. Establish the power plant power output deviation formula and electric vehicle aggregate power output formula based on the system frequency regulation characteristics of the networked power system; A200. Construct a vehicle-grid frequency coordinated control mechanism based on the power plant power output deviation formula and the electric vehicle aggregate power output formula.

6. The adaptive power system frequency control method according to claim 5, characterized in that: Before the step A100 is executed, there are preparatory steps, specifically: Based on the networked power system, a corresponding network transmission interval time-varying delay is defined.

7. The adaptive power system frequency control method according to claim 6, characterized in that: In A100, the specific process of establishing the power plant power output deviation formula based on the system frequency regulation characteristics of the networked power system is as follows: A111. Construct the primary frequency regulation signal of the power plant based on the system frequency regulation characteristics of the networked power system and the droop characteristic parameters of the power plant; A112. Constructing a secondary frequency modulation signal of the power plant based on the primary frequency modulation signal of the power plant and the time-varying time delay of the network transmission interval; A113. Construct a power plant power output deviation formula based on the power plant primary frequency modulation signal and the power plant secondary frequency modulation signal.

8. The adaptive power system frequency control method according to claim 6, characterized in that: In the A100, the specific process of establishing the electric vehicle aggregate power output formula based on the network transmission interval time-varying delay is as follows: A121. Construct the primary frequency modulation signal of electric vehicles based on the system frequency modulation characteristics of the networked power system and the droop characteristic parameters of electric vehicles; A122, constructing a secondary frequency modulation signal for the electric vehicle based on the primary frequency modulation signal of the electric vehicle and combining the time-varying time delay of the network transmission interval; A123. Based on the primary frequency modulation signal of the electric vehicle and the secondary frequency modulation signal of the electric vehicle and the dynamic characteristics of the electric vehicle in the networked power system, a formula for the aggregated power output of the electric vehicle is constructed.

9. The adaptive power system frequency control method according to claim 6, characterized in that: In A200, the specific process of constructing the vehicle-grid frequency coordinated control mechanism based on the power plant power output deviation formula and the electric vehicle aggregate power output formula is as follows: A211. Based on the power plant power output deviation formula and the electric vehicle aggregate power output formula combined with the regional control deviation, a networked power system state space equation with electric vehicle access is constructed; A212. Construct a state feedback controller, and use the constructed state feedback controller to improve the state space equation of the networked power system with electric vehicles connected to obtain a vehicle-grid frequency coordinated control mechanism.

10. The adaptive power system frequency control method according to claim 1, characterized in that: In S4, the specific process of constructing the frequency control model verification mechanism based on the Lyapunov method is as follows: A frequency control model verification mechanism is constructed based on the Lyapunov method combined with positive definite matrices.

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