Micro-grid grid-connected operation optimization scheduling method based on intelligent optimization algorithm

Abstract

The invention relates to the technical field of power grid dispatching, in particular to a micro-grid grid-connected operation optimization dispatching method based on an intelligent optimization algorithm, and the method comprises the steps: obtaining target power demand information through the historical power demand of an industrial park, and building a power quality demand analysis model; analyzing the feature data and calculating a corresponding feature interval to obtain first power demand data and second power demand data so as to obtain power quality requirements in different time intervals; power supply capabilities of the main power grid and different micro-grids are obtained, corresponding power dispatching parameters are obtained, and optimization calculation is carried out by taking power quality requirements in different time intervals as targets and taking the power dispatching parameters of the micro-grids as particle swarms through a particle swarm algorithm dispatching model so as to obtain an optimal dispatching scheme; according to the method, grid-connected optimal scheduling can be carried out for different microgrid energy supply conditions under the condition that the power quality requirement of an industrial park is met, and the accuracy of microgrid scheduling is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to a microgrid grid-connected operation optimization dispatching method based on an intelligent optimization algorithm. Background Art

[0002] A microgrid is a small grid facility independent of the main grid, mainly composed of distributed power sources, distribution facilities, energy storage systems and protection devices. It can generate electricity and store electricity on its own. The microgrid includes a grid-connected operation mode parallel to the main grid and an independent island operation mode. Through the self-regulation of the microgrid, when the power supply of the main grid is tight, it can cooperate with the main grid for power supply or independently supply power. At the same time, since microgrids mainly use clean energy and renewable energy, they play an important role in economic benefits, environmental protection and energy saving, and emergency power supply.

[0003] Since microgrids are used in a variety of scenarios such as industrial parks, hospitals, and residential areas, the dispatching strategies of microgrids are different according to the actual conditions of different scenarios. For industrial parks with more complex power systems, in order to achieve reasonable dispatching of the grid-connected operation between microgrids and the main grid, the current technology uses intelligent optimization algorithms to achieve the optimal dispatching strategy.

[0004] Current intelligent optimization algorithms, such as particle swarm optimization and genetic optimization, all use the power parameters and cost parameters in the microgrid system as the main influencing parameters to optimize the scheduling of the microgrid. However, the problem is that the power quality in industrial scenarios has high requirements for industrial scenarios, and the microgrid contains a variety of distributed energy sources. If only unified power parameters and cost parameters are used as targets for optimization scheduling, poor power transmission quality is likely to occur, resulting in reduced accuracy in the optimization scheduling of the microgrid.

[0005] Therefore, a microgrid grid-connected operation optimization scheduling method based on intelligent optimization algorithm is proposed. Summary of the invention

[0006] The purpose of the present invention is to provide a microgrid grid-connected operation optimization scheduling method based on an intelligent optimization algorithm, obtain target power demand information through the historical power demand of the industrial park, establish a power quality demand analysis model, analyze characteristic data and calculate the corresponding characteristic interval to obtain first power demand data and second power demand data, so as to obtain power quality requirements in different time intervals; obtain the power supply capacity of the main power grid and different microgrids, and obtain corresponding power scheduling parameters, and use the particle swarm algorithm scheduling model to take the power quality requirements in different time intervals as the target, and use the power scheduling parameters of the microgrid as the particle swarm to perform optimization calculation to obtain the best scheduling solution; through this method, while meeting the power quality requirements of the industrial park, grid-connected optimization scheduling can be performed for different microgrid energy supply conditions, thereby improving the accuracy of microgrid scheduling.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A microgrid grid-connected operation optimization scheduling method based on an intelligent optimization algorithm, comprising:

[0009] Obtaining historical power demand of the industrial park, and generating target power demand information based on the historical power demand;

[0010] Establishing a power quality demand analysis model to analyze the power quality characteristic data and abnormal power quality characteristic data of the target power demand information;

[0011] Further, the power quality demand analysis model includes a demand data processing unit, a demand data feature analysis unit, a demand data anomaly marking unit, a demand data similarity calculation unit and a demand anomaly data merging and outputting unit;

[0012] The target power demand information includes target voltage demand information and target power demand information;

[0013] The target power demand information is input into the demand data processing unit for preprocessing to generate preprocessed demand data;

[0014] The demand data characteristic analysis unit analyzes the characteristics of the pre-processed demand data to generate power quality characteristic data;

[0015] The demand data feature analysis unit includes a Transformer encoding layer, a residual convolution group, a long short-term memory neural network and a fully connected layer; the Transformer encoding layer is respectively connected to the residual convolution group and the long short-term memory neural network; the results of the residual convolution group and the long short-term memory neural network are input into the fully connected layer;

[0016] The residual convolution group includes a first residual convolution group and a second residual convolution group, the first residual convolution group includes 3 convolution layers and 1 relu activation layer; the second residual convolution group includes 3 convolution layers and 1 relu activation layer; the number of the long short-term memory neural network is 6;

[0017] Further, the first residual convolution group is connected to the second residual convolution group, and the output of the first residual convolution group is input to the first residual convolution group; the first residual convolution group RC1 and the second residual convolution group RC2 are constructed as follows:

[0018]

[0019] in, The convolution layer has 64 channels and a convolution kernel size of 1*3. is the convolution layer with 64 channels and a convolution kernel size of 1*1, relu is the relu activation layer, Indicates series connection, Indicates parallel connection;

[0020] The demand data abnormality marking unit performs feature comparison based on the features of the power quality feature data, marks and generates abnormal power quality feature data;

[0021] The demand data similarity calculation unit performs similarity calculation on the abnormal power quality characteristic data and the non-abnormal power quality characteristic data to generate demand abnormality similarity data and demand non-abnormality similarity data;

[0022] κ=α1·f c +α2·f σ +α3·f v ;

[0023] Among them, κ is the similarity calculation result, f c is the cluster analysis result, f σ is the standard deviation analysis result, f v is the voting analysis result, α1, α2 and α3 are similarity weights, α1+α2+α3=1;

[0024] Acquire a characteristic interval according to the power quality characteristic data and generate a plurality of first power demand data; the first power demand data is the target power demand information acquired according to the characteristic interval of the non-abnormal power quality characteristic;

[0025] Acquire an abnormal characteristic interval according to the abnormal power quality characteristic data and generate a plurality of second power demand data; the second power demand data is the target power demand information acquired according to the characteristic interval of the demand abnormality similarity data;

[0026] Wherein, the second power demand data includes independent second power demand data and interval second power demand data;

[0027] Perform data interval calculation on the demand anomaly similarity data that is not less than the similarity threshold, and obtain the target power demand information of the characteristic interval, wherein the second power demand data of the interval is the target power demand information;

[0028] The independent second power demand data is the demand abnormality similarity data less than the similarity threshold, and the data interval calculation is performed to obtain the target power demand information of the characteristic interval;

[0029] The demand abnormality data merging and outputting unit performs data interval calculation on the target power demand information according to the demand abnormality similarity data and the demand non-abnormality similarity data according to a similarity threshold, and outputs the characteristic interval;

[0030] Acquire first power supply information according to the main power grid system of the industrial park, and acquire corresponding second power supply information and microgrid power dispatch parameters according to each microgrid system of the industrial park;

[0031] Establishing a particle swarm algorithm scheduling model, wherein the particle swarm algorithm scheduling model schedules a plurality of microgrid power scheduling parameters through a plurality of the first power demand data, the first power supply information, and a plurality of the second power supply information, and generates a first microgrid scheduling strategy to schedule each of the microgrid systems;

[0032] Further, the corresponding second power supply information and a plurality of microgrid power dispatching parameters are obtained according to each microgrid system; the power dispatching parameters and the microgrid power dispatching parameters include voltage and power;

[0033] Obtaining a priority according to the second power supply information of each of the microgrid systems; setting a corresponding priority weight for a plurality of the microgrid power dispatching parameters of each of the microgrid systems according to the priority; initializing the speed and position of the particle swarm with the microgrid power dispatching parameters, taking the priority weight and cost as the penalty coefficient for iterative update of the particle swarm, taking the difference between the first power demand data and the first power supply information as the target condition of the particle swarm, and iterating the position and speed of the particle swarm for multiple times until the particle swarm reaches the first power demand data;

[0034] Using the microgrid power dispatching parameter of the last iteration as the target parameter, generating the first microgrid dispatching strategy;

[0035] The particle swarm algorithm scheduling model schedules the plurality of microgrid power scheduling parameters through the plurality of the second power demand data, the first power supply information and the plurality of the second power supply information, and generates a second microgrid scheduling strategy to schedule each of the microgrid systems;

[0036] Further, the corresponding second power supply information and a plurality of microgrid power dispatching parameters are obtained according to each microgrid system;

[0037] Obtaining a priority according to the second power supply information of each of the microgrid systems; setting a corresponding priority weight for a plurality of the microgrid power dispatching parameters of each of the microgrid systems according to the priority; initializing the speed and position of the particle swarm with the microgrid power dispatching parameters, and using the priority weight and cost as a penalty coefficient for iterative updating of the particle swarm;

[0038] Taking the difference between the interval second power demand data of the second power demand data and the first power supply information as the target condition of the particle swarm, iterating the position and the speed of the particle swarm for multiple times until the particle swarm reaches the interval second power demand data;

[0039] Taking the difference between the independent second power demand data of the second power demand data and the first power supply information as the target condition of the particle swarm, iterating the position and the speed of the particle swarm for multiple times until the particle swarm reaches the independent second power demand data;

[0040] The second microgrid dispatching strategy is generated by taking the microgrid power dispatching parameters of the two particle swarms of the last iteration as target parameters.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention obtains target power demand information through the historical power demand of the industrial park, analyzes the abnormal power demand data and abnormal power demand data of the target power demand information based on the power quality demand analysis model, and calculates the characteristic intervals of each target power demand information according to the similarity between the abnormal data and the non-abnormal data, so as to obtain power quality requirement data of different demands at different times; compared with other methods, this method can ensure that the microgrid of the industrial park meets different power quality requirements at different times during the grid-connected optimization scheduling, so as to ensure the power quality requirements of the industrial park and improve the accuracy of microgrid optimization scheduling.

[0043] 2. In order to improve the accuracy of similarity calculation between abnormal data and non-abnormal data, after obtaining the marked abnormal power quality characteristic data and non-abnormal power quality characteristic data, the present invention uses a clustering algorithm, a standard deviation method and a voting method and performs weighted calculations, and calculates the similarity between the abnormal power quality characteristic data and the non-abnormal power quality characteristic data through multiple dimensions to ensure the accuracy of the similarity calculation results. Different characteristic intervals can be accurately divided through high-accuracy similarities, and accurate power demand data in different intervals can be obtained, which provides an accurate data basis for subsequent microgrid optimization scheduling to improve the accuracy of microgrid optimization scheduling.

[0044] 3. The present invention uses the difference between the power demand information and the main power grid power supply data under different characteristic intervals as the adjustment target data, and the current supply weights of different microgrid systems as the penalty coefficient, and combines the particle swarm optimization algorithm to optimize the power dispatching parameters of the microgrid, and uses the final result after multiple iterations of the target as the power dispatching strategy; compared with other methods, the present invention can perform grid-connected parameter dispatching of the microgrid system on the basis of ensuring the power quality requirements of the industrial park, and can ensure the quality requirements of electricity in addition to the cost while optimizing, and can also perform reasonable dispatching according to the power supply quality of different microgrid systems to improve the accuracy of microgrid optimization dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0046] Figure 2 A schematic diagram of the flow chart of the power quality demand analysis model of the present invention;

[0047] Figure 3 A schematic diagram of a flow chart of a demand data feature analysis unit of a power quality demand analysis model of the present invention;

[0048] Figure 4 It is a flow chart of the residual convolution group of the power quality demand analysis model of the present invention;

[0049] Figure 5 This is a graph of microgrid power results after the power quality demand analysis model of the present invention and particle swarm algorithm optimization. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Reference Figure 1 As shown, the microgrid system has good advantages in economy, energy saving, environmental protection and emergency response, and is therefore widely used in many fields; for the microgrid system of the industrial park, since the power supply system is relatively complex, in order to reduce the pressure of the main power grid, the microgrid system needs to be dispatched, but since the power quality requirements of the industrial park in different time intervals are also different, and the current optimization scheduling method is to optimize the scheduling by using unified power parameters and cost parameters as the target, resulting in poor accuracy of the microgrid optimization scheduling. For this reason, the present invention provides a microgrid grid-connected operation optimization scheduling method based on an intelligent optimization algorithm, and the technical solution is as follows:

[0052] Obtaining historical power demand of the industrial park, and generating target power demand information based on the historical power demand;

[0053] Establishing a power quality demand analysis model to analyze the power quality characteristic data and abnormal power quality characteristic data of the target power demand information; obtaining a characteristic interval based on the power quality characteristic data and generating a plurality of first power demand data; obtaining an abnormal characteristic interval based on the abnormal power quality characteristic data and generating a plurality of second power demand data;

[0054] Acquire first power supply information according to the main power grid system of the industrial park, and acquire corresponding second power supply information and microgrid power dispatch parameters according to each microgrid system of the industrial park;

[0055] Establishing a particle swarm algorithm scheduling model, wherein the particle swarm algorithm scheduling model schedules a plurality of microgrid power scheduling parameters through a plurality of the first power demand data, the first power supply information, and a plurality of the second power supply information, and generates a first microgrid scheduling strategy to schedule each of the microgrid systems;

[0056] The particle swarm algorithm scheduling model schedules multiple microgrid power scheduling parameters through multiple second power demand data, the first power supply information and multiple second power supply information, and generates a second microgrid scheduling strategy to schedule each microgrid system.

[0057] In order to obtain more accurate power quality requirements in different time intervals, the target power demand information is generated through the historical power demand of the industrial park as reference information for analysis; by establishing a power quality demand analysis model, the situation where the power quality requirements are greatly increased is regarded as abnormal data, and it is marked through abnormal data analysis to achieve the purpose of marking high power quality requirement data, and through similarity calculation, the same quality requirement data is merged to obtain the corresponding target power demand information and the corresponding time interval;

[0058] This method can reliably understand the power quality requirements of each industrial park in different time intervals. These data can provide an accurate data basis for the subsequent optimization and scheduling of the microgrid system, so as to improve the accuracy of the subsequent microgrid optimization and scheduling;

[0059] Afterwards, the power supply parameter information of the main grid system and each microgrid system is obtained to obtain the power quality of different microgrid systems and the power quality of the main grid system. Combined with the dispatch parameters of each microgrid system, the difference between the power quality requirements of different time intervals and the power supply of the main grid system is used as the target parameter, and the cost and the power quality of the microgrid power supply are used as the penalty coefficient. The relevant parameters of the microgrid system are optimized according to the particle swarm algorithm to generate the corresponding microgrid grid-connected optimization dispatching strategy;

[0060] This method can optimize the grid-connected scheduling of microgrids while ensuring the power quality of the industrial park in different time periods. While ensuring the power cost and power quality requirements of the industrial park, it can also achieve reasonable power output in combination with the microgrid's own power supply capacity, thereby improving the accuracy of microgrid optimization scheduling.

[0061] Embodiment 1

[0062] Obtaining historical power demand of the industrial park, and generating target power demand information based on the historical power demand;

[0063] Establishing a power quality demand analysis model to analyze the power quality characteristic data and abnormal power quality characteristic data of the target power demand information;

[0064] Furthermore, the power quality demand analysis model includes a demand data processing unit, a demand data feature analysis unit, a demand data abnormality marking unit, a demand data similarity calculation unit and a demand abnormality data merging and outputting unit, referring to Figure 2 As shown; Since the demand for power quality in the industrial park is different in different time periods and seasons, if the power supply of corresponding quality cannot be generated, it will have a huge impact on the work of the industrial park. Therefore, the historical power demand data of the industrial park is used as a reference, and the part with increased or decreased voltage demand is analyzed as abnormal data. Finally, the similarity of these abnormal data is classified and calculated and mapped to the corresponding time. The time interval corresponding to different power demands can be analyzed to facilitate the subsequent grid-connected optimization scheduling of the microgrid and the main grid, and adjustments are made according to the actual power demand to improve the accuracy of the grid-connected scheduling of the microgrid system;

[0065] The target power demand information includes target voltage demand information and target power demand information; the target voltage demand information and the target power demand information are time series data acquired and recorded in time;

[0066] The target power demand information is input into the demand data processing unit for preprocessing to generate preprocessed demand data;

[0067] The preprocessing of the demand data processing unit mainly includes denoising, standardization, missing value filling and value screening, and the data is segmented and spliced. For example, the data of one year is divided into one week, that is, 7 days of data as a unit, forming data with a dimension of 7*(365 / 7), and then rounded down to form data with a dimension of 7*51. In order to ensure the integrity of the preprocessing demand data, the remaining data that cannot be divided is discarded; in the specific implementation process, corresponding processing can be carried out according to the specific situation;

[0068] The demand data characteristic analysis unit analyzes the characteristics of the pre-processed demand data to generate power quality characteristic data;

[0069] The demand data feature analysis unit includes a Transformer encoding layer, a residual convolution group, a long short-term memory neural network and a fully connected layer; the Transformer encoding layer is respectively connected to the residual convolution group and the long short-term memory neural network; the results of the residual convolution group and the long short-term memory neural network are input into the fully connected layer; Figure 3 As shown;

[0070] The residual convolution group includes a first residual convolution group and a second residual convolution group, the first residual convolution group includes 3 convolution layers and 1 relu activation layer; the second residual convolution group includes 3 convolution layers and 1 relu activation layer; the number of the long short-term memory neural network is 6;

[0071] In order to accurately identify abnormal data of high power demand and low power demand in power demand data, the attention mechanism of the Transformer encoding layer can analyze the long-distance dependency characteristics between the data in the time series data formed by power data, and the residual convolution group can capture the morphological change characteristics in space, and the long-term and short-term neural network can capture the continuous change characteristics in time. Through the combined analysis of multiple models, the change characteristics of power data can be accurately analyzed, so as to accurately confirm the abnormal data in power demand, provide an accurate abnormal data time interval data basis, facilitate the subsequent microgrid grid connection adjustment and optimization, and improve the accuracy of optimization;

[0072] Further, the first residual convolution group is connected to the second residual convolution group, and the output of the first residual convolution group is input to the first residual convolution group; the first residual convolution group RC1 and the second residual convolution group RC2 are constructed as follows:

[0073]

[0074] in, The convolution layer has 64 channels and a convolution kernel size of 1*3. is the convolution layer with 64 channels and 1*1 convolution kernel size, relu is the relu activation layer, Indicates series connection, Indicates parallel connection, refer to Figure 4 As shown in the figure, by adopting the parallel analysis method, the accuracy of spatial features can be further improved in the process of extracting spatial features. Through the convolution kernel sizes of different dimensions, the target power demand information can be analyzed from multiple aspects to improve the accuracy of spatial information, so as to ensure the time intervals of different power quality requirements in the future and improve the accuracy of subsequent microgrid grid-connected optimization scheduling adjustment.

[0075] The demand data abnormality marking unit performs feature comparison based on the features of the power quality feature data, marks and generates abnormal power quality feature data;

[0076] The demand data similarity calculation unit performs similarity calculation on the abnormal power quality characteristic data and the non-abnormal power quality characteristic data to generate demand abnormality similarity data and demand non-abnormality similarity data;

[0077] κ=α1·f c +α2·f σ +α3·f v ;

[0078] Among them, κ is the similarity calculation result, f c is the cluster analysis result, f σ is the standard deviation analysis result, f v is the result of voting analysis, α1, α2 and α3 are similarity weights, α1+α2+α3=1; α1 defaults to 0.4, α2 defaults to 0.4, and α3 defaults to 0.2. The specific weight values ​​can be changed according to the actual situation;

[0079] The clustering model uses the k-means model, and the value of k is set based on historical data and the experience of relevant personnel;

[0080] The standard deviation and voting method is to traverse all feature data, subtract a certain data from all other data to obtain the change fluctuation data, and after traversing all data, the similarity is calculated based on the change fluctuation data of each data using the standard deviation and voting method;

[0081] The standard deviation is calculated by counting the maximum fluctuation value between a large number of non-abnormal related historical data and the maximum fluctuation value between abnormal related historical data. The result is obtained based on the ratio of the maximum fluctuation values. The specific calculation is as follows:

[0082]

[0083] Among them, fl is the fluctuation data after subtracting the current data from other data, fl max is the maximum fluctuation value;

[0084] The voting rule is to set multiple similarity standard values ​​based on expert experience, and judge whether they are similar based on the maximum ratio of the similarity standard values; the specific calculation is as follows:

[0085]

[0086] Among them, max is the maximum value, flt n is the nth similarity standard value;

[0087] In order to ensure the accuracy of similarity calculation of abnormal data after marking, this embodiment clusters all data through cluster analysis, and the accuracy of similarity calculation can be ensured by the clustering results; at the same time, in order to further improve the accuracy of similarity results, standard deviation analysis and voting method analysis are also added, combined with statistical methods, the similarity of data is calculated from multiple aspects to ensure the accuracy of data similarity calculation and the accuracy of subsequent time interval division, so as to improve the accuracy of subsequent microgrid grid-connected optimization scheduling adjustment;

[0088] Acquire a characteristic interval according to the power quality characteristic data and generate a plurality of first power demand data; the first power demand data is the target power demand information acquired according to the characteristic interval of the non-abnormal power quality characteristic;

[0089] In order to improve the accuracy of subsequent microgrid optimization and scheduling, due to abnormal conditions, the high power demand and low power demand of the industrial park are the top priority power quality requirements. Therefore, the data will be stored after calculating multiple time intervals based on historical data. After the time intervals of high power data and low power data are confirmed, the time intervals under non-abnormal conditions are confirmed, and the average power quality data of each interval is calculated to generate the power demand value under the non-abnormal interval as the target power demand for the microgrid and main grid grid-connected optimization scheduling;

[0090] The corresponding power quality data under non-abnormal conditions is obtained through the divided time interval as the target power demand information, so as to facilitate the subsequent optimization scheduling of microgrid grid connection and improve the accuracy of the optimization scheduling of microgrid system grid connection;

[0091] Acquire an abnormal characteristic interval according to the abnormal power quality characteristic data and generate a plurality of second power demand data; the second power demand data is the target power demand information acquired according to the characteristic interval of the demand abnormality similarity data;

[0092] After obtaining abnormal high power demand data and low power demand data, they are mapped to the time interval of the target power demand information, and the corresponding time feature interval is obtained to ensure that accurate power quality data can be supplied in the time feature interval; for this purpose, after calculating the similarity, the minimum time span of the abnormal time feature interval in all historical data is determined, and the maximum time span in the time feature interval containing the minimum time span is determined, and the mean value of the power demand data with the maximum time span is used as the target power demand for the optimal scheduling of the microgrid and the main grid;

[0093] Through the divided abnormal data, the time interval of power quality requirements of the industrial park under high voltage and low voltage conditions can be accurately determined. The power quality requirement data within this time interval is used as the target data to optimize the scheduling of the microgrid, which can provide the industrial park with power quality that meets the demand in different time periods and improve the accuracy of the grid-connected optimization scheduling of the microgrid system;

[0094] Wherein, the second power demand data includes independent second power demand data and interval second power demand data;

[0095] Perform data interval calculation on the demand anomaly similarity data that is not less than the similarity threshold, and obtain the target power demand information of the characteristic interval, wherein the second power demand data of the interval is the target power demand information;

[0096] The independent second power demand data is the demand abnormality similarity data less than the similarity threshold, and the data interval calculation is performed to obtain the target power demand information of the characteristic interval;

[0097] The similarity threshold is set to 0.95 by default, which can be modified according to actual conditions;

[0098] Since data fluctuations or additional power demand increases or decreases may occur in the time feature interval of abnormal data calculation, after the similarity calculation, these data with low similarity are classified and stored to generate independent time intervals. Based on the previous interval division method, the frequency of occurrence of these time intervals, if the frequency is high, is saved as a special time interval, if the frequency is low, these data are stored as special cases and not recorded as adjusted time intervals; this method can be used to grasp the high-quality power demand and low-quality power demand of the industrial park in different time periods, and obtain some characteristic abnormal time feature intervals, further improve the accuracy of the acquisition of different power quality requirements of the industrial park at different times, facilitate the optimization and adjustment of the subsequent microgrid grid-connected data, and improve the accuracy of the microgrid system grid-connected optimization scheduling;

[0099] Frequency judgment is mainly carried out by comparing data of the same time interval of each year. For example, the data of July 2017 is compared with the data of July of different years, such as the data of July 2016 and July 2015. The upper and lower thresholds of frequency judgment are 3. If it is not less than 3 times, it is judged as a high frequency and recorded as a special time feature interval. If it is less than 3 times, it is judged as a characteristic situation and is not recorded as a special time feature interval. The data is stored. The specific frequency number can be adjusted according to expert experience and actual conditions.

[0100] Furthermore, if the time interval that exceeds the threshold frequency is smaller than the actual interval, for example, the time interval of the abnormal comparison data is 10 days, and after comparison, it is found that only 5 days of data exceed the threshold frequency, in this case, the data of these 5 days will be used as the abnormal time interval, and the other 5 days will be used as the quasi-abnormal time interval and stored as alternative data; in the process of grid-connected tuning of microgrid parameters, the 5-day data will be used as the main tuning target data to generate the main strategy plan, and the 10-day data will be used as the alternative tuning target data to generate the alternative strategy plan. Through the generation of multiple cases, the accuracy of the target data for microgrid tuning can be guaranteed from multiple aspects, so as to improve the accuracy of the grid-connected optimization scheduling of the microgrid system;

[0101] The demand abnormality data merging and outputting unit performs data interval calculation on the target power demand information according to the demand abnormality similarity data and the demand non-abnormality similarity data according to a similarity threshold, and outputs the characteristic interval;

[0102] Acquire first power supply information according to the main power grid system of the industrial park, and acquire corresponding second power supply information and microgrid power dispatch parameters according to each microgrid system of the industrial park;

[0103] Establishing a particle swarm algorithm scheduling model, wherein the particle swarm algorithm scheduling model schedules a plurality of microgrid power scheduling parameters through a plurality of the first power demand data, the first power supply information, and a plurality of the second power supply information, and generates a first microgrid scheduling strategy to schedule each of the microgrid systems;

[0104] Further, the corresponding second power supply information and a plurality of microgrid power dispatching parameters are obtained according to each microgrid system; the power dispatching parameters and the microgrid power dispatching parameters include voltage and power; the first power supply information is the average power supply of the main power grid system, and the second power supply information is the stored power and average supply of each microgrid system;

[0105] Obtaining a priority according to the second power supply information of each of the microgrid systems; setting a corresponding priority weight for a plurality of the microgrid power dispatching parameters of each of the microgrid systems according to the priority; initializing the speed and position of the particle swarm with the microgrid power dispatching parameters, taking the priority weight and cost as the penalty coefficient for iterative update of the particle swarm, taking the difference between the first power demand data and the first power supply information as the target condition of the particle swarm, and iterating the position and speed of the particle swarm for multiple times until the particle swarm reaches the first power demand data;

[0106] Using the microgrid power dispatching parameter of the last iteration as the target parameter, generating the first microgrid dispatching strategy;

[0107] By taking the difference between the power quality requirements of the industrial park and the power supply of the main power grid in different time intervals under non-abnormal conditions, and using these data as target data, the power quality requirements of the industrial park can be met during the optimization of the microgrid grid connection; at the same time, in order to ensure the accuracy of the optimization process of each parameter adjustment, the cost data of the power data is used as the penalty coefficient, and the actual supply and actual storage of each microgrid system are used as another penalty coefficient; this method can be used to make adaptive adjustments according to the specific parameters of the microgrid system in the optimization process while ensuring that the cost is not too high. This method ensures that the microgrid system will not exceed the parameters of the microgrid system supply while ensuring that the power quality requirements of the industrial park are met, so as to improve the accuracy of the microgrid system grid connection optimization scheduling;

[0108] The particle swarm algorithm scheduling model schedules the plurality of microgrid power scheduling parameters through the plurality of the second power demand data, the first power supply information and the plurality of the second power supply information, and generates a second microgrid scheduling strategy to schedule each of the microgrid systems;

[0109] Further, the corresponding second power supply information and a plurality of microgrid power dispatching parameters are obtained according to each microgrid system;

[0110] Obtaining a priority according to the second power supply information of each of the microgrid systems; setting a corresponding priority weight for a plurality of the microgrid power dispatching parameters of each of the microgrid systems according to the priority; initializing the speed and position of the particle swarm with the microgrid power dispatching parameters, and using the priority weight and cost as a penalty coefficient for iterative updating of the particle swarm;

[0111] Taking the difference between the interval second power demand data of the second power demand data and the first power supply information as the target condition of the particle swarm, iterating the position and the speed of the particle swarm for multiple times until the particle swarm reaches the interval second power demand data;

[0112] Taking the difference between the independent second power demand data of the second power demand data and the first power supply information as the target condition of the particle swarm, iterating the position and the speed of the particle swarm for multiple times until the particle swarm reaches the independent second power demand data;

[0113] Using the microgrid power dispatching parameters of the two particle swarms of the last iteration as target parameters, generating the second microgrid dispatching strategy;

[0114] In the industrial park, high power quality requirements or low power quality requirements in different time characteristic intervals are used as optimization target data, so that the optimization and regulation of the microgrid can meet the power quality requirements of the actual industrial park; at the same time, combined with the actual power supply situation of each microgrid, multiple priority weights are divided as penalty coefficients, and appropriate optimization and regulation are performed on each microgrid system during the optimization and regulation process to prevent the situation of exceeding the power supply data of the microgrid system and improve the accuracy of the grid-connected optimization scheduling of the microgrid system.

[0115] Embodiment 2

[0116] The present invention collects 6 years of data from Industrial Park A, selects 360 days of data each year, pre-processes all the data, divides the data into units of 30 days, collects data every half an hour, generates data with a dimension of (48*30)*60, uses the data of the first 5 years as training data, and uses the data of the last year as test data for abnormal training and recognition, selects the model with the highest training accuracy as the test model to recognize the test data of the 6th year, and the data of the 6th year is divided into the same dimension size, i.e. 48*30, and selects 6 groups of data from 60 groups of data for abnormal data recognition and marking test, 3 groups in the array contain abnormal data, including high power demand data and low power demand data, and 3 groups are non-abnormal data. Table 1 shows the recognition accuracy results:

[0117] Table 1. The accuracy of abnormal and normal identification marks for the 6 test data sets in the 6th year

[0118] Data group label Does it contain abnormal data? Anomaly recognition accuracy Normal recognition accuracy Data 1 yes 94.13％ 93.28％ Data 2 no none 94.15％ Data 3 no none 93.66％ Data 4 yes 93.22％ 92.94％ Data 5 yes 93.79％ 92.86％ Data 6 no none 93.47％

[0119] From the results in Table 1, we can see that the accuracy of abnormal data recognition is above 93%, and the recognition result is good. The recognition of normal data is also above 92.5%, which can show that the model has good recognition and labeling results for different data.

[0120] In order to further test the recognition accuracy of the model, this embodiment also collected one year of data from another B industrial park. According to the same preprocessing method and division method, 4 groups of data were selected for testing, of which 2 groups of data contained abnormal data and 2 groups of data did not contain abnormal data. The test results are shown in Table 2.

[0121] Table 2. Accuracy of abnormal and normal recognition marks for another industrial park test data

[0122] Data group label Does it contain abnormal data? Anomaly recognition accuracy Normal recognition accuracy Data 1 yes 91.13％ 92.34％ Data 2 no none 91.98％ Data 3 yes 90.68％ 91.72％ Data 4 no none 92.56％

[0123] From the results in Table 2, we can see that although the data is not from the same industrial park, the recognition accuracy has decreased, but it still remains above 90%, proving that the recognition effect of this method is good;

[0124] This example optimizes the 24-hour data of Industrial Park A on a certain day. The optimization results refer to Figure 5 As shown in the figure, the diamond block line is the expected power data of the initially set microgrid, and the square block line is the dispatch power data optimized after the power quality demand analysis model and particle swarm algorithm. Figure 5 It can be seen that the data is optimized for high power demand and low power demand in different time periods.

[0125] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A microgrid grid-connected operation optimization scheduling method based on intelligent optimization algorithm, characterized in that: include: Obtaining historical power demand of the industrial park, and generating target power demand information based on the historical power demand; Establishing a power quality demand analysis model to analyze the power quality characteristic data and abnormal power quality characteristic data of the target power demand information; obtaining a characteristic interval based on the power quality characteristic data and generating a plurality of first power demand data; Acquire an abnormal characteristic interval according to the abnormal power quality characteristic data and generate a plurality of second power demand data; Acquire first power supply information according to the main power grid system of the industrial park, and acquire corresponding second power supply information and microgrid power dispatch parameters according to each microgrid system of the industrial park; Establishing a particle swarm algorithm scheduling model, wherein the particle swarm algorithm scheduling model schedules a plurality of microgrid power scheduling parameters through a plurality of the first power demand data, the first power supply information, and a plurality of the second power supply information, and generates a first microgrid scheduling strategy to schedule each of the microgrid systems; The particle swarm algorithm scheduling model schedules multiple microgrid power scheduling parameters through multiple second power demand data, the first power supply information and multiple second power supply information, and generates a second microgrid scheduling strategy to schedule each microgrid system.

2. A microgrid grid-connected operation optimization scheduling method based on an intelligent optimization algorithm according to claim 1, characterized in that: Establishing a power quality demand analysis model to analyze the power quality characteristic data and abnormal power quality characteristic data of the target power demand information includes: The power quality demand analysis model includes a demand data processing unit, a demand data feature analysis unit, a demand data anomaly marking unit, a demand data similarity calculation unit and a demand anomaly data merging and outputting unit; The target power demand information includes target voltage demand information and target power demand information; The target power demand information is input into the demand data processing unit for preprocessing to generate preprocessed demand data; The demand data characteristic analysis unit analyzes the characteristics of the pre-processed demand data to generate power quality characteristic data; The demand data abnormality marking unit performs feature comparison based on the features of the power quality feature data, marks and generates abnormal power quality feature data; The demand data similarity calculation unit performs similarity calculation on the abnormal power quality characteristic data and the non-abnormal power quality characteristic data to generate demand abnormality similarity data and demand non-abnormality similarity data; The abnormal demand data merging and outputting unit calculates a data interval of the target power demand information according to the abnormal demand similarity data and the non-abnormal demand similarity data and a similarity threshold, and outputs the characteristic interval.

3. A microgrid grid-connected operation optimization scheduling method based on intelligent optimization algorithm according to claim 2, characterized in that: The demand data feature analysis unit performs the feature analysis of the pre-processed demand data including: The demand data feature analysis unit includes a Transformer encoding layer, a residual convolution group, a long short-term memory neural network and a fully connected layer; the Transformer encoding layer is respectively connected to the residual convolution group and the long short-term memory neural network; the results of the residual convolution group and the long short-term memory neural network are input into the fully connected layer; The residual convolution group includes a first residual convolution group and a second residual convolution group, the first residual convolution group includes 3 convolution layers and 1 relu activation layer; the second residual convolution group includes 3 convolution layers and 1 relu activation layer; the number of the long short-term memory neural networks is 6.

4. A microgrid grid-connected operation optimization scheduling method based on intelligent optimization algorithm according to claim 3, characterized in that: The first residual convolution group and the second residual convolution group include: The first residual convolution group is connected to the second residual convolution group, and the output of the first residual convolution group is input to the first residual convolution group; the first residual convolution group RC1 and the second residual convolution group RC2 are constructed as follows: in, The convolution layer has 64 channels and a convolution kernel size of 1*3. is the convolution layer with 64 channels and a convolution kernel size of 1*1, relu is the relu activation layer, Indicates series connection, Indicates parallel connection.

5. The microgrid grid-connected operation optimization scheduling method based on intelligent optimization algorithm according to claim 2 is characterized in that: The demand data similarity calculation unit performs similarity calculation on the abnormal power quality characteristic data and the non-abnormal power quality characteristic data, including: κ=α1·f c +α2·f σ +α3·f v ; Among them, κ is the similarity calculation result, f c is the cluster analysis result, f σ is the standard deviation analysis result, f v is the result of voting analysis, α1, α2 and α3 are similarity weights, α1+α2+α3=1.

6. The microgrid grid-connected operation optimization scheduling method based on intelligent optimization algorithm according to claim 1 is characterized in that: A characteristic interval is acquired according to the power quality characteristic data and a plurality of first power demand data are generated, wherein the first power demand data is the target power demand information acquired according to the characteristic interval of the non-abnormal power quality characteristic.

7. The microgrid grid-connected operation optimization scheduling method based on intelligent optimization algorithm according to claim 1 is characterized in that: The abnormal characteristic interval is obtained through the abnormal power quality characteristic data and a plurality of second power demand data are generated. The second power demand data is the target power demand information obtained according to the characteristic interval of the demand abnormality similarity data.

8. A microgrid grid-connected operation optimization scheduling method based on intelligent optimization algorithm according to claim 7, characterized in that: The second power demand data includes independent second power demand data and interval second power demand data: Perform data interval calculation on the demand anomaly similarity data that is not less than the similarity threshold, and obtain the target power demand information of the characteristic interval, wherein the second power demand data of the interval is the target power demand information; The independent second power demand data is the demand abnormality similarity data less than the similarity threshold to perform data interval calculation to obtain the target power demand information of the characteristic interval.

9. The microgrid grid-connected operation optimization scheduling method based on intelligent optimization algorithm according to claim 1 is characterized in that: The particle swarm algorithm scheduling model schedules a plurality of microgrid power scheduling parameters through a plurality of the first power demand data, the first power supply information and a plurality of the second power supply information, and generates a first microgrid scheduling strategy to schedule each of the microgrid systems, including: Acquire the corresponding second power supply information and a plurality of microgrid power dispatching parameters according to each microgrid system; the power dispatching parameters and the microgrid power dispatching parameters include voltage and power; Obtaining a priority according to the second power supply information of each of the microgrid systems; setting a corresponding priority weight for a plurality of the microgrid power dispatching parameters of each of the microgrid systems according to the priority; initializing the speed and position of the particle swarm with the microgrid power dispatching parameters, taking the priority weight and cost as the penalty coefficient for iterative update of the particle swarm, taking the difference between the first power demand data and the first power supply information as the target condition of the particle swarm, and iterating the position and speed of the particle swarm for multiple times until the particle swarm reaches the first power demand data; The first microgrid dispatching strategy is generated by taking the microgrid power dispatching parameter of the last iteration as the target parameter.

10. The microgrid grid-connected operation optimization scheduling method based on intelligent optimization algorithm according to claim 1 is characterized in that: The particle swarm algorithm scheduling model schedules the plurality of microgrid power scheduling parameters through the plurality of the second power demand data, the first power supply information and the plurality of the second power supply information, and generates a second microgrid scheduling strategy to schedule each of the microgrid systems, including: Acquire a plurality of the microgrid power dispatching parameters according to the corresponding second power supply information of each microgrid system; acquire a priority according to the second power supply information of each microgrid system; set a corresponding priority weight for the plurality of microgrid power dispatching parameters of each microgrid system according to the priority; initialize the speed and position of the particle swarm with the microgrid power dispatching parameters, and use the priority weight and cost as the penalty coefficient for iterative update of the particle swarm; Taking the difference between the interval second power demand data of the second power demand data and the first power supply information as the target condition of the particle swarm, iterating the position and the speed of the particle swarm for multiple times until the particle swarm reaches the interval second power demand data; taking the difference between the independent second power demand data of the second power demand data and the first power supply information as the target condition of the particle swarm, iterating the position and the speed of the particle swarm for multiple times until the particle swarm reaches the independent second power demand data; The second microgrid dispatching strategy is generated by taking the microgrid power dispatching parameters of the two particle swarms of the last iteration as target parameters.

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