CN118157226A - Low-load AGC performance optimization method and system - Google Patents
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Abstract
The application discloses a low-load AGC performance optimization method and a system, which relate to the technical field of power generation control, wherein the method comprises the following steps: monitoring data of a target power grid to obtain a power grid load level; when the power grid load level is lower than a preset load level, the real-time operation data of the unit of the target power grid are called; performing simulation optimization adjustment on the real-time running data of the unit to generate a plurality of optimization adjustment schemes; performing unit stability analysis on the plurality of optimized adjustment schemes to generate a plurality of unit stability data; carrying out scheme selection based on the plurality of unit stability data to obtain an optimal adjustment scheme; and controlling the unit of the target power grid according to the optimal regulation scheme. Thereby achieving the technical effect of improving the response performance and the economical performance of AGC (automatic power generation control) under the low-load working condition.
Description
Technical Field
The invention relates to the technical field of power generation control, in particular to a low-load AGC performance optimization method and system.
Background
The thermal power generating unit is required to have deep peak regulation capability and further increase load response rate under the influence of the continuous rising of the new energy duty ratio, the continuous increasing of the scale of the power grid heat supply unit, the change of the energy structure and the strong random fluctuation of the operation of the new energy grid-connected unit. With the change of the structure of the power generation side in the power grid, the assessment standard of the power grid is changed, and the requirements of AGC, primary frequency modulation, low-load operation and deep peak regulation are improved, so that a new target is brought to the operation level and economic operation of the unit. Especially under the low-load operation condition, the fluctuation of the power grid demand is smaller, but the requirements on the response speed and the stability of the unit are higher, and the existing AGC (automatic power generation control) has the technical problems of poor response performance and poor economic performance under the low-load working condition.
Disclosure of Invention
The application aims to provide a low-load AGC performance optimization method and system. The method is used for solving the technical problems of poor response performance and poor economic performance of AGC (automatic power generation control) under a low-load working condition in the prior art.
In view of the above technical problems, the application provides a low-load AGC performance optimization method and a system.
In a first aspect, the present application provides a low load AGC performance optimization method, where the method includes:
monitoring data of a target power grid to obtain a power grid load level;
When the power grid load level is lower than a preset load level, the real-time operation data of the unit of the target power grid are called;
performing simulation optimization adjustment on the real-time running data of the unit to generate a plurality of optimization adjustment schemes;
Performing unit stability analysis on the plurality of optimized adjustment schemes to generate a plurality of unit stability data;
Carrying out scheme selection based on the plurality of unit stability data to obtain an optimal adjustment scheme;
And controlling the unit of the target power grid according to the optimal regulation scheme.
In a second aspect, the present application also provides a low load AGC performance optimization system, wherein the system includes:
The load detection module is used for monitoring data of a target power grid and obtaining a power grid load grade;
The judging and calling module is used for calling the real-time running data of the unit of the target power grid when the power grid load level is lower than a preset load level;
the simulation generation module is used for performing simulation optimization adjustment on the real-time operation data of the unit to generate a plurality of optimization adjustment schemes;
the scheme evaluation module is used for analyzing the unit stability of the plurality of optimization and adjustment schemes and generating a plurality of unit stability data;
the scheme optimization module is used for selecting a scheme based on the plurality of unit stability data to acquire an optimal adjustment scheme;
and the control execution module is used for controlling the unit of the target power grid according to the optimal regulation scheme.
In a third aspect, the present application also provides an electronic device, including: a memory for storing executable instructions; and the processor is used for realizing the low-load AGC performance optimization method provided by the application when executing the executable instructions stored in the memory.
In a fourth aspect, the present application also provides a computer readable storage medium storing a computer program, which when executed by a processor, implements a low load AGC performance optimization method provided by the present application.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The method comprises the steps of monitoring data of a target power grid to obtain a power grid load level; when the power grid load level is lower than a preset load level, the real-time operation data of the unit of the target power grid are called; performing simulation optimization adjustment on the real-time running data of the unit to generate a plurality of optimization adjustment schemes; performing unit stability analysis on the plurality of optimized adjustment schemes to generate a plurality of unit stability data; carrying out scheme selection based on the plurality of unit stability data to obtain an optimal adjustment scheme; and controlling the unit of the target power grid according to the optimal regulation scheme. Thereby achieving the technical effect of improving the response performance and the economical performance of AGC (automatic power generation control) under the low-load working condition.
The foregoing description is only an overview of the present application, and is intended to more clearly illustrate the technical means of the present application, be implemented according to the content of the specification, and be more apparent in view of the above and other objects, features and advantages of the present application, as follows.
Drawings
Embodiments of the invention and the following brief description are described with reference to the drawings, in which:
Fig. 1 is a schematic flow chart of a low load AGC performance optimization method of the present application;
Fig. 2 is a schematic structural diagram of a low load AGC performance optimization system according to the present application;
fig. 3 is a schematic structural view of an exemplary electronic device of the present application.
Reference numerals illustrate: a processor 31, a memory 32, an input device 33, an output device 34.
Detailed Description
The application solves the technical problems of poor response performance and poor economic performance of AGC (automatic power generation control) under a low-load working condition in the prior art by providing the low-load AGC performance optimization method and the system.
In order to solve the above problems, the technical embodiment adopts the following overall concept:
First, data of a target power grid is monitored to obtain a power grid load level. And then, when the load level of the power grid is lower than the preset load level, calling the real-time running data of the unit of the target power grid. And then, performing simulation optimization adjustment on the real-time running data of the unit to generate a plurality of optimization adjustment schemes. And then, performing unit stability analysis on the plurality of optimized adjustment schemes to form a plurality of unit stability data. And finally, carrying out scheme selection based on the plurality of unit stability data to obtain an optimal adjustment scheme, and controlling the unit of the target power grid according to the scheme. Thereby achieving the technical effect of improving the response performance and the economical performance of AGC (automatic power generation control) under the low-load working condition.
In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments, and it should be noted that the described embodiments are only some embodiments of the present application, and not all embodiments of the present application, and it should be understood that the present application is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present application are shown.
Example 1
As shown in fig. 1, the present application provides a low load AGC performance optimization method, which includes:
s100: monitoring data of a target power grid to obtain a power grid load level;
And carrying out data monitoring on the target power grid to acquire the power grid load level, and acquiring and analyzing real-time data related to the power grid. Illustratively, first, a sensor, monitoring device, or other data acquisition device is deployed to acquire real-time operational data of a target grid. The real-time operational data includes information in terms of current, voltage, frequency, power, etc. The collected data is then transmitted to a data center or monitoring system. This may be accomplished by way of a network connection, wireless communication, etc.
Optionally, the target AGC load partitioning rule is based on using data analysis techniques. And processing and analyzing the acquired data, and classifying or grading. Which relates to decision criteria in terms of grid conditions, load levels, etc.
Optionally, a real-time monitoring mechanism is established to continuously monitor the state of the power grid and update the information of the load level in time.
S200: when the power grid load level is lower than a preset load level, the real-time operation data of the unit of the target power grid are called;
In other words, when the load level of the power grid is lower than the preset load level, it is indicated that the unit of the target power grid is in a low-load running state, and low-load automatic power generation control AGC (Automatic Generation Control) is required.
In some embodiments, the unit real-time operation data of the target power grid includes information of power generation power, operation states, operation control parameters, fuel consumption rate, thermal efficiency and the like of the plurality of units. The real-time operation data reflects real-time control and corresponding operation state parameters of the unit of the target power grid.
S300: performing simulation optimization adjustment on the real-time running data of the unit to generate a plurality of optimization adjustment schemes;
Further, performing simulation optimization adjustment on the real-time operation data of the unit to generate a plurality of optimization adjustment schemes, wherein the steps comprise:
Adjusting the real-time operation data of the unit to generate an alternative adjustment scheme set;
Performing unit modeling on the target power grid to generate a unit simulation model;
based on the unit simulation model, performing simulation operation on the alternative adjustment scheme set to generate a simulation operation result;
And screening the alternative adjustment scheme set according to the simulation operation result to generate a plurality of optimized adjustment schemes.
Optionally, the real-time operation data of the unit is utilized to change the adjustment parameters to generate an alternative adjustment scheme set. This includes adjusting the start-up and shut-down of the unit, adjusting the fuel feed rate, adjusting the steam temperature, adjusting the medium pressure, etc.
Illustratively, a unit simulation model is generated, first, key data for each unit in the target grid is collected, including but not limited to unit type, rated power, start-stop time, fuel consumption rate, response time, and the like. And classifying the collected unit data, and dividing the unit data into different unit groups according to the model of the unit. Characteristic parameters including start-up time, minimum and maximum load ranges, fuel efficiency, response speed, etc. are then set for each unit group, and a basic behavior model of the unit is established, wherein the basic behavior model can be an empirical model or a mathematical model, and is established by analyzing specifications or characteristic curves of the target unit.
Optionally, the simulation operation is performed on the alternative adjustment scheme set by establishing a simulation model of each unit. This involves applying the tuning scheme to the unit simulation model and then simulating the operating conditions of the unit. In the simulation operation process, recording the output state, the unit operation state, the load condition and other key parameters at each moment, and generating the simulation operation result. The simulation operation result is used for subsequent analysis and evaluation of the alternative adjustment scheme.
Further, the real-time operation data of the unit is adjusted to generate an alternative adjustment scheme set, and the steps further include:
acquiring a plurality of adjustable parameters based on the real-time operation data of the unit;
Acquiring a unit control interval, and randomly adjusting the plurality of adjustable parameters in the unit control interval to acquire a plurality of adjustment parameter sets;
and carrying out random matching on the plurality of adjustment parameter sets to generate the alternative adjustment scheme set.
Optionally, the real-time operation data of the unit is a set of operation parameters, which can be divided into forced parameters and active parameters, wherein the active parameters are parameters which can be manually controlled by reasonable adjustment control means, and the forced parameters generate corresponding changes along with the adjustment of the active parameters based on the operation characteristics of the target generator unit.
Optionally, for each adjustable parameter, an adjustable interval is determined that is within the allowable range to ensure that the generated adjustment scheme is reasonably viable. The adjustable interval is a unit control interval.
Optionally, the random adjustment is performed in a control interval of each adjustable parameter, so as to generate a plurality of adjustment parameter sets, and the adjustment parameters are generated in the control interval according to a certain rule or random distribution. The method includes moving in random directions in a control section of the unit according to random step sizes, wherein coordinates of the control section corresponding to each moving point are newly generated adjusting parameters, after repeated iterative random movements, the adjusting parameter sets are obtained, and each combination corresponds to an adjusting scheme to be distinguished.
Further, the unit control interval is obtained, and the method further comprises:
acquiring a historical operation data set of a unit;
Extracting extremum from the set of historical operating data based on the plurality of adjustable parameters to obtain a plurality of extremum sets, wherein the plurality of extremum sets comprise a plurality of maxima and a plurality of minima;
and establishing a unit control interval according to the maximum values and the minimum values.
Optionally, historical operation data of the unit are collected through a unit monitoring system, a sensor, a log file and the like, and a unit historical operation data set is generated. These data include the operating status of the unit, measurements from various sensors, control commands, fault records, etc.
Optionally, for each adjustable parameter, its extremum is extracted from the historical operating data set. This includes maxima (maxima) and minima (minima). Determining extrema of the parameters facilitates understanding of the extremes of the parameters by analyzing the historical data.
Further, a control interval for each adjustable parameter is determined from the resulting plurality of sets of extrema. For example, if the historical operating data for a certain parameter indicates a range of values [ a, b ] over a period of time, this range may be taken as the control interval for that parameter.
Optionally, the unit control interval is an extremum range of the adjustable parameter within a certain preset time interval.
Further, the alternative adjustment scheme set is screened according to the simulation operation result to generate a plurality of optimization adjustment schemes, including:
Acquiring operation evaluation indexes including, but not limited to, a unit response speed index, a boiler pressure index and a thermal efficiency index;
Randomly extracting a first simulation operation result of a first alternative adjustment scheme, and carrying out fitness evaluation on the first simulation operation result based on the operation evaluation index to generate a first fitness;
traversing the alternative adjustment scheme set to generate an adaptability set;
And screening the fitness set according to a preset fitness threshold value, and acquiring a plurality of optimal regulation schemes according to screening results.
The fitness is an evaluation value generated based on the target scene requirement and the target unit operation evaluation rule, and is used for quantitatively evaluating and reflecting the operation state of the target unit. The fitness is determined from operation evaluation indexes including a response speed index, a boiler pressure index, a thermal efficiency index, a stability index (fluctuation depth of output, fluctuation frequency, fluctuation amplitude, etc.), and the like.
Optionally, performing simulation operation on each scheme in the alternative adjustment scheme set, and acquiring a corresponding operation evaluation index. Taking the first alternative adjustment scheme as an example, firstly, a simulation operation result is extracted, and then, the fitness evaluation is performed based on an operation evaluation index. The evaluation is achieved by defining a fitness function, wherein a higher fitness value indicates a better performance of the mediation scheme.
Optionally, traversing all the alternative adjustment schemes, executing simulation operation on each scheme, performing fitness evaluation, generating a fitness set, and screening the fitness set according to a preset fitness threshold. Only those schemes with fitness above the threshold are considered satisfactory optimal adjustment schemes. Based on the screening result, a plurality of optimized adjustment schemes meeting the requirements are obtained, and a plurality of optimized adjustment schemes are generated.
Further, performing fitness evaluation on the first simulation operation result to generate a first fitness, including:
establishing an fitness evaluation function, wherein the fitness evaluation function is as follows:
；
Wherein, For the fitness of the ith simulation run result,、、Respectively a first weight, a second weight and a third weight,For the response speed of the unit of the ith simulation operation result,For the boiler pressure as a result of the ith simulation run,Thermal efficiency for the ith simulated operation result;
And carrying out fitness evaluation on the first simulation operation result by adopting the fitness evaluation function to generate first fitness.
Optionally, an adaptability evaluation function is constructed based on the unit response speed, the boiler pressure and the thermal efficiency of the simulation operation result, the evaluation function synthesizes the indexes, the relative importance of the indexes is regulated through weight, and the adaptability evaluation of the unit operation performance is carried out.
Optionally, when evaluating the fitness of the first simulation running result, substituting the corresponding value into the function to obtain the first fitness. By defining the fitness function, comprehensive evaluation of different simulation operation results is facilitated, and a scheme with better performance is selected from a plurality of optimization and adjustment schemes.
Optionally, based on the requirements of different target power plants or target scenes on the running states of the unit, the first, second and third weights are adaptively adjusted according to the importance degree changes of a plurality of running indexes, so that the fitness evaluation function has good mobility.
S400: performing unit stability analysis on the plurality of optimized adjustment schemes to generate a plurality of unit stability data;
further, performing unit stability analysis on the plurality of optimal adjustment schemes to generate a plurality of unit stability data, including:
acquiring unit history abnormal data of the target power grid, wherein the unit history abnormal data comprises a history power generation fluctuation value and a history power generation fluctuation frequency;
And carrying out power generation stability calculation on the plurality of optimization and adjustment schemes according to the historical power generation fluctuation value and the historical power generation fluctuation frequency to obtain a plurality of unit stability data.
Optionally, unit history abnormal data of the target power grid is obtained, wherein the unit history abnormal data comprises a history power generation fluctuation value and a history power generation fluctuation frequency. The data reflects the abnormal condition of the unit in the history operation and is used for evaluating the stability of the unit.
For example, the power generation stability calculation is performed for a plurality of optimal adjustment schemes using data of the historical power generation fluctuation value and the historical power generation fluctuation frequency. The method comprises the steps of calculating amplitude improvement rate, average value improvement rate and the like of simulation operation results of a plurality of optimization adjustment schemes compared with historical power generation fluctuation values and historical power generation fluctuation frequencies. Stability data of the related machine set under different running conditions can be obtained by carrying out unit stability analysis on a plurality of optimized adjustment schemes, and the stability data are used for comparing the advantages and disadvantages of the schemes.
S500: carrying out scheme selection based on the plurality of unit stability data to obtain an optimal adjustment scheme;
optionally, first, a plurality of optimization tuning schemes are ranked based on the fitness evaluation. And selecting the first N schemes with high fitness as primary selection adjustment schemes, then sorting the schemes according to the unit stability data corresponding to the N primary selection adjustment schemes, and selecting the primary selection adjustment scheme with the highest unit stability data to store as a first optimal adjustment scheme.
Optionally, the primary selected adjustment scheme with the second highest unit stability data is selected and stored as a second optimal adjustment scheme, and the second optimal adjustment scheme is used as a preparation scheme, and is used for replacing and performing unit control of the target power grid through the scheme when the first optimal adjustment scheme is not good in performance.
Optionally, the first optimal adjustment scheme and the second optimal adjustment scheme are used for generating the optimal adjustment scheme. The robustness and emergency handling capability of the system can be improved, and the system is more flexible and controllable in the face of different working states and environmental changes.
S600: and controlling the unit of the target power grid according to the optimal regulation scheme.
Optionally, a first optimal adjustment scheme is implemented, and the performance of the system and the operation condition of the unit are continuously monitored. When the system performance is poor or the unit is abnormal, a replacement mechanism is started, and the system is automatically or manually switched to a second optimal adjustment scheme, so that the stability and the reliability of the system are ensured.
Optionally, the performance of the unit and the whole grid is evaluated periodically. By comparing actual operation data with expected performance, feedback and adjustment are carried out, an optimal adjustment scheme is updated, the matching between the actual operation data and system change and demand is ensured, and the system can be operated efficiently, stably and reliably under different working states.
In summary, the low-load AGC performance optimization method provided by the invention has the following technical effects:
The method comprises the steps of monitoring data of a target power grid to obtain a power grid load level; when the power grid load level is lower than a preset load level, the real-time operation data of the unit of the target power grid are called; performing simulation optimization adjustment on the real-time running data of the unit to generate a plurality of optimization adjustment schemes; performing unit stability analysis on the plurality of optimized adjustment schemes to generate a plurality of unit stability data; carrying out scheme selection based on the plurality of unit stability data to obtain an optimal adjustment scheme; and controlling the unit of the target power grid according to the optimal regulation scheme. Thereby achieving the technical effect of improving the response performance and the economical performance of AGC (automatic power generation control) under the low-load working condition.
Example two
Based on the same concept as the low-load AGC performance optimization method in the embodiment, as shown in fig. 2, the present application further provides a low-load AGC performance optimization system, where the system includes:
The load monitoring module is used for monitoring the data of the target power grid and obtaining the power grid load level;
The judging and calling module is used for calling the real-time running data of the unit of the target power grid when the power grid load level is lower than a preset load level;
The simulation generation module is used for performing simulation optimization adjustment on the real-time operation data of the unit to generate a plurality of optimization adjustment schemes;
the scheme evaluation module is used for carrying out unit stability analysis on the plurality of optimized adjustment schemes and generating a plurality of unit stability data;
the scheme optimization module is used for selecting a scheme based on the plurality of unit stability data and acquiring an optimal adjustment scheme;
and the control execution module is used for controlling the unit of the target power grid according to the optimal regulation scheme.
Further, the simulation generating module further includes:
the scheme expansion unit is used for adjusting the real-time operation data of the unit to generate an alternative adjustment scheme set;
The simulation modeling unit is used for performing unit modeling on the target power grid to generate a unit simulation model;
The simulation operation unit is used for performing simulation operation on the alternative adjustment scheme set based on the unit simulation model to generate a simulation operation result;
and the alternative screening unit is used for screening the alternative adjustment scheme set according to the simulation operation result to generate a plurality of optimal adjustment schemes.
Further, the solution expansion unit further includes:
The adjusting parameter unit is used for acquiring a plurality of adjustable parameters based on the real-time running data of the unit;
The constraint adjusting unit is used for acquiring a unit control interval, randomly adjusting the plurality of adjustable parameters in the unit control interval and acquiring a plurality of adjustment parameter sets;
and the random matching unit is used for carrying out random matching on the plurality of adjustment parameter sets and generating the alternative adjustment scheme set.
Further, the constraint adjusting unit further includes:
The method comprises the steps of obtaining a data unit, wherein the data unit is used for obtaining a historical operation data set of a unit;
The extremum extraction unit is used for extracting extremum from the set of historical operation data based on the plurality of adjustable parameters to obtain a plurality of extremum sets, wherein the plurality of extremum sets comprise a plurality of maximum values and a plurality of minimum values;
And the interval generating unit is used for establishing a unit control interval according to the maximum values and the minimum values.
Further, the alternative screening unit further comprises:
The operation evaluation index unit is used for acquiring operation evaluation indexes including, but not limited to, a unit response speed index, a boiler pressure index and a thermal efficiency index;
The fitness evaluation unit is used for randomly extracting a first simulation operation result of the first alternative adjustment scheme, and performing fitness evaluation on the first simulation operation result based on the operation evaluation index to generate first fitness;
The fitness level set unit is used for traversing the alternative adjustment scheme set and generating a fitness level set;
and the screening acquisition unit is used for screening the fitness set according to a preset fitness threshold value and acquiring a plurality of optimization adjustment schemes according to screening results.
Further, the fitness evaluation unit further includes:
and the evaluation function unit is used for establishing an adaptability evaluation function, wherein the adaptability evaluation function is as follows:
；
Wherein, For the fitness of the ith simulation run result,、、Respectively a first weight, a second weight and a third weight,For the response speed of the unit of the ith simulation operation result,For the boiler pressure as a result of the ith simulation run,Thermal efficiency for the ith simulated operation result;
And carrying out fitness evaluation on the first simulation operation result by adopting the fitness evaluation function to generate first fitness.
Further, the solution evaluation module further includes:
The abnormal data unit is used for acquiring unit history abnormal data of the target power grid, wherein the unit history abnormal data comprises a history power generation fluctuation value and a history power generation fluctuation frequency;
And the stability calculation unit is used for calculating the power generation stability of the plurality of optimization adjustment schemes according to the historical power generation fluctuation value and the historical power generation fluctuation frequency to obtain a plurality of unit stability data.
Example III
Fig. 3 is a schematic structural diagram of an exemplary electronic device provided by the present invention, showing a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present invention. The electronic device shown in fig. 3 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 3, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 3, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 3, by bus connection is taken as an example.
The memory 32 is a computer readable storage medium that can be used to store software programs, computer executable programs, and modules, such as program instructions/modules corresponding to a low load AGC performance optimization method in an embodiment of the present invention. The processor 31 executes various functional applications of the computer device and data processing by running software programs, instructions and modules stored in the memory 32, i.e. implements a low load AGC performance optimization method as described above.
It should be understood that the embodiments mentioned in this specification focus on the differences from other embodiments, and that the specific embodiment in the first embodiment is equally applicable to a low load AGC performance optimization system described in the second embodiment, and is not further developed herein for brevity of description.
It is to be understood that both the foregoing description and the embodiments of the present application enable one skilled in the art to utilize the present application. While the application is not limited to the above-mentioned embodiments, obvious modifications, combinations and substitutions of the above-mentioned embodiments are also within the scope of the application.
Claims (10)
1. A method for optimizing performance of a low load AGC, comprising:
monitoring data of a target power grid to obtain a power grid load level;
When the power grid load level is lower than a preset load level, the real-time operation data of the unit of the target power grid are called;
performing simulation optimization adjustment on the real-time running data of the unit to generate a plurality of optimization adjustment schemes;
Performing unit stability analysis on the plurality of optimized adjustment schemes to generate a plurality of unit stability data;
Carrying out scheme selection based on the plurality of unit stability data to obtain an optimal adjustment scheme;
And controlling the unit of the target power grid according to the optimal regulation scheme.
2. The method of claim 1, wherein simulating optimal adjustment of the real-time operational data of the unit to generate a plurality of optimal adjustment schemes comprises:
Adjusting the real-time operation data of the unit to generate an alternative adjustment scheme set;
Performing unit modeling on the target power grid to generate a unit simulation model;
based on the unit simulation model, performing simulation operation on the alternative adjustment scheme set to generate a simulation operation result;
And screening the alternative adjustment scheme set according to the simulation operation result to generate a plurality of optimized adjustment schemes.
3. The method of claim 2, wherein adjusting the crew real-time operational data to generate a set of alternative adjustment schemes comprises:
acquiring a plurality of adjustable parameters based on the real-time operation data of the unit;
Acquiring a unit control interval, and randomly adjusting the plurality of adjustable parameters in the unit control interval to acquire a plurality of adjustment parameter sets;
and carrying out random matching on the plurality of adjustment parameter sets to generate the alternative adjustment scheme set.
4. The method of claim 3, wherein obtaining a crew control interval comprises:
acquiring a historical operation data set of a unit;
Extracting extremum from the set of historical operating data based on the plurality of adjustable parameters to obtain a plurality of extremum sets, wherein the plurality of extremum sets comprise a plurality of maxima and a plurality of minima;
and establishing a unit control interval according to the maximum values and the minimum values.
5. The method of claim 2, wherein screening the set of alternative adjustment schemes according to the simulation run results to generate a plurality of optimal adjustment schemes comprises:
Acquiring operation evaluation indexes including, but not limited to, a unit response speed index, a boiler pressure index and a thermal efficiency index;
Randomly extracting a first simulation operation result of a first alternative adjustment scheme, and carrying out fitness evaluation on the first simulation operation result based on the operation evaluation index to generate a first fitness;
traversing the alternative adjustment scheme set to generate an adaptability set;
And screening the fitness set according to a preset fitness threshold value, and acquiring a plurality of optimal regulation schemes according to screening results.
6. The method of claim 5, wherein performing fitness evaluation on the first simulation run result to generate a first fitness comprises:
establishing an fitness evaluation function, wherein the fitness evaluation function is as follows:
；
Wherein, For the adaptation degree of the ith simulation operation result,/>、/>、/>First, second and third weights,/>, respectivelySet response speed for ith simulation operation result,/>Boiler pressure for the ith simulation run result,/>Thermal efficiency for the ith simulated operation result;
And carrying out fitness evaluation on the first simulation operation result by adopting the fitness evaluation function to generate first fitness.
7. The method of claim 1, wherein performing a unit stability analysis on the plurality of optimal tuning schemes to generate a plurality of unit stability data comprises:
acquiring unit history abnormal data of the target power grid, wherein the unit history abnormal data comprises a history power generation fluctuation value and a history power generation fluctuation frequency;
And carrying out power generation stability calculation on the plurality of optimization and adjustment schemes according to the historical power generation fluctuation value and the historical power generation fluctuation frequency to obtain a plurality of unit stability data.
8. A low load AGC performance optimization system comprising:
The load detection module is used for monitoring data of a target power grid and obtaining a power grid load grade;
The judging and calling module is used for calling the real-time running data of the unit of the target power grid when the power grid load level is lower than a preset load level;
the simulation generation module is used for performing simulation optimization adjustment on the real-time operation data of the unit to generate a plurality of optimization adjustment schemes;
the scheme evaluation module is used for analyzing the unit stability of the plurality of optimization and adjustment schemes and generating a plurality of unit stability data;
the scheme optimization module is used for selecting a scheme based on the plurality of unit stability data to acquire an optimal adjustment scheme;
and the control execution module is used for controlling the unit of the target power grid according to the optimal regulation scheme.
9. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
a processor for implementing a low load AGC performance optimization method according to any one of claims 1 to 7 when executing executable instructions stored in said memory.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a low load AGC performance optimization method according to any one of claims 1 to 7.
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