CN111724049B - Research and judgment method for potential electric power energy efficiency service clients - Google Patents
Research and judgment method for potential electric power energy efficiency service clients Download PDFInfo
- Publication number
- CN111724049B CN111724049B CN202010515519.9A CN202010515519A CN111724049B CN 111724049 B CN111724049 B CN 111724049B CN 202010515519 A CN202010515519 A CN 202010515519A CN 111724049 B CN111724049 B CN 111724049B
- Authority
- CN
- China
- Prior art keywords
- value
- evaluation
- user
- power consumption
- index
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000011160 research Methods 0.000 title claims abstract description 11
- 238000011156 evaluation Methods 0.000 claims abstract description 78
- 230000005611 electricity Effects 0.000 claims abstract description 41
- 239000011159 matrix material Substances 0.000 claims description 33
- 238000005381 potential energy Methods 0.000 claims description 20
- 230000008859 change Effects 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000007781 pre-processing Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 2
- 238000004422 calculation algorithm Methods 0.000 abstract description 3
- 238000004134 energy conservation Methods 0.000 abstract description 3
- 230000009467 reduction Effects 0.000 abstract description 3
- 230000006399 behavior Effects 0.000 description 15
- 230000002354 daily effect Effects 0.000 description 13
- 125000003275 alpha amino acid group Chemical group 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000005065 mining Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 150000001413 amino acids Chemical class 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000001737 promoting effect Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/82—Energy audits or management systems therefor
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Entrepreneurship & Innovation (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a research and judgment method for potential electric power efficiency service clients, which comprehensively considers factors such as power factor, peak section electricity utilization ratio, maximum overload rate, average load capacity ratio, maximum unbalance of load and the like, constructs an index evaluation system for the irrational behavior of a power consumption user, evaluates the irrational behavior of the power consumption user based on an AHP-TOPSIS algorithm, obtains a potential electric power efficiency service evaluation value of the power consumption user according to an evaluation result and electricity consumption, and further finds out the potential electric power efficiency service clients. The method can support power supply enterprises to develop energy efficiency service markets, help special-purpose transformer users to reduce electricity cost, and further promote energy conservation and emission reduction of the whole society.
Description
Technical Field
The invention belongs to the technical field of power big data application, and particularly relates to a research and judgment method for a potential power efficiency service customer.
Background
Along with the expansion of comprehensive energy business of power supply enterprises, providing power energy efficiency service (hereinafter referred to as "energy efficiency service") for special-purpose power-variable users has become one of the key working directions of the power supply enterprises.
Because of the lack of effective monitoring means and professional electric energy management personnel, the phenomenon that the power consumption of special transformer users is unreasonable is common, such as low power factor, high unbalanced degree of three-phase load and the like, the power consumption cost is improved intangibly, so that a large number of special transformer users have the requirements of standardizing the power consumption and reducing the power consumption cost, and a huge energy efficiency service potential market is provided for power supply enterprises. However, at present, due to the lack of an effective means for comprehensively evaluating the irrational power consumption of a specially-changed user, when a power supply enterprise digs energy efficiency to serve potential clients, the power supply enterprise is often difficult to achieve a certain vector.
At present, researches related to mining of potential customers of energy efficiency service are still quite fresh, only a part of documents are used for researching the electricity consumption efficiency evaluation of industrial users (one of special transformer users), but the comprehensive analysis of electricity consumption behaviors of the industrial users is lacking, for example, the influence of factors such as peak electricity utilization ratio, super capacity electricity utilization and the like on electricity consumption cost is not considered, so that effective support cannot be provided for mining work of the potential customers of energy efficiency service.
Disclosure of Invention
The invention aims to provide a research and judgment method for potential power efficiency service customers, which is used for evaluating the power consumption behaviors of special transformer users, checking out the special transformer users with unreasonable power consumption behaviors, supporting power supply enterprises to develop energy efficiency service markets, helping the special transformer users to reduce the power consumption cost, and further promoting the energy conservation and emission reduction of the whole society.
The invention adopts the following technical scheme:
a research and judgment method for potential electric energy efficiency service clients comprises the following steps:
(1) Extracting special variant user data and calculating an evaluation index value of the special variant user;
(2) Preprocessing the evaluation index value of each special-variant user;
(3) Determining weight coefficients of all evaluation indexes based on an AHP analytic hierarchy process;
(4) Evaluating the irrational power consumption behavior of each special change user based on a TOPSIS comprehensive evaluation method;
(5) Calculating a potential energy efficiency service evaluation value;
(6) And obtaining the potential special variant users of the energy efficiency service according to the research judgment threshold value and the potential energy efficiency service evaluation value.
Further, in the step (1), the private user data includes the number, capacity, daily power consumption, daily idle power consumption, daily peak power consumption, total active load of each acquisition point, A phase load, B phase load and C phase load of the period to be evaluated by the private user.
Further, in step (1), the evaluation index value of the specific user includes: power factor, peak section power utilization ratio, number of times of daily average heavy overload, average load capacity ratio, and maximum unbalance of load.
Further, the preprocessing of the evaluation index value in the step (2) includes: the average load capacity ratio is transformed, and the peak section power utilization ratio, the average daily frequency of heavy overload and the maximum unbalance degree index are subjected to capping value processing.
Further, the average load capacity ratio transformation is calculated by:
wherein RL is a avg For average load-to-capacity ratio, V max 、V min Respectively RL (RL) avg Upper and lower threshold values of the normal range of (2).
Further, the power factor, peak section electricity utilization ratio, average daily times of heavy overload and maximum unbalance degree index are calculated by the following formula:
I t i as a transformed index value o The original value of the evaluation index is obtained; i top The capping value of the evaluation index.
Further, the step (3) specifically includes the following steps:
(A) Constructing a judgment matrix;
(B) Consistency verification is carried out on the judgment matrix;
(C) After the consistency check of the judgment matrix, the weight coefficient of each evaluation index is calculated by the feature vector corresponding to the maximum feature root.
Further, the step (4) specifically includes the following steps:
(a) Constructing an evaluation matrix;
(b) Normalizing the matrix;
(c) Determining index weights;
(d) Constructing a weighted normalized evaluation matrix;
(e) Determining positive and negative ideal solutions;
(f) Calculating Euclidean distance
(g) And calculating an evaluation value.
Further, the step (5) specifically includes the following steps:
calculating a potential energy efficiency service evaluation value of the special change user by the following steps:
e i ＝g i q i
in the formula e i A potential energy efficiency service index for the ith private variant user; g i Improving the index, q for the energy efficiency of the ith private variant user i The power consumption index for the ith industrial user is calculated by the following steps:
g i ＝1-f i
wherein E is i The electricity consumption of the ith industrial user in a statistical period is calculated; e (E) max 、E min The maximum value and the preset minimum value of the electricity consumption of the industrial user in the statistical range are respectively set; for E, using Laida criterion before calculation max 、E min Pretreatment:
wherein E is avg The average value of the electricity consumption of all industrial users in the statistical range is obtained; sigma is the standard deviation of the power consumption of the special variation user in the statistical range.
Further, the step (6) is as follows: and when the potential energy efficiency service evaluation value is larger than the research judgment threshold value, judging that the special change client is a potential energy efficiency service client. Determination threshold value
The invention has the beneficial effects that:
1. and the potential power efficiency service special change users can be screened out only by analyzing the data in the power consumption information acquisition system, and no additional equipment investment is required.
2. The special transformer users with obvious unreasonable electricity consumption behaviors can be used as energy efficiency service potential clients, and by the method, proper energy efficiency service strategies can be formulated for the energy efficiency service potential clients according to various evaluation index values of the energy efficiency service potential clients.
3. The invention can support power supply enterprises to develop energy efficiency service markets, and simultaneously help special-purpose transformer users to reduce electricity cost, thereby promoting energy conservation and emission reduction of the whole society. Therefore, the invention can obtain great economic benefit.
4. The whole analysis process does not need manual intervention, so that a large amount of human resources are saved, and meanwhile, the accuracy of an analysis result is improved.
5. The method is combined with an AHP-TOPSIS algorithm to evaluate the irrational performance of the power consumption of the special transformer user, and meanwhile, the power consumption data of the special transformer user is combined to comprehensively judge the energy efficiency to serve the potential user.
6. The power consumption information acquisition system basically realizes full coverage and full acquisition, so the invention has extremely strong popularization.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a block diagram of an evaluation index system according to the present invention.
FIG. 3 is a block diagram of a hardware system embodying the present invention.
Detailed Description
The invention provides a potential customer mining method for electric power efficiency service based on an electric power consumption acquisition system, which comprises the steps of firstly comprehensively considering factors of power factor, peak section electricity utilization ratio, average daily number of times of heavy overload, average load capacity ratio and maximum unbalance degree of load, constructing an index evaluation system for the irrational electric power consumption of a special transformer user, then comprehensively evaluating the irrational electric power consumption of the special transformer user based on an AHP-TOPSIS algorithm, and obtaining a potential energy efficiency service evaluation value of the special transformer user according to the irrational comprehensive evaluation result of the electric power consumption and the electric power consumption of the transformer user, so as to screen out the potential customer for the electric power efficiency service. The flow diagram is shown in fig. 1.
The above method is described in detail below with reference to the accompanying drawings and specific examples.
1. And extracting the special variant user data, and calculating the evaluation index value of the special variant user.
And extracting the number, capacity, daily available power consumption, daily idle power consumption, daily peak power consumption, total active load of each acquisition point, A-phase load, B-phase load and C-phase load data of the period to be evaluated of the special transformer user from the power consumption information acquisition system, and constructing an evaluation index system for the irrational power consumption of the special transformer user.
As shown in fig. 2, the evaluation index system is composed of 5 indexes such as a power factor, a peak section electricity utilization ratio, a maximum overload rate, an average load capacity ratio, and a maximum load unbalance degree.
1.1 Power factor f p
The power factor refers to the average power factor of a specific user in a statistical period, and can be obtained according to the formula (1).
In the formula (1), E i For the active consumption of a special change user in a statistical period, Q t Reactive power consumption of a private transformer user in a statistical period. f (f) p The smaller the value, the less reasonable the power usage behavior of the private transformer user.
1.2 peak section Power utilization ratio RE peak
The peak electricity utilization ratio refers to the ratio of the electricity consumption of a peak section to the total electricity consumption of a specific user in a statistical period, and can be obtained according to the formula (2).
In the formula (2), E pi Peak power consumption of the ith day in the statistical period for the special transformer user, E ai For the special change of the power consumption of the peak section of the user on the ith day in the statistical period, N t The number of days of operation for the statistical period. RE (RE) peak The larger the value is, the more electricity fees need to be paid by the special transformer users under the condition of consuming the same electric quantity, so that the electricity consumption behavior is unreasonable.
1.3 times of daily average of heavy overload C zgz
Heavy overload refers to the load rate r of a distribution transformer acquisition point i 80% or more, and 3 or more collection points are continuously used, which is defined as heavy overload phenomenon of the distribution transformer, and the average number of times of heavy overload of the special transformer user in the period to be analyzed is counted and recorded as C zgz . Which can be obtained according to the formula (3).
In the formula (3), N t C, for the running days of the special change user in the period to be analyzed zgz The number of times of heavy overload phenomenon of the distribution transformer every day is shown. C (C) zgz The larger the value, the more unreasonable the power usage behavior of the private transformer user is indicated.
1.4 average load capacity ratio RL avg
The average load capacity ratio refers to the ratio of the average load value to the power supply capacity of a specific variable user in the production state in a statistical period, and can be obtained according to the formula (4).
In the formula (4), p i The power load value of the special transformer user is acquired by the power consumption information acquisition system at the ith acquisition point in the statistical period; n is the total number of acquisition points of the electricity consumption information acquisition system in a statistical period; l (L) i For producing the judging coefficient, the definition is shown in the formula (5).
In the formula (5), p set For the set production electricity load threshold, when the electricity load value at a certain moment is larger than p set And if not, judging that the special change user is in a production state, otherwise, judging that the special change user is in a non-production state.
RL avg The more the value is out of the normal range, the greater the possibility that the value is penalized by the power supply enterprise due to the excess capacity, and the more unreasonable the power consumption behavior of the private transformer user. At the same time RL avg The more the value is lower than the normal range, the more serious the light load operation phenomenon of the transformer is (the unnecessary basic electric charge expenditure is possibly caused), and the more unreasonable the power consumption of the special transformer user is.
1.5 maximum load imbalance
The maximum load unbalance degree refers to the maximum value of the three-phase load unbalance degree of a specific user in a statistical period, and can be obtained according to the formula (6).
f lmax ＝max(f li ) (6)
In the formula (6), f li The calculating method for the three-phase load unbalance degree of a specific variable user at the moment i is shown as a formula (7).
In the formula (7), p vi The calculation method for the three-phase load average value of the special transformer user at the time i is shown as a formula (8).
f lmax The larger the value, the more unreasonable the power usage behavior of the private transformer user.
2. And preprocessing the evaluation index value of each special variant user.
The average load capacity ratio index is converted by the formula (9).
In the formula (9), V max 、V min Respectively RL (RL) avg Upper and lower threshold values of the normal range of (2).
And (3) carrying out capping value processing on the power factor, peak section power utilization ratio, heavy overload average times per day and maximum load unbalance degree index by adopting the method (10).
In formula (10), I t I as a transformed index value o Is the original value of the evaluation index. I top The capping value of the evaluation index. I top The calculation rule is as follows.
Taking the average daily number of heavy overload as an example, firstly, setting the number of all special transformer users to be evaluated as m, setting the proportion, such as r, according to which the upper limit value is selected, and calculating to obtain the upper limit valueAccording to the number value m set For example m set ＝r×m。
Secondly, grouping all special transformer users to be evaluated according to the average daily number of times of heavy overload, and searching the number of the special transformer users to be evaluated from large to small according to the number, wherein the number of the special transformer users is larger than or equal to m for the first time set The average number of times of heavy overload is the upper limit value.
3. And determining the weight coefficient of each evaluation index based on an AHP analytic hierarchy process.
3.1 construction of a judgment matrix
N evaluation indexes are provided, and the evaluation indexes are compared with each other to obtain a judgment matrix B.
Element B in B ij The importance comparison result of the ith evaluation index and the jth evaluation index is shown, and the comparison result is marked by a 1-9 scale method.
3.2 consistency check
Since the judgment matrix B is affected by subjective judgment of a decision maker, a certain error is unavoidable, and consistency verification is required, and a consistency ratio CR is defined as shown in a formula (12).
In the formula (12), CI is a consistency index, which can be obtained according to the formula (13); RI is an average random uniformity index, and its value is shown in table 2.
In the formula (13), lambda max To determine the largest feature root of matrix B.
Table 2 RI values
When CR <0.1, the consistency of the judgment matrix meets the requirement, otherwise, the judgment matrix is reconstructed.
3.3 weight coefficient calculation
After the judgment matrix B passes the consistency check, the maximum characteristic root lambda of the judgment matrix B max The corresponding feature vector W is represented by the formula (14), and the weight coefficient of each evaluation index can be obtained by the formula (15).
W＝[w 1 ,w 2 ,…,w n ] T (14)
In the formula (15), u i Weight coefficient for the ith evaluation index, w i Is the i-th element in W. The matrix composed of the weight coefficients of the respective evaluation indexes is denoted by U as shown in the formula (16).
3.4 evaluation index System weight setting
The evaluation index system is constructed by 5 indexes of power factor, peak section electricity utilization ratio, maximum overload rate, average load capacity ratio and maximum load unbalance degree to form a judgment matrix B 1 Solving each characteristic weight according to the formulas (12) to (16), wherein the weight matrix is as follows
4. And evaluating the irrational power consumption behaviors of all special-purpose transformer users based on a TOPSIS comprehensive evaluation method.
4.1 construction of an evaluation matrix
N evaluation indexes are provided, m objects to be evaluated are provided, x is the number ij The j-th evaluation index value for the i-th object to be evaluatedThe evaluation matrix X is shown in formula (17).
4.2 matrix normalization
To eliminate the influence of the magnitude and order of each index, each element X is normalized according to the formula (18).
And finally obtaining a normalized matrix R.
4.3 determining the index weight
And determining the weight coefficient of each evaluation index by adopting an AHP analytic hierarchy process to form an evaluation index weight coefficient matrix U.
4.4 construction of weighted normalized evaluation matrix
A weighted normalized evaluation matrix Y is obtained by the formula (20).
4.5 determining the Positive and negative ideal solutions
Solving the positive ideal solution Y according to the formula (21) -formula (24) + And negative ideal solution Y - 。
In the formula (21), the amino acid sequence of the amino acid,
the maximum value of column 1 in Y can be obtained by the formula (22).
Y + The values of the other elements can be obtained by referring to the formula (22).
In the formula (23), the amino acid sequence of the compound,
the minimum value of column 1 in Y can be obtained as in (24).
Y - The values of the other elements can be obtained by referring to the formula (24).
4.6 calculation of Euclidean distance
The Euclidean distance between each object to be evaluated and the positive and negative ideal solutions is calculated according to the formula (25) and the formula (26).
In the formula (25), the amino acid sequence of the amino acid,
the Euclidean distance between the ith object to be evaluated and the positive ideal solution.
In the formula (26), the amino acid sequence of the compound,
the Euclidean distance between the ith object to be evaluated and the negative ideal solution.
4.7 calculating evaluation value
The evaluation value of each object to be evaluated is calculated according to formula (27).
In the formula (27), f i Is the evaluation value of the i-th object to be evaluated.
4.8 Power consumption behavior irrational evaluation of specially-changed users based on index evaluation system
5 indexes such as power factor, peak section electricity utilization ratio, maximum overload rate, average load capacity ratio, maximum unbalance degree of load and the like are constructed to form a matrix X 1 Combining the index weights U 1 Calculating a scoring value f of the power consumption irrational behavior of the special transformer user to be evaluated according to the formulas (18) to (27) i Score value f i The smaller the power consumption discomfort of the private transformer user is indicated to be stronger.
5. Calculating a potential energy efficiency service valuation
Due to the scale effect, under the same condition, the greater the electricity consumption, the greater the benefit obtained by the energy efficiency service of the industrial user. Also, under the condition of considerable electricity consumption, the worse the electricity consumption, the greater the income obtained for the industrial users who have poorer the electricity consumption. To accurately characterize the potential energy efficiency service mining value of an industrial user, the potential energy efficiency service evaluation value of a specially-changed user is calculated by comprehensively considering the power utilization behavior irrational degree and the power consumption amount, as shown in a formula (28).
e i ＝g i q i (28)
In formula (28), e i A potential energy efficiency service index for the ith private variant user; g i The energy efficiency improving index of the ith special change user is used for representing the energy efficiency improving space of the special change user, and the calculation method is shown as a formula (29); q i And (3) representing the power consumption of the special transformer user for the power consumption index of the ith industrial user, wherein the calculation method is shown in the formula (30).
g i ＝1-f i (29)
In formula (30), E i The electricity consumption of the ith industrial user in a statistical period is calculated; e (E) max 、E min Respectively, the maximum value and the preset minimum value of the electricity consumption of the industrial users in the statistical range, and the electricity consumption difference of each industrial user is larger, so as to avoid unreasonable calculation result of the potential energy efficiency service index caused by overlarge or overlarge electricity consumption of some industrial users, and to avoid the above situation, the Laida criterion is utilized before calculation, the E is calculated max 、E min Pretreatment is performed.
In formula (31), E avg The average value of the electricity consumption of all industrial users in the statistical range is obtained; sigma is the standard deviation of the electricity consumption of the industrial user in the statistical range.
The greater the potential energy efficiency service index of an industrial user, the greater the necessity for developing energy efficiency services thereto. When the potential energy efficiency service index of the industrial user satisfies (32), it is selected to serve the potential energy efficiency service customer of the power supply enterprise.
e i ＞e set (32)
In formula (32), e set The value range of the judgment threshold is 0.3-0.6, and the judgment threshold is set by a power supply enterprise according to specific conditions.
6. Case analysis
By using the method, the irrational property of the electricity utilization behavior of 1148 special transformer users in the range of the area A is evaluated. Calculating the evaluation index-average load capacity ratio, p set Take 0.15S, where S is the private variable user capacity. V when the evaluation index-average load capacity ratio is converted in application (9) max 、V min Taking 0.8 and 0.3 respectively, setting r value as 1%, comparing 5 evaluation indexes two by two to obtain a judgment matrix B, and the evaluation results are shown in Table 3Show (rank by the evaluation value from big to small).
Table 3 evaluation results
Setting a research and judgment threshold e according to the service capability, stage index, power efficiency service plan and other conditions of the regional A power supply company set 0.38, then C1145, C1146, C1147, C1148 are potential energy efficiency service customers. Meanwhile, the power supply enterprise can also formulate an energy efficiency service strategy for the energy efficiency service potential customer according to various evaluation index values of the energy efficiency service potential customer, and the efficiency of the energy efficiency service market expansion work of the power supply enterprise is greatly improved. The invention can be used as a functional module of big data of Hebei corporation and big data analysis platform of artificial intelligent laboratory, and according to the principle and flow chart of the invention, the computer program is compiled and then deployed on the big data analysis platform.
As shown in fig. 3, the big data analysis platform obtains relevant data of all special transformer users to be analyzed from the electricity consumption information acquisition system through a unified interface program, then analyzes the relevant data through a compiled computer program, screens out potential electricity energy efficiency service clients, stores screening results in a database server of the data interaction platform, and then responds to requests of province, city, county and all levels of power supply units through a WEB server of the data interaction platform, and displays the screening results to monitoring terminals of the province, city, county and all levels of power supply units.
Claims (1)
1. The method for studying and judging the potential electric energy efficiency service customer is characterized by comprising the following steps:
(1) Extracting special transformer user data, and calculating an evaluation index value of the power consumption irrational property of the special transformer user; the private transformer user data comprise the number, the capacity, the daily power consumption, the daily reactive power consumption, the daily peak section power consumption, the total active load of each acquisition point, the A phase load, the B phase load and the C phase load of a period to be evaluated by the private transformer user; the evaluation index value of the special variant user comprises: power factor, peak section power utilization ratio, average number of times of heavy overload, average load capacity ratio and maximum unbalance of load;
(2) Preprocessing the evaluation index value of each special-variant user; the preprocessing of the evaluation index value comprises: the average load capacity ratio is transformed, and the peak section power utilization ratio, the average daily frequency of heavy overload and the maximum unbalance index of the load are subjected to capping value processing;
the average load capacity ratio transformation is calculated by:
wherein RL is a avg For average load-to-capacity ratio, V max 、V min Respectively RL (RL) avg Upper and lower threshold values of the normal range;
the power factor, peak section electricity utilization ratio, heavy overload average daily frequency and load maximum unbalance degree index are calculated by the following formula:
I t i as a transformed index value o The original value of the evaluation index is obtained; i top A capping value for the evaluation index;
(3) Determining weight coefficients of all evaluation indexes based on an AHP analytic hierarchy process;
(A) Constructing a judgment matrix;
(B) Consistency verification is carried out on the judgment matrix;
(C) After the consistency check of the judgment matrix, calculating the weight coefficient of each evaluation index by the feature vector corresponding to the maximum feature root of the judgment matrix;
(4) Evaluating the irrational power consumption behavior of each special change user based on a TOPSIS comprehensive evaluation method;
(a) Constructing an evaluation matrix;
(b) Normalizing the matrix;
(c) Determining index weights;
(d) Constructing a weighted normalized evaluation matrix;
(e) Determining positive and negative ideal solutions;
(f) Calculating Euclidean distance;
(g) Calculating an evaluation value;
(5) Calculating a potential energy efficiency service evaluation value:
e i ＝g i q i
in the formula e i A potential energy efficiency service index for the ith private variant user; g i Improving the index, q for the energy efficiency of the ith private variant user i The power consumption index for the ith special transformer user is calculated as follows:
g i ＝1-f i
wherein f i An evaluation value of the ith object to be evaluated; e (E) i The power consumption of the ith private transformer user in the statistical period is calculated; e (E) max 、E min The power consumption maximum value and the preset minimum value of the special variable users in the statistical range are respectively set; for E, using Laida criterion before calculation max 、E min Pretreatment:
wherein E is avg The average value of the electricity consumption of all special-purpose variable users in the statistical range is obtained; sigma is standard deviation of power consumption of a specially-changed user in a statistical range;
(6) And when the potential energy efficiency service evaluation value is larger than the research judgment threshold value, judging that the special change user is a potential energy efficiency service client.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010515519.9A CN111724049B (en) | 2020-06-08 | 2020-06-08 | Research and judgment method for potential electric power energy efficiency service clients |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010515519.9A CN111724049B (en) | 2020-06-08 | 2020-06-08 | Research and judgment method for potential electric power energy efficiency service clients |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111724049A CN111724049A (en) | 2020-09-29 |
| CN111724049B true CN111724049B (en) | 2023-05-23 |
Family
ID=72566246
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010515519.9A Active CN111724049B (en) | 2020-06-08 | 2020-06-08 | Research and judgment method for potential electric power energy efficiency service clients |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111724049B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112862535B (en) * | 2021-02-25 | 2022-10-04 | 国网河北省电力有限公司营销服务中心 | Method for determining power price responsiveness of power-dedicated transformer client and terminal equipment |
| CN117726232B (en) * | 2023-12-22 | 2024-06-14 | 国网湖北省电力有限公司营销服务中心(计量中心) | Comprehensive evaluation method for power system service users |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106156950A (en) * | 2016-07-06 | 2016-11-23 | 燕山大学 | A kind of active distribution network economical operation evaluation methodology based on Hypersphere Support Vector Machine |
| CN107316125A (en) * | 2017-05-12 | 2017-11-03 | 燕山大学 | A kind of active distribution network economical operation evaluation method based on economical operation domain |
| CN109191189A (en) * | 2018-08-20 | 2019-01-11 | 国网河南省电力公司经济技术研究院 | Power sales decontrol lower power customer value assessment method |
| CN109685552A (en) * | 2018-12-06 | 2019-04-26 | 国网山东省电力公司青岛供电公司 | The analysis of non-intrusion type residential electricity consumption efficiency and method of servicing |
| CN111062581A (en) * | 2019-11-26 | 2020-04-24 | 国网浙江省电力有限公司电力科学研究院 | Enterprise client high-voltage value-added service system construction method based on AHP (advanced high Performance packet protocol) hierarchy |
-
2020
- 2020-06-08 CN CN202010515519.9A patent/CN111724049B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106156950A (en) * | 2016-07-06 | 2016-11-23 | 燕山大学 | A kind of active distribution network economical operation evaluation methodology based on Hypersphere Support Vector Machine |
| CN107316125A (en) * | 2017-05-12 | 2017-11-03 | 燕山大学 | A kind of active distribution network economical operation evaluation method based on economical operation domain |
| CN109191189A (en) * | 2018-08-20 | 2019-01-11 | 国网河南省电力公司经济技术研究院 | Power sales decontrol lower power customer value assessment method |
| CN109685552A (en) * | 2018-12-06 | 2019-04-26 | 国网山东省电力公司青岛供电公司 | The analysis of non-intrusion type residential electricity consumption efficiency and method of servicing |
| CN111062581A (en) * | 2019-11-26 | 2020-04-24 | 国网浙江省电力有限公司电力科学研究院 | Enterprise client high-voltage value-added service system construction method based on AHP (advanced high Performance packet protocol) hierarchy |
Non-Patent Citations (4)
| Title |
|---|
| 基于AHP-TOPSIS的电力能效项目综合评价;李慧玲;《现代电力》;20140831;第31卷(第4期);88-94页 * |
| 基于可拓云模型的配电网经济运行综合评价及灵敏度分析;马丽叶等;《电工电能新技术》;20160723(第07期);8-16页 * |
| 用户用电设备评估的区间熵权法;祝恩国等;《电测与仪表》;20141225(第24期);106-112页 * |
| 电力客户能效状态模糊综合评估;吴剑飞等;《电力***保护与控制》;20100701(第13期);94-103页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111724049A (en) | 2020-09-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110909983B (en) | Multidimensional assessment method for electric energy quality of active power distribution network | |
| CN104809658B (en) | A kind of rapid analysis method of low-voltage distribution network taiwan area line loss | |
| CN109829604A (en) | A kind of grid side energy-accumulating power station operational effect comprehensive estimation method | |
| CN103679544A (en) | Comprehensive assessment method for running of intelligent power distribution network | |
| CN110738435A (en) | distribution network project investment decision evaluation method | |
| CN111724049B (en) | Research and judgment method for potential electric power energy efficiency service clients | |
| CN111260198A (en) | Method and system for judging degree of rationality of line loss in transformer area synchronization and terminal equipment | |
| CN110109971A (en) | A kind of low-voltage platform area user power utilization Load Characteristic Analysis method | |
| CN104504508A (en) | Area closed-loop data analysis method on basis of analytical hierarchy process and wavelet regression | |
| CN106447108A (en) | Power utilization demand analysis prediction method taking business-expansion installation data into consideration | |
| CN111612326A (en) | Comprehensive evaluation method for power supply reliability of distribution transformer | |
| CN106447198A (en) | Power consumption checking method based on business expanding installation data | |
| CN110807598A (en) | User load regulation and control value evaluation method participating in orderly power utilization | |
| CN104834975A (en) | Power network load factor prediction method based on intelligent algorithm optimization combination | |
| CN113450031B (en) | Method and device for selecting intelligent energy consumption service potential transformer area of residents | |
| CN111798333A (en) | Energy utilization evaluation and electricity utilization safety analysis method and system | |
| CN113642933A (en) | Power distribution station low-voltage diagnosis method and device | |
| CN106651425A (en) | User electricity stealing and electricity leakage behavior monitoring method considering business expanding installation data | |
| CN116432437A (en) | Comprehensive evaluation method and system for photovoltaic power generation unit | |
| CN112488360B (en) | Distribution variation routine analysis early warning method based on artificial intelligence | |
| Carpaneto et al. | Customer classification by means of harmonic representation of distinguishing features | |
| CN112734274A (en) | Mining and comprehensive evaluation method for low-carbon power grid operation leading influence factors | |
| CN111724057A (en) | Post-investment evaluation method for distribution network area | |
| Tao et al. | Power consumption behavior analysis for customer side flexible resources based on data mining | |
| CN108335009A (en) | The method of electric power demand side power consumption management implementation result analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |