Method and device for controllable load resources to participate in primary frequency modulation of power system

Abstract

The invention relates to the technical field of power system automatic control, and discloses a primary frequency modulation method and device for controllable load resources to participate in a power system, and the method comprises the steps: obtaining the load state data of the power system, carrying out the multi-dimensional load clustering of the load state data, and obtaining a load feature time sequence data set; based on the load characteristic time sequence data set, establishing a Koopman operator high-dimensional linear model of the power system by using an extended dynamic mode decomposition algorithm; performing optimization solution on the load state of the power system by using the Koopman operator high-dimensional linear model of the power system to obtain an optimal load control sequence; and performing frequency adjustment on controllable load resources participating in the power system by using the optimal load control sequence. According to the method, the stable prediction of the load state of the power system and the accurate adjustment of the frequency in the controllable load resources participating in the power system are realized, and the operation efficiency and the stability of the power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control of electric power systems, and in particular to a method and device for controlling load resources to participate in primary frequency regulation of an electric power system. Background Art

[0002] The power grid is a huge inertial system. According to the rotor motion equation, when the active power of the power grid is insufficient, the generator rotor accelerates and the power grid frequency increases, and vice versa. Therefore, the primary frequency regulation function is one of the means to dynamically ensure the active power balance of the power grid.

[0003] The power system relies on the speed regulator on the generator side to maintain the stability of the system frequency. However, with the large-scale access of distributed energy resources (DERs), the inertia of the power system decreases, resulting in faster acceleration or deceleration of the machine rotor during disturbance events. Summary of the invention

[0004] In view of this, the present invention provides a method and device for controlling load resources to participate in primary frequency regulation of an electric power system, so as to solve the problems of reduced inertia and insufficient frequency stability of the electric power system caused by the large-scale access of distributed energy resources.

[0005] In a first aspect, the present invention provides a method for controlling load resources to participate in primary frequency regulation of a power system, the method comprising:

[0006] Obtain the load status data of the power system, perform multi-dimensional load clustering on the load status data, and obtain a load characteristic time series data set;

[0007] Based on the load characteristic time series data set, the Koopman operator high-dimensional linear model of the power system is established using the extended dynamic mode decomposition algorithm;

[0008] The Koopman operator high-dimensional linear model of the power system is used to optimize the load state of the power system and obtain the optimal load control sequence;

[0009] The optimal load control sequence is used to adjust the frequency of controllable load resources participating in the power system.

[0010] The present embodiment provides a method for controlling load resources to participate in primary frequency regulation of an electric power system. By performing multi-dimensional load clustering on load status data, the accurate distribution of the load of the electric power system in the load characteristic time series data set is ensured, and the coordination and stability of the load are ensured. Then, based on the load characteristic time series data set, a Koopman operator high-dimensional linear model of the electric power system is established by using an extended dynamic mode decomposition algorithm. The Koopman operator high-dimensional linear model of the electric power system is used to map complex nonlinear load dynamics into a high-dimensional linear space, thereby simplifying the prediction complexity of the load state of the electric power system and realizing global linearization of each load state. Finally, an optimal load control sequence is obtained by optimizing and solving the load state of the electric power system. The optimal load control sequence is used to perform frequency regulation on the controllable load resources participating in the electric power system, thereby realizing stable prediction of the load state of the electric power system and accurate regulation of the frequency in the controllable load resources participating in the electric power system, thereby improving the operating efficiency and stability of the electric power system.

[0011] In an optional implementation, multi-dimensional load clustering is performed on the load state data to obtain a load characteristic time series data set, including:

[0012] Normalize the load status data of the power system;

[0013] Clustering the normalized load state data to obtain an initial load feature group;

[0014] The initial load characteristic group is adaptively adjusted to obtain a load characteristic time series data set.

[0015] The present embodiment provides a method for controlling load resources to participate in the primary frequency regulation of the power system. By normalizing the load state data of the power system, it is ensured that each characteristic data in the load characteristic time series data set has the same weight in the subsequent model construction process, avoiding the prediction error amplification caused by the magnitude difference between the load characteristics; and by clustering the load state data after normalization, it is ensured that the load characteristics in each initial load characteristic group are similar, while the initial load characteristic groups have large differences, which is convenient for the subsequent distributed optimization and regulation of the power system load; finally, by adaptively adjusting the initial load characteristic group, the load in the load characteristic time series data set responds synchronously, ensuring that the load management of the power system is always in the optimal state, and improving the coordination and stability of the power system load. Furthermore, by dynamically adjusting the group, a consistency control strategy is introduced to make the loads in the group respond synchronously, ensuring the coordination of the load and the stability of the power grid, and realizing efficient management of the power system load.

[0016] In an optional implementation, based on the load characteristic time series data set, an extended dynamic mode decomposition algorithm is used to establish a Koopman operator high-dimensional linear model of the power system, including:

[0017] The load characteristic time series data set is embedded into delayed coordinates to obtain a high-dimensional state vector;

[0018] The high-dimensional state vector is selected as the basis function, and the basis function is fitted using the least squares method to obtain the Koopman operator;

[0019] The control input matrix and the control variables are obtained, and a Koopman operator high-dimensional linear model of the power system is established based on the Koopman operator, the control input matrix and the control variables.

[0020] The present embodiment provides a method for controllable load resources to participate in the primary frequency regulation of the power system. By embedding delayed coordinates, the one-dimensional time series data in the load characteristic time series data set is converted into a state representation in a multi-dimensional space, so that the nonlinear dynamics of the power system is more linear in the high-dimensional space, and a reliable data basis is provided for the subsequent basis function selection and extended dynamic mode decomposition algorithm; and the basis function is fitted by the least squares method to obtain the Koopman operator, which realizes the accurate mapping of the linear relationship between the load states, makes the Koopman operator high-dimensional linear model more accurate, and realizes the linear description and dynamic prediction of the load state of the power system.

[0021] In an optional implementation, based on the load characteristic time series data set, an extended dynamic mode decomposition algorithm is used to establish a Koopman operator high-dimensional linear model of the power system, which also includes:

[0022] The validity of the basis function is verified, and the basis function is optimized based on the verification results.

[0023] The present embodiment provides a method for controllable load resources to participate in the primary frequency regulation of the power system. By verifying the validity of the basis function, the linear validity of the Koopman operator high-dimensional linear model of the power system is ensured, so that the optimized basis function can effectively capture the dynamic behavior of the power system, thereby realizing the linear description and dynamic prediction of the load state of the power system.

[0024] In an optional implementation, the Koopman operator high-dimensional linear model of the power system is used to optimize and solve the load state of the power system to obtain an optimal load control sequence, including:

[0025] Collect the current power system load state data, input the current power system load state data into the Koopman operator high-dimensional linear model of the power system, and obtain the power system load state control sequence at the prediction time;

[0026] Optimizing the load state control sequence of the power system at the prediction time to obtain the load state optimization control sequence;

[0027] Taking the minimum frequency deviation and control cost as the goal, the optimization objective function is constructed based on the load state optimization control sequence;

[0028] The optimization objective function is solved to obtain the optimal load control sequence.

[0029] The present embodiment provides a method for controlling load resources to participate in primary frequency regulation of an electric power system, with the goal of minimizing frequency deviation and control cost, constructing an optimization objective function based on a load state optimization control sequence, and then solving the optimization objective function to obtain an optimal load control sequence, so that the electric power system can minimize frequency deviation and control cost while satisfying constraint conditions, thereby achieving optimal load regulation and ensuring the stability of the electric power system.

[0030] In an optional implementation, the load state control sequence of the power system at the prediction time is optimized to obtain a load state optimization control sequence, including:

[0031] Obtain the load state data of the power system at the initial moment, and use the Koopman operator to linearly map the load state data of the power system at the initial moment to the prediction moment, so as to obtain the load state data of the power system at the prediction moment;

[0032] Based on the power system load status data at the prediction time, the prediction confidence interval is determined using the Bayesian estimation algorithm;

[0033] Based on the prediction confidence interval, the load state control sequence of the power system at the prediction moment is dynamically adjusted to obtain the load state optimization control sequence.

[0034] The present embodiment provides a method for controlling load resources to participate in primary frequency regulation of an electric power system. Based on the load state data of the electric power system at the prediction time, a prediction confidence interval is determined using a Bayesian estimation algorithm. Then, the load state control sequence of the electric power system at the prediction time is dynamically adjusted through the prediction confidence interval, thereby ensuring stable control of the load of the electric power system in the face of complex disturbances and uncertainties, enhancing the robustness of the electric power system to errors, measurement noise and external disturbances, and improving the reliability of load regulation of the electric power system.

[0035] In a second aspect, the present invention provides a controllable load resource participating in a primary frequency regulation device of a power system, the device comprising:

[0036] A clustering module is used to obtain the load status data of the power system, perform multi-dimensional load clustering on the load status data, and obtain a load characteristic time series data set;

[0037] Establish a module for establishing a Koopman operator high-dimensional linear model of the power system based on the load characteristic time series data set using the extended dynamic mode decomposition algorithm;

[0038] The optimization solution module is used to optimize the load state of the power system by using the Koopman operator high-dimensional linear model of the power system to obtain the optimal load control sequence;

[0039] The regulation module is used to perform frequency regulation on controllable load resources participating in the power system using an optimal load control sequence.

[0040] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for controllable load resources participating in primary frequency regulation of an electric power system according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for controllable load resources to participate in primary frequency regulation of a power system according to the first aspect or any corresponding embodiment thereof.

[0042] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, wherein the computer instructions are used to enable a computer to execute the method for controllable load resources to participate in primary frequency regulation of an electric power system according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a flow chart of a method for controlling load resources to participate in primary frequency regulation of a power system according to an embodiment of the present invention;

[0045] Figure 2 is a flow chart of another method for controlling load resources to participate in primary frequency regulation of a power system according to an embodiment of the present invention;

[0046] Figure 3 is a flow chart of another method for controlling load resources to participate in primary frequency regulation of a power system according to an embodiment of the present invention;

[0047] Figure 4 is a structural block diagram of a primary frequency regulation device in which controllable load resources participate in a power system according to an embodiment of the present invention;

[0048] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0050] At present, grid demand-side control, especially intelligent load control to adapt to grid dynamics, has become an important means of frequency regulation. Koopman Theory provides a method to convert nonlinear dynamic systems into linear problems, which reduces the computational complexity and is suitable for big data-driven prediction and control.

[0051] Therefore, an embodiment of the present invention provides a method for primary frequency regulation of a power system in which controllable load resources participate. By integrating advanced Koopman model predictive control technology and an intelligent load management system, the primary frequency of a power system in which controllable load resources participate is effectively regulated to improve the frequency stability and response speed of the power system.

[0052] The embodiment of the present invention provides a method for controllable load resources to participate in the primary frequency regulation of the power system. It should be noted that the method for controllable load resources to participate in the primary frequency regulation of the power system provided by the embodiment of the present invention may be executed by a device for controllable load resources to participate in the primary frequency regulation of the power system. The device for controllable load resources to participate in the primary frequency regulation of the power system may be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware. The electronic device may be a server or a terminal. The server in the embodiment of the present application may be a single server or a server cluster composed of multiple servers. The terminal in the embodiment of the present application may be a smart phone, a personal computer, a tablet computer, a wearable device, an intelligent robot, or other intelligent hardware devices. In the following method embodiments, the execution subject is an electronic device as an example for explanation.

[0053] According to an embodiment of the present invention, an embodiment of a method for controllable load resources to participate in primary frequency regulation of an electric power system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0054] In this embodiment, a method for controlling load resources to participate in primary frequency modulation of a power system is provided, which can be used for the above-mentioned electronic equipment. Figure 1 is a flow chart of a method for controlling load resources to participate in primary frequency regulation of a power system according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0055] Step S101, acquiring load status data of the power system, performing multi-dimensional load clustering on the load status data, and obtaining a load characteristic time series data set.

[0056] Specifically, by deploying PMU (Phasor Measurement Units) and RTU (Remote Terminal Units) in the power system, the load status data of the power system can be monitored in real time. The load status data includes the voltage, current, power and frequency of the load. Among them, PMU can provide high-precision phasor measurement data, and RTU is responsible for collecting and transmitting data from remote devices to ensure the stability and reliability of the power system.

[0057] Furthermore, a central controller is deployed in the power system, which receives monitoring data from PMU and RTU (i.e., load status data of the power system) and analyzes the real-time load status of the power system. By communicating with the distributed actuators at the load point, each actuator is integrated with an intelligent controller, which can dynamically adjust the load according to the instructions of the central controller to cope with load changes in the power grid and improve the operating efficiency and stability of the power system.

[0058] Furthermore, in order to effectively manage the load of the power system, a multidimensional load clustering method was introduced, and then a load characteristic time series data set was constructed based on load characteristics (such as geographical location, load type and historical response characteristics), meteorological data (such as temperature, wind speed, etc.) and real-time status of the load (such as fluctuation frequency, fluctuation amplitude, etc.).

[0059] Step S102: Based on the load characteristic time series data set, a Koopman operator high-dimensional linear model of the power system is established using an extended dynamic mode decomposition algorithm.

[0060] Step S103, using the Koopman operator high-dimensional linear model of the power system to optimize and solve the load state of the power system to obtain an optimal load control sequence.

[0061] Step S104: frequency regulation of controllable load resources participating in the power system is performed using the optimal load control sequence.

[0062] Specifically, the load is adjusted according to the optimal load control sequence, and the prediction and control are continuously updated through a real-time feedback mechanism to form a closed-loop control.

[0063] Furthermore, when a frequency disturbance occurs in the power system, the load of each group is adjusted, and the power system quickly identifies and predicts local frequency changes. Based on the prediction results, the MPC (model predictive control) strategy is used to dynamically adjust the power of each group load and provide upward / downward backup power, thereby quickly restoring the system frequency to the rated value.

[0064] The present embodiment provides a method for controlling load resources to participate in primary frequency regulation of an electric power system. By performing multi-dimensional load clustering on load status data, the accurate distribution of the load of the electric power system in the load characteristic time series data set is ensured, and the coordination and stability of the load are ensured. Then, based on the load characteristic time series data set, a Koopman operator high-dimensional linear model of the electric power system is established by using an extended dynamic mode decomposition algorithm. The Koopman operator high-dimensional linear model of the electric power system is used to map complex nonlinear load dynamics into a high-dimensional linear space, thereby simplifying the prediction complexity of the load state of the electric power system and realizing global linearization of each load state. Finally, an optimal load control sequence is obtained by optimizing and solving the load state of the electric power system. The optimal load control sequence is used to perform frequency regulation on the controllable load resources participating in the electric power system, thereby realizing stable prediction of the load state of the electric power system and accurate regulation of the frequency in the controllable load resources participating in the electric power system, thereby improving the operating efficiency and stability of the electric power system.

[0065] In this embodiment, a method for controlling load resources to participate in primary frequency modulation of a power system is provided, which can be used for the above-mentioned electronic equipment. Figure 2 is a flow chart of a method for controlling load resources to participate in primary frequency regulation of a power system according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0066] Step S201, acquiring load status data of the power system, performing multi-dimensional load clustering on the load status data, and obtaining a load characteristic time series data set.

[0067] Specifically, the above step S201 includes:

[0068] Step S2011, normalizing the load status data of the power system.

[0069] Specifically, the load state data of the power system is normalized to eliminate the dimension effect and ensure that the data with different characteristics are within the same numerical range. The Z-score (standard score) normalization method is used for normalization. The calculation process is as follows:

[0070]

[0071] Among them, x norm represents the normalized load state data, x represents the load state data, μ represents the mean, and σ represents the standard deviation.

[0072] Furthermore, the time series data window length is set for the normalized load state data to ensure that the model can capture short-term and long-term dynamic behaviors according to the periodic or non-periodic changes of different load characteristics, thereby improving the prediction accuracy; wherein, in data preprocessing, the appropriate time series data window length is selected, a multi-window mechanism is introduced, and time series data with different window lengths are used in combination with long-term and short-term load fluctuations, so that the model can capture both short-term rapid changes and long-term trends.

[0073] Step S2012, clustering the normalized load status data to obtain an initial load characteristic group.

[0074] Specifically, the K-means++ algorithm (an improvement of the traditional K-means algorithm) is used to determine the initial cluster center, which avoids the disadvantage of the K-means algorithm (an unsupervised learning algorithm for cluster analysis) being sensitive to the initial point. The clustering goal is to minimize the sum of the squares of the distances between the load samples (i.e., the load state data after normalization) and their cluster centers, ensuring that the load characteristics within the group are similar and the differences between groups are large, thereby achieving more reasonable load grouping; the expression of the K-means++ algorithm is expressed as:

[0075]

[0076] Where N is the total number of samples of load status data after normalization, q is the number of clusters, and c is j represents the jth cluster center, x i Represents the i-th load status data.

[0077] Step S2013, adaptively adjust the initial load characteristic group to obtain a load characteristic time series data set.

[0078] Specifically, clustering makes the load characteristic distribution of the power system clearer. However, the load characteristics are not static. Therefore, in order to cope with the situation where the load changes over time, an adaptive online clustering update mechanism is used to ensure that the power system is always in the optimal state. Since the real-time changes in the power grid load require dynamic adjustment of the group configuration, an online clustering update algorithm is introduced to recalculate the cluster center according to the real-time behavior of the load, and adapt to the adjustment of the group to ensure that the load management of the power system is always in the optimal state.

[0079] Furthermore, a progressive update mechanism is adopted to gradually adjust the cluster center with a smaller step size when new load status data arrives to avoid large mutations; by introducing an online clustering algorithm, the group configuration is dynamically adjusted according to the real-time behavior of the load to keep the load characteristic group optimal; at the same time, a collaborative control strategy is adopted within the load characteristic group, such as a consistency algorithm, to ensure synchronous response of the load within the group and reduce individual deviations; the calculation formula for cluster center update is as follows:

[0080]

[0081] in, represents the updated cluster center, C j Indicates the initial load characteristic group.

[0082] Step S202: Based on the load characteristic time series data set, the extended dynamic mode decomposition algorithm is used to establish a Koopman operator high-dimensional linear model of the power system. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0083] Step S203, using the Koopman operator high-dimensional linear model of the power system to optimize and solve the load state of the power system to obtain the optimal load control sequence. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0084] Step S204: Use the optimal load control sequence to adjust the frequency of controllable load resources participating in the power system. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0085] The present embodiment provides a method for controlling load resources to participate in the primary frequency regulation of the power system. By normalizing the load state data of the power system, it is ensured that each characteristic data in the load characteristic time series data set has the same weight in the subsequent model construction process, avoiding the prediction error amplification caused by the magnitude difference between the load characteristics; and by clustering the load state data after normalization, it is ensured that the load characteristics in each initial load characteristic group are similar, while the initial load characteristic groups have large differences, which is convenient for the subsequent distributed optimization and regulation of the power system load; finally, by adaptively adjusting the initial load characteristic group, the load in the load characteristic time series data set responds synchronously, ensuring that the load management of the power system is always in the optimal state, and improving the coordination and stability of the power system load. Furthermore, by dynamically adjusting the group, a consistency control strategy is introduced to make the loads in the group respond synchronously, ensuring the coordination of the load and the stability of the power grid, and realizing efficient management of the power system load.

[0086] In this embodiment, a method for controlling load resources to participate in primary frequency modulation of a power system is provided, which can be used for the above-mentioned electronic equipment. Figure 3 is a flow chart of a method for controlling load resources to participate in primary frequency regulation of a power system according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0087] Step S301, obtain the load status data of the power system, perform multi-dimensional load clustering on the load status data, and obtain a load characteristic time series data set. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0088] Step S302: Based on the load characteristic time series data set, a Koopman operator high-dimensional linear model of the power system is established using an extended dynamic mode decomposition algorithm.

[0089] Specifically, the above step S302 includes:

[0090] Step S3021, embedding the load characteristic time series data set into delayed coordinates to obtain a high-dimensional state vector.

[0091] Specifically, the goal of delayed coordinate embedding is to incorporate temporal information into the state representation so that the system dynamics appear more linear in a higher-dimensional space. Therefore, by combining the states of the data in the load characteristic time series dataset at different time points into a high-dimensional vector, the load state space of the power system is reconstructed to capture the nonlinear characteristics of the load dynamics.

[0092] Furthermore, the delay coordinate embedding step includes: determining the optimal embedding dimension m and the delay interval τ to ensure that the embedded state vector can effectively capture the dynamic characteristics of the system, and constructing the power system load state space based on the optimal embedding dimension and the delay interval τ to capture the dynamic characteristics. The power system load state space can be expressed as:

[0093] ζ(x)=x(t),x(t-τ),...,x(t-(m-1)τ)) (4)

[0094] Among them, i(x) represents the state vector after delayed coordinate embedding, that is, the high-dimensional state vector, t-τ represents the load state after a delay of τ (the observation data at different time points are combined into a vector by embedding delayed coordinates to reconstruct the state space), and x(t) represents the load state corresponding to the current time point t in the load characteristic time series data set.

[0095] Furthermore, the delay interval τ is determined using a correlation function method or a mutual information method, and the optimal embedding dimension m is determined using a false nearest neighbor method or a saturated dimension method.

[0096] Step S3022: Select a high-dimensional state vector as a basis function, and use the least squares method to fit the basis function to obtain a Koopman operator.

[0097] Specifically, the high-dimensional state vector after the delayed coordinates are embedded is used as the input of the basis function selection and EDMD (Extended Dynamic Mode Decomposition) algorithm. The EDMD algorithm extracts linear characteristics using the high-dimensional state vector and constructs the Koopman operator by selecting the appropriate basis function.

[0098] Furthermore, basis functions are used to describe the linear representation of the load state of the power system in the state space, where the basis functions can be polynomial functions, trigonometric functions or radial basis functions, etc. The specific selection depends on the complexity of the power system and the application scenario. For example, if the dynamics of the power system has obvious periodicity, such as periodic fluctuations in data such as wind speed, power generation, and load, the basis function uses trigonometric basis functions.

[0099] Furthermore, the basis function can be expressed as:

[0100] φ(x)=[φ 1 (ζ(x)),φ 2 (ζ(x)),...,φ n (ζ(x))] (5)

[0101] Among them, φ(x) represents the basis function set, n represents the number of basis functions, φ n (ζ(x)) represents a basis function.

[0102] Furthermore, EDMD constructs the Koopman operator by fitting the basis functions using the least squares method. The Koopman operator is used to represent the linear relationship of the power system evolving from one load state to another. The calculation formula of the Koopman operator K in matrix form is as follows:

[0103]

[0104] Among them, x t Represents the high-dimensional state vector at time t.

[0105] Furthermore, the validity of the basis function is verified, and the basis function is optimized based on the verification result.

[0106] Furthermore, the verification process of the validity of the basis function includes the reconstruction capability verification of the basis function and the Koopman operator verification; among them, the reconstruction error is used to evaluate the reconstruction effect of the basis function, and the reconstruction error adopts the mean square error MSE. The calculation formula of the mean square error MSE is as follows:

[0107]

[0108] Among them, u∈n, if the reconstruction error MSE is less than the preset error, it means that the selected basis function can effectively capture the dynamic characteristics of the system; if the reconstruction error MSE is greater than or equal to the preset error, the basis function is reselected and the Koopman operator is recalculated.

[0109] Furthermore, by calculating the reconstruction error, the accuracy of the basis function in reconstructing the load state of the power system can be quantified, and it can be determined whether the basis function is sufficient to express the nonlinear characteristics; if the reconstruction error is small, it means that the basis function set is powerful enough to effectively describe the complex dynamic behavior of the system; on the contrary, a larger error may mean that the basis function is not sufficient to describe all the characteristics of the system, and the basis function needs to be adjusted or replaced.

[0110] Furthermore, the Koopman operator is verified by calculating the eigenvalues ​​and modes of the Koopman operator and then analyzing its spectral distribution. After the Koopman operator K is calculated, the calculation of the eigenvalues ​​and modes can be performed by the following steps:

[0111] (1) Eigenvalue decomposition: The eigenvalue and eigenvector of the Koopman operator K are calculated using the following formula:

[0112] Kz=λz (8)

[0113] Among them, λ represents the eigenvalue and z represents the eigenvector.

[0114] (2) Modal calculation: Koopman mode refers to the system response at a specific eigenvector. The calculation formula of Koopman modal vector is as follows:

[0115] Φ=φ(x)V (9)

[0116] Wherein, V represents the eigenvector matrix, Φ represents the Koopman mode, and the Koopman mode describes the influence of each high-dimensional state vector on a specific eigenvalue (ie, frequency and growth / decay characteristics).

[0117] (3) Spectral analysis: By analyzing the eigenvalues ​​and Koopman modes, the frequency and attenuation / growth mode of the power system are obtained. The specific analysis methods are as follows: Eigenvalue analysis: The modulus of the eigenvalue reflects the dynamic characteristics of the system. An eigenvalue with a modulus less than 1 indicates that the corresponding mode will decay, that is, the power system is stable. An eigenvalue with a modulus greater than 1 indicates that the mode will grow, indicating that the power system is unstable. Modal analysis: Koopman modes are associated with eigenvalues ​​and describe how the system responds to different disturbances. For example, a mode with an eigenvalue modulus close to 1 indicates that the mode of the power system changes slowly, while a mode with a modulus much greater than 1 indicates that the power system may experience violent fluctuations or instability.

[0118] If the modulus of the eigenvalue is found to be too large during the analysis, it indicates that the power system may be highly unstable. The stability of the power system can be improved by adjusting the basis function. For example, a more suitable basis function can be selected (such as increasing the order of the polynomial basis function or changing the basis function type) to improve the stability of the Koopman operator and accurately capture the dynamic characteristics of the system.

[0119] Step S3023, obtaining a control input matrix and control variables, and establishing a Koopman operator high-dimensional linear model of the power system based on the Koopman operator, the control input matrix and the control variables.

[0120] Specifically, based on the Koopman operator, a Koopman operator high-dimensional linear model of the power system is established, and then a recursive formula is used to perform multi-step prediction; the expression of the Koopman operator high-dimensional linear model is as follows:

[0121]

[0122] in, represents the load state of the power system at time k+1, represents the load state of the power system at time k, B represents the control input matrix, u k represents the control variable.

[0123] Furthermore, the Koopman operator high-dimensional linear model of the power system can predict the load status at multiple prediction moments, ensuring that the load regulation system can respond to possible grid changes in advance.

[0124] Step S303, using the Koopman operator high-dimensional linear model of the power system to optimize and solve the load state of the power system to obtain an optimal load control sequence.

[0125] Specifically, the above step S303 includes:

[0126] Step S3031, collecting the current power system load state data, inputting the current power system load state data into the Koopman operator high-dimensional linear model of the power system, and obtaining the power system load state control sequence at the prediction time.

[0127] Step S3032, optimizing the load state control sequence of the power system at the prediction time to obtain a load state optimized control sequence.

[0128] In some optional implementations, the above step S3032 includes:

[0129] Step a1, obtaining the load state data of the power system at the initial moment, and using the Koopman operator to linearly map the load state data of the power system at the initial moment to the prediction moment, to obtain the load state data of the power system at the prediction moment.

[0130] Specifically, the Koopman operator is used to predict the load state data of the power system at the prediction time, and the following recursive formula is used for multi-step prediction:

[0131]

[0132] Furthermore, the load state data of the power system at the initial moment may include the voltage, power, frequency and other state information of the load. By embedding the delayed coordinates to form a high-dimensional state vector, and recursively using the constructed Koopman operator, the state of the power system load at multiple moments in the future can be predicted, thereby providing forward-looking information for dynamic regulation of the power grid.

[0133] Step a2, based on the power system load state data at the prediction time, using the Bayesian estimation algorithm to determine the prediction confidence interval.

[0134] Specifically, in order to deal with power system errors, measurement noise and external disturbances, the Bayesian estimation algorithm is used to quantify the uncertainty of future states. The Bayesian estimation algorithm combines the load state data of the power system (i.e., the load state data of the power system at the initial moment) and the load state data of the power system at the prediction moment to derive the confidence interval of the future load state, i.e., the prediction confidence interval. The prediction confidence interval can indicate the possible deviation range of the prediction result, so that the load regulation can be dynamically adjusted according to the uncertainty of the prediction. By introducing the prediction confidence interval, the prediction model can estimate the upper and lower limits of the state at multiple future moments, i.e., the possible fluctuation range at each prediction moment.

[0135] Furthermore, the Bayesian estimation algorithm includes the following steps: prior distribution: setting the prior distribution of the power system load state based on the power system load state data at the initial moment, providing a preliminary estimate of the future state; likelihood function: calculating the likelihood function of the state through the power system load state data at the prediction moment, indicating the degree of match between the observed data and the actual state; posterior distribution: combining the prior distribution and the likelihood function, updating the posterior distribution, thereby obtaining an uncertainty prediction about the future load state.

[0136] Step a3: dynamically adjust the load state control sequence of the power system at the prediction time based on the prediction confidence interval to obtain the load state optimization control sequence.

[0137] Specifically, the load state control sequence of the power system at the prediction moment is dynamically adjusted to ensure that the load state control sequence of the power system at the prediction moment conforms to the prediction confidence interval, ensuring that the power system can make robust load regulation decisions even in the presence of disturbances or noise.

[0138] The present embodiment provides a method for controlling load resources to participate in primary frequency regulation of an electric power system. Based on the load state data of the electric power system at the prediction time, a prediction confidence interval is determined using a Bayesian estimation algorithm. Then, the load state control sequence of the electric power system at the prediction time is dynamically adjusted through the prediction confidence interval, thereby ensuring stable control of the load of the electric power system in the face of complex disturbances and uncertainties, enhancing the robustness of the electric power system to errors, measurement noise and external disturbances, and improving the reliability of load regulation of the electric power system.

[0139] Step S3033, with the goal of minimizing frequency deviation and control cost, construct an optimization objective function based on the load state optimization control sequence.

[0140] Specifically, the core task of MPC is to achieve precise load regulation by optimizing the control sequence. In this process, considering the actual constraints of the power system (such as power upper and lower limits, frequency deviation tolerance) and control costs, the MPC problem can be expressed as an optimization objective function. The expression of the optimization objective function is as follows:

[0141]

[0142] Among them, J * represents the optimization objective function, u 0 :N p -1 indicates the number of steps from the current moment to the prediction step N p The control sequence of l(x k ,u k ) represents the load state of the power system and the control action u at time k k The stage cost, V f (xN p ) represents the control cost of the final load state, which is used to guide the power system to the desired state.

[0143] Step S3034, solving the optimization objective function to obtain the optimal load control sequence.

[0144] Specifically, by solving the optimization objective function, the frequency deviation and control cost can be minimized while ensuring that the constraints are met, thereby achieving optimal load regulation.

[0145] Furthermore, the optimization objective function is solved by the interior point method or the gradient descent method to obtain the optimal load control sequence.

[0146] Step S304: Use the optimal load control sequence to adjust the frequency of controllable load resources participating in the power system. Figure 2 Step S204 of the illustrated embodiment will not be described in detail here.

[0147] The present embodiment provides a method for controlling load resources to participate in the primary frequency regulation of the power system. By embedding delayed coordinates, the one-dimensional time series data in the load characteristic time series data set is converted into a state representation in a multi-dimensional space, so that the nonlinear dynamics of the power system is more linear in the high-dimensional space, and a reliable data basis is provided for the subsequent basis function selection and the expansion of the dynamic mode decomposition algorithm; and the basis function is fitted by the least squares method to obtain the Koopman operator, so that the accurate mapping of the linear relationship between the load states is achieved, so that the high-dimensional linear model of the Koopman operator is more accurate, and the linear description and dynamic prediction of the load state of the power system are achieved; finally, with the minimum frequency deviation and control cost as the goal, an optimization objective function is constructed based on the load state optimization control sequence, and then the optimization objective function is solved to obtain the optimal load control sequence, so that the power system minimizes the frequency deviation and control cost under the premise of satisfying the constraints, thereby achieving the optimal load regulation and ensuring the stability of the power system.

[0148] In this embodiment, a controllable load resource is also provided to participate in the primary frequency regulation device of the power system, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0149] This embodiment provides a controllable load resource participating in the primary frequency regulation device of the power system, such as Figure 4 As shown, including:

[0150] The clustering module 401 is used to obtain the load status data of the power system, perform multi-dimensional load clustering on the load status data, and obtain a load characteristic time series data set;

[0151] Establishing module 402, for establishing a Koopman operator high-dimensional linear model of the power system based on the load characteristic time series data set using an extended dynamic mode decomposition algorithm;

[0152] The optimization solution module 403 is used to optimize and solve the load state of the power system by using the Koopman operator high-dimensional linear model of the power system to obtain the optimal load control sequence;

[0153] The regulation module 404 is used to regulate the frequency of controllable load resources participating in the power system by using the optimal load control sequence.

[0154] In some optional implementations, the clustering module 401 includes:

[0155] A normalization processing unit, used for normalizing the load state data of the power system;

[0156] A clustering unit, used for clustering the normalized load state data to obtain an initial load characteristic group;

[0157] The adjustment unit is used to adaptively adjust the initial load characteristic group to obtain a load characteristic time series data set.

[0158] In some optional implementations, the establishing module 402 includes:

[0159] An embedding unit is used to embed the load characteristic time series data set into delayed coordinates to obtain a high-dimensional state vector;

[0160] A fitting unit is used to select a high-dimensional state vector as a basis function and fit the basis function using the least square method to obtain a Koopman operator;

[0161] A unit is established to obtain a control input matrix and a control variable, and a Koopman operator high-dimensional linear model of the power system is established based on the Koopman operator, the control input matrix and the control variable.

[0162] In some optional implementations, the establishing module 402 further includes:

[0163] The verification unit is used to verify the validity of the basis function and optimize the basis function based on the verification result.

[0164] In some optional implementations, the optimization solution module 403 includes:

[0165] A collection unit is used to collect the load state data of the power system at the current moment, input the load state data of the power system at the current moment into the Koopman operator high-dimensional linear model of the power system, and obtain the load state control sequence of the power system at the prediction moment;

[0166] An optimization unit, used for optimizing the load state control sequence of the power system at the prediction time to obtain the load state optimization control sequence;

[0167] The construction unit takes the minimum frequency deviation and control cost as the goal and constructs the optimization objective function based on the load state optimization control sequence;

[0168] The solving unit is used to solve the optimization objective function and obtain the optimal load control sequence.

[0169] In some optional embodiments, the optimization unit includes:

[0170] A mapping subunit is used to obtain the load state data of the power system at the initial moment, and use the Koopman operator to linearly map the load state data of the power system at the initial moment to the prediction moment to obtain the load state data of the power system at the prediction moment;

[0171] A determination subunit is used to determine a prediction confidence interval using a Bayesian estimation algorithm based on power system load state data at the prediction time;

[0172] The adjustment subunit is used to dynamically adjust the load state control sequence of the power system at the prediction moment based on the prediction confidence interval to obtain the load state optimization control sequence.

[0173] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0174] In this embodiment, a controllable load resource participating in the primary frequency regulation device of the power system is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0175] The embodiment of the present invention also provides a computer device having the above Figure 4 A controllable load resource is shown participating in a primary frequency regulation device of an electric power system.

[0176] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0177] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0178] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0179] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0180] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0181] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0182] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0183] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0184] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0185] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for controlling load resources to participate in primary frequency regulation of a power system, characterized in that: The method comprises: Acquire load status data of the power system, perform multi-dimensional load clustering on the load status data, and obtain a load characteristic time series data set; Based on the load characteristic time series data set, a Koopman operator high-dimensional linear model of the power system is established using an extended dynamic mode decomposition algorithm; The Koopman operator high-dimensional linear model of the power system is used to optimize and solve the load state of the power system to obtain an optimal load control sequence; The optimal load control sequence is used to perform frequency regulation on controllable load resources participating in the power system.

2. The method according to claim 1, characterized in that The multi-dimensional load clustering is performed on the load state data to obtain a load characteristic time series data set, including: Normalizing the load status data of the power system; Clustering the normalized load state data to obtain an initial load feature group; The initial load characteristic group is adaptively adjusted to obtain the load characteristic time series data set.

3. The method according to claim 1, characterized in that The method of establishing a Koopman operator high-dimensional linear model of the power system based on the load characteristic time series data set and using an extended dynamic mode decomposition algorithm includes: Embedding the load characteristic time series data set with delayed coordinates to obtain a high-dimensional state vector; Selecting the high-dimensional state vector as a basis function, and fitting the basis function using a least squares method to obtain the Koopman operator; A control input matrix and a control variable are obtained, and a Koopman operator high-dimensional linear model of the power system is established based on the Koopman operator, the control input matrix and the control variable.

4. The method according to claim 3, characterized in that: The method of establishing a Koopman operator high-dimensional linear model of the power system based on the load characteristic time series data set by using an extended dynamic mode decomposition algorithm also includes: The validity of the basis function is verified, and the basis function is optimized based on the verification result.

5. The method according to claim 3, characterized in that: The method of optimizing and solving the load state of the power system by using the Koopman operator high-dimensional linear model of the power system to obtain the optimal load control sequence includes: Collecting the load state data of the power system at the current moment, inputting the load state data of the power system at the current moment into the Koopman operator high-dimensional linear model of the power system, and obtaining the load state control sequence of the power system at the prediction moment; Optimizing the load state control sequence of the power system at the prediction moment to obtain a load state optimization control sequence; Taking the minimum frequency deviation and control cost as the goal, constructing an optimization objective function based on the load state optimization control sequence; The optimization objective function is solved to obtain the optimal load control sequence.

6. The method according to claim 5, characterized in that The step of optimizing the load state control sequence of the power system at the prediction moment to obtain the load state optimization control sequence includes: Acquire the load state data of the power system at the initial moment, and use the Koopman operator to linearly map the load state data of the power system at the initial moment to the prediction moment to obtain the load state data of the power system at the prediction moment; Based on the power system load state data at the prediction time, a prediction confidence interval is determined using a Bayesian estimation algorithm; The load state control sequence of the power system at the prediction moment is dynamically adjusted based on the prediction confidence interval to obtain the load state optimization control sequence.

7. A controllable load resource participating in a primary frequency regulation device of a power system, characterized in that: The device comprises: A clustering module, used to obtain load status data of the power system, perform multi-dimensional load clustering on the load status data, and obtain a load characteristic time series data set; Establishing a module for establishing a Koopman operator high-dimensional linear model of the power system based on the load characteristic time series data set using an extended dynamic mode decomposition algorithm; An optimization solution module is used to optimize and solve the load state of the power system by using the Koopman operator high-dimensional linear model of the power system to obtain an optimal load control sequence; The regulation module is used to use the optimal load control sequence to perform frequency regulation on controllable load resources participating in the power system.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the primary frequency regulation method of a controllable load resource participating in an electric power system as described in any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for controllable load resources to participate in primary frequency regulation of an electric power system as described in any one of claims 1 to 6.

10. A computer program product, characterized in that It comprises computer instructions, and the computer instructions are used to enable a computer to execute the method for controllable load resources participating in the primary frequency regulation of the power system as described in any one of claims 1 to 6.

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