Wind power plant optimal scheduling method and system based on dual-power prediction system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of wind power generation, in particular to a wind power plant optimal scheduling method and system based on a double-power prediction system, electronic equipment and a storage medium, and the method comprises the steps: extracting a first power prediction data set and a second power prediction data set based on the double-power prediction system; extracting a target prediction report rate and a power prediction accuracy rate in the first power prediction data set and the second power prediction data set; constructing an accuracy rate evaluation model, and extracting a power prediction accuracy rate evaluation result based on the influence of historical data in the same period on the prediction accuracy rate; and outputting an optimal scheduling strategy by taking minimization of power prediction deviation as a target based on a power prediction accuracy evaluation result. The objective of the invention is to realize wind power plant optimization scheduling based on a double-power prediction system and improve the feasibility of a scheduling strategy of a wind power plant under different power prediction results.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and particularly to an optimized scheduling method, system, electronic device and storage medium for a wind farm based on a dual power prediction system. Background Art

[0002] In the field of wind power generation, power dispatching agencies have strict assessments on the power prediction results reported by wind farm sites, covering various indicators such as the reporting rate, medium-term and short-term and ultra-short-term power prediction accuracy rates. At the same time, there are also detailed assessment requirements for the data quality, equipment availability, link status and telemetry signals of various automation devices for the interaction between the site and the dispatching. For example, the medium-term power prediction reporting rate needs to reach 100%, and the ultra-short-term reporting rate also has high standards, and different assessment items correspond to different electricity assessment ratios or rules. However, the current centralized control center faces many dilemmas in the management process. On the one hand, the acquisition of assessment data is extremely passive and there is a delay. Since the dispatching releases the assessment results monthly, it is difficult to detect anomalies in the reporting of prediction data and non-compliance with the accuracy rate in a timely manner, resulting in frequent assessments of the power prediction reporting rate and accuracy rate. On the other hand, the obtained assessment data is only the final summary result, and it is impossible to conduct refined stage-by-stage and time-by-time analysis of each stage of power prediction, which makes it difficult to deeply analyze the root causes of problems. Moreover, for power prediction manufacturers, due to the lack of refined management means, it is impossible to specifically require them to optimize the prediction model. Even if the manufacturers optimize regularly, the assessment results are still not ideal. Some sites are equipped with dual power prediction systems, but because the dispatching only provides a set of general assessment data, it is impossible to conduct refined accuracy analysis on the two systems, and it is difficult to discover their respective advantages and disadvantages and optimize them specifically. In addition, for important grid-connected equipment connected to the dispatching by the site, since the communication and data quality information are in the hands of the dispatching end, the centralized control center and the site are in a passive waiting state for notification and cannot actively monitor and analyze. Once the dispatching fails to detect in time, it will cause continuous assessment problems.

[0003] Currently, the optimized scheduling of a wind farm based on a dual power prediction system aims to comprehensively utilize the two systems to improve the scheduling efficiency and prediction accuracy. However, there is a key problem of insufficient feasibility of the scheduling strategy. Existing scheduling strategies often lack sufficient consideration of the complex and changeable operating environment of the wind farm and fail to comprehensively integrate various influencing factors. When formulating the scheduling strategy, although there is data from the dual power prediction system, due to the inability to conduct refined analysis of its accuracy characteristics and change trends, it is difficult to dynamically adjust the strategy weights according to the actual prediction capabilities of the two systems under different time periods and different meteorological conditions, resulting in the strategy may overly rely on a certain system or evenly distribute the weights and cannot achieve the optimal decision. Summary of the Invention

[0004] The purpose of the present invention is to realize the optimal dispatching of wind farms based on the dual power prediction system, improve the feasibility of the dispatching strategy of wind farms under different power prediction results, and provide a method, system, electronic device and storage medium for optimizing the dispatching of wind farms based on the dual power prediction system. The technical solutions adopted are as follows: The technical solution of the first aspect of the present invention provides a wind farm optimization scheduling method based on a dual power prediction system, the method comprising: Extracting a first power prediction data set and a second power prediction data set based on a dual power prediction system; Extracting a target prediction reporting rate and a power prediction accuracy rate from the first power prediction data set and the second power prediction data set; Construct an accuracy evaluation model, extract the power prediction accuracy evaluation results based on the impact of historical data on the prediction accuracy; Based on the power prediction accuracy evaluation results, the optimal scheduling strategy is output with the goal of minimizing the power prediction deviation.

[0005] Further, extracting the first power prediction data set and the second power prediction data set based on the dual power prediction system includes: Extracting a first power prediction value sequence, a second power prediction value sequence, a first meteorological data sequence, and a second meteorological data sequence from the dual power prediction system according to a preset time resolution; Performing data cleaning and alignment preprocessing on the first power prediction value sequence, the second power prediction value sequence, the first meteorological data sequence, and the second meteorological data sequence; Associating the preprocessed first power prediction value sequence and the first meteorological data sequence to obtain a first power prediction data set; The preprocessed second power prediction value sequence and the second meteorological data sequence are associated to obtain a second power prediction data set.

[0006] Further, extracting the target prediction reporting rate in the first power prediction data set and the second power prediction data set includes: Traversing the timestamp and related record information corresponding to each power prediction value in the first power prediction data set and the second power prediction data set; According to the preset reporting time interval, the actual reporting times of the dual power prediction system within the statistical period are extracted; The predicted reporting rate is extracted based on the actual number of reports of the dual power prediction system.

[0007] Further, extracting the power prediction accuracy rates in the first power prediction data set and the second power prediction data set includes: Based on the first power prediction data set and the second power prediction data set, the root mean square error between the power prediction value and the actual power measurement value at each time point is calculated respectively, and the expression is:

[0008] In the formula, represents the root mean square error; Indicates the total number of data; Indicates The power prediction value at each time point; Indicates The actual power measurement value at a point in time.

[0009] Furthermore, an accuracy evaluation model is constructed, and based on the impact of historical data of the same period on the prediction accuracy, the power prediction accuracy evaluation results are extracted, including: Acquire historical data of the dual power prediction system in the same period, extract features related to the power prediction accuracy from the historical data in the same period, and construct a first feature matrix and a second feature matrix, wherein the first feature matrix and the second feature matrix at least include: meteorological data, unit operation status data, and root mean square error; The accuracy evaluation model is constructed using the random forest algorithm, and the accuracy evaluation model is trained using the first feature matrix and the second feature matrix respectively; The trained accuracy evaluation model is used to output the accuracy evaluation results of the dual power prediction system.

[0010] Furthermore, the expression of the accuracy evaluation result of the dual power prediction system is:

[0011] In the formula, Indicates The accuracy evaluation results of the power prediction system; Indicates The self-use weight adjustment factor of the power forecast system, ; Indicates The number of decision trees included in the accuracy evaluation model constructed by each power prediction system; Indicates In the power prediction system, A decision tree is used to calculate the input feature vector The output prediction accuracy value; represents the number of features; Indicates The feature importance weight vector corresponding to the power prediction system The elements; Indicates The value of the -th feature in the feature matrix of a power prediction system.

[0012] Furthermore, based on the power prediction accuracy evaluation result, the optimal scheduling strategy output with the goal of minimizing the power prediction deviation includes: Establish the operation constraints of the wind farm units and the grid connection constraints; Construct the objective function and use the linear programming algorithm to solve the optimal scheduling strategy. The expression of the objective function is:

[0013] In the formula, represents the minimization operator; represents the time period of the scheduling decision; represents the first balance coefficient, calculated according to the self - use weight adjustment factor of the first power prediction system; represents the second balance coefficient, calculated according to the self - use weight adjustment factor of the second power prediction system; represents the power prediction value of the first power prediction system at time; represents the power prediction value of the second power prediction system at time; represents the actual power measurement value at time; represents the weight given to the first power prediction system at time based on the accuracy evaluation result, ; represents the weight given to the second power prediction system at time based on the accuracy evaluation result, ; represents the accuracy evaluation result of the first power prediction system; represents the accuracy evaluation result of the second power prediction system.

[0014] The technical solution of the second aspect of the present invention provides a wind farm optimal scheduling system based on a dual - power prediction system, which adopts the wind farm optimal scheduling method based on a dual - power prediction system provided by the technical solution of the first aspect of the present invention. The system includes: A data acquisition module configured to extract a first power prediction data set and a second power prediction data set based on the dual - power prediction system; A feature extraction module configured to extract the target prediction reporting rate and the power prediction accuracy in the first power prediction data set and the second power prediction data set; An accuracy evaluation module, configured to build an accuracy evaluation model, extract the power prediction accuracy evaluation result based on the influence of historical data in the same period on the prediction accuracy; A scheduling strategy optimization module, configured to output an optimal scheduling strategy with the goal of minimizing the power prediction deviation based on the power prediction accuracy evaluation result.

[0015] The technical solution of the third aspect of the present invention provides an electronic device, which includes: a processor and a memory communicatively connected to the processor; wherein, the memory stores instructions executable by the processor, and when the instructions are executed by the processor, the processor is enabled to execute the steps of the wind farm optimal scheduling method based on the dual power prediction system according to the technical solution of the first aspect of the present invention.

[0016] The technical solution of the fourth aspect of the present invention provides a computer-readable storage medium, on which a program for implementing the wind farm optimal scheduling method based on the dual power prediction system is stored, and when the program for implementing the wind farm optimal scheduling method based on the dual power prediction system is executed by a processor, the steps of the wind farm optimal scheduling method based on the dual power prediction system according to the technical solution of the first aspect of the present invention are implemented.

[0017] The present invention has the following beneficial effects: The wind farm optimal scheduling method based on the dual power prediction system provided by the present invention operates through multi-step collaboration. The extracted dual power prediction data set ensures that the prediction information of the dual power prediction system can be fully considered; the constructed accuracy evaluation model uses historical data in the same period to deeply analyze the influencing factors, so that the output accuracy evaluation result can more scientifically reflect the reliability of the dual power prediction system under different working conditions; finally, an optimal scheduling strategy with the goal of minimizing the power prediction deviation is formulated based on the accuracy evaluation result, and the weight allocation can be dynamically adjusted according to the real-time accuracy of each system. When the accuracy of one system is high, more reliance is placed on its prediction result, avoiding decision-making mistakes caused by blind averaging or fixed weight allocation. At the same time, this method comprehensively considers the associated influence of historical data in the same period and the power prediction accuracy, enabling the wind farm to more reasonably arrange the power generation plan, optimize the unit load distribution, reduce the risks brought by inaccurate prediction or unreasonable scheduling in the complex and changeable operating environment, and thus significantly improve the stability of the wind farm operation. Description of the Drawings

[0018] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] Figure 1 It is a flowchart of a wind farm optimal scheduling method based on a dual power prediction system provided by an embodiment of the present invention. Figure 2 It is a schematic structural diagram of a wind farm optimal scheduling system based on a dual power prediction system provided by an embodiment of the present invention. Detailed implementation manners

[0020] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a wind farm optimal scheduling method, system, electronic device, and storage medium based on the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0022] The following specifically describes the specific solutions of a wind farm optimal scheduling method, system, electronic device, and storage medium based on the present invention with reference to the accompanying drawings.

[0023] Please refer to Figure 1 , which shows a flowchart of a wind farm optimal scheduling method based on a dual power prediction system provided by an embodiment of the present invention. The method includes: Step S100: Extract the first power prediction dataset and the second power prediction dataset based on the dual - power prediction system. Specifically, the dual - power prediction system is composed of two independently operating power prediction systems configured in a wind farm. They each use different prediction algorithms, models, and data - processing methods to predict the future power generation of the wind farm. Usually, one system focuses on accurate prediction under a specific meteorological condition, and the other may have better power prediction at different time scales or different operating conditions. By using these two systems simultaneously, power prediction information can be provided for the dispatching decision - making of the wind farm from multiple perspectives, increasing the scientific nature and reliability of the decision - making, and thus better coping with the complex and variable power generation changes in the wind farm and meeting the relevant requirements of the power dispatching agency. Step S100 specifically includes: Step S110: Extract the first power prediction value sequence, the second power prediction value sequence, the first meteorological data sequence, and the second meteorological data sequence from the dual - power prediction system according to a preset time resolution. For the extraction of the meteorological data sequence, relying on the meteorological data sources integrated or accessed by the dual - power prediction system itself, meteorological data such as wind speed, wind direction, air pressure, temperature, and humidity can be extracted respectively at the same time resolution to form the first meteorological data sequence and the second meteorological data sequence. Step S120: Perform data cleaning and alignment pre - processing on the first power prediction value sequence, the second power prediction value sequence, the first meteorological data sequence, and the second meteorological data sequence. Specifically, for the power prediction value sequence, reasonable threshold ranges are set based on the installed capacity of the wind farm and the statistical characteristics of historical power data to judge outliers, and the outliers are removed from the sequence. For the meteorological data sequence, outliers can be identified based on meteorological common sense and the range of long - term accumulated local meteorological data, and the abnormal data are determined and removed. For the missing values in the data, linear interpolation or spline interpolation can be used to fill them to ensure the integrity and rationality of the data. Due to possible time - synchronization errors or inconsistent data - recording frequencies between the power prediction system and the meteorological data acquisition system, a time - alignment operation is required. Specifically, based on the time stamps of the power prediction value sequence, the corresponding meteorological data sequences are matched in chronological order. If the time points of the meteorological data do not exactly coincide with the time points of the power prediction values, the nearest - neighbor principle can be used to select the meteorological data record closest to the power prediction time point to ensure that each power prediction value can correspond to accurate meteorological data, making the different sequences accurately aligned in time for subsequent correlation and analysis.

[0024] Step S130: Correlate the preprocessed first power prediction value sequence and the first meteorological data sequence to obtain the first power prediction data set. Specifically, through database operations, each power prediction value in the first power prediction value sequence after cleaning and alignment preprocessing is correlated one by one with the meteorological data items in the first meteorological data sequence corresponding to the same time point, forming a two-dimensional data table structure containing power prediction values and related meteorological data. Each row represents a data record at a specific time point, and the columns are the power prediction value and different meteorological element values, thus constructing the first power prediction data set. Step S140: Correlate the preprocessed second power prediction value sequence and the second meteorological data sequence to obtain the second power prediction data set. The extraction method of the second power prediction data set is the same as that of the first power prediction data set. The sub-steps of Step S100 cooperate closely, from data acquisition, processing to integration, providing a high-quality and strongly correlated data basis for the optimal dispatching of the wind farm based on the dual power prediction system.

[0025] Step S200: Extract the target prediction reporting rate and power prediction accuracy rate from the first power prediction data set and the second power prediction data set. Step S200 specifically includes: Step S210: Extracting the target prediction reporting rate from the first power prediction data set and the second power prediction data set includes: Step S211: Traverse the time stamps and related record information corresponding to each power prediction value in the first power prediction data set and the second power prediction data set. Specifically, read the first power prediction data set and the second power prediction data set. These two data sets are both structured data sets (in the form of two-dimensional data tables) containing information such as power prediction values and related meteorological data processed in the previous steps. For the first power prediction data set, starting from the first row of data, sequentially read the time stamp field information corresponding to the power prediction value in each row, and at the same time obtain other possible related record information in this row of data, such as data source identification, data quality marking, etc. In the same way, perform a traversal operation on the second power prediction data set to ensure that the complete time stamps and related record information corresponding to all power prediction values in the two data sets are obtained. The above information will be used as the basis for determining the reporting situation later. Through the time stamp, the specific time point corresponding to each power prediction value can be determined. Step S212: Extract the actual reporting times of the dual power prediction system within the statistical period according to the preset reporting time interval. In this embodiment, according to the internal management requirements of the wind farm, the preset reporting time interval is determined. Preferably, the power prediction data is reported once every 30 minutes. Then, taking a day as the statistical period, the number of times to be reported within a day is 48 times. For the first power prediction system, after extracting the timestamp information from its dataset, according to the preset reporting time interval, check whether there is a corresponding power prediction value record at each reporting time point in turn. If there is, count it once; after traversing all the data within the statistical period, obtain the actual reporting times of the first power prediction system within the statistical period. Using the same method, operate on the second power prediction system; Step S213: Extract the prediction reporting rate based on the actual reporting times of the dual power prediction system. Specifically, after obtaining the actual reporting times of the first power prediction system and the second power prediction system within the statistical period, calculate the prediction reporting rate according to the following formula: Prediction reporting rate = (Actual reporting times / Should - report times) × 100%. Record and organize the two calculated prediction reporting rates. The reporting rate data will be used as one of the indicators to evaluate the operation stability and compliance of the dual power prediction system, and can be used for comparative analysis with other wind farms or industry standards, and can also provide a reference basis for the internal management decision - making of the wind farm. By systematically traversing, analyzing, and calculating the first power prediction dataset and the second power prediction dataset, the prediction reporting rate of the dual power prediction system can be accurately extracted. It helps the wind farm comprehensively and accurately grasp the reporting situation of the two power prediction systems within the specified statistical period, and promptly discover possible problems such as untimely reporting and missed reporting, so as to effectively monitor the operation stability of the power prediction system and the integrity of data transmission. Based on the obtained prediction reporting rate, the wind farm can better evaluate its own performance in meeting the reporting requirements of the power dispatching agency, early - warning potential assessment risks, and providing data support for subsequent in - depth analysis of the performance differences of the power prediction system and the formulation of targeted optimization measures, which helps to improve the operation management level and the scientific nature of dispatching decisions of the entire wind farm.

[0026] Step S220: Extracting the power prediction accuracy rates in the first power prediction dataset and the second power prediction dataset includes: Step S221: Based on the first power prediction dataset and the second power prediction dataset, calculate the root - mean - square error between the power prediction value and the actual power measurement value at each time point respectively. Its expression is:

[0027] In the formula, represents the root - mean - square error; represents the total number of data; represents the The power prediction value at each time point; Indicates The actual power measurement value at a time point; this formula uses the root mean square error as a quantitative indicator, and accurately calculates the prediction accuracy of each power prediction system in different time periods based on the first power prediction data set and the second power prediction data set; it can intuitively and accurately measure the degree of deviation between the two power prediction systems and the actual power, so that wind farm operators can clearly understand the reliability of the prediction results of each system under different working conditions; by comparing the root mean square errors of the two systems, it is helpful to discover the advantages and disadvantages of different systems in terms of prediction accuracy, and then provide indicators for the subsequent selection of better prediction results for scheduling decisions, evaluation of power prediction system performance, and targeted optimization and improvement of prediction models. Step S300: construct an accuracy evaluation model, and extract power prediction accuracy evaluation results based on the impact of historical data of the same period on prediction accuracy; Step S300 specifically includes: Step S310: Obtain historical data of the dual power prediction system for the same period, extract features related to the accuracy of power prediction from the historical data for the same period, and construct a first feature matrix and a second feature matrix. The first feature matrix and the second feature matrix include at least: meteorological data, unit operation status data, and root mean square error; specifically, first, determine the time range of the historical data for the same period, and select historical data within 3-5 years corresponding to the time period for which power prediction is currently required. For example, if a prediction is made for a certain period in the current winter, then extract data from the same period in previous winters; the historical data for the same period must cover the historical power prediction value records generated by the dual power prediction system, and should also include relevant records of actual power measurement values ​​at corresponding moments; from the data storage system of the wind farm, extract the power prediction data of the first power prediction system and the second power prediction system in the corresponding historical period respectively through data query statements according to timestamps and corresponding power prediction system identifiers; at the same time, it is also necessary to collect other relevant auxiliary data in the time period, such as meteorological data and unit operation status data; Extraction of meteorological data features: Basic meteorological element characteristics: For the collected historical meteorological data, common meteorological elements are extracted as features, including but not limited to average wind speed, wind speed standard deviation, wind direction and its frequency of change, daily maximum temperature, daily minimum temperature, and average air pressure; these basic characteristics can intuitively reflect the potential impact of the meteorological conditions at that time on the accuracy of power forecasting; Meteorological change trend characteristics: Extract the change rate of meteorological elements over time, including but not limited to the wind speed change rate and temperature change rate; these change trend characteristics help capture the impact of meteorological dynamic change processes on power forecasting; For unit operation status data feature extraction: Characteristics of basic operation parameters of the unit: From the operation records of the wind farm units, extract characteristics including but not limited to the cumulative operation duration, cumulative start-stop times, average operation duration per time, and average fault-free operation time interval of each unit; these characteristics can reflect the impact of the operation stability and fatigue degree of the units on power prediction as a whole; Fault-related characteristics: Conduct a detailed classification and statistics on the faults that occurred in the history of the units, and record the occurrence frequencies of different types of faults; at the same time, calculate the first operation power deviation and fault recovery time after each fault repair; through these characteristics, the model can learn the relationship between fault factors and the power prediction accuracy rate; For the extraction of root mean square error characteristics: According to the calculation formula of the root mean square error (RMSE), calculate the root mean square error corresponding to the first power prediction system and the second power prediction system respectively for each historical same-period time period; the root mean square error can intuitively reflect the deviation degree between the power prediction value and the actual value during this time period, and the accuracy evaluation model can directly learn the size of the prediction deviation in different situations; Construct the first feature matrix and the second feature matrix based on the above meteorological data characteristics, unit operation status data characteristics, and fault-related characteristics: For the first feature matrix, arrange the feature values corresponding to each historical same-period time period in sequence as rows, that is, the data in the first row are all the feature values extracted from the first historical same-period time period, and fill them into the corresponding columns of the matrix in sequence according to the feature order, and so on to construct the entire first feature matrix; in the same way, construct the second feature matrix to ensure that the feature meanings at the corresponding positions in the two feature matrices are the same, but they respectively correspond to different power prediction systems; finally, the two feature matrices completely integrate the multi-dimensional information related to the historical same-period power prediction accuracy rate of the dual power prediction system, providing a standardized data structure basis for the subsequent training of the accuracy evaluation model; Step S310 systematically obtains the historical same-period data of the dual power prediction system, and then constructs the first feature matrix and the second feature matrix. This process provides a data basis for the subsequent construction of the accuracy evaluation model, can quantitatively integrate various key factors in the complex operation environment of the wind farm, enables the model to fully learn the mutual relationship between different factors and their comprehensive impact on the power prediction accuracy rate, so as to more accurately discover the laws in historical data, reliably evaluate the power prediction accuracy rate of the dual power prediction system under different working conditions, provide strong data support for the optimization dispatching decision of the wind farm, and effectively improve the rationality and adaptability of the dispatching strategy.

[0028] Step S320: Construct an accuracy evaluation model using the random forest algorithm, and train the accuracy evaluation model using the first feature matrix and the second feature matrix respectively; specifically, construct an accuracy evaluation model using the random forest algorithm, and perform operations on the first power prediction system and the second power prediction system respectively; First, initialize two random forest regression models. Since what needs to be evaluated is accuracy, and accuracy can be regarded as a prediction problem of continuous numerical values, a regression model is preferably used in this embodiment; then set the value range of key parameters. For example, the value range of the number of decision trees is preferably between 100 and 500 trees, the maximum depth of each tree is preferably about 5 to 20 layers, and the minimum number of samples required for internal node splitting is preferably 2 to 10; It should be noted that the approximate ranges of these initial parameters will be determined by hyperparameter optimization to obtain the best values later; For the first feature matrix and the second feature matrix, their corresponding historical data in the same period are respectively divided into a training set, a validation set, and a test set. 70% of the data is used as the training set, 20% as the validation set, and 10% as the test set. At the same time, the division is carried out by random sampling, but it is necessary to ensure that the distribution of various features in different subsets is relatively balanced to avoid data skew affecting the model training effect; Then, use the training set to train the two random forest models respectively. Take the features in the feature matrix as the input (for the first power prediction system and the second power prediction system, they respectively correspond to each row of data in their respective feature matrices), and the corresponding historical power prediction accuracy as the output target value. During the training process, use the grid search hyperparameter optimization method combined with cross-validation to find the optimal combination of hyperparameters; In this way, an accuracy evaluation model for the first power prediction system is trained for the first feature matrix, and an accuracy evaluation model for the second power prediction system is trained for the second feature matrix. Each of the two models learns the complex relationship between the features and the accuracy in the corresponding system for subsequent accuracy evaluation.

[0029] Step S330: Use the trained accuracy evaluation model to output the accuracy evaluation result of the dual power prediction system; specifically, the expression of the accuracy evaluation result of the dual power prediction system is:

[0030] In the formula, represents the accuracy evaluation result of the th power prediction system; represents the self-use weight adjustment factor of the th power prediction system, ; represents the number of decision trees included in the accuracy evaluation model constructed by the th power prediction system; represents the th in the A decision tree is used to calculate the input feature vector The output prediction accuracy value; represents the number of features; Indicates The feature importance weight vector corresponding to the power prediction system The elements; Indicates The characteristic matrix of the power prediction system The The value of a feature; specifically, obtain the relevant data required for power prediction at the current moment, and extract the corresponding features from the real-time dual power prediction system data, meteorological data, unit operation status data and other information sources according to the same feature extraction method as when constructing the feature matrix to form the current feature vector; for example, when constructing the feature matrix in step S320, the average wind speed, the cumulative running time of the unit, the root mean square error and other features are extracted, then at this time, the specific values ​​of these corresponding features should also be obtained to form the feature vector; the current feature vector corresponding to the first power prediction system is input into the trained accuracy evaluation model of the first power prediction system, and each decision tree in the model will output a prediction accuracy value according to the input feature vector, and then calculate according to the expression of the above accuracy evaluation result; the same operation steps are used to input the current feature vector corresponding to the second power prediction system into its trained accuracy evaluation model, and the current accuracy evaluation result of the second power prediction system is calculated according to the expression of the accuracy evaluation result; finally, the accuracy evaluation result of the dual power prediction system in the current situation can be obtained, which can intuitively understand the reliability of the prediction results of the two systems under the current environment and working conditions, and provide a key basis for the subsequent formulation of scheduling strategies; Step S300 uses the random forest algorithm to build and train the accuracy evaluation model, and then outputs the accuracy evaluation results of the dual power prediction system. It can fully explore the complex relationship between the hidden features in the historical data of the same period and the power prediction accuracy. With the help of the random forest algorithm's good ability to handle nonlinear problems and resist overfitting, the constructed model can better fit the data rules and accurately capture the impact of different factors on the accuracy. The final output accuracy evaluation results provide an intuitive and quantitative reference for wind farm operators, which helps to reasonably select better prediction results or dynamically adjust the use of different systems in subsequent scheduling decisions based on the accuracy performance of different systems, thereby improving the scientificity and rationality of wind farm scheduling strategies and reducing the risks and losses caused by inaccurate power predictions.

[0031] Step S400: Based on the power prediction accuracy evaluation result, outputting the optimal scheduling strategy with the goal of minimizing the power prediction deviation; Step S400 specifically includes: Step S410: Establish the operating constraints of the wind farm units and the grid connection constraints; specifically, first obtain the rated power parameters of each unit in the wind farm and determine the minimum and maximum of the generated power of each unit; for all units in the wind farm, the lower and upper limits of the total generated power must be satisfied at any time, which can be expressed as:

[0032]

[0033] In the formula, represents the total number of wind turbines; represents the th unit's generated power at time; and are the lower and upper limits of the total generated power of the wind farm respectively; Consult the technical manual of the unit or analyze the actual operation data to determine the upward ramp rate and downward ramp rate of each unit; the ramp rate represents the maximum amplitude that the unit's power can increase or decrease per unit time, and there are constraint conditions:

[0034] This constraint condition is used to ensure that the unit's power change is within a reasonable range, avoid damaging the unit equipment and ensure the stability of the grid connection, and prevent problems such as mechanical stress and electrical shock caused by too fast power adjustment of the unit; According to the operation specifications of the grid and the requirements for the wind farm connection point, determine the allowable range of the power factor, which can be expressed as:

[0035] Specifically, the grid requires the power factor at the wind farm connection point to be maintained between 0.9 and 1 to ensure the power quality and reactive power balance of the grid. By monitoring the active power and reactive power of the wind farm, calculate the power factor , and meet this constraint condition by adjusting the reactive power output of the unit or using reactive power compensation equipment; for example, when the power factor is lower than the lower limit , the input of the reactive power compensation device can be increased or the excitation current of the unit can be adjusted to increase the reactive power output, so that the power factor rises back to the allowable range; conversely, when the power factor is higher than the upper limit , the reactive power output is reduced.

[0036] Obtain the maximum power capacity that the grid can accept the wind farm at the current moment , which is usually comprehensively determined by the power grid dispatching department according to the overall load condition of the power grid, the capacity of the transmission line, the power generation condition of other power sources, etc. The constraint condition for the total power generation of the wind farm is as follows:

[0037] This constraint condition is used to prevent the safe and stable operation of the power grid from being affected due to excessive power generation of the wind farm.

[0038] Step S420: Construct the objective function and use the linear programming algorithm to solve the optimal scheduling strategy. The expression of the objective function is:

[0039] In the formula, represents the minimization operator; represents the time period of the scheduling decision; represents the first balance coefficient, which is calculated according to the self-use weight adjustment factor of the first power prediction system; represents the second balance coefficient, which is calculated according to the self-use weight adjustment factor of the second power prediction system; represents the power prediction value of the first power prediction system at moment; represents the power prediction value of the second power prediction system at moment; represents the actual power measurement value at moment; represents the weight given to the first power prediction system at moment based on the accuracy evaluation result, ; represents the weight given to the second power prediction system at moment based on the accuracy evaluation result, ; represents the accuracy evaluation result of the first power prediction system; represents the accuracy evaluation result of the second power prediction system; , ; Finally, the objective function and the established operating constraints of the wind farm units and grid connection constraints are transformed into the standard form of a linear programming problem, and a suitable linear programming solver is selected, such as the PuLP library in Python or CPLEX and Gurobi linear programming software; the transformed objective function and constraint conditions are input into the solver, and according to the linear programming algorithm, the optimal solution that minimizes the objective function is searched within the feasible solution space that satisfies the constraint conditions, that is, the power generation power setting values of each unit at different time points are obtained, and these values constitute the optimal scheduling strategy. The obtained power setting values can maximize the actual output power of the wind farm close to the predicted power and reduce the deviation impact caused by inaccurate prediction; in the process of constructing the wind farm optimal scheduling method based on the dual power prediction system, it involves multiple complex links such as the analysis of historical data, the application of the accuracy evaluation model, and the consideration of different constraint conditions. The ultimate goal is to generate an optimal decision after comprehensively weighing various factors; the power setting value is to present the results of these complex analyses and trade-offs in an intuitive and executable manner, facilitating the wind farm operators to directly arrange the operation of the units and conduct power control based on these values, and effectively applying the results of the optimal scheduling to the daily operation of the wind farm to ensure the effective implementation of the efficient optimal scheduling of the entire wind farm.

[0040] In step S400, by establishing the operating constraints of the wind farm units and the grid connection constraints, and constructing an objective function based on the power prediction accuracy evaluation results, and using the linear programming algorithm to solve the optimal scheduling strategy, a scientific scheduling decision considering various factors such as the operating characteristics of the internal units of the wind farm, the grid connection requirements, and the power prediction accuracy is realized; by constructing the objective function and solving the optimal scheduling strategy, the prediction information of the dual power prediction system is fully utilized, and the weights are reasonably allocated according to its accuracy evaluation results, aiming at minimizing the power prediction deviation, so that the power generation plan of the wind farm can be closer to the actual power demand, improving the power generation efficiency of the wind farm and reducing problems such as wind curtailment or grid scheduling difficulties caused by inaccurate power prediction, thus enhancing the competitiveness and reliability of the wind farm in the electricity market and promoting the effective utilization and sustainable development of wind power resources.

[0041] Please refer to Figure 2 As shown in the figure, the technical solution of the second aspect of the present invention provides a wind farm optimal scheduling system based on a dual power prediction system, which adopts the wind farm optimal scheduling method based on the dual power prediction system provided by the technical solution of the first aspect of the present invention. The system includes: A data acquisition module configured to extract a first power prediction data set and a second power prediction data set based on the dual power prediction system; A feature extraction module configured to extract the target prediction reporting rate and the power prediction accuracy in the first power prediction data set and the second power prediction data set; An accuracy evaluation module is configured to construct an accuracy evaluation model and extract a power prediction accuracy evaluation result based on the impact of historical contemporaneous data on prediction accuracy; The scheduling strategy optimization module is configured to output the optimal scheduling strategy based on the power prediction accuracy evaluation result with the goal of minimizing the power prediction deviation.

[0042] The technical solution of the third aspect of the present invention provides an electronic device, comprising: a processor and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor so that the processor can execute the steps of the wind farm optimization scheduling method based on the dual power prediction system described in the technical solution of the first aspect of the present invention.

[0043] The technical solution of the fourth aspect of the present invention provides a computer-readable storage medium, on which is stored a program for implementing a wind farm optimization scheduling method based on a dual power prediction system. The program for implementing a wind farm optimization scheduling method based on a dual power prediction system is executed by a processor to implement the steps of the wind farm optimization scheduling method based on a dual power prediction system described in the technical solution of the first aspect of the present invention.

[0044] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0045] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A wind farm optimization scheduling method based on a dual power prediction system, characterized in that: The method comprises: Extracting a first power prediction data set and a second power prediction data set based on a dual power prediction system; Extracting a target prediction reporting rate and a power prediction accuracy rate from the first power prediction data set and the second power prediction data set; Construct an accuracy evaluation model, extract the power prediction accuracy evaluation results based on the impact of historical data on the prediction accuracy; Based on the power prediction accuracy evaluation results, the optimal scheduling strategy is output with the goal of minimizing the power prediction deviation.

2. The wind farm optimization scheduling method based on the dual power prediction system according to claim 1, characterized in that: Extracting the first power prediction data set and the second power prediction data set based on the dual power prediction system includes: Extracting a first power prediction value sequence, a second power prediction value sequence, a first meteorological data sequence, and a second meteorological data sequence from the dual power prediction system according to a preset time resolution; Performing data cleaning and alignment preprocessing on the first power prediction value sequence, the second power prediction value sequence, the first meteorological data sequence, and the second meteorological data sequence; Associating the preprocessed first power prediction value sequence and the first meteorological data sequence to obtain a first power prediction data set; The preprocessed second power prediction value sequence and the second meteorological data sequence are associated to obtain a second power prediction data set.

3. The wind farm optimization scheduling method based on the dual power prediction system according to claim 1, characterized in that: Extracting the target prediction reporting rate from the first power prediction data set and the second power prediction data set includes: Traversing the timestamp and related record information corresponding to each power prediction value in the first power prediction data set and the second power prediction data set; According to the preset reporting time interval, the actual reporting times of the dual power prediction system within the statistical period are extracted; The predicted reporting rate is extracted based on the actual number of reports of the dual power prediction system.

4. The wind farm optimization scheduling method based on the dual power prediction system according to claim 3, characterized in that: Extracting the power prediction accuracy rates from the first power prediction data set and the second power prediction data set includes: Based on the first power prediction data set and the second power prediction data set, the root mean square error between the power prediction value and the actual power measurement value at each time point is calculated respectively, and the expression is: In the formula, represents the root mean square error; Indicates the total number of data; Indicates The power prediction value at each time point; Indicates The actual power measurement value at a point in time.

5. The wind farm optimization scheduling method based on the dual power prediction system according to claim 1, characterized in that: Construct an accuracy evaluation model based on the impact of historical data on prediction accuracy, and extract the power prediction accuracy evaluation results including: Acquire historical data of the dual power prediction system in the same period, extract features related to the power prediction accuracy from the historical data in the same period, and construct a first feature matrix and a second feature matrix, wherein the first feature matrix and the second feature matrix at least include: meteorological data, unit operation status data, and root mean square error; The accuracy evaluation model is constructed using the random forest algorithm, and the accuracy evaluation model is trained using the first feature matrix and the second feature matrix respectively; The trained accuracy evaluation model is used to output the accuracy evaluation results of the dual power prediction system.

6. The wind farm optimization scheduling method based on the dual power prediction system according to claim 5, characterized in that: The expression of the accuracy evaluation result of the dual power prediction system is: In the formula, Indicates The accuracy evaluation results of the power prediction system; Indicates The self-use weight adjustment factor of the power forecast system, ; Indicates The number of decision trees included in the accuracy evaluation model constructed by each power prediction system; Indicates In the power prediction system, A decision tree is used to calculate the input feature vector The output prediction accuracy value; represents the number of features; Indicates The feature importance weight vector corresponding to the power prediction system The elements; Indicates The characteristic matrix of the power prediction system The The value of a feature.

7. The wind farm optimization scheduling method based on the dual power prediction system according to any one of claims 1 to 6, characterized in that: Based on the power prediction accuracy evaluation results, the optimal scheduling strategy is output with the goal of minimizing the power prediction deviation, including: Establish wind farm unit operation constraints and grid access constraints; Construct the objective function and use the linear programming algorithm to solve the optimal scheduling strategy. The expression of the objective function is: In the formula, represents the minimization operator; Indicates the time period of scheduling decision; represents a first balancing coefficient, calculated according to a self-use weight adjustment factor of the first power prediction system; represents a second balancing coefficient, calculated according to a self-use weight adjustment factor of the second power prediction system; Indicates that the first power prediction system Power prediction value at the moment; Indicates that the second power prediction system Power prediction value at the moment; express The actual power measurement value at the moment; Indicates that based on the accuracy evaluation results The weight given to the first power prediction system at all times, ; Indicates that based on the accuracy evaluation results The weight given to the second power prediction system at all times, ; represents the accuracy evaluation result of the first power prediction system; Represents the accuracy evaluation result of the second power prediction system.

8. The wind farm optimization dispatching system based on the dual power prediction system is characterized by: The wind farm optimization scheduling method based on the dual power prediction system according to any one of claims 1 to 7 is adopted, and the system comprises: A data acquisition module, configured to extract a first power prediction data set and a second power prediction data set based on the dual power prediction system; A feature extraction module configured to extract a target prediction reporting rate and a power prediction accuracy rate from the first power prediction data set and the second power prediction data set; An accuracy evaluation module is configured to construct an accuracy evaluation model and extract a power prediction accuracy evaluation result based on the impact of historical contemporaneous data on prediction accuracy; The scheduling strategy optimization module is configured to output the optimal scheduling strategy based on the power prediction accuracy evaluation result with the goal of minimizing the power prediction deviation.

9. An electronic device, characterized in that: The electronic device includes: a processor and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor so that the processor can execute the steps of the wind farm optimization scheduling method based on the dual power prediction system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program for implementing a wind farm optimization scheduling method based on a dual power prediction system, and the program for implementing a wind farm optimization scheduling method based on a dual power prediction system is executed by a processor to implement the steps of the wind farm optimization scheduling method based on a dual power prediction system as described in any one of claims 1 to 7.

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