CN114862177A - Energy interconnection energy storage and distribution method and system - Google Patents
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Abstract
The invention discloses an energy interconnection energy storage and distribution method and system, wherein the method comprises the following steps: periodically collecting historical electricity utilization data of a target electricity utilization area to obtain historical periodic electricity utilization data; obtaining a historical electricity utilization data line graph by carrying out mathematical statistics and graph conversion; acquiring preset nodes of the power consumption system to obtain a historical power consumption wave crest node set and a historical power consumption wave trough node set; acquiring energy output information to obtain an energy output set; deeply mining output data of the device to obtain a first output characteristic, a second output characteristic and a third output characteristic; taking the historical electricity utilization wave crest node set and the historical electricity utilization wave trough node set as training data, and taking the first, second and third output characteristics as supervision data, and building an energy storage distribution model; external power consumption requirements are collected and input to the energy storage distribution model for training, energy output distribution information is obtained, and energy output is carried out on the target power consumption area.
Description
Technical Field
The invention relates to the field of energy distribution, in particular to an energy interconnection energy storage and distribution method and system.
Background
With the development of society and the increase of population, the consumption of energy is rapidly increased, and the shortage of energy becomes a bottleneck restricting the economic development, so how to develop an advanced and safe new energy using technology and how to improve the energy utilization rate also become problems which need to be solved urgently, and the utilization rate of energy can be improved by effectively and reasonably distributing and utilizing various energy sources in local areas.
However, in the prior art, when various energy sources are distributed, the available multi-energy source supply conditions cannot be reasonably distributed based on the generation cost, the generation period and the generation efficiency of various energy sources, so that the energy utilization rate is reduced.
Disclosure of Invention
The invention aims to provide an energy interconnection energy storage and distribution method and system, which are used for solving the technical problem that when various energy sources are distributed in the prior art, the available multi-energy source supply condition cannot be reasonably distributed based on the generation cost, the generation period and the generation efficiency of the various energy sources, so that the energy utilization rate is reduced.
In view of the above problems, the present invention provides an energy interconnection energy storage allocation method and system.
In a first aspect, the present invention provides an energy interconnection energy storage allocation method, including: based on the big data, periodically collecting historical electricity utilization data of the target electricity utilization area to obtain historical periodic electricity utilization data; obtaining a historical electricity utilization data line graph by carrying out mathematical statistics and graph conversion on the historical periodic electricity utilization data; performing preset node acquisition on the historical electricity consumption data line graph to obtain a historical electricity consumption peak node set and a historical electricity consumption trough node set; acquiring energy output information of the target power utilization area to obtain an energy output set; obtaining a first output characteristic, a second output characteristic and a third output characteristic by deeply mining the output data of the energy output set; taking the historical electricity peak node set and the historical electricity valley node set as training data, and taking the first output characteristic, the second output characteristic and the third output characteristic as supervision data, and building an energy storage and distribution model of the target electricity utilization area; and acquiring the external power demand of the target power utilization area, inputting the external power demand into the energy storage distribution model for training, acquiring energy output distribution information, and outputting energy to the target power utilization area.
In another aspect, the present invention further provides an energy interconnected energy storage allocation system, configured to execute the energy interconnected energy storage allocation method according to the first aspect, wherein the system includes: the first acquisition unit is used for periodically acquiring historical electricity utilization data of the target electricity utilization area based on the big data to obtain historical periodic electricity utilization data; the first obtaining unit is used for obtaining a historical electricity utilization data line graph by carrying out mathematical statistics and graph conversion on the historical periodic electricity utilization data; the second acquisition unit is used for carrying out preset node acquisition on the historical electricity consumption data line graph to obtain a historical electricity consumption peak node set and a historical electricity consumption trough node set; the third acquisition unit is used for acquiring the energy output information of the target power utilization area to obtain an energy output set; the first mining unit is used for carrying out deep mining on the output data of the energy output set to obtain a first output characteristic, a second output characteristic and a third output characteristic; the first building unit is used for building an energy storage distribution model of the target power utilization area by taking the historical power utilization wave crest node set and the historical power utilization wave trough node set as training data and the first output characteristic, the second output characteristic and the third output characteristic as supervision data; and the fourth acquisition unit is used for acquiring the external power consumption requirement of the target power consumption area, inputting the external power consumption requirement to the energy storage distribution model for training, acquiring energy output distribution information and outputting energy to the target power consumption area.
In a third aspect, an electronic device comprises a processor and a memory;
the memory is used for storing;
the processor is configured to execute the method according to any one of the first aspect above by calling.
In a fourth aspect, a computer program product comprises a computer program and/or instructions which, when executed by a processor, performs the steps of the method of any of the first aspect described above.
One or more technical schemes provided by the invention at least have the following technical effects or advantages:
carry out data acquisition through the historical power consumption data to the target area, and carry out the analysis of power consumption extreme value to the data of gathering, gather the energy output of this place simultaneously, and carry out energy output's feature excavation to the collection result, based on power consumption extreme value data and excavation feature, build energy storage distribution model, the realization is distributed the training to the required energy of the regional outside demand power consumption of target power consumption, make and carry out reasonable optimization output to available energy, reached the cost of production based on various energies, production cycle and production efficiency, supply with the condition and carry out rational distribution to available many energy, make the technological effect of promotion energy utilization rate.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained according to the provided drawings without inventive effort.
Fig. 1 is a schematic flow chart of an energy interconnection energy storage allocation method according to the present invention;
fig. 2 is a schematic flow chart illustrating deep mining of output data of the energy output set in the energy interconnection energy storage allocation method according to the present invention;
fig. 3 is a schematic flow chart illustrating parameter correction of distribution of the energy output-geographic environment related parameters in the energy interconnection energy storage and distribution method according to the present invention;
fig. 4 is a schematic flow chart of building an energy storage allocation model of the target power utilization region in the energy interconnection energy storage allocation method of the present invention;
fig. 5 is a schematic structural diagram of an energy interconnection energy storage and distribution system according to the present invention;
fig. 6 is a schematic structural diagram of an exemplary electronic device of the present invention.
Description of reference numerals:
the system comprises a first acquisition unit 11, a first obtaining unit 12, a second acquisition unit 13, a third acquisition unit 14, a first mining unit 15, a first building unit 16, a fourth acquisition unit 17, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304 and a bus interface 305.
Detailed Description
The invention provides an energy interconnection energy storage and distribution method and system, and solves the technical problem that when various energy sources are distributed in the prior art, the available multi-energy supply condition cannot be reasonably distributed based on the generation cost, the generation period and the generation efficiency of the various energy sources, so that the energy utilization rate is reduced.
In the technical scheme of the invention, the data acquisition, storage, use, processing and the like all conform to relevant regulations of national laws and regulations.
In the following, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention. It should be further noted that, for the convenience of description, only some but not all of the elements associated with the present invention are shown in the drawings.
The invention provides an energy interconnection energy storage and distribution method, which comprises the following steps: carry out data acquisition through the historical power consumption data to the target area, and carry out the analysis of power consumption extreme value to the data of gathering, gather the energy output of this place simultaneously, and carry out energy output's feature excavation to the collection result, based on power consumption extreme value data and excavation feature, build energy storage distribution model, the realization is distributed the training to the required energy of the regional outside demand power consumption of target power consumption, make and carry out reasonable optimization output to available energy, reached the cost of production based on various energies, production cycle and production efficiency, supply with the condition and carry out rational distribution to available many energy, make the technological effect of promotion energy utilization rate.
Having described the general principles of the invention, reference will now be made in detail to various non-limiting embodiments of the invention, examples of which are illustrated in the accompanying drawings.
Example one
Referring to fig. 1, the present invention provides an energy interconnection energy storage and distribution method, which specifically includes the following steps:
step S100: periodically collecting historical electricity utilization data of the target electricity utilization area based on the big data to obtain historical periodic electricity utilization data;
step S200: obtaining a historical electricity utilization data line graph by carrying out mathematical statistics and graph conversion on the historical periodic electricity utilization data;
specifically, with the development of society and the increase of population, the consumption of energy is rapidly increased, and energy shortage becomes a bottleneck restricting economic development, so how to develop an advanced and safe new energy using technology and how to improve the energy utilization rate also become problems to be solved urgently, and the utilization rate of energy can be improved by effectively and reasonably distributing and utilizing a plurality of kinds of energy in a local area.
In the prior art, when various energy sources are distributed, the available multi-energy source supply conditions cannot be reasonably distributed based on the generation cost, the generation period and the generation efficiency of various energy sources, so that the energy utilization rate is reduced.
In order to solve the problems, the application provides an energy interconnection energy storage and distribution method, data acquisition is carried out on historical electricity utilization data of a target area, analysis of an electricity utilization extreme value is carried out on the acquired data, meanwhile, energy output of the place is acquired, feature mining of energy output is carried out on an acquisition result, based on the electricity utilization extreme value data and the mining features, an energy storage and distribution model is built, distribution training of energy required by external demand electricity utilization of the target electricity utilization area is achieved, reasonable optimization output of available energy is carried out, the generation cost based on various energy is achieved, the generation cycle and the generation efficiency are achieved, reasonable distribution is carried out on the available multi-energy supply condition, and the technical effect of energy utilization rate is improved.
Specifically, the target power utilization area is a target area which needs energy distribution, the area meets the supply of various energy sources including wind energy, water energy, photovoltaic energy and the like, the historical power utilization data is a set of all historical power utilization data of the area at present, the historical power utilization data is a monthly statistical power utilization data set of the area through periodic collection of the power utilization data of the area, the historical periodic power utilization data is a monthly statistical power utilization data set of the area, the historical periodic power utilization data is subjected to mathematical statistics, namely, the historical power utilization data is subjected to proper graph conversion according to the development rule of the power utilization data, the graph can be generally converted into a line graph with remarkable data change, the monthly power utilization condition of the area can be visually displayed, and the historical power utilization data line graph is a result obtained by performing graph conversion on the monthly power utilization data.
Step S300: performing preset node acquisition on the historical electricity consumption data line graph to obtain a historical electricity consumption peak node set and a historical electricity consumption trough node set;
step S400: acquiring energy output information of the target power utilization area to obtain an energy output set;
specifically, after the historical electricity consumption data line graph is obtained, preset node collection can be performed on the historical electricity consumption data line graph, the preset nodes are the broken line statistical graph which is analyzed, and the highest points and the lowest points of broken line changes are collected, wherein the historical electricity consumption peak node set is the node set corresponding to each highest point, the monthly node set with the highest electricity consumption data is represented, the historical electricity consumption trough node set is the node set corresponding to each lowest point, and the monthly node set with the lowest electricity consumption data is represented.
Meanwhile, the energy output of the region can be collected, the energy output set comprises various renewable energy sets, non-renewable energy sets and the like, and illustratively, if the region is traversed by water consumption and the terrain difference is high, water energy can be utilized; if the area is flat and wide, the illumination is sufficient, and wind energy, photovoltaic energy and the like can be utilized; but also conventional thermal power generation, etc.
Step S500: obtaining a first output characteristic, a second output characteristic and a third output characteristic by deeply mining the output data of the energy output set;
further, as shown in fig. 2, step S500 includes:
step S510: collecting geographic environment information of the target power utilization area to obtain geographic environment data;
step S520: performing parameter calculation on the association degree between the output data and the geographic environment data to obtain the distribution of each energy output-geographic environment association parameter;
step S530: analyzing the geographic environment data to obtain a geographic climate development rule;
step S540: obtaining a geographical change abundance period and a geographical change deficiency period by deeply analyzing the geographical climate development rule;
step S550: and performing parameter correction on the distribution of the energy output-geographic environment associated parameters based on the geographic change full period and the geographic change short period to obtain the first output characteristic, the second output characteristic and the third output characteristic of the output data.
Wherein, step S520 includes:
step S521: defining the geographic environment data as a set of items A and the outcome data as a transaction B;
step S522: counting the occurrence frequency of each transaction in the transaction B in the item set A to generate the support degree distribution of each transaction;
step S523: generating association rule confidence distribution between every two affairs in the affair B by performing probability calculation on the association rule between every two affairs;
step S524: and performing parameter conversion on the transaction support degree distribution and the association rule confidence degree distribution to obtain the energy output-geographic environment association parameter distribution.
Specifically, after the set of energy output is obtained, it may be deep mined to obtain a first output characteristic, a second output characteristic, and a third output characteristic. The deep mining is to analyze the development law or influence characteristics behind the data by analyzing the depth analysis of the data through the appearance characteristics of the data so as to utilize the potential value of the data and assist the subsequent data analysis process. The first output characteristic may be understood as a cost characteristic of the generation of the various sources of energy available for the area, the second output characteristic may be understood as a cycle characteristic of the generation of the various sources of energy available for the area, and the third output characteristic may be understood as an efficiency characteristic of the generation of the various sources of energy available for the area.
Specifically, in the process of data mining, the geographic environment of the area may be collected first, the geographic environment data includes multiple data of geographic topography, climate environment, economic development and the like of the area, and then the correlation degree calculation is performed on the suppliable energy of the area and the geographic environment data, generally, the suppliable energy of an area and the geographic data of the area have a direct correlation degree, such as the above easily-developed water energy with a poor geography, the easily-developed photovoltaic energy with a flat and open geography, the easily-developed thermal power generation with rich coal resources and the like, and the distribution of the energy output-geographic environment correlation parameters is the distribution of the correlation parameters of the suppliable energy and the geographic environment.
Specifically, when calculating the degree of association between the two, the geographic environment data may be defined as an item set a, and the output data may be defined as a transaction B, where the item set a includes various geographic environment data of the area, and the transaction B is a set of various sources of available energy, and by counting the occurrence times of the set of various sources of available energy in the various geographic environment data of the area, a support degree distribution of each transaction may be obtained, where the support degree distribution of each transaction reflects the set of the occurrence times of the set of various sources of available energy, and the greater the number of the occurrence times, the greater the source of an item of available energy is greatly dependent on the geographic environment of the area, and thus the degree of association between the two is relatively large, and it should be noted that the degree of support is only one influence parameter of the degree of association between things, in order to perform more detailed data mining, confidence calculation can be performed between every two of various source sets capable of supplying energy, and for example, if the region is mainly photovoltaic power generation and is assisted by thermal power generation, potential hydraulic power generation does not exist, which indicates that the confidence between photovoltaic power generation and thermal power generation is higher and the confidence between the photovoltaic power generation and the potential hydraulic power generation is lower. Therefore, the confidence coefficient is also another influence parameter of the association degree between the objects, the association degree between the objects can be comprehensively and finely analyzed through calculation of the support degree and the confidence coefficient, the association rule confidence coefficient distribution reflects the association degree distribution between every two of various source sets capable of supplying energy, and the development rules of various source sets are reflected from the side. The distribution of the energy yield-geographic environment related parameters is obtained by performing parameter transformation on the distribution of the transaction support degree and the distribution of the association rule confidence degree, in other words, by transforming the percentage expression form of the two distributions into the expression form of the parameters, for example, if the support degree of a certain type of available energy is 80%, the support degree of the certain type of available energy can be transformed into the parameter 0.8, and finally the distribution of the energy yield-geographic environment related parameters is obtained.
After obtaining the distribution of each energy output-geographic environment associated parameter, analyzing the geographic environment data to obtain a geographic climate development law, wherein the geographic climate development law can be summarized into a climate development law of the area, the geographic climate development law comprises weather factors such as rainy season, rainfall, windy and the like, and a geographic change full period and a geographic change short period can be obtained by deep analyzing the weather factors, wherein the geographic change full period can be understood as a condition that the rainy season is concentrated, the rainfall is large, the rainy period can be fully utilized to perform power generation, energy storage and the like of water conservancy potential energy, the geographic change short period is opposite to a condition that the rainy season is dispersed, the rainfall is small, and parameter correction can be performed on the distribution of each energy output-geographic environment associated parameter through different change conditions of the geographic factors, namely the change of the geographic factor, the method comprises the steps of influencing each suppliable energy source, further enabling the output characteristic of each suppliable energy source to be changed, wherein the change can be good or bad, finally analyzing and obtaining the output cost, the generation period and the generation efficiency of each suppliable energy source, and the specific data of the characteristic are dynamically changed.
Further, as shown in fig. 3, step S550 includes:
step S551: obtaining a first starting node and a first end node of the geographic change in the full period;
step S552: acquiring data of the parameter distribution of each energy output-geographic environment association parameter distribution at the first initial node and the first end node respectively to obtain a first initial node parameter distribution and a first end node parameter distribution;
step S553: obtaining a second starting node and a second end node of the geographic change shortage period;
step S554: acquiring data of the parameter distribution of each energy output-geographic environment association parameter distribution at the second starting node and the second end node respectively to obtain a second starting node parameter distribution and a second end node parameter distribution;
step S555: and obtaining the first output characteristic, the second output characteristic and the third output characteristic by performing differential traversal analysis on the first starting node parameter distribution and the second starting node parameter distribution and the first end node parameter distribution and the second end node parameter distribution.
Specifically, when the parameter correction is performed on the energy yield-geographical environment-related parameter distributions, a first start node and a first end node of the geographical change plumpness may be obtained, wherein the first start node may be understood as a start time of a rainy season and the first end node may be understood as an end time of the rainy season, and the first start node parameter distribution, that is, the parameter distribution related to each suppliable energy at the start time of the rainy season and the first end node parameter distribution, that is, the parameter distribution related to each suppliable energy at the end time of the rainy season may be obtained by marking the energy yield-geographical environment-related parameter distributions with corresponding time nodes. Otherwise, the second starting node is the starting time of the poor rainy season, the second last node is the ending time of the poor rainy season, the second starting node parameter distribution is the associated parameter distribution of each suppliable energy source at the starting time of the poor rainy season, and the second last node parameter distribution is the associated parameter distribution of each suppliable energy source at the ending time of the poor rainy season.
Furthermore, by performing data differentiation analysis on the associated parameter distribution of each energy source capable of being supplied at the starting time of rainy season, the associated parameter distribution of each energy source capable of being supplied at the ending time of rainy season, and the associated parameter distribution of each energy source capable of being supplied at the ending time of rainy season, a certain difference result can be obtained, which represents the conversion distribution data among the energy sources capable of being supplied under different geographic environments, during the conversion process, a certain influence is inevitably caused on the generation cost, the generation period and the generation efficiency of the energy sources to be converted, and by extracting the influence characteristics, the subsequent energy distribution can be reasonably corrected, so that the system can operate under the condition of ensuring the benefit, and optimizing to obtain the energy production, storage and distribution mode with the lowest cost and higher profit.
Step S600: taking the historical electricity peak node set and the historical electricity valley node set as training data, and taking the first output characteristic, the second output characteristic and the third output characteristic as supervision data, and building an energy storage and distribution model of the target electricity utilization area;
further, as shown in fig. 4, step S600 includes:
step S610: splitting the training data to obtain a training data set and a test data set;
step S620: continuously splitting the training data set into K parts based on a K-fold cross validation method;
step S630: screening any one of the k training data, and carrying out algorithm training on the remaining k-1 data to generate k training model sets;
step S640: based on the k training model sets, simultaneously testing the test data sets to obtain an initial energy storage distribution model;
step S650: obtaining a preliminary predictive allocation result of the test data set based on the initial energy storage allocation model;
step S660: obtaining an actual energy distribution result of the test data set based on the historical periodic electricity consumption data;
step S670: comparing the data of the preliminary prediction distribution result with the actual energy distribution result to generate a first inspection parameter, a second inspection parameter and a third inspection parameter;
step S680: and performing model verification on the initial energy storage distribution model according to the first verification parameter, the second verification parameter and the third verification parameter to generate the energy storage distribution model.
Specifically, after the historical electricity peak node set, the historical electricity valley node set, the first yield characteristic, the second yield characteristic and the third yield characteristic are obtained, an energy storage allocation model of the region can be built based on the historical electricity peak node set, the historical electricity valley node set, the first yield characteristic, the second yield characteristic and the third yield characteristic. Specifically, the historical electricity utilization wave crest node set and the historical electricity utilization wave trough node set can be used as training data, and the first output characteristic, the second output characteristic and the third output characteristic can be used as supervision data to build a model.
Specifically, when the model is built, in order to ensure the training accuracy of the model, the training data can be split, the training data set is used for training the model, the testing data set is used for testing and verifying the accuracy of the trained model, in order to improve the training accuracy of the model, data training can be carried out based on a K-fold cross verification method, the general condition is that the K-fold cross verification is used for model tuning optimization, a super-parameter value enabling the generalization performance of the model to be optimal is found, and the K-fold cross verification uses the advantages of a non-repeated sampling technology: each sample point has only one chance to be drawn into the training or test set during each iteration. Firstly, dividing all samples into k sample subsets with equal size; sequentially traversing the k subsets, taking the current subset as a verification set each time, taking all the other samples as training sets, and training and evaluating the model; and finally, taking the average value of the k evaluation indexes as a final evaluation index. And taking the training model corresponding to the final evaluation index as the initial energy storage distribution model.
After the initial energy storage allocation model is trained, the initial energy storage allocation model can be tested, that is, the test data set is input into the initial energy storage allocation model, and a corresponding preliminary prediction allocation result can be obtained through allocation training, which is a result trained by the model, and the comparison result with an actual energy allocation result can be analyzed by performing data comparison on the initial energy storage allocation model and the actual energy allocation result, wherein the first inspection parameter is a training accuracy rate displayed by the difference result, that is, a correct proportion is predicted for an interested category in all samples, the second inspection parameter is a data precision rate displayed by the difference result, that is, a correct prediction capability in the prediction result, and the third inspection parameter is a recall rate displayed by the difference result, that is, a capability of finding a category from the actual category. Finally, model verification is carried out on the initial energy storage distribution model through the three inspection parameters, so that the energy storage distribution model is generated, and the training accuracy of the model is improved.
Step S700: and acquiring the external power demand of the target power utilization area, inputting the external power demand into the energy storage distribution model for training, acquiring energy output distribution information, and outputting energy to the target power utilization area.
Further, step S700 includes:
step S710: carrying out power utilization feature extraction on the external power utilization requirement to obtain a power utilization feature set;
step S720: inputting the electricity utilization characteristic set into the energy storage distribution model, and performing distribution training to obtain a primary distribution result;
step S730: and optimizing the preliminary distribution result based on preset benefits to generate the energy output distribution information.
Specifically, after the energy storage allocation model is built, actual data training allocation can be performed based on the model. Specifically, can gather the regional outside power consumption demand of target power consumption, through carrying out power consumption feature extraction to it, can obtain power consumption feature set, wherein, power consumption feature set includes power consumption quantity, power consumption period (power consumption centralization time and power consumption decentralized time) and power consumption region etc. distributes the training through inputting the power consumption feature of extracting to the energy storage distribution model that has built, can obtain preliminary distribution result, for regional development with the reality is coordinated mutually, can be based on predetermined benefit, right preliminary distribution result optimizes, generates energy output distribution information. The preset benefits can be determined based on the actual economic development level of the region, reasonable distribution of energy demands is guaranteed on the premise that the benefits of the region are guaranteed, and reasonable energy distribution can be carried out on the external power demand through the energy output distribution information, so that energy production with low cost and high profit is obtained.
In summary, the energy interconnection energy storage and distribution method provided by the invention has the following technical effects:
1. carry out data acquisition through the historical power consumption data to the target area, and carry out the analysis of power consumption extreme value to the data of gathering, gather the energy output of this place simultaneously, and carry out energy output's feature excavation to the collection result, based on power consumption extreme value data and excavation feature, build energy storage distribution model, the realization is distributed the training to the required energy of the regional outside demand power consumption of target power consumption, make and carry out reasonable optimization output to available energy, reached the cost of production based on various energies, production cycle and production efficiency, supply with the condition and carry out rational distribution to available many energy, make the technological effect of promotion energy utilization rate.
2. And optimizing the preliminary distribution result based on the preset benefit to generate energy output distribution information. According to the actual economic development level of the region, reasonable distribution of energy demands is ensured on the premise of ensuring self benefits, and the energy output distribution information can reasonably distribute energy to the external power demand, so that energy production with low cost and high profit is obtained.
Example two
Based on the same inventive concept as the method for allocating energy storage for energy interconnection in the foregoing embodiment, the present invention further provides an energy storage allocation system for energy interconnection, referring to fig. 5, where the system includes:
the first acquisition unit 11 is used for periodically acquiring historical electricity utilization data of a target electricity utilization area based on big data to obtain historical periodic electricity utilization data;
a first obtaining unit 12, where the first obtaining unit 12 is configured to obtain a historical electricity consumption data line graph by performing mathematical statistics and graph transformation on the historical periodic electricity consumption data;
the second acquisition unit 13 is configured to perform preset node acquisition on the historical electricity consumption data line graph to obtain a historical electricity consumption peak node set and a historical electricity consumption valley node set;
a third collecting unit 14, where the third collecting unit 14 is configured to collect energy output information of the target power utilization area to obtain an energy output set;
a first mining unit 15, wherein the first mining unit 15 is configured to perform deep mining on the output data of the energy output set to obtain a first output characteristic, a second output characteristic, and a third output characteristic;
the first building unit 16 is configured to build an energy storage and distribution model of the target power utilization area by using the historical power utilization peak node set and the historical power utilization valley node set as training data, and using the first output feature, the second output feature and the third output feature as supervision data;
and the fourth acquisition unit 17 is used for acquiring the external power consumption requirement of the target power consumption region, inputting the external power consumption requirement to the energy storage distribution model for training, acquiring energy output distribution information, and outputting energy to the target power consumption region.
Further, the system further comprises:
the fifth acquisition unit is used for acquiring the geographic environment information of the target power utilization area to obtain geographic environment data;
the first computing unit is used for carrying out parameter computation on the association degree between the output data and the geographic environment data to obtain the distribution of each energy output-geographic environment association parameter;
the second obtaining unit is used for analyzing the geographic environment data to obtain a geographic climate development rule;
the first analysis unit is used for deeply analyzing the geographical climate development rule to obtain a geographical change abundance period and a geographical change deficiency period;
a first correcting unit, configured to perform parameter correction on the distribution of the energy output-geographic environment related parameters based on the geographic change rich period and the geographic change deficient period, so as to obtain the first output characteristic, the second output characteristic, and the third output characteristic of the output data.
Further, the system further comprises:
a first defining unit for defining the geographic environment data as an item set A and the output data as a transaction B;
the first statistical unit is used for counting the occurrence frequency of each transaction in the transaction B in the item set A and generating the support degree distribution of each transaction;
the second calculation unit is used for performing probability calculation on the association rule between every two affairs in the affair B to generate the confidence coefficient distribution of the association rule between every two affairs;
and the first conversion unit is used for performing parameter conversion on the transaction support degree distribution and the association rule confidence degree distribution to obtain the energy output-geographic environment association parameter distribution.
Further, the system further comprises:
a third obtaining unit, configured to obtain a first start node and a first end node of the geographic change rich period;
a sixth acquisition unit, configured to acquire data of the parameter distributions of the energy yield-geographic environment associated parameters at the first start node and the first end node, respectively, to obtain a first start node parameter distribution and a first end node parameter distribution;
a fourth obtaining unit, configured to obtain a second start node and a second end node of the geographic change shortage period;
a seventh acquisition unit, configured to acquire data of the parameter distributions of the energy yield-geographic environment associated parameters at the second start node and the second end node, respectively, to obtain a second start node parameter distribution and a second end node parameter distribution;
a second parsing unit, configured to perform differential traversal parsing on the first and second starting node parameter distributions, the first and second last node parameter distributions, and obtain the first, second, and third output characteristics.
Further, the system further comprises:
the first splitting unit is used for splitting the training data to obtain a training data set and a test data set;
the second splitting unit is used for continuously splitting the training data set into K parts based on a K-fold cross verification method;
the first training unit is used for screening any one of the k parts of training data and carrying out algorithm training on the rest k-1 parts of data to generate k training model sets;
and the first testing unit is used for simultaneously testing the test data set based on the k training model sets to obtain an initial energy storage distribution model.
Further, the system further comprises:
a fifth obtaining unit, configured to obtain a preliminary prediction allocation result of the test data set based on the initial energy storage allocation model;
a sixth obtaining unit, configured to obtain an actual energy allocation result of the test data set based on the historical periodic electricity consumption data;
the first comparison unit is used for carrying out data comparison on the preliminary prediction distribution result and the actual energy distribution result to generate a first inspection parameter, a second inspection parameter and a third inspection parameter;
the first checking unit is used for carrying out model checking on the initial energy storage distribution model according to the first checking parameter, the second checking parameter and the third checking parameter to generate the energy storage distribution model.
Further, the system further comprises:
the first extraction unit is used for extracting power utilization characteristics of the external power utilization requirement to obtain a power utilization characteristic set;
the first input unit is used for inputting the electricity utilization characteristic set to the energy storage distribution model, performing distribution training and obtaining a preliminary distribution result;
and the first optimization unit is used for optimizing the preliminary distribution result based on preset benefits and generating the energy output distribution information.
In the present description, the embodiments are described in a progressive manner, and each embodiment focuses on differences from other embodiments, and the foregoing energy interconnection energy storage allocation method and specific example in the first embodiment of fig. 1 are also applicable to an energy interconnection energy storage allocation system of this embodiment, and through the foregoing detailed description of an energy interconnection energy storage allocation method, a person skilled in the art can clearly know an energy interconnection energy storage allocation system in this embodiment, so for brevity of the description, detailed descriptions are omitted here. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Exemplary electronic device
The electronic device of the present invention is described below with reference to fig. 6.
Fig. 6 illustrates a schematic structural diagram of an electronic device according to the present invention.
Based on the inventive concept of the energy interconnection energy storage allocation method in the foregoing embodiments, the present invention further provides an energy interconnection energy storage allocation system, on which a computer program is stored, which, when executed by a processor, implements the steps of any one of the foregoing energy interconnection energy storage allocation methods.
Where in fig. 6 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The invention provides an energy interconnection energy storage and distribution method, which comprises the following steps: periodically collecting historical electricity utilization data of the target electricity utilization area based on the big data to obtain historical periodic electricity utilization data; obtaining a historical electricity utilization data line graph by carrying out mathematical statistics and graph conversion on the historical periodic electricity utilization data; performing preset node acquisition on the historical electricity consumption data line graph to obtain a historical electricity consumption peak node set and a historical electricity consumption trough node set; acquiring energy output information of the target power utilization area to obtain an energy output set; obtaining a first output characteristic, a second output characteristic and a third output characteristic by deeply mining the output data of the energy output set; taking the historical electricity peak node set and the historical electricity valley node set as training data, and taking the first output characteristic, the second output characteristic and the third output characteristic as supervision data, and building an energy storage and distribution model of the target electricity utilization area; and acquiring the external power demand of the target power utilization area, inputting the external power demand into the energy storage distribution model for training, acquiring energy output distribution information, and outputting energy to the target power utilization area. The technical problem that when multiple energy sources are distributed in the prior art, the available multi-energy source supply conditions can not be reasonably distributed based on the generation cost, the generation period and the generation efficiency of the various energy sources, so that the energy utilization rate is reduced is solved. Carry out data acquisition through the historical power consumption data to the target area, and carry out the analysis of power consumption extreme value to the data of gathering, gather the energy output of this place simultaneously, and carry out energy output's feature excavation to the collection result, based on power consumption extreme value data and excavation feature, build energy storage distribution model, the realization is distributed the training to the required energy of the regional outside demand power consumption of target power consumption, make and carry out reasonable optimization output to available energy, reached the cost of production based on various energies, production cycle and production efficiency, supply with the condition and carry out rational distribution to available many energy, make the technological effect of promotion energy utilization rate.
The invention also provides an electronic device, which comprises a processor and a memory;
the memory is used for storing;
the processor is configured to execute the method according to any one of the first embodiment through calling.
The invention also provides a computer program product comprising a computer program and/or instructions which, when executed by a processor, performs the steps of the method of any of the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely software embodiment, an entirely hardware embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention is in the form of a computer program product that may be embodied on one or more computer-usable storage media having computer-usable program code embodied therewith. And such computer-usable storage media include, but are not limited to: various media capable of storing program codes, such as a usb disk, a portable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk Memory, a Compact Disc Read-Only Memory (CD-ROM), and an optical Memory.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, it is intended that the present invention also include such modifications and variations.
Claims (10)
1. An energy interconnection energy storage allocation method, the method comprising:
periodically collecting historical electricity utilization data of the target electricity utilization area based on the big data to obtain historical periodic electricity utilization data;
obtaining a historical electricity utilization data line graph by carrying out mathematical statistics and graph conversion on the historical periodic electricity utilization data;
performing preset node acquisition on the historical electricity consumption data line graph to obtain a historical electricity consumption peak node set and a historical electricity consumption trough node set;
acquiring energy output information of the target power utilization area to obtain an energy output set;
obtaining a first output characteristic, a second output characteristic and a third output characteristic by deeply mining the output data of the energy output set;
taking the historical electricity peak node set and the historical electricity valley node set as training data, and taking the first output characteristic, the second output characteristic and the third output characteristic as supervision data, and building an energy storage and distribution model of the target electricity utilization area;
and acquiring the external power demand of the target power utilization area, inputting the external power demand into the energy storage distribution model for training, acquiring energy output distribution information, and outputting energy to the target power utilization area.
2. The method of claim 1, wherein said deep mining through production data of said set of energy production comprises:
collecting geographic environment information of the target power utilization area to obtain geographic environment data;
performing parameter calculation on the association degree between the output data and the geographic environment data to obtain the distribution of each energy output-geographic environment association parameter;
analyzing the geographic environment data to obtain a geographic climate development rule;
obtaining a geographical change abundance period and a geographical change deficiency period by deeply analyzing the geographical climate development rule;
and performing parameter correction on the distribution of the energy yield-geographic environment associated parameters based on the geographic change full period and the geographic change short period to obtain the first yield characteristic, the second yield characteristic and the third yield characteristic of the yield data.
3. The method of claim 2, wherein said parametrically calculating a degree of correlation between said yield data and said geographic environment data comprises:
defining the geographic environment data as a set of items A and the outcome data as a transaction B;
counting the occurrence frequency of each transaction in the transaction B in the item set A to generate the support degree distribution of each transaction;
generating association rule confidence distribution between every two affairs in the affair B by performing probability calculation on the association rule between every two affairs;
and performing parameter conversion on the transaction support degree distribution and the association rule confidence degree distribution to obtain the energy output-geographic environment association parameter distribution.
4. The method of claim 3, wherein said performing a parameter modification on said energy yield-geographic environment associated parameter distributions comprises:
obtaining a first starting node and a first end node of the geographic change in the full period;
acquiring data of the parameter distribution of each energy output-geographic environment association parameter distribution at the first initial node and the first end node respectively to obtain a first initial node parameter distribution and a first end node parameter distribution;
obtaining a second starting node and a second end node of the geographic change shortage period;
acquiring data of the parameter distribution of each energy output-geographic environment association parameter distribution at the second starting node and the second end node respectively to obtain a second starting node parameter distribution and a second end node parameter distribution;
and obtaining the first output characteristic, the second output characteristic and the third output characteristic by performing differential traversal analysis on the first starting node parameter distribution and the second starting node parameter distribution and the first end node parameter distribution and the second end node parameter distribution.
5. The method of claim 4, wherein said modeling an energy storage allocation model for said target electricity usage area comprises:
splitting the training data to obtain a training data set and a test data set;
continuously splitting the training data set into K parts based on a K-fold cross validation method;
screening any one of the k training data, and carrying out algorithm training on the remaining k-1 data to generate k training model sets;
and simultaneously testing the test data set based on the k training model sets to obtain an initial energy storage distribution model.
6. The method of claim 5, wherein the method comprises:
obtaining a preliminary predictive allocation result of the test data set based on the initial energy storage allocation model;
obtaining an actual energy distribution result of the test data set based on the historical periodic electricity consumption data;
comparing the data of the preliminary prediction distribution result with the actual energy distribution result to generate a first inspection parameter, a second inspection parameter and a third inspection parameter;
and performing model verification on the initial energy storage distribution model according to the first verification parameter, the second verification parameter and the third verification parameter to generate the energy storage distribution model.
7. The method of claim 6, wherein said inputting said external power demand into said energy storage allocation model for training comprises:
carrying out power utilization feature extraction on the external power utilization requirement to obtain a power utilization feature set;
inputting the electricity utilization characteristic set into the energy storage distribution model, and performing distribution training to obtain a primary distribution result;
and optimizing the preliminary distribution result based on preset benefits to generate the energy output distribution information.
8. An energy interconnection and energy storage distribution system, the system comprising:
the first acquisition unit is used for periodically acquiring historical electricity utilization data of the target electricity utilization area based on the big data to obtain historical periodic electricity utilization data;
the first obtaining unit is used for obtaining a historical electricity utilization data line graph by carrying out mathematical statistics and graph conversion on the historical periodic electricity utilization data;
the second acquisition unit is used for carrying out preset node acquisition on the historical electricity consumption data line graph to obtain a historical electricity consumption peak node set and a historical electricity consumption trough node set;
the third acquisition unit is used for acquiring the energy output information of the target power utilization area to obtain an energy output set;
the first mining unit is used for carrying out deep mining on the output data of the energy output set to obtain a first output characteristic, a second output characteristic and a third output characteristic;
the first building unit is used for building an energy storage distribution model of the target power utilization area by taking the historical power utilization wave crest node set and the historical power utilization wave trough node set as training data and the first output characteristic, the second output characteristic and the third output characteristic as supervision data;
and the fourth acquisition unit is used for acquiring the external power consumption requirement of the target power consumption area, inputting the external power consumption requirement to the energy storage distribution model for training, acquiring energy output distribution information and outputting energy to the target power consumption area.
9. An electronic device comprising a processor and a memory;
the memory is used for storing;
the processor is used for executing the method of any one of claims 1-7 through calling.
10. A computer program product comprising a computer program and/or instructions, characterized in that the computer program and/or instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.
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