CN118432087B - Power distribution network load curve modeling and early warning method based on optical storage and charging system - Google Patents
Power distribution network load curve modeling and early warning method based on optical storage and charging system Download PDFInfo
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Abstract
The invention relates to the technical field of power distribution, and provides a power distribution network load curve modeling and early warning method based on an optical storage and charging system, which comprises the following steps: acquiring a photovoltaic output curve, an energy storage and power storage curve and an energy storage and power discharge curve based on the acquired working and operation data of the optical storage and power charging system; the photovoltaic output curve, the energy storage and accumulation curve and the energy storage and discharge curve are predicted and analyzed by utilizing a neural network model, and a comprehensive distribution influence value affecting the work of the distribution network is obtained; according to the acquired working data of the power distribution network, combining the comprehensive influence value of power distribution, and outputting a load curve of the power distribution network by using a load prediction model; and setting typical characteristic data on a load curve of the power distribution network as an early warning trigger condition, and carrying out early warning when the load data of the power distribution network reaches the early warning trigger condition. The method can improve the modeling quality of the load curve of the power distribution network, realize the early warning after the early warning condition is triggered, accurately find out the condition of overlarge load and avoid the condition of damaging the power distribution working quality of the optical storage and charging system.
Description
Technical Field
The invention relates to the technical field of power distribution, in particular to a power distribution network load curve modeling and early warning method based on an optical storage and charging system.
Background
On the charging and cruising problem of the electric automobile, the integrated optical storage and charging is an ideal scheme for solving the problem of quick charging of the electric automobile. The integrated optical storage and charging device has the advantages that the power is digested in situ through energy storage and charging, the pressure of a power grid is reduced, the arrangement of optical storage and charging stations is flexible, the application is convenient, the integrated optical storage and charging device is suitable for various fields, and the integrated optical storage and charging device is an advantage of integrated optical storage and charging.
The photovoltaic power generation is utilized by the photovoltaic power storage and charging system, the electric quantity is stored in the energy storage battery, and when the electric quantity is needed, the energy storage battery supplies the electric quantity to the charging pile for use; the power distribution network of the optical storage and charging system needs to consider the access of a distributed power supply so as to meet the requirements of users on power supply reliability and power quality. The electric automobile is used as a special load, the construction of the electric automobile and charging facilities is developed on a large scale, the load of a new round is possibly increased rapidly, meanwhile, the load structure of a distribution network is changed, the electric automobile is used as the load of an optical storage charging system, the electric automobile is charged in a normal electricity using period or an electricity peak period, the charging requirement can increase the burden of the optical storage charging system, the charging characteristics of different electric automobiles are different, the influence on a power grid is different when the electric automobiles are charged, the battery charger for vehicles belongs to a nonlinear device, the adverse effect is brought to the power quality of the power supply system, meanwhile, the charging facilities are distributed at multiple points, the charging of vehicles has randomness, the running condition of the system is possibly changed at any time, and the uncertainty of the power grid is increased. Under the influence of the factors, the load has strong randomness in a state of lacking orderly control, and especially, the local area congestion and the overload of the power grid can be caused due to special events or adjustment of production life information.
Patent application number CN201710331489.4 discloses a power distribution network load modeling method, which comprises the following steps: determining average electrical characteristics of various typical loads; counting the proportion of various typical loads in the form of a power grid; estimating the average characteristic of the load under the form of the power grid; and establishing a distribution network load model according to the proportion of the typical load and the average characteristic of the load. The method comprehensively considers the differences of the loads of the distribution networks with different development forms, establishes a distribution network load model through a statistical method, and provides reasonable basis for selecting a typical daily load curve to represent the load of the whole time period. According to the method, load modeling is conducted according to the proportion of various typical loads in the form of a traditional power distribution network, analysis modeling is conducted from the diversity of the loads, and the influence on the power consumption of the loads is not considered in consideration of the power distribution stability of the power grid;
The optical storage and charging system is different from a conventional power grid in the power distribution working principle, the persistence of photovoltaic power generation and the stability of an energy storage power station can influence the power consumption of the load of the power distribution network, and the existing modeling of the load curve of the power distribution network has less consideration on the energy efficiency of photovoltaic charging and energy storage and discharging working links, so that the modeling quality of the power distribution network is influenced;
Therefore, it is necessary to provide a power distribution network load curve modeling and early warning method based on the optical storage and charging system.
Disclosure of Invention
The invention provides a power distribution network load curve modeling and early warning method based on an optical storage and charging system, which monitors the operation of power consumption data acquisition equipment and power consumption data transmission by utilizing a monitoring platform and processes the power consumption data transmission, so that the operation stability of the power consumption data acquisition equipment can be improved, the quality of the power consumption data transmission is ensured, the stable operation of the power consumption data acquisition and transmission is ensured, and the efficiency of the power consumption data acquisition and transmission is improved.
The invention provides a power distribution network load curve modeling and early warning method based on an optical storage and charging system, which comprises the following steps:
S1: acquiring a photovoltaic output curve, an energy storage and power storage curve and an energy storage and power discharge curve based on the acquired working and operation data of the optical storage and power charging system;
S2: the photovoltaic output curve, the energy storage and accumulation curve and the energy storage and discharge curve are predicted and analyzed by utilizing a neural network model, and a comprehensive distribution influence value affecting the work of the distribution network is obtained;
s3: according to the acquired working data of the power distribution network, combining the comprehensive influence value of power distribution, and outputting a load curve of the power distribution network by using a load prediction model;
S4: setting typical characteristic data on a load curve of the power distribution network as an early warning trigger condition, and carrying out early warning when the load data of the power distribution network reaches the early warning trigger condition;
s5, controlling and managing the quantity and the type of the power distribution network loads to be charged according to the early warning reminding of the power distribution network load curve, wherein the specific steps are as follows:
S501: according to the typical characteristic data, the corresponding load quantity and load type under charging are obtained;
S502: constructing an objective function; based on the objective function, adjusting and optimizing the load quantity and the load type to obtain an optimal load quantity type ratio; the ratio of the optimal load quantity type ratio is the optimal load quantity and load type ratio;
S503: acquiring the charging demand priority of the load waiting for charging and the charging completion time of different types of loads;
s504: according to the optimal load quantity and type ratio, the charging demand priority of the load and the charging completion time, a preset load charging process monitoring model is utilized, and an artificial intelligent algorithm is utilized to intelligently control and manage the load quantity and the load type to be charged.
Further, S1 includes:
s101: acquiring working and operating data of an optical storage and filling system;
S102: according to the working and running data of the optical storage and filling system, combining the illumination intensity historical data in the meteorological big data to obtain a photovoltaic output curve;
s103: and obtaining an energy storage and accumulation curve and an energy storage and discharge curve according to the working and operation data of the optical storage and accumulation system.
Further, S102 includes:
s1021: according to the working operation data of the optical storage and filling system, extracting and obtaining photovoltaic output working data;
S1022: acquiring illumination intensity historical data based on meteorological big data;
s1023: generating a plurality of photovoltaic working scenes by utilizing a scene simulation model according to the illumination intensity historical data, and extracting to obtain photovoltaic working simulation data;
s1024: integrating the photovoltaic output work data and the photovoltaic work simulation data, and drawing to obtain a photovoltaic output curve.
Further, S103 includes:
s1031: extracting and obtaining energy storage working data according to the working and operation data of the optical storage and charging system;
S1032: and according to the energy storage working data, extracting parameters forming an energy storage and accumulation curve and an energy storage and discharge curve by using a parameter extraction template, and drawing and generating the energy storage and accumulation curve and the energy storage and discharge curve by using the parameters.
Further, S2 includes:
S201: constructing a distribution influence analysis model by using the neural network model;
S202: extracting and obtaining a plurality of characteristic data affecting the work of the power distribution network in a photovoltaic output curve, an energy storage and power storage curve and an energy storage and power discharge curve;
S203: predicting the plurality of characteristic data by using a distribution influence analysis model to obtain a plurality of distribution influence values;
S204: and after the multiple distribution influence values are weighted and summed, the distribution comprehensive influence value is obtained.
Further, S3 includes:
s301: acquiring power distribution network working data of an optical storage and charging system, and extracting load curve construction data according to the power distribution network working data;
S302: and according to the load curve composition data, combining the comprehensive influence value of power distribution, and outputting a load curve of the power distribution network by using a load prediction model.
Further, the load prediction model in S302 is created based on a convolutional neural network model, and training and verification are performed based on a data set; the data set is: the load curves constitute historical data in the data and historical data in the comprehensive influence value of power distribution.
Further, S4 includes:
s401: typical characteristic data on a load curve of the power distribution network are obtained;
s402: based on the typical characteristic data, setting an early warning triggering condition;
s403: and when the load data of the power distribution network is matched with the typical characteristic data, early warning and reminding are carried out.
Further, the method further comprises the step S6 of evaluating and monitoring the power distribution working stability of the optical storage and charging system according to a power distribution network load curve, and specifically comprises the following steps:
S601: acquiring evaluation parameter data based on evaluation standards and evaluation requirements of the distribution work stability of the optical storage and filling system;
S602: characteristic data in a load curve of the power distribution network and working and running data of the optical storage and charging system are used as evaluation content items, and a preset matching relation library of evaluation parameter data and the evaluation content items is utilized to obtain evaluation content items matched with the evaluation parameter data;
S603: according to historical data of a plurality of evaluation content items, constructing a distribution work stability evaluation calculation formula, carrying out simulation calculation to obtain a simulation calculation result, and extracting threshold results for judging distribution work stability and distribution work instability from the simulation calculation result;
s604: and carrying out real-time data monitoring on the evaluation content item according to the threshold result to obtain real-time evaluation content item data, carrying out real-time calculation on the real-time evaluation content item data by using a power distribution work stability evaluation calculation formula, and carrying out risk reminding of unstable power distribution work if the real-time calculation result reaches the threshold result.
Compared with the prior art, the invention has the following advantages and beneficial effects: the photovoltaic output curve, the energy storage and electricity storage curve and the energy storage and discharge curve are obtained through the obtained working operation data of the optical storage and charging system, the power distribution comprehensive influence value is obtained through the neural network model, the load curve of the power distribution network is output through the load prediction model according to the working data of the power distribution network and the power distribution comprehensive influence value, early warning reminding is achieved, modeling quality of the load curve of the power distribution network can be improved, early warning after triggering early warning conditions is achieved, the condition that the load is overlarge can be found accurately, and the condition that the power distribution working quality of the optical storage and charging system is damaged is avoided.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a power distribution network load curve modeling and early warning method based on an optical storage and charging system;
FIG. 2 is a schematic diagram of method steps for obtaining a photovoltaic output curve, an energy storage and storage curve, and an energy storage and discharge curve;
FIG. 3 is a schematic diagram of method steps for obtaining a comprehensive impact value of power distribution.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The invention provides a power distribution network load curve modeling and early warning method based on an optical storage and charging system, which is shown in figure 1 and comprises the following steps:
S1: acquiring a photovoltaic output curve, an energy storage and power storage curve and an energy storage and power discharge curve based on the acquired working and operation data of the optical storage and power charging system;
S2: the photovoltaic output curve, the energy storage and accumulation curve and the energy storage and discharge curve are predicted and analyzed by utilizing a neural network model, and a comprehensive distribution influence value affecting the work of the distribution network is obtained;
s3: according to the acquired working data of the power distribution network, combining the comprehensive influence value of power distribution, and outputting a load curve of the power distribution network by using a load prediction model;
S4: setting typical characteristic data on a load curve of the power distribution network as an early warning trigger condition, and carrying out early warning when the load data of the power distribution network reaches the early warning trigger condition;
s5, controlling and managing the quantity and the type of the power distribution network loads to be charged according to the early warning reminding of the power distribution network load curve, wherein the specific steps are as follows:
S501: according to the typical characteristic data, the corresponding load quantity and load type under charging are obtained;
S502: constructing an objective function; based on the objective function, adjusting and optimizing the load quantity and the load type to obtain an optimal load quantity type ratio; the ratio of the optimal load quantity type ratio is the optimal load quantity and load type ratio;
S503: acquiring the charging demand priority of the load waiting for charging and the charging completion time of different types of loads;
s504: according to the optimal load quantity and type ratio, the charging demand priority of the load and the charging completion time, a preset load charging process monitoring model is utilized, and an artificial intelligent algorithm is utilized to intelligently control and manage the load quantity and the load type to be charged.
The working principle of the technical scheme is as follows: in order to realize modeling and early warning of a power distribution network load curve based on an optical storage and charging system, the invention obtains a photovoltaic output curve, an energy storage and power storage curve and an energy storage and discharge curve according to collected and obtained working operation data of the optical storage and charging system, considers the influence of the photovoltaic output curve, the energy storage and power storage curve and the energy storage and discharge curve on the work of the power distribution network, obtains a comprehensive power distribution influence value by using a neural network model, outputs the power distribution network load curve by using a load prediction model according to the working data and the comprehensive power distribution influence value, sets an early warning triggering condition for triggering early warning, and can accurately output the power distribution network load curve and realize early warning and reminding when the load is overlarge;
The method also comprises the steps of controlling and managing the quantity and the type of the load of the power distribution network to be charged according to the early warning reminding of the load curve of the power distribution network, and specifically comprises the following steps: according to the typical characteristic data, the corresponding load quantity and load type under charging are obtained; constructing an objective function; based on the objective function, adjusting and optimizing the load quantity and the load type to obtain an optimal load quantity type ratio; the ratio of the optimal load quantity type ratio is the optimal load quantity and load type ratio; acquiring the charging demand priority of the load waiting for charging and the charging completion time of different types of loads; according to the optimal load quantity and type ratio, the charging demand priority of the load and the charging completion time, a preset load charging process monitoring model is utilized, and an artificial intelligent algorithm is utilized to intelligently control and manage the load quantity and the load type to be charged.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the photovoltaic output curve, the energy storage and power storage curve and the energy storage and discharge curve are obtained through the obtained working operation data of the optical storage and charging system, the power distribution comprehensive influence value is obtained through the neural network model, the load curve of the power distribution network is output and early warning reminding is realized through the load prediction model according to the working data of the power distribution network and the power distribution comprehensive influence value, the modeling quality of the load curve of the power distribution network can be improved, early warning after triggering early warning conditions is realized, the condition of overlarge load can be accurately found, and the condition of damaging the power distribution working quality of the optical storage and charging system is avoided; by intelligently controlling and managing the number and the type of the loads waiting to be charged, the intelligent management of the number and the type of the loads can be improved, and the management efficiency and quality can be improved.
In one embodiment, as shown in fig. 2, S1 includes:
s101: acquiring working and operating data of an optical storage and filling system;
S102: according to the working and running data of the optical storage and filling system, combining the illumination intensity historical data in the meteorological big data to obtain a photovoltaic output curve;
s103: and obtaining an energy storage and accumulation curve and an energy storage and discharge curve according to the working and operation data of the optical storage and accumulation system.
The working principle of the technical scheme is as follows: in order to achieve the acquisition of a photovoltaic output curve, an energy storage and power storage curve and an energy storage and power discharge curve, the photovoltaic energy storage and power storage system is obtained by combining the working operation data of the photovoltaic energy storage and power storage system and the illumination intensity historical data in meteorological big data, and can be comprehensively and systematically acquired.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the photovoltaic output curve, the energy storage and power storage curve and the energy storage and power discharge curve are obtained according to the working and running data of the optical storage and power charging system, so that the obtaining quality of the photovoltaic output curve, the energy storage and power storage curve and the energy storage and power discharge curve is ensured.
In one embodiment, S102 includes:
s1021: according to the working operation data of the optical storage and filling system, extracting and obtaining photovoltaic output working data;
S1022: acquiring illumination intensity historical data based on meteorological big data;
s1023: generating a plurality of photovoltaic working scenes by utilizing a scene simulation model according to the illumination intensity historical data, and extracting to obtain photovoltaic working simulation data;
s1024: integrating the photovoltaic output work data and the photovoltaic work simulation data, and drawing to obtain a photovoltaic output curve.
The working principle of the technical scheme is as follows: in order to obtain a photovoltaic output curve, the photovoltaic output working data is extracted according to working operation data of the photovoltaic storage and charging system, then the illumination intensity historical data is obtained based on meteorological big data, a scene simulation model is utilized to generate a photovoltaic working scene, photovoltaic working simulation data are obtained, the comprehensiveness of the photovoltaic output working data can be guaranteed, and finally the photovoltaic output working data and the photovoltaic working simulation data are integrated and drawn to obtain the photovoltaic output curve.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the photovoltaic output curve is obtained by using the scene simulation model according to the working and running data of the optical storage and charging system and based on meteorological big data, so that the obtaining quality of the photovoltaic output curve is ensured.
In one embodiment, S103 includes:
s1031: extracting and obtaining energy storage working data according to the working and operation data of the optical storage and charging system;
S1032: and according to the energy storage working data, extracting parameters forming an energy storage and accumulation curve and an energy storage and discharge curve by using a parameter extraction template, and drawing and generating the energy storage and accumulation curve and the energy storage and discharge curve by using the parameters.
The working principle of the technical scheme is as follows: in order to achieve the acquisition of the energy storage and power storage curve and the energy storage and power discharge curve, the invention extracts and obtains the energy storage working data according to the working operation data of the optical storage and power charging system, extracts and obtains parameters composing the energy storage and power storage curve and the energy storage and power discharge curve by utilizing a parameter extraction template, draws and generates the energy storage and power storage curve and the energy storage and power discharge curve, and can ensure the acquisition quality of the energy storage and power storage curve and the energy storage and power discharge curve.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, parameters forming the energy storage and electricity storage curve and the energy storage and electricity discharge curve are extracted and obtained by utilizing the parameter extraction template, and the energy storage and electricity storage curve and the energy storage and electricity discharge curve are drawn and generated, so that the acquisition quality of the energy storage and electricity storage curve and the energy storage and electricity discharge curve is ensured.
In one embodiment, as shown in fig. 3, S2 includes:
S201: constructing a distribution influence analysis model by using the neural network model;
S202: extracting and obtaining a plurality of characteristic data affecting the work of the power distribution network in a photovoltaic output curve, an energy storage and power storage curve and an energy storage and power discharge curve;
S203: predicting the plurality of characteristic data by using a distribution influence analysis model to obtain a plurality of distribution influence values;
S204: and after the multiple distribution influence values are weighted and summed, the distribution comprehensive influence value is obtained.
The working principle of the technical scheme is as follows: in order to obtain a comprehensive distribution influence value, a neural network model is utilized to construct a distribution influence analysis model, a plurality of characteristic data which are extracted from a photovoltaic output curve, an energy storage and power storage curve and an energy storage and power discharge curve and influence the work of a distribution network are predicted, a plurality of distribution influence values are obtained, and after weighted summation is implemented, the comprehensive distribution influence value is obtained, so that the comprehensive distribution influence value can be ensured to be scientifically and accurately obtained.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the power distribution comprehensive influence value is obtained by constructing the power distribution influence analysis model by utilizing the neural network model, so that the scientific and accurate acquisition of the power distribution comprehensive influence value is ensured.
In one embodiment, S3 comprises:
s301: acquiring power distribution network working data of an optical storage and charging system, and extracting load curve construction data according to the power distribution network working data;
S302: and according to the load curve composition data, combining the comprehensive influence value of power distribution, and outputting a load curve of the power distribution network by using a load prediction model.
The working principle of the technical scheme is as follows: in order to output the load curve of the power distribution network, the invention extracts the load curve forming data according to the power distribution network working data of the optical storage and charging system, combines the comprehensive influence value of power distribution, outputs the load curve by using a load prediction model, and can ensure the output quality of the load curve of the power distribution network.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the load prediction model is utilized for outputting, so that the output quality of the load curve of the power distribution network is ensured.
In one embodiment, the load prediction model in S302 is created based on a convolutional neural network model and trained and validated based on a dataset; the data set is: the load curves constitute historical data in the data and historical data in the comprehensive influence value of power distribution.
The working principle of the technical scheme is as follows: and a load prediction model is established by utilizing the convolutional neural network model, and training and verification are performed based on the data set, so that the construction quality of the load prediction model can be ensured.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the load prediction model is created by using the convolutional neural network model, so that the construction quality of the load prediction model is ensured.
In one embodiment, S4 comprises:
s401: typical characteristic data on a load curve of the power distribution network are obtained;
s402: based on the typical characteristic data, setting an early warning triggering condition;
s403: and when the load data of the power distribution network is matched with the typical characteristic data, early warning and reminding are carried out.
The working principle of the technical scheme is as follows: in order to realize the early warning and reminding functions, typical characteristic data on a load curve of the power distribution network are acquired, early warning triggering conditions are set, and when the load data of the power distribution network is matched with the typical characteristic data, early warning and reminding are carried out, so that the accuracy of early warning and reminding can be guaranteed.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the early warning reminding is set, so that the safety reminding when the load is overlarge is realized.
In one embodiment, the method further includes S6, according to a load curve of the power distribution network, of evaluating and monitoring the power distribution working stability of the optical storage and charging system, specifically:
S601: acquiring evaluation parameter data based on evaluation standards and evaluation requirements of the distribution work stability of the optical storage and filling system;
S602: characteristic data in a load curve of the power distribution network and working and running data of the optical storage and charging system are used as evaluation content items, and a preset matching relation library of evaluation parameter data and the evaluation content items is utilized to obtain evaluation content items matched with the evaluation parameter data;
S603: according to historical data of a plurality of evaluation content items, constructing a distribution work stability evaluation calculation formula, carrying out simulation calculation to obtain a simulation calculation result, and extracting threshold results for judging distribution work stability and distribution work instability from the simulation calculation result;
s604: and carrying out real-time data monitoring on the evaluation content item according to the threshold result to obtain real-time evaluation content item data, carrying out real-time calculation on the real-time evaluation content item data by using a power distribution work stability evaluation calculation formula, and carrying out risk reminding of unstable power distribution work if the real-time calculation result reaches the threshold result.
The working principle of the technical scheme is as follows: in order to realize evaluation and monitoring of the distribution work stability of the optical storage and filling system, the invention acquires evaluation parameter data based on the evaluation standard and the evaluation requirement of the distribution work stability of the optical storage and filling system; characteristic data in a load curve of the power distribution network and working operation data of the optical storage and charging system are used as evaluation content items, and a preset matching relation library of evaluation parameter data and the evaluation content items is utilized to obtain evaluation content items matched with the evaluation parameter data; then constructing a power distribution work stability evaluation calculation formula, performing simulation calculation, and obtaining threshold results for judging power distribution work stability and power distribution work instability according to simulation calculation results; and carrying out real-time data monitoring on the evaluation content item according to the threshold result to obtain real-time evaluation content item data, carrying out real-time calculation on the real-time evaluation content item data by utilizing a power distribution work stability evaluation calculation formula, and if the real-time calculation result reaches the threshold result, realizing the risk reminding of unstable power distribution work.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the power distribution working stability of the optical storage and charging system can be evaluated and monitored according to the power distribution network load curve, so that the power distribution working energy efficiency of the optical storage and charging system can be mastered in time, and the risk of being unfavorable for stable operation can be found in a targeted manner.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. The power distribution network load curve modeling and early warning method based on the optical storage and charging system is characterized by comprising the following steps of:
S1: acquiring a photovoltaic output curve, an energy storage and power storage curve and an energy storage and power discharge curve based on the acquired working and operation data of the optical storage and power charging system;
S2: the photovoltaic output curve, the energy storage and accumulation curve and the energy storage and discharge curve are predicted and analyzed by utilizing a neural network model, and a comprehensive distribution influence value affecting the work of the distribution network is obtained;
s3: according to the acquired working data of the power distribution network, combining the comprehensive influence value of power distribution, and outputting a load curve of the power distribution network by using a load prediction model;
S4: setting typical characteristic data on a load curve of the power distribution network as an early warning trigger condition, and carrying out early warning when the load data of the power distribution network reaches the early warning trigger condition;
S2 comprises the following steps:
S201: constructing a distribution influence analysis model by using the neural network model;
S202: extracting and obtaining a plurality of characteristic data affecting the work of the power distribution network in a photovoltaic output curve, an energy storage and power storage curve and an energy storage and power discharge curve;
S203: predicting the plurality of characteristic data by using a distribution influence analysis model to obtain a plurality of distribution influence values;
S204: after weighting and summing the multiple distribution influence values, obtaining a distribution comprehensive influence value;
s5, controlling and managing the quantity and the type of the power distribution network loads to be charged according to the early warning reminding of the power distribution network load curve, wherein the specific steps are as follows:
S501: according to the typical characteristic data, the corresponding load quantity and load type under charging are obtained;
S502: constructing an objective function; based on the objective function, adjusting and optimizing the load quantity and the load type to obtain an optimal load quantity type ratio; the ratio of the optimal load quantity type ratio is the optimal load quantity and load type ratio;
S503: acquiring the charging demand priority of the load waiting for charging and the charging completion time of different types of loads;
s504: according to the optimal load quantity and type ratio, the charging demand priority of the load and the charging completion time, a preset load charging process monitoring model is utilized, and an artificial intelligent algorithm is utilized to intelligently control and manage the load quantity and the load type to be charged.
2. The method for modeling and early warning of a load curve of a power distribution network based on an optical storage and inflation system according to claim 1, wherein S1 comprises:
s101: acquiring working and operating data of an optical storage and filling system;
S102: according to the working and running data of the optical storage and filling system, combining the illumination intensity historical data in the meteorological big data to obtain a photovoltaic output curve;
s103: and obtaining an energy storage and accumulation curve and an energy storage and discharge curve according to the working and operation data of the optical storage and accumulation system.
3. The method for modeling and early warning a load curve of a power distribution network based on an optical storage and inflation system according to claim 2, wherein S102 comprises:
s1021: according to the working operation data of the optical storage and filling system, extracting and obtaining photovoltaic output working data;
S1022: acquiring illumination intensity historical data based on meteorological big data;
s1023: generating a plurality of photovoltaic working scenes by utilizing a scene simulation model according to the illumination intensity historical data, and extracting to obtain photovoltaic working simulation data;
s1024: integrating the photovoltaic output work data and the photovoltaic work simulation data, and drawing to obtain a photovoltaic output curve.
4. The method for modeling and early warning a load curve of a power distribution network based on an optical storage and inflation system according to claim 2, wherein S103 comprises:
s1031: extracting and obtaining energy storage working data according to the working and operation data of the optical storage and charging system;
S1032: and according to the energy storage working data, extracting parameters forming an energy storage and accumulation curve and an energy storage and discharge curve by using a parameter extraction template, and drawing and generating the energy storage and accumulation curve and the energy storage and discharge curve by using the parameters.
5. The method for modeling and early warning of a load curve of a power distribution network based on an optical storage and inflation system according to claim 1, wherein S3 comprises:
s301: acquiring power distribution network working data of an optical storage and charging system, and extracting load curve construction data according to the power distribution network working data;
S302: and according to the load curve composition data, combining the comprehensive influence value of power distribution, and outputting a load curve of the power distribution network by using a load prediction model.
6. The method for modeling and early warning a load curve of a power distribution network based on an optical storage and inflation system according to claim 5, wherein the load prediction model in S302 is created based on a convolutional neural network model and trained and verified based on a data set; the data set is: the load curves constitute historical data in the data and historical data in the comprehensive influence value of power distribution.
7. The method for modeling and early warning of a load curve of a power distribution network based on an optical storage and inflation system according to claim 1, wherein S4 comprises:
s401: typical characteristic data on a load curve of the distribution network is obtained,
S402: based on the typical characteristic data, setting an early warning triggering condition;
s403: and when the load data of the power distribution network is matched with the typical characteristic data, early warning and reminding are carried out.
8. The method for modeling and early warning of a power distribution network load curve based on an optical storage and charging system according to claim 1, further comprising S6, according to the power distribution network load curve, evaluating and monitoring the power distribution working stability of the optical storage and charging system, specifically:
S601: acquiring evaluation parameter data based on evaluation standards and evaluation requirements of the distribution work stability of the optical storage and filling system;
S602: characteristic data in a load curve of the power distribution network and working and running data of the optical storage and charging system are used as evaluation content items, and a preset matching relation library of evaluation parameter data and the evaluation content items is utilized to obtain evaluation content items matched with the evaluation parameter data;
S603: according to historical data of a plurality of evaluation content items, constructing a distribution work stability evaluation calculation formula, carrying out simulation calculation to obtain a simulation calculation result, and extracting threshold results for judging distribution work stability and distribution work instability from the simulation calculation result;
s604: and carrying out real-time data monitoring on the evaluation content item according to the threshold result to obtain real-time evaluation content item data, carrying out real-time calculation on the real-time evaluation content item data by using a power distribution work stability evaluation calculation formula, and carrying out risk reminding of unstable power distribution work if the real-time calculation result reaches the threshold result.
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| CN105743126A (en) * | 2016-04-14 | 2016-07-06 | 华南理工大学 | Microgrid energy management system capable of realizing load management |
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