Quick-response unit compatible frequency modulation adjusting method and system

Abstract

The invention belongs to the technical field of power systems, and discloses a quick-response unit compatible frequency modulation method and system, and the method comprises the steps: obtaining the real-time frequency data of a power grid, and calculating the deviation between the frequency of the power grid and the standard frequency; determining a frequency adjustment amplitude by using the fuzzy control rule base, and selecting a corresponding unit response mode according to the real-time frequency change to perform frequency adjustment; on the basis of a particle swarm optimization algorithm, the optimal unit response time and power output are determined by combining the unit response capability, the frequency modulation power range and the start-stop time; and generating a frequency control instruction according to the optimal unit response time and power output, and controlling the frequency modulation unit to perform power adjustment based on the frequency control instruction to realize adjustment of the power grid frequency. According to the method, the optimal unit response time and power output can be accurately determined, the response speed and precision of frequency adjustment are remarkably improved, and the intelligent level and the overall operation efficiency of a power grid frequency modulation system are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a fast-response unit-compatible frequency regulation method and system. Background Art

[0002] As the scale of the power system continues to expand, the operating load and dispatching of the power grid are becoming more complex. How to ensure the stability of the power grid frequency has become a core issue that needs to be solved in the operation of the power system. The deviation of the power grid frequency usually comes from factors such as load fluctuations and power supply access. Too high or too low frequency will affect the safety of power equipment and the reliability of the power grid. Therefore, frequency regulation has become a crucial link in power grid dispatching.

[0003] Traditional frequency regulation methods mainly rely on conventional unit regulation, such as increasing or decreasing the power output of generator sets. However, these methods have the problems of slow response time and low regulation accuracy. Especially when the grid frequency fluctuates violently, traditional regulation methods often cannot quickly stabilize the frequency. In recent years, with the development of intelligent control technology, frequency regulation methods based on fuzzy control and optimization algorithms have gradually become an effective frequency regulation solution. These methods can adjust the response strategy of the unit according to the changes in real-time frequency data, thereby improving the frequency stability of the grid.

[0004] However, the existing frequency modulation methods often have the following shortcomings in practical applications:

[0005] Slow response speed: Due to the limited response capability and frequency regulation power range of the unit, traditional frequency regulation methods usually cannot respond quickly in a short period of time.

[0006] Inaccurate frequency adjustment: The frequency recovery time is long and the power regulation process may cause unnecessary fluctuations, affecting the stability of the power grid.

[0007] Ignoring the actual capacity and start-stop time of the unit: The existing frequency regulation method does not take into account the actual response capacity of the unit during the start-stop process, resulting in unsatisfactory optimization effect.

[0008] Therefore, how to improve the unit regulation capability and response speed while ensuring the stability of the grid frequency has become a difficult problem in current frequency regulation technology. Summary of the invention

[0009] The embodiment of the present invention provides a fast-response unit-compatible frequency regulation method and system to solve the above-mentioned technical problems in the prior art.

[0010] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not a general review, nor is it intended to identify key / important components or to describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0011] According to a first aspect of an embodiment of the present invention, a fast-response unit-compatible frequency regulation method is provided.

[0012] In one embodiment, the fast-response unit-compatible frequency regulation method includes:

[0013] Obtain real-time frequency data of the power grid, calculate the deviation between the power grid frequency and the standard frequency, perform fuzzy processing on the frequency deviation and change rate, and define fuzzy variables;

[0014] The frequency adjustment amplitude is determined by using the fuzzy control rule base, and the corresponding unit response mode is selected to adjust the frequency according to the real-time frequency change;

[0015] Based on the particle swarm optimization algorithm, the optimal unit response time and power output are determined by combining the unit response capability, frequency modulation power range and start-stop time;

[0016] Generate frequency control instructions based on the optimal unit response time and power output, and control the frequency-modulating unit to adjust power based on the frequency control instructions to achieve grid frequency adjustment.

[0017] In one embodiment, the real-time frequency data of the power grid is obtained, the deviation between the power grid frequency and the standard frequency is calculated, and the frequency deviation and the rate of change are fuzzy processed, and the fuzzy variables are defined, including:

[0018] Collect the frequency data of the power grid in real time, and calculate the deviation between the power grid frequency and the standard frequency and the rate of change of the frequency deviation over time;

[0019] The frequency deviation and the frequency deviation change rate are divided into different fuzzy sets according to the frequency deviation value and the frequency deviation change rate value respectively. The frequency deviation and the change rate are converted into fuzzy values ​​through the membership function, and the input variables of the fuzzy control rules are defined according to the results of the fuzzification processing.

[0020] In one embodiment, the real-time acquisition of the frequency data of the power grid and the calculation of the deviation between the power grid frequency and the standard frequency and the rate of change of the frequency deviation over time include:

[0021] The real-time frequency data of the power grid is obtained through the power grid monitoring system, and the deviation between the current power grid frequency and the standard frequency is calculated. If the deviation is positive, it means that the power grid frequency is higher than the standard frequency. If the deviation is negative, it means that the power grid frequency is lower than the standard frequency.

[0022] A time window is set to sample the frequency data, the difference between the frequency deviation at the current moment and the frequency deviation at the previous moment is calculated, and the ratio of the difference to the time interval is taken as the rate of change of the frequency deviation.

[0023] In one embodiment, converting the frequency deviation and the rate of change into a fuzzy value through a membership function includes:

[0024] The triangular membership function is used as the membership function of the frequency deviation, and the frequency deviation is converted into a fuzzy value by using the triangular membership function;

[0025] The Gaussian membership function is used as the membership function of the frequency deviation change rate, and the frequency deviation change rate is converted into a fuzzy value using the Gaussian membership function.

[0026] In one embodiment, the expression of the triangle membership function is:

[0027] ;

[0028] The expression of Gaussian membership function is:

[0029] ;

[0030] Where μ(x) represents the triangular membership function, x represents the frequency deviation value, a represents the minimum value of membership 0, b represents the position of membership 1, and c represents the maximum value of membership 0. represents the Gaussian membership function, represents the frequency deviation change rate, E represents the expected value of the fuzzy set, Represents the standard deviation of the Gaussian function.

[0031] In one embodiment, the method of determining the frequency adjustment amplitude by using the fuzzy control rule base and selecting the corresponding unit response mode to adjust the frequency according to the real-time frequency change includes:

[0032] A fuzzy control rule base is formulated according to the fuzzification results of the power grid frequency deviation and the frequency change rate, and the fuzzified frequency deviation and the frequency change rate are input into the fuzzy controller as input variables;

[0033] According to the input fuzzy variables, the fuzzy inference algorithm is used to infer the rule base and calculate the frequency adjustment amplitude;

[0034] The frequency adjustment amplitude is converted from the fuzzy domain to the actual control value to obtain the specific power adjustment instruction, and the corresponding unit response mode is selected according to the frequency adjustment amplitude to perform power adjustment.

[0035] In one embodiment, the determination of the optimal unit response time and power output based on the particle swarm optimization algorithm in combination with the unit response capability, frequency modulation power range and start-stop time includes:

[0036] With the optimization objectives of minimizing the frequency regulation time, maximizing the unit response accuracy, and minimizing the time it takes for the frequency to recover to the target range, the constraints of the unit response capability, frequency regulation power range, and start-stop time are defined;

[0037] Initialize the particle swarm, set the initial position and velocity of the particles, and determine the search space, where the search space includes the power range and response time of the unit;

[0038] The fitness function is determined according to the frequency recovery time, power output accuracy and unit start and stop time, and the fitness of each particle is calculated according to the fitness function;

[0039] According to the speed update formula and position update formula, adjust the position and speed of the particle to find a better solution;

[0040] Evaluate the fitness of each particle and update the particle's historical best solution, and update the global best solution to the particle with the best current fitness;

[0041] Determine whether the maximum number of iterations or convergence conditions are reached. If not, continue to iterate. If so, output the global optimal solution of the particle swarm to obtain the optimal unit response time and power output.

[0042] In one embodiment, the fitness function is expressed as:

[0043] ;

[0044] In the formula, f(x) represents the fitness function, T r Indicates the frequency recovery time, P a Indicates the actual regulated power output of the unit, P d Indicates the target power output required by the unit, T s Indicates the start and stop time of the unit. They respectively represent the weights of frequency recovery time, power output accuracy, and unit start and stop time.

[0045] In one embodiment, the speed update formula is:

[0046] ;

[0047] The position update formula is:

[0048] ;

[0049] In the formula, v i(t+1) represents the velocity of the ith particle at the t+1th iteration, v i (t) represents the velocity of the ith particle at the tth iteration, represents the inertia weight, k1 and k2 represent two different learning factors, r1 and r2 represent random numbers in the interval [0, 1], and pbest i represents the best historical position of the ith particle, x i (t) represents the position of the i-th particle at the t-th iteration, gbest represents the global best position, and x i (t+1) represents the position of the ith particle at the t+1th iteration, x i (t) represents the position of the ith particle at the tth iteration.

[0050] According to a second aspect of an embodiment of the present invention, a fast-response unit-compatible frequency regulation system is provided.

[0051] In one embodiment, the fast-response unit-compatible frequency regulation system comprises a frequency data processing module, a unit response mode determination module, an optimization algorithm solution module and a power grid frequency regulation module;

[0052] The frequency data processing module is used to obtain real-time frequency data of the power grid, calculate the deviation between the power grid frequency and the standard frequency, and perform fuzzy processing on the frequency deviation and the rate of change to define fuzzy variables;

[0053] The unit response mode determination module is used to determine the frequency adjustment amplitude using the fuzzy control rule base, and select the corresponding unit response mode to perform frequency adjustment according to the real-time frequency change;

[0054] The optimization algorithm solving module is used to determine the optimal unit response time and power output based on the particle swarm optimization algorithm, combined with the unit response capability, frequency modulation power range and start and stop time;

[0055] The grid frequency adjustment module is used to generate a frequency control instruction according to the optimal unit response time and power output, and control the frequency modulation unit to adjust the power based on the frequency control instruction to achieve grid frequency adjustment.

[0056] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0057] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0058] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0059] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0060] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0061] The present invention can significantly improve the response speed and accuracy of frequency regulation by combining fuzzy control algorithm and particle swarm optimization algorithm; by acquiring grid frequency data in real time and performing fuzzy processing, it can quickly capture frequency deviation and change rate, and make adjustment instructions in time; by using particle swarm optimization algorithm, comprehensively considering the response capability of the unit, frequency modulation power range and start-stop time, it can accurately determine the optimal unit response time and power output, reduce fluctuations in the frequency adjustment process, and ensure that the grid frequency is quickly and stably restored to the target value. In addition, the present invention can dynamically adapt to grid changes, automatically select the appropriate unit response mode, avoid adjustment errors caused by inappropriate unit status, and improve the intelligence level and overall operation efficiency of the grid frequency modulation system.

[0062] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0064] Figure 1 is a flow chart showing a fast-response unit-compatible frequency regulation method according to an exemplary embodiment;

[0065] Figure 2 is a structural block diagram of a fast-response unit-compatible frequency regulation system according to an exemplary embodiment;

[0066] Figure 3 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0067] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0068] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0069] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0070] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0071] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0072] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0073] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0074] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0075] Figure 1 An embodiment of a fast-response unit-compatible frequency regulation method of the present invention is shown.

[0076] In this optional embodiment, the fast-response unit-compatible frequency regulation method includes:

[0077] Step S101, obtaining real-time frequency data of the power grid, calculating the deviation between the power grid frequency and the standard frequency, and performing fuzzy processing on the frequency deviation and the rate of change, and defining fuzzy variables;

[0078] Specifically, the real-time frequency data of the power grid is obtained, the deviation between the power grid frequency and the standard frequency is calculated, and the frequency deviation and the rate of change are fuzzy processed. The fuzzy variables are defined as follows:

[0079] Collect the frequency data of the power grid in real time, and calculate the deviation between the power grid frequency and the standard frequency and the rate of change of the frequency deviation over time; specifically, obtain the real-time frequency data of the power grid through the power grid monitoring system (such as the SCADA system or other real-time monitoring equipment), and calculate the deviation between the current power grid frequency and the standard frequency (such as 50Hz or 60Hz). If the deviation is positive, it means that the power grid frequency is higher than the standard frequency; if the deviation is negative, it means that the power grid frequency is lower than the standard frequency; set a time window (such as every second or every minute) to sample the frequency data, calculate the difference between the frequency deviation at the current moment and the frequency deviation at the previous moment, and use the ratio of the difference to the time interval as the rate of change of the frequency deviation, that is, the rate of change of the frequency deviation = (the frequency deviation at the current moment - the frequency deviation at the previous moment) / time interval;

[0080] According to the frequency deviation value and the frequency deviation change rate value, the frequency deviation and the frequency deviation change rate are divided into different fuzzy sets respectively, the frequency deviation and the change rate are converted into fuzzy values ​​through the membership function, and the input variables of the fuzzy control rules are defined according to the results of the fuzzification processing;

[0081] Specifically, according to the frequency deviation value, it is divided into different fuzzy sets including:

[0082] Large positive deviation (positive value with deviation greater than a certain threshold), small positive deviation (positive value with smaller deviation), zero deviation (close to standard frequency), small negative deviation (negative value with smaller deviation), large negative deviation (negative value with deviation greater than a certain threshold);

[0083] According to the frequency deviation change rate value, it is divided into different fuzzy sets including:

[0084] Rapid increase (frequency deviation increases rapidly), slow increase (frequency deviation increases slowly), zero change (frequency deviation change rate is zero), slow decrease (frequency deviation decreases slowly), rapid decrease (frequency deviation decreases rapidly);

[0085] Specifically, converting the frequency deviation and the rate of change into a fuzzy value through a membership function includes:

[0086] The triangular membership function is used as the membership function of the frequency deviation, and the frequency deviation is converted into a fuzzy value by using the triangular membership function;

[0087] The expression of the triangle membership function is:

[0088] ;

[0089] Wherein, μ(x) represents the triangular membership function, x represents the frequency deviation value, a represents the minimum value where the membership is 0, b represents the position where the membership is 1, and c represents the maximum value where the membership is 0.

[0090] The Gaussian membership function is used as the membership function of the frequency deviation change rate, and the frequency deviation change rate is converted into a fuzzy value by using the Gaussian membership function;

[0091] The expression of Gaussian membership function is:

[0092] ;

[0093] In the formula, represents the Gaussian membership function, represents the frequency deviation change rate, E represents the expected value of the fuzzy set, Represents the standard deviation of the Gaussian function.

[0094] Step S102, using the fuzzy control rule base to determine the frequency adjustment amplitude, and selecting the corresponding unit response mode to perform frequency adjustment according to the real-time frequency change;

[0095] Specifically, the method of determining the frequency adjustment amplitude by using the fuzzy control rule base and selecting the corresponding unit response mode to adjust the frequency according to the real-time frequency change includes:

[0096] A fuzzy control rule base is developed based on the fuzzification results of the grid frequency deviation and frequency change rate. The rule base includes various possible frequency adjustment strategies and selects appropriate adjustment responses based on different inputs (deviation and change rate). The rule base is set according to experience or system requirements as follows:

[0097] If the frequency deviation is a "large positive deviation" and the rate of change is "rapidly increasing", the power should be "increased"; if the frequency deviation is a "zero deviation" and the rate of change is "zero change", the power should be "remained unchanged"; if the frequency deviation is a "small negative deviation" and the rate of change is "slowly decreasing", the power should be "reduced";

[0098] The fuzzified frequency deviation and frequency change rate are input into the fuzzy controller as input variables;

[0099] According to the input fuzzy variables, the rule base is inferred using a fuzzy reasoning algorithm (such as the Mamdani reasoning method) to calculate the frequency adjustment amplitude, which specifically includes: matching the corresponding rules in the rule base according to the current deviation and change rate values; calculating the output variable (i.e., the frequency adjustment amplitude) according to the matched rules. This step is usually completed through fuzzy reasoning calculation; for example, if the rule matches "the frequency deviation is a large positive deviation, and the frequency change rate is rapidly increasing", the inferred output is "increase power";

[0100] The frequency adjustment amplitude is converted from the fuzzy domain to the actual control value to obtain a specific power adjustment instruction, and the corresponding unit response mode is selected for power adjustment according to the frequency adjustment amplitude, which specifically includes: using a defuzzification method (such as the center of gravity method) to convert the fuzzy control result (such as "increase power" and "reduce power") into a specific power adjustment value. For example, if the amplitude corresponding to "increase power" is 10MW, the adjustment instruction is obtained after defuzzification: increase the unit power by 10MW; select the appropriate unit to respond according to the system status. Usually, the unit with fast response (such as gas turbine) is adjusted first; adjust the output power of the corresponding unit according to the control instruction. For example, if the adjustment instruction is "increase power by 10MW", select the unit with the fastest response to increase the output;

[0101] Step S103: Based on the particle swarm optimization algorithm, the optimal unit response time and power output are determined in combination with the unit response capability, frequency modulation power range and start-stop time;

[0102] Specifically, the particle swarm optimization algorithm is used to determine the optimal unit response time and power output in combination with the unit response capability, frequency modulation power range and start-stop time, including:

[0103] Define optimization objectives and constraints: Clarify optimization objectives and constraints to ensure that the optimization process can meet the grid frequency regulation requirements, including minimizing frequency regulation time, maximizing unit response accuracy, and the shortest time for frequency to recover to the target range as optimization objectives, and defining unit response capability, frequency regulation power range (for example, minimum power and maximum power limits), and start and stop time constraints;

[0104] Initialize the parameters of the particle swarm optimization algorithm: The particle swarm optimization (PSO) algorithm searches for the optimal solution by simulating the search process of particles. In this step, it is necessary to initialize the relevant parameters of the particles and define the search space of the particle swarm, including initializing the particle swarm (each particle represents a solution, and the solution includes the unit response time and power output), setting the initial position and speed of the particle (the position of the particle represents the current unit response time and power output, and the speed represents the change of the particle), and determining the search space. The search space should include the adjustable power range of the unit (minimum power and maximum power) and the allowable response time range (such as response time from seconds to minutes);

[0105] Calculate the fitness function: The fitness function is calculated based on the unit response time and power output. The fitness function measures the quality of each particle solution. Specifically, the fitness function is determined based on the frequency recovery time, power output accuracy and unit start and stop time, and the fitness of each particle is calculated based on the fitness function.

[0106] The expression of the fitness function is:

[0107] ;

[0108] In the formula, f(x) represents the fitness function, T r Indicates the frequency recovery time, P a Indicates the actual regulated power output of the unit, P d Indicates the target power output required by the unit, T s Indicates the start and stop time of the unit. They represent the weights of frequency recovery time, power output accuracy, and unit start and stop time respectively;

[0109] Particle swarm update rule: Update the speed and position of particles according to the movement law of the particle swarm, so as to continuously optimize the quality of the solution. Specifically, it includes adjusting the position and speed of particles according to the speed update formula and the position update formula to find a better solution;

[0110] The speed update formula is:

[0111] ;

[0112] The position update formula is:

[0113] ;

[0114] In the formula, v i (t+1) represents the velocity of the ith particle at the t+1th iteration, v i (t) represents the velocity of the ith particle at the tth iteration, represents the inertia weight, k1 and k2 represent two different learning factors, r1 and r2 represent random numbers in the interval [0, 1], and pbest i represents the best historical position of the ith particle, x i (t) represents the position of the i-th particle at the t-th iteration, gbest represents the global best position, and x i (t+1) represents the position of the ith particle at the t+1th iteration, x i (t) represents the position of the ith particle at the tth iteration;

[0115] Evaluate particles and update the optimal solution: By evaluating the fitness of each particle, update the historical best solution of each particle and the global best solution. Specifically, it includes evaluating the fitness of each particle and updating the historical best solution of the particle, that is, comparing the fitness of the current particle with its historical best fitness. If the current fitness is better, update the historical best solution; update the global best solution to the particle with the best current fitness, that is, compare the historical best solutions of all particles and select the particle with the best fitness as the global best solution;

[0116] Convergence judgment and termination conditions: During the particle swarm search process, the termination conditions are set to determine whether the optimization has converged. If the convergence conditions are met, the optimization process is terminated. Specifically, it is determined whether the maximum number of iterations has been reached, or when the fitness of the global optimal solution does not change much in several iterations, the optimization process is considered to have converged. If not, the iteration is continued. If so, the global optimal solution of the particle swarm is output to obtain the optimal unit response time and power output.

[0117] Step S104: Generate a frequency control instruction according to the optimal unit response time and power output, and control the frequency modulation unit to perform power regulation based on the frequency control instruction to achieve grid frequency adjustment;

[0118] Specifically, a frequency control instruction is generated according to the optimal unit response time and power output, and the frequency modulation unit is controlled to perform power regulation based on the frequency control instruction to achieve the adjustment of the grid frequency, including:

[0119] Generate frequency control instructions: Calculate specific frequency adjustment instructions based on the optimal unit response time and power output, combined with the deviation and change rate of the real-time grid frequency; the frequency recovery target is to determine the target range (such as 50Hz or 60Hz) to which the grid frequency should be adjusted; the power adjustment amount is based on the calculated frequency deviation and change rate, combined with the unit response time and frequency regulation power range, to determine the power value to be adjusted; the adjustment direction is to determine the direction of power adjustment (increase or decrease) according to the frequency deviation (positive or negative);

[0120] Transmit control instructions to frequency-regulating units: Frequency control instructions (including power adjustment amount and response time) are transmitted to corresponding frequency-regulating units through the power dispatching system, ensuring that the transmission speed of the instructions is fast enough so that the frequency-regulating units can respond to frequency changes in a timely manner;

[0121] Control the frequency modulation unit to adjust the power: The frequency modulation unit adjusts the power output according to the received instructions:

[0122] Increase power: When the grid frequency is low, increase the generating power of the unit to push the frequency back up;

[0123] Reduce power: When the grid frequency is high, reduce the generating power of the unit to drive the frequency down;

[0124] The regulation process should be precisely controlled based on factors such as the unit’s start and stop time, response capability, and power range;

[0125] Real-time monitoring and feedback: During the regulation process, the grid frequency will continue to change. The frequency regulation system needs to monitor the frequency change in real time and feed back to the control system. If the grid frequency does not reach the target value, the system will continue to regenerate new control instructions according to the optimization algorithm for adjustment;

[0126] Continuous adjustment and optimization: If the grid frequency fluctuates or deviates during the adjustment process, the system will continuously optimize the control instructions based on the new frequency data and adjustment results to ensure the stability of the grid frequency; the frequency-adjusting unit will adjust the power again according to the new instructions until the frequency stabilizes within the target value range;

[0127] By generating frequency control instructions based on the optimal unit response time and power output, the frequency regulation system can quickly and accurately adjust the unit power, promote grid frequency stability, and ensure that the grid can maintain stable operation when facing load fluctuations or other factors.

[0128] Figure 2 An embodiment of a fast-response unit-compatible frequency regulation system of the present invention is shown.

[0129] In this optional embodiment, the fast-response unit-compatible frequency regulation system includes:

[0130] The frequency data processing module 201 is used to obtain real-time frequency data of the power grid, calculate the deviation between the power grid frequency and the standard frequency, and perform fuzzy processing on the frequency deviation and the rate of change to define fuzzy variables;

[0131] The unit response mode determination module 202 is used to determine the frequency adjustment amplitude by using the fuzzy control rule base, and select the corresponding unit response mode to perform frequency adjustment according to the real-time frequency change;

[0132] The optimization algorithm solving module 203 is used to determine the optimal unit response time and power output based on the particle swarm optimization algorithm, combined with the unit response capability, frequency modulation power range and start-stop time;

[0133] The grid frequency adjustment module 204 is used to generate a frequency control instruction according to the optimal unit response time and power output, and control the frequency-adjusting unit to adjust power based on the frequency control instruction to achieve grid frequency adjustment.

[0134] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0135] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0136] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0137] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0138] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0139] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A fast-response unit-compatible frequency regulation method, characterized in that: include: Obtain real-time frequency data of the power grid, calculate the deviation between the power grid frequency and the standard frequency, perform fuzzy processing on the frequency deviation and change rate, and define fuzzy variables; The frequency adjustment amplitude is determined by using the fuzzy control rule base, and the corresponding unit response mode is selected to adjust the frequency according to the real-time frequency change; Based on the particle swarm optimization algorithm, the optimal unit response time and power output are determined by combining the unit response capability, frequency modulation power range and start-stop time; Generate frequency control instructions based on the optimal unit response time and power output, and control the frequency-modulating unit to adjust power based on the frequency control instructions to achieve grid frequency adjustment.

2. The fast-response unit-compatible frequency regulation method according to claim 1, characterized in that: The real-time frequency data of the power grid is obtained, the deviation between the power grid frequency and the standard frequency is calculated, and the frequency deviation and the rate of change are fuzzy processed. The fuzzy variables are defined as follows: Collect the frequency data of the power grid in real time, and calculate the deviation between the power grid frequency and the standard frequency and the rate of change of the frequency deviation over time; The frequency deviation and the frequency deviation change rate are divided into different fuzzy sets according to the frequency deviation value and the frequency deviation change rate value respectively. The frequency deviation and the change rate are converted into fuzzy values ​​through the membership function, and the input variables of the fuzzy control rules are defined according to the results of the fuzzification processing.

3. The fast-response unit-compatible frequency regulation method according to claim 2, characterized in that: The real-time collection of the frequency data of the power grid and the calculation of the deviation between the power grid frequency and the standard frequency and the rate of change of the frequency deviation over time include: The real-time frequency data of the power grid is obtained through the power grid monitoring system, and the deviation between the current power grid frequency and the standard frequency is calculated. If the deviation is positive, it means that the power grid frequency is higher than the standard frequency. If the deviation is negative, it means that the power grid frequency is lower than the standard frequency. A time window is set to sample the frequency data, the difference between the frequency deviation at the current moment and the frequency deviation at the previous moment is calculated, and the ratio of the difference to the time interval is taken as the rate of change of the frequency deviation.

4. The fast-response unit-compatible frequency regulation method according to claim 2, characterized in that: The converting of the frequency deviation and the rate of change into a fuzzy value by means of a membership function comprises: The triangular membership function is used as the membership function of the frequency deviation, and the frequency deviation is converted into a fuzzy value by using the triangular membership function; The Gaussian membership function is used as the membership function of the frequency deviation change rate, and the frequency deviation change rate is converted into a fuzzy value using the Gaussian membership function.

5. The fast-response unit-compatible frequency regulation method according to claim 4, characterized in that: The expression of the triangle membership function is: ； The expression of Gaussian membership function is: ； Where μ(x) represents the triangular membership function, x represents the frequency deviation value, a represents the minimum value of membership 0, b represents the position of membership 1, and c represents the maximum value of membership 0. represents the Gaussian membership function, represents the frequency deviation change rate, E represents the expected value of the fuzzy set, Represents the standard deviation of the Gaussian function.

6. The fast-response unit-compatible frequency regulation method according to claim 1, characterized in that: The method of determining the frequency adjustment amplitude by using the fuzzy control rule base and selecting the corresponding unit response mode to adjust the frequency according to the real-time frequency change includes: A fuzzy control rule base is formulated according to the fuzzification results of the power grid frequency deviation and the frequency change rate, and the fuzzified frequency deviation and the frequency change rate are input into the fuzzy controller as input variables; According to the input fuzzy variables, the fuzzy inference algorithm is used to infer the rule base and calculate the frequency adjustment range; The frequency adjustment amplitude is converted from the fuzzy domain to the actual control value to obtain the specific power adjustment instruction, and the corresponding unit response mode is selected according to the frequency adjustment amplitude to perform power adjustment.

7. The fast-response unit-compatible frequency regulation method according to claim 1, characterized in that: The particle swarm optimization algorithm is used to determine the optimal unit response time and power output in combination with the unit response capability, frequency modulation power range and start-stop time. With the optimization objectives of minimizing the frequency regulation time, maximizing the unit response accuracy, and minimizing the time it takes for the frequency to recover to the target range, the constraints of the unit response capability, frequency regulation power range, and start-stop time are defined; Initialize the particle swarm, set the initial position and velocity of the particles, and determine the search space, where the search space includes the power range and response time of the unit; The fitness function is determined according to the frequency recovery time, power output accuracy and unit start and stop time, and the fitness of each particle is calculated according to the fitness function; According to the speed update formula and position update formula, adjust the position and speed of the particle to find a better solution; Evaluate the fitness of each particle and update the particle's historical best solution, and update the global best solution to the particle with the best current fitness; Determine whether the maximum number of iterations or convergence conditions are reached. If not, continue to iterate. If so, output the global optimal solution of the particle swarm to obtain the optimal unit response time and power output.

8. The fast-response unit-compatible frequency regulation method according to claim 7, characterized in that: The expression of the fitness function is: ； In the formula, f(x) represents the fitness function, T r Indicates the frequency recovery time, P a Indicates the actual regulated power output of the unit, P d Indicates the target power output required by the unit, T s Indicates the start and stop time of the unit. They respectively represent the weights of frequency recovery time, power output accuracy, and unit start and stop time.

9. The fast-response unit-compatible frequency regulation method according to claim 7, characterized in that: The speed update formula is: ； The position update formula is: ； In the formula, v i (t+1) represents the velocity of the ith particle at the t+1th iteration, v i (t) represents the velocity of the ith particle at the tth iteration, represents the inertia weight, k1 and k2 represent two different learning factors, r1 and r2 represent random numbers in the interval [0, 1], and pbest i represents the best historical position of the ith particle, x i (t) represents the position of the ith particle at the tth iteration, gbest represents the global best position, and x i (t+1) represents the position of the ith particle at the t+1th iteration, x i (t) represents the position of the ith particle at the tth iteration.

10. A fast-response unit-compatible frequency regulation system, characterized in that: It includes frequency data processing module, unit response mode determination module, optimization algorithm solution module and power grid frequency regulation module; The frequency data processing module is used to obtain real-time frequency data of the power grid, calculate the deviation between the power grid frequency and the standard frequency, and perform fuzzy processing on the frequency deviation and the rate of change to define fuzzy variables; The unit response mode determination module is used to determine the frequency adjustment amplitude using the fuzzy control rule base, and select the corresponding unit response mode to perform frequency adjustment according to the real-time frequency change; The optimization algorithm solving module is used to determine the optimal unit response time and power output based on the particle swarm optimization algorithm, combined with the unit response capability, frequency modulation power range and start and stop time; The grid frequency adjustment module is used to generate a frequency control instruction according to the optimal unit response time and power output, and control the frequency modulation unit to adjust the power based on the frequency control instruction to achieve grid frequency adjustment.

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