CN111507766A - Microgrid electric power big data transaction management system applying block chains and artificial intelligence - Google Patents
Microgrid electric power big data transaction management system applying block chains and artificial intelligence Download PDFInfo
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Abstract
The invention discloses a microgrid power big data transaction management system applying a block chain and artificial intelligence, which comprises a power purchase receiving module, a power purchase adjusting module, an intelligent matching module and a data processing module, wherein the power purchase unit price of the completed transaction is sorted out according to the past power utilization data records of a power utilization side, the electric quantity required to be purchased by the power utilization side at this time is recorded, the average value of the power purchase unit prices of the completed transaction is used as an initial power price by the power price adjusting module, the total power generation amount of all current power generation sides and the total electric quantity required to be purchased by all the power utilization sides are compared, the intelligent matching module screens the power generation sides capable of meeting the electric quantity required to be purchased by the power utilization side, and the proper; the invention combines the block chain technology on the basis of artificial intelligence, and enhances the intelligence, decentralization and data non-tamper property of the transaction management system.
Description
Technical Field
The invention relates to electric power transaction management, in particular to a microgrid electric power big data transaction management system applying block chains and artificial intelligence.
Background
At present, electric power transactions are concentrated on a small number of large platforms, the centralization degree is serious, benefit control of a power generation side and a power utilization side is concentrated on the platforms, faults of an electric power system are easy to occur, and the influence range is enlarged; because the transactions of the power generation side and the power utilization side do not need to meet, the key requirements of all parties participating in the transactions need to be solved, the accuracy and rapidness of the transactions are strived for, and thus the traditional power transactions lack personalized adjustment and the unrelated transactions are influenced. The generation and development of the block chain technology can seek some ways for solving the problem of over-centralized power transaction, so that the power transaction is more fair and all parties can obtain benefits from the power transaction.
Many power trading systems allocate power generation side resources according to matching of power supply and power utilization, so that real-time and effective allocation of power resources is achieved, but the problems of geographical positions of power allocation, power utilization loss, power utilization power and the like are not comprehensively considered in such a mode. Along with the reduction of the main bodies of electric power participation, the appearance of productive consumers and the electricity consumption of some flexible loads, the complexity of the electric power transaction layer is higher, the difficulty degree of electric power transaction operation is greatly increased by the simultaneous participation of all the main bodies, the uneven distribution of electric power resources sometimes occurs, and the stability is poor. In the aspect of transaction matching, most systems can guess the transaction situation, but the existing prediction means has disadvantages due to large fluctuation of historical data, strong time-varying characteristics, nonlinearity and the like, so that the guess error is possibly large, and the generation and the execution of the transaction are further influenced.
Disclosure of Invention
The invention provides a microgrid electric power big data transaction management system applying a block chain and artificial intelligence to solve the problems.
A microgrid electric power big data transaction management system applying block chains and artificial intelligence comprises:
the electricity purchasing receiving module is used for sorting out the electricity purchasing unit price after the transaction is finished according to the past electricity utilization data record of the electricity utilization side and recording the electric quantity required to be purchased by the electricity utilization side at this time;
the electricity price adjusting module is used for taking the average value of the electricity purchasing prices which have finished the transaction as the initial electricity price, comparing the total electricity generation quantity of all current electricity generation sides with the total electricity quantity which needs to be purchased by all electricity utilization sides, reducing the initial electricity price as the current electricity price according to a preset value when the total electricity generation quantity is larger than the total electricity quantity which needs to be purchased, and increasing the initial electricity price as the current electricity price according to the preset value when the total electricity generation quantity is smaller than the total electricity quantity which needs to be purchased;
the intelligent matching module is used for screening power generation sides capable of meeting the electric quantity required to be purchased by the power utilization side, and selecting proper power generation sides for matching according to response signals generated by the power generation sides during past transactions;
the total price calculating module is used for calculating the total electricity purchasing price of the electricity utilization side according to the electric quantity required to be purchased by the electricity utilization side and the current electricity price and sending the total electricity purchasing price to the electricity generation side;
after the power generation side confirms the total price of electricity purchase, the transaction generation module generates the transaction and receives a response signal generated by the power generation side, and transmits the transaction electric quantity, the total price of electricity purchase, the information of the power utilization side, the information of the power generation side and the response signal generated by the power generation side corresponding to the transaction to the block chain;
and the butt joint arrangement module is used for sending the total price of electricity purchase to the electricity utilization side for payment, distributing corresponding electric quantity after the payment is successful and finishing the transaction.
The transaction management system provided by the invention enables the transaction to be more accurate and rapid, the progress among the transactions cannot be influenced mutually, and less system resources are occupied; the demand characteristics of each transaction participant are provided, the transaction is generated in a personalized manner, and the intellectualization, the decentralization and the data non-tamper property of the transaction system are enhanced by combining the block chain technology on the basis of artificial intelligence.
According to the invention, the response signal of the power generation side is serialized and decomposed into a plurality of intermediate signal sets and residual signal sets, and the response of the power generation side to the transaction is conjectured by using the recurrent neural network, so that the accuracy of conjecture of the current transaction is improved, the matching degree between the power generation side and the power utilization side is enhanced, the distribution of electric power is more reasonable, and users of the power generation side and the power utilization side can obtain profits from the electric power.
The probability condition that the power generation side is separated from the microgrid is used for evaluating, the dynamic stability between the power supply and the load of the microgrid when the microgrid receives disturbance is predicted, and the corresponding power is obtained from the power generation side in the later period through the mean value calculation of the emergency power, so that the microgrid can keep the power supply to the power utilization side when the power generation side is separated, and the normal operation of power transaction cannot be lost due to the separation of the power generation side.
Drawings
FIG. 1 is a system diagram of the transaction management system of the present invention.
Detailed Description
The invention is further illustrated by the following specific examples:
a microgrid electric power big data transaction management method applying block chains and artificial intelligence comprises the following steps:
the electricity purchasing receiving module sorts out the electricity purchasing unit price of the completed transaction according to the past electricity consumption data record of the electricity consumption side and records the electric quantity required to be purchased by the electricity consumption side at this time;
the electricity price adjusting module takes the average value of the electricity purchasing prices which have finished the transaction as the initial electricity price, compares the total electricity generation amount of all current electricity generation sides with the total electricity quantity which needs to be purchased by all electricity utilization sides, reduces the initial electricity price according to a preset value to be taken as the current electricity price when the total electricity generation amount is larger than the total electricity quantity which needs to be purchased, and increases the initial electricity price according to the preset value to be taken as the current electricity price when the total electricity generation amount is smaller than the total electricity quantity which needs to be purchased;
the intelligent matching module screens the power generation side capable of meeting the electric quantity required to be purchased by the power utilization side, and selects a proper power generation side for matching according to a response signal generated by the power generation side in the past transaction;
the total price calculating module calculates the total electricity purchasing price of the electricity utilization side according to the electric quantity required to be purchased by the electricity utilization side and the current electricity price, and sends the total electricity purchasing price to the electricity generation side;
after the power generation side confirms the total price of electricity purchase, the transaction generation module generates the transaction and receives a response signal generated by the power generation side, and transmits the transaction electric quantity, the total price of electricity purchase, the information of the power utilization side, the information of the power generation side and the response signal generated by the power generation side corresponding to the transaction to the block chain;
and the butt joint arrangement module sends the total price of the electricity purchase to the electricity utilization side for payment, and after the payment is successful, corresponding electric quantity is distributed and the transaction is ended.
Further, the specific steps of selecting a proper power generation side for matching according to a response signal generated by the power generation side during the past transaction are as follows:
(1) acquiring a response signal of the power generation side during the past transaction from the block chain, checking the response signal, and marking the incomplete signal as a incomplete signal;
(2) arranging the rest signals from small to large and dividing the rest signals into four equal parts, wherein the signals at the three division positions are respectively a first division signal, a second division signal and a third division signal, and 1.5 times of the difference between the first division signal and the third division signal is marked as an abnormal difference;
(3) marking the signals which are higher than the abnormal difference above the third segmentation signal or lower than the abnormal difference below the first segmentation signal as abnormal signals;
(4) analyzing a change trend curve of the signal by using a linear regression mode on the non-incomplete and non-abnormal signal by taking time as a variable;
(5) extracting an error value of each signal and a corresponding point on the change trend curve, training a distributed gradient enhancement library according to the time corresponding to the signal and the error value, and predicting the error values of the incomplete signal and the abnormal signal according to the distributed gradient enhancement library;
(6) correcting the incomplete signals and the abnormal signals based on the variation trend curve and the predicted error values to obtain normal signals, and arranging the normal signals into a signal set according to a time sequence;
(7) determining local extreme points of the signal set, fitting an envelope of a relative maximum point and an envelope of a relative minimum point by using Spline interpolation, and obtaining an average value of the envelope and the envelope, namely an average envelope;
(8) judging the difference between the number of times of the signal set passing through the zero point and the number of opposite poles, if not more than one, taking the signal set as an intermediate signal set and skipping the step (9), and if more than one, judging whether the average envelope is zero;
(9) if the average envelope is zero, taking the signal set as an intermediate signal set, if the average envelope is not zero, subtracting the average envelope from the signal set to obtain a new signal set, and repeating the step (7);
(10) storing the intermediate signal set and judging whether the result of subtracting the intermediate signal set from the signal set is a monotone queue, if so, taking the result as a residual signal set and skipping the step (11), and if not, judging whether all signals in the result are the same;
(11) if all the signals in the result are the same, taking the result as a residual signal set, and if the signals in the result are not the same, taking the result as a new signal set and repeating the step (7);
(12) for all the signals in the stored intermediate signal set and residual signal set, reducing the amplitude of each signal, and inputting the intermediate signal set and residual signal set with reduced amplitudes into a recurrent neural network for training; the method of reducing the amplitude of each signal is: recording the difference between the minimum value in a signal and the non-reduced signal as a first difference value, recording the difference between the maximum value and the minimum value in the non-reduced signal as a second difference value, multiplying the ratio of the first difference value to the second difference value by two, and recording as a third calculation value, wherein the result of subtracting one from the third calculation value is the value of the signal after the amplitude is reduced;
(13) and outputting the speculative value of the response signal of the current transaction power generation side by using the trained recurrent neural network, and selecting the power generation side with the maximum signal speculative value to match with the power utilization side.
When a certain power generation side is separated from the microgrid, the probability that the power generation side is separated from the microgrid is obtained from historical data and recorded as ht；
And disconnecting the electric quantity transaction corresponding to the power generation side, and calculating the descending emergency electric quantity mean value w of the micro-grid within the time T in the process that the power generation side is separated from the micro-grid as follows:
wherein OB ═ MT-Pt,T-Mt,T；
g (E) is the probability of the generation of a non-random error E of the microgrid within a time T, and obeys standard normal distribution, MTIs the maximum non-random error value, M, that the microgrid can bear during the time Tt,TIs the maximum non-random error value, P, that the microgrid can bear within the time T after the power generation side is separated from the microgridt,TThe generated power at the power generation side in the time T;
and when the power generation side is reconnected to the microgrid, the microgrid acquires corresponding electric quantity from the power generation side according to the calculated average value of the descending emergency electric quantity.
A microgrid electric power big data transaction management system applying block chains and artificial intelligence comprises:
the electricity purchasing receiving module is used for sorting out the electricity purchasing unit price after the transaction is finished according to the past electricity utilization data record of the electricity utilization side and recording the electric quantity required to be purchased by the electricity utilization side at this time;
the electricity price adjusting module is used for taking the average value of the electricity purchasing prices which have finished the transaction as the initial electricity price, comparing the total electricity generation quantity of all current electricity generation sides with the total electricity quantity which needs to be purchased by all electricity utilization sides, reducing the initial electricity price as the current electricity price according to a preset value when the total electricity generation quantity is larger than the total electricity quantity which needs to be purchased, and increasing the initial electricity price as the current electricity price according to the preset value when the total electricity generation quantity is smaller than the total electricity quantity which needs to be purchased;
the intelligent matching module is used for screening power generation sides capable of meeting the electric quantity required to be purchased by the power utilization side, and selecting proper power generation sides for matching according to response signals generated by the power generation sides during past transactions;
the total price calculating module is used for calculating the total electricity purchasing price of the electricity utilization side according to the electric quantity required to be purchased by the electricity utilization side and the current electricity price and sending the total electricity purchasing price to the electricity generation side;
the transaction generating module is used for generating a secondary transaction after the power generation side confirms the total power purchase price, receiving a response signal generated by the power generation side, and transmitting the transaction electric quantity, the total power purchase price, the power utilization side information, the power generation side information and the response signal generated by the power generation side corresponding to the transaction to the block chain;
and the butt joint arrangement module is used for sending the total price of electricity purchase to the electricity utilization side for payment, distributing corresponding electric quantity after the payment is successful and finishing the transaction.
Claims (4)
1. The utility model provides an application block chain and artificial intelligence's little electric wire netting electric power big data transaction management system which characterized in that includes:
the electricity purchasing receiving module is used for sorting out the electricity purchasing unit price after the transaction is finished according to the past electricity utilization data record of the electricity utilization side and recording the electric quantity required to be purchased by the electricity utilization side at this time;
the electricity price adjusting module is used for taking the average value of the electricity purchasing prices which have finished the transaction as the initial electricity price, comparing the total electricity generation quantity of all current electricity generation sides with the total electricity quantity which needs to be purchased by all electricity utilization sides, reducing the initial electricity price as the current electricity price according to a preset value when the total electricity generation quantity is larger than the total electricity quantity which needs to be purchased, and increasing the initial electricity price as the current electricity price according to the preset value when the total electricity generation quantity is smaller than the total electricity quantity which needs to be purchased;
the intelligent matching module is used for screening power generation sides capable of meeting the electric quantity required to be purchased by the power utilization side, and selecting proper power generation sides for matching according to response signals generated by the power generation sides during past transactions;
the total price calculating module is used for calculating the total electricity purchasing price of the electricity utilization side according to the electric quantity required to be purchased by the electricity utilization side and the current electricity price and sending the total electricity purchasing price to the electricity generation side;
after the power generation side confirms the total price of electricity purchase, the transaction generation module generates the transaction and receives a response signal generated by the power generation side, and transmits the transaction electric quantity, the total price of electricity purchase, the information of the power utilization side, the information of the power generation side and the response signal generated by the power generation side corresponding to the transaction to the block chain;
and the butt joint arrangement module is used for sending the total price of electricity purchase to the electricity utilization side for payment, distributing corresponding electric quantity after the payment is successful and finishing the transaction.
2. The microgrid power big data transaction management system based on the application block chain and the artificial intelligence as claimed in claim 1, characterized in that according to a response signal generated by a power generation side during previous transaction, a proper power generation side is selected for matching, and the specific steps are as follows:
(1) acquiring a response signal of the power generation side during the past transaction from the block chain, checking the response signal, and marking the incomplete signal as a incomplete signal;
(2) arranging the rest signals from small to large and dividing the rest signals into four equal parts, wherein the signals at the three division positions are respectively a first division signal, a second division signal and a third division signal, and 1.5 times of the difference between the first division signal and the third division signal is marked as an abnormal difference;
(3) marking the signals which are higher than the abnormal difference above the third segmentation signal or lower than the abnormal difference below the first segmentation signal as abnormal signals;
(4) analyzing a change trend curve of the signal by using a linear regression mode on the non-incomplete and non-abnormal signal by taking time as a variable;
(5) extracting an error value of each signal and a corresponding point on the change trend curve, training a distributed gradient enhancement library according to the time corresponding to the signal and the error value, and predicting the error values of the incomplete signal and the abnormal signal according to the distributed gradient enhancement library;
(6) correcting the incomplete signals and the abnormal signals based on the variation trend curve and the predicted error values to obtain normal signals, and arranging the normal signals into a signal set according to a time sequence;
(7) determining local extreme points of the signal set, fitting an envelope of a relative maximum point and an envelope of a relative minimum point by using Spline interpolation, and obtaining an average value of the envelope and the envelope, namely an average envelope;
(8) judging the difference between the number of times of the signal set passing through the zero point and the number of opposite poles, if not more than one, taking the signal set as an intermediate signal set and skipping the step (9), and if more than one, judging whether the average envelope is zero;
(9) if the average envelope is zero, taking the signal set as an intermediate signal set, if the average envelope is not zero, subtracting the average envelope from the signal set to obtain a new signal set, and repeating the step (7);
(10) storing the intermediate signal set and judging whether the result of subtracting the intermediate signal set from the signal set is a monotone queue, if so, taking the result as a residual signal set and skipping the step (11), and if not, judging whether all signals in the result are the same;
(11) if all the signals in the result are the same, taking the result as a residual signal set, and if the signals in the result are not the same, taking the result as a new signal set and repeating the step (7);
(12) for all the signals in the stored intermediate signal set and residual signal set, reducing the amplitude of each signal, and inputting the intermediate signal set and residual signal set with reduced amplitudes into a recurrent neural network for training;
(13) and outputting the speculative value of the response signal of the current transaction power generation side by using the trained recurrent neural network, and selecting the power generation side with the maximum signal speculative value to match with the power utilization side.
3. The microgrid power big data transaction management system applying blockchains and artificial intelligence is characterized in that the method for reducing the amplitude of each signal is as follows:
and taking the difference between the minimum value in a signal and the non-reduced signal as a first difference value, taking the difference between the maximum value and the minimum value in the non-reduced signal as a second difference value, multiplying the ratio of the first difference value to the second difference value by two, and taking the result obtained by subtracting one from the third difference value as the value of the signal after the amplitude is reduced.
4. The microgrid power big data transaction management system applying blockchains and artificial intelligence according to claim 1 or 3,
when a certain power generation side is separated from the microgrid, the probability that the power generation side is separated from the microgrid is obtained from historical data and recorded as ht；
And disconnecting the electric quantity transaction corresponding to the power generation side, and calculating the descending emergency electric quantity mean value w of the micro-grid within the time T in the process that the power generation side is separated from the micro-grid as follows:
wherein OB ═ MT-Pt,T-Mt,T；
g (E) is the probability of the generation of a non-random error E of the microgrid within a time T, and obeys standard normal distribution, MTIs the maximum non-random error value, M, that the microgrid can bear during the time Tt,TIs the maximum non-random error value, P, that the microgrid can bear within the time T after the power generation side is separated from the microgridt,TFor the hair on the power generation side within the time TElectrical power;
and when the power generation side is reconnected to the microgrid, the microgrid acquires corresponding electric quantity from the power generation side according to the calculated average value of the descending emergency electric quantity.
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