CN113689112B - Intelligent energy station energy efficiency evaluation method and system by utilizing cloud computing improved analytic hierarchy process - Google Patents
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Abstract
The invention discloses an intelligent energy station energy efficiency evaluation method and system by utilizing a cloud computing improved analytic hierarchy process, wherein the method comprises the following steps: 1) Collecting electric energy consumption data of an intelligent transformer station, an energy storage station and a data center in the intelligent energy station respectively; 2) Uploading the collected electric energy consumption data to a cloud end, performing cloud computing by utilizing a cloud server of the cloud end, and respectively performing energy efficiency index computing on an intelligent transformer station, an energy storage station and a data center in an intelligent energy station; 3) And inputting the energy efficiency index into a pre-established analytic hierarchy process model to obtain an energy efficiency evaluation result of the intelligent energy station. The method aims to solve the problems that the traditional analytic hierarchy process is complex in steps of judging the matrix and consistency test, high in calculation difficulty and high in influence of subjective factors on the weight coefficient, and the problems of calculation and correction of a large amount of data are solved by utilizing cloud calculation too quickly, so that the result of the analytic hierarchy process is more convinced, and the method has the advantages of fine calculation granularity, simplicity in operation and high sensitivity in calculation.
Description
Technical Field
The invention belongs to an intelligent energy station transformer energy efficiency detection technology, and particularly relates to an intelligent energy station energy efficiency evaluation method and system using a cloud computing improved analytic hierarchy process.
Background
The analytic hierarchy process is a decision making method of decomposing elements always related to decision making into layers of targets, criteria, schemes and the like, and performing qualitative and quantitative analysis on the basis of the layers. The analytic hierarchy process is to decompose the decision problem into different hierarchical structures according to the sequence of the total target, the sub-targets of each layer and the evaluation criteria until a specific spare power switching scheme, then to calculate the priority weight of each element of each layer to a certain element of the previous layer by solving the matrix feature vector, and finally to merge the final weight of each alternative scheme to the total target in a hierarchical manner by a weighted sum method, wherein the final weight with the largest weight is the optimal scheme. The method has the defects of less quantitative data, more qualitative ingredients and difficult convincing. The analytic hierarchy process is one method with human brain simulating decision mode and with more qualitative colors. When the index is too many, the data statistics are large, the weight is difficult to determine, and the accurate calculation of the characteristic value and the characteristic vector is complex. If there are more and more indexes, it may be difficult to judge the importance degree between every two indexes, and even the consistency of the hierarchical single ordering and the total ordering may be affected, so that the consistency test cannot be passed.
Disclosure of Invention
The invention aims to solve the technical problems: aiming at the problems in the prior art, the invention provides an energy efficiency evaluation method and system based on an analytic hierarchy process improved by cloud computing, wherein the cloud computing is a result of mixed evolution and jump of computer technologies such as distributed computing, utility computing, parallel computing, network storage, hot backup redundancy, virtualization and the like.
In order to solve the technical problems, the invention adopts the following technical scheme:
an intelligent energy station energy efficiency evaluation method utilizing cloud computing to improve analytic hierarchy process, comprising:
1) Collecting electric energy consumption data of an intelligent transformer station, an energy storage station and a data center in the intelligent energy station respectively;
2) Uploading the collected electric energy consumption data to a cloud end, performing cloud computing by utilizing a cloud server of the cloud end, and respectively performing energy efficiency index computing on an intelligent transformer station, an energy storage station and a data center in an intelligent energy station;
3) And inputting the energy efficiency index into a pre-established analytic hierarchy process model to obtain an energy efficiency evaluation result of the intelligent energy station.
Optionally, in step 2), when energy efficiency index calculation is performed on the intelligent substation, the energy storage station and the data center in the intelligent energy station, a function expression for performing energy efficiency index calculation on the intelligent substation is shown as follows:
W＝T×[P 0 +(S％) 2 P k ]
in the above formula, W represents the power consumption of a transformer in the intelligent substation, T represents the energy efficiency level of the intelligent substation, and P 0 Representing no-load loss of a transformer in an intelligent substation, S% representing average load rate of the transformer in the intelligent substation, and P k Representing the load loss of a transformer in an intelligent substation at rated capacity; h 1 Represents the total power consumption of a main transformer in the intelligent substation, H 2 Representing total power consumption of substation equipment in intelligent substation, S p*i Forward index for representing loss of intelligent substation S xi Representing the original value of the loss of the intelligent substation, S oi A reference value representing the loss of the intelligent substation.
Optionally, in step 2), when energy efficiency index calculation is performed on the intelligent substation, the energy storage station and the data center in the intelligent energy station, a function expression for performing energy efficiency index calculation on the energy storage station is shown as follows:
P 2grid ＝P in -P out ＝P in (1-η(P in ))
in the above formula, S represents an average energy efficiency evaluation index of the energy storage station, T is running time, and N d For the number of load branches of the energy storage station, P d,i (t) is the load value of the ith load branch of the energy storage station at the moment t, delta t is the time variation, P con,k (t) is the converter loss of the kth load branch of the energy storage station at the time t, P line,k (t) is the line loss of the kth load branch of the energy storage station at the time t; p (P) 2load Representing load branch and energy storage charging converter losses of energy storage station, P in Input power to an energy storage charging converter of an energy storage station, P out The power output by the energy storage and charging converter of the energy storage station is calculated, and eta is the efficiency of the energy storage and charging converter of the energy storage station.
Optionally, in step 2), when energy efficiency index calculation is performed on the intelligent substation, the energy storage station and the data center in the intelligent energy station, a function expression for performing energy efficiency index calculation on the data center is shown as follows:
in the above formula, E represents the host energy consumption of the data center, t 0 For the initial time, t 1 For the end time, m is the number of hosts in the data center, p i (α (t)) is the α (t) host power consumption of the ith host of the data center in the period of time;
PUE＝P 1 /P 2
in the above formula, PUE represents an energy efficiency evaluation index of the data center, P 1 Representing total base station energy consumption of data center, P 2 Representing the total base station master energy consumption of the data center.
Optionally, the pre-established hierarchical analysis model in the step 3) is of a three-layer structure, the energy efficiency level of the intelligent energy station in the hierarchical structure model is used as the highest layer of the hierarchical structure model, the energy efficiency level of the intelligent transformer station, the energy storage station and the data center in the intelligent energy station is used as the middle layer of the hierarchical structure model, and each energy efficiency index is used as the lowest layer of the hierarchical structure model.
Optionally, step 4) further includes a step of establishing a hierarchical analysis model:
s1) pairing all energy efficiency indexes, and obtaining the importance degree between any energy efficiency index pair based on a nine-level scale method, so as to construct a judgment matrix of an analytic hierarchy process according to the importance degree of each energy efficiency index pair;
s2) sending the judgment matrix to a cloud server, calculating the characteristic value of the judgment matrix by the cloud server, solving the characteristic vector corresponding to the characteristic value, normalizing the characteristic vector to obtain a corresponding weight vector, and finally obtaining a weight matrix formed by the weight vectors of each energy efficiency index, thereby completing the establishment of the analytic hierarchy model.
Optionally, step S2) includes:
s2.1) sending the judgment matrix to a cloud server, calculating the characteristic value of the judgment matrix by using the cloud server, solving the characteristic vector corresponding to the characteristic value, and normalizing the characteristic vector to obtain a corresponding weight vector;
s2.2) calculating a consistency index, and if the consistency ratio is smaller than a preset threshold value, obtaining a weight matrix formed by weight vectors of each energy efficiency index, thereby completing the establishment of a hierarchical analysis model; otherwise, the jump performs step S1).
Optionally, the functional expression for calculating the consistency ratio in step S2.2) is: cr=ci/RI, where RI is a random consistency index generated randomly, CI is a consistency index, and a calculation function expression of the consistency index CI is:
in the above, lambda max For the maximum eigenvalue of the judgment matrix, n is the order of the judgment matrix.
In addition, the invention also provides an intelligent energy station energy efficiency evaluation system utilizing the cloud computing improved analytic hierarchy process, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the intelligent energy station energy efficiency evaluation method utilizing the cloud computing improved analytic hierarchy process.
Furthermore, the present invention provides a computer readable storage medium having stored therein a computer program programmed or configured to perform the intelligent energy station energy efficiency assessment method using cloud computing improved analytic hierarchy process.
Compared with the prior art, the invention has the following advantages: the method aims to solve the problems of complicated steps, high calculation difficulty and great influence of subjective factors on the weight coefficient of the matrix judgment and consistency check of the traditional analytic hierarchy process, and the problems of calculation and correction of a large amount of data are solved by utilizing cloud calculation too quickly, and the method has the advantages of fine calculation granularity, simplicity in operation and high calculation speed. The existing analytic hierarchy process has the problem of more quantitative data, and has the advantages that the energy efficiency grade of the transformer substation can be evaluated on different layers according to the weight values, the evaluation result is simple and accurate, the steps of judging matrixes and consistency inspection are complex, the calculation difficulty is high, the influence of subjective factors on the weight coefficient is large in the process of evaluating the energy efficiency of the transformer substation by performing the analytic hierarchy process, and even errors of evaluation results are caused in serious cases. The cloud computing is a result of mixed evolution and jump of computer technologies such as distributed computing, utility computing, parallel computing, network storage, hot standby redundancy, virtualization and the like, and the invention can effectively quantify the energy-saving index of the intelligent energy station and find the high energy consumption problem of the intelligent energy station by using the hierarchical analysis method improved by the cloud computing, thereby providing a referential basis for scientific evaluation of energy saving and emission reduction of the intelligent energy station, solving the problems of complicated steps, high computing difficulty and great influence of subjective factors on weight coefficients of the traditional hierarchical analysis method for judging matrixes and consistency inspection, and solving the computing and correcting problems of a large amount of data too fast, so that the result of the hierarchical analysis method is more convincer, and the invention has the advantages of fine computing granularity, simple operation and extremely agile computing.
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FIG. 1 is a basic flow chart of a method according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, the intelligent energy station energy efficiency evaluation method using the cloud computing improved analytic hierarchy process of the present embodiment includes:
1) Collecting electric energy consumption data of an intelligent transformer station, an energy storage station and a data center in the intelligent energy station respectively;
2) Uploading the collected electric energy consumption data to a cloud end, performing cloud computing by utilizing a cloud server of the cloud end, and respectively performing energy efficiency index computing on an intelligent transformer station, an energy storage station and a data center in an intelligent energy station;
3) And inputting the energy efficiency index into a pre-established analytic hierarchy process model to obtain an energy efficiency evaluation result of the intelligent energy station.
In this embodiment, when energy efficiency index calculation is performed on the intelligent substation, the energy storage station, and the data center in the intelligent energy station in step 2), a function expression for performing energy efficiency index calculation on the intelligent substation is shown as follows:
W＝T×[P 0 +(S％) 2 P k ]
in the above formula, W represents the power consumption of a transformer in the intelligent substation, T represents the energy efficiency level of the intelligent substation, and P 0 Representing no-load loss of transformer in intelligent substation, S% representing intelligent substationAverage load ratio of transformers in station, P k Representing the load loss of a transformer in an intelligent substation at rated capacity; h 1 Represents the total power consumption of a main transformer in the intelligent substation, H 2 Representing total power consumption of substation equipment in intelligent substation, S p*i Forward index for representing loss of intelligent substation S xi Representing the original value of the loss of the intelligent substation, S oi A reference value representing the loss of the intelligent substation.
In this embodiment, when the energy efficiency index calculation is performed on the intelligent substation, the energy storage station, and the data center in the intelligent energy station in step 2), the function expression for performing the energy efficiency index calculation on the energy storage station is shown as follows:
P 2grid ＝P in -P out ＝P in (1-η(P in ))
in the above formula, S represents an average energy efficiency evaluation index of the energy storage station, T is running time, and N d For the number of load branches of the energy storage station, P d,i (t) is the load value of the ith load branch of the energy storage station at the moment t, delta t is the time variation, P con,k (t) is the converter loss of the kth load branch of the energy storage station at the time t, P line,k (t) is the line loss of the kth load branch of the energy storage station at the time t; p (P) 2load Representing load branch and energy storage charging converter losses of energy storage station, P in Input power to an energy storage charging converter of an energy storage station, P out The power output by the energy storage and charging converter of the energy storage station is calculated, and eta is the efficiency of the energy storage and charging converter of the energy storage station.
In this embodiment, in step 2), when energy efficiency index calculation is performed on the intelligent substation, the energy storage station, and the data center in the intelligent energy station, a function expression for performing energy efficiency index calculation on the data center is shown as follows:
in the above formula, E represents the host energy consumption of the data center, t 0 For the initial time, t 1 For the end time, m is the number of hosts in the data center, p i (α (t)) is the α (t) host power consumption of the ith host of the data center in the period of time;
PUE＝P 1 /P 2
in the above formula, PUE represents an energy efficiency evaluation index of the data center, P 1 Representing total base station energy consumption of data center, P 2 Representing the total base station master energy consumption of the data center.
In this embodiment, the pre-established hierarchical analysis model in step 3) is a three-layer structure, and the energy efficiency level of the intelligent energy station in the hierarchical structure model is used as the highest layer of the hierarchical structure model, and the energy efficiency levels of the intelligent transformer station, the energy storage station and the data center in the intelligent energy station are used as the middle layer of the hierarchical structure model, and each energy efficiency index is used as the lowest layer of the hierarchical structure model.
In this embodiment, the step 4) further includes a step of establishing a hierarchical analysis model:
s1) pairing all energy efficiency indexes, and obtaining the importance degree between any energy efficiency index pair based on a nine-level scale method, so as to construct a judgment matrix of an analytic hierarchy process according to the importance degree of each energy efficiency index pair;
s2) sending the judgment matrix to a cloud server, calculating the characteristic value of the judgment matrix by the cloud server, solving the characteristic vector corresponding to the characteristic value, normalizing the characteristic vector to obtain a corresponding weight vector, and finally obtaining a weight matrix formed by the weight vectors of each energy efficiency index, thereby completing the establishment of the analytic hierarchy model.
In this embodiment, step S2) includes:
s2.1) sending the judgment matrix to a cloud server, calculating the characteristic value of the judgment matrix by using the cloud server, solving the characteristic vector corresponding to the characteristic value, and normalizing the characteristic vector to obtain a corresponding weight vector;
s2.2) calculating a consistency index, and if the consistency ratio is smaller than a preset threshold value, obtaining a weight matrix formed by weight vectors of each energy efficiency index, thereby completing the establishment of a hierarchical analysis model; otherwise, the jump performs step S1).
In this embodiment, the functional expression for calculating the consistency ratio in step S2.2) is: cr=ci/RI, where RI is a random consistency index generated randomly, CI is a consistency index, and a calculation function expression of the consistency index CI is:
in the above, lambda max For the maximum eigenvalue of the judgment matrix, n is the order of the judgment matrix.
In summary, the method of this embodiment includes: 1) Collecting electric energy consumption data of an intelligent transformer station, an energy storage station and a data center in the intelligent energy station respectively; 2) Uploading the collected electric energy consumption data to a cloud end, performing cloud computing by utilizing a cloud server of the cloud end, and respectively performing energy efficiency index computing on an intelligent transformer station, an energy storage station and a data center in an intelligent energy station; 3) And inputting the energy efficiency index into a pre-established analytic hierarchy process model to obtain an energy efficiency evaluation result of the intelligent energy station. The method aims to solve the problems that the traditional analytic hierarchy process is complex in steps of judging the matrix and consistency test, high in calculation difficulty and high in influence of subjective factors on the weight coefficient, and the problems of calculation and correction of a large amount of data are solved by utilizing cloud calculation too quickly, so that the result of the analytic hierarchy process is more convinced, and the method has the advantages of fine calculation granularity, simplicity in operation and high sensitivity in calculation.
In addition, the embodiment also provides an intelligent energy station energy efficiency evaluation system using the cloud computing improved analytic hierarchy process, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the intelligent energy station energy efficiency evaluation method using the cloud computing improved analytic hierarchy process.
In addition, the present embodiment also provides a computer-readable storage medium having stored therein a computer program programmed or configured to perform the foregoing intelligent energy station energy efficiency assessment method using cloud computing improved hierarchical analysis.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 means 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 instruction means 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.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.
Claims (5)
1. An intelligent energy station energy efficiency evaluation method utilizing a cloud computing improved analytic hierarchy process is characterized by comprising the following steps:
1) Collecting electric energy consumption data of an intelligent transformer station, an energy storage station and a data center in the intelligent energy station respectively;
2) Uploading collected electric energy consumption data to a cloud end, performing cloud calculation by utilizing a cloud server of the cloud end, and respectively performing energy efficiency index calculation on an intelligent transformer substation, an energy storage station and a data center in an intelligent energy station, wherein the energy efficiency index calculation comprises power consumption W of a transformer in the intelligent transformer substation and forward indexes of intelligent transformer substation loss respectivelyS p*i Average energy efficiency evaluation index S of energy storage station, load branch of energy storage station and loss of energy storage charging converterP 2load Difference between input and output power of energy storage charging converter of energy storage stationP 2grid Energy consumption of data centerEAnd an energy efficiency evaluation index PUE of the data center, and pue=P 1 /P 2 ，P 1 Representing the total base station energy consumption of the data center,P 2 representing the total energy consumption of base station main equipment of the data center;
3) Inputting the energy efficiency index into a pre-established analytic hierarchy process model to obtain an energy efficiency evaluation result of the intelligent energy station; the pre-established hierarchical analysis model is of a three-layer structure, the energy efficiency level of an intelligent energy station in the hierarchical structure model is used as the highest layer of the hierarchical structure model, the energy efficiency level of an intelligent transformer station, an energy storage station and a data center in the intelligent energy station is used as the middle layer of the hierarchical structure model, and each energy efficiency index is used as the lowest layer of the hierarchical structure model;
in the step 2), when energy efficiency index calculation is performed on an intelligent substation, an energy storage station and a data center in the intelligent energy station respectively, a function expression for performing energy efficiency index calculation on the intelligent substation is shown as follows:
in the above-mentioned method, the step of,Wrepresents the power consumption of the transformer in the intelligent substation,T 1 representing energy efficiency level, P of intelligent substation 0 Representing the no-load loss of the transformer in the intelligent substation, S% representing the average load factor of the transformer in the intelligent substation,P k representing the load loss of a transformer in an intelligent substation at rated capacity;H 1 represents the total power consumption of the main transformer in the intelligent substation,H 2 represents the total power consumption of substation equipment in the intelligent substation,S p*i a forward index representing the loss of the intelligent substation,S xi representing the original value of the loss of the intelligent substation,S oi a reference value representing the loss of the intelligent substation;
the functional expression for calculating the energy efficiency index of the energy storage station is shown as follows:
in the above formula, S represents an average energy efficiency evaluation index of the energy storage station,Tin order for the run-time to be run,N d for the number of load branches of the energy storage station,
to the first energy storage stationiThe load branch is at the moment->
Load value of>
For the amount of time change, +.>
To the first energy storage stationkThe load branch is at the moment->
Converter loss of->
To the first energy storage stationkThe load branch is at the moment->
Line loss of (2); />
Indicating load branch and energy storage charging converter losses of the energy storage station,/->
Input power to the energy storage charging converter of the energy storage station, < >>
Output power of an energy storage charging converter of an energy storage station, < >>
The efficiency of the energy storage charging converter for the energy storage station;
the functional expression for calculating the energy efficiency index of the data center is shown as follows:
in the above-mentioned method, the step of,Erepresenting the host power consumption of the data center,
for the initial moment +.>
For the end time->
Number of hosts for data center,/->
Data center NoiThe individual hosts are +.>
Host power consumption;
the step 3) is also preceded by the step of establishing a hierarchical analysis model:
s1) pairing all energy efficiency indexes, and obtaining the importance degree between any energy efficiency index pair based on a nine-level scale method, so as to construct a judgment matrix of an analytic hierarchy process according to the importance degree of each energy efficiency index pair;
s2) sending the judgment matrix to a cloud server, calculating the characteristic value of the judgment matrix by the cloud server, solving the characteristic vector corresponding to the characteristic value, normalizing the characteristic vector to obtain a corresponding weight vector, and finally obtaining a weight matrix formed by the weight vectors of each energy efficiency index, thereby completing the establishment of the analytic hierarchy model.
2. The intelligent energy station energy efficiency assessment method using cloud computing improved analytic hierarchy process of claim 1, wherein step S2) comprises:
s2.1) sending the judgment matrix to a cloud server, calculating the characteristic value of the judgment matrix by using the cloud server, solving the characteristic vector corresponding to the characteristic value, and normalizing the characteristic vector to obtain a corresponding weight vector;
s2.2) calculating a consistency index, and if the consistency ratio is smaller than a preset threshold value, obtaining a weight matrix formed by weight vectors of each energy efficiency index, thereby completing the establishment of a hierarchical analysis model; otherwise, the jump performs step S1).
3. The intelligent energy station energy efficiency assessment method using cloud computing improved analytic hierarchy process of claim 2, wherein the functional expression for computing the uniformity ratio in step S2.2) is:CR=CI/RIwhereinRIFor a randomly generated random consistency index,CIis a consistency indexCIThe expression of the calculation function of (c) is:
in the above-mentioned method, the step of,λ max for the maximum eigenvalue of the judgment matrix, n is the order of the judgment matrix.
4. An intelligent energy station energy efficiency assessment system using cloud computing modified analytic hierarchy process comprising a microprocessor and a memory interconnected, wherein the microprocessor is programmed or configured to perform the steps of the intelligent energy station energy efficiency assessment method using cloud computing modified analytic hierarchy process of any one of claims 1 to 3.
5. A computer readable storage medium having stored therein a computer program programmed or configured to perform the intelligent energy station energy efficiency assessment method using cloud computing improved hierarchical analysis as set forth in any one of claims 1-3.
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