CN113112136A - Comprehensive evaluation method and system for reliability of power distribution network - Google Patents
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Abstract
The invention relates to a method and a system for comprehensively evaluating reliability of a power distribution network, which comprise the following steps: acquiring reliability data of a power distribution network user based on a power distribution internet of things sensing layer, and preprocessing the data to obtain basic reliability data with reliability; transmitting the obtained basic reliability data to a comprehensive reliability evaluation index system of the power distribution network, and calculating main reliability evaluation indexes of the power distribution network; based on the main evaluation index calculation result, further calculating a power distribution network reliability reference evaluation index based on the reliability electricity price; and obtaining a reliability evaluation result of the power distribution network according to the evaluation index calculation result and a preset grading standard, and providing data support for power supply regulation and control of the power distribution network. The method can provide theoretical support for a power distribution network differentiation planning technology and a power supply recovery method, and improves the adaptability, the economy and the competitiveness of power distribution network planning on the whole. The method can be widely applied to the field of power distribution network planning.
Description
Technical Field
The invention relates to the field of operation planning of a power distribution network, in particular to a method and a system for comprehensively evaluating reliability of the power distribution network.
Background
The reliability of the power distribution network refers to the measurement of the capability of the whole power distribution system and equipment from a power supply point to users, including a transformer substation, a high-voltage line, a low-voltage line and a service line, of meeting the power and electric energy demands of the users according to an acceptable standard and an expected quantity. A power distribution network is an electrical power network consisting of a variety of distribution equipment (or components) and distribution facilities that transform voltage and distribute electrical energy directly to end users. It is a critical phase of electrical energy supply and distribution. It is known that the generation, transmission and use of electric energy are accomplished almost instantaneously, i.e. the supply and demand of electric power must be instantaneously balanced. The operation of the power distribution system is directly related to normal power utilization of users, when the equipment is stopped due to faults, daily maintenance or other reasons, the whole power system stops supplying power to the users, and normal power supply cannot be continued until the faults of the power distribution network and the equipment of the power distribution network are eliminated or repaired, so that the reliability indexes of the power distribution network centrally reflect the structure and the operation characteristics of the whole power system. The distribution system, which is an important component of the power system, is located primarily at the end locations of the system and is responsible for providing and distributing power directly to the users. The power distribution system generally mainly uses a radiation type network as a main part, and has strong sensitivity to faults, and if any branch circuit has a fault problem, the whole distribution line is broken down, so that the reliability of power supply is influenced. How to effectively improve the power supply reliability of the distribution network is the central importance of the daily work of the current power enterprises.
With the continuous promotion of the innovation of the power system, the Chinese power industry gradually tends to be marketized. The reform of the electricity selling side enables users to have the right of independent selection and has the right of providing higher requirements for power supply companies, so that the safety and the reliability of self electricity utilization are guaranteed. Price is the most core function of the market, and in a value chain of the loop of power production to consumption, the power market can capture value fluctuation of different time, different spaces and different links and express the value fluctuation in a price form. The user is different to power supply quality, the difference of power supply reliability demand, must lead to the difference of electric energy price, and the user can be according to the difference of self demand and the difference of market electric energy price, provides the electric energy quality and the reliability requirement that are fit for oneself to power supply enterprise, and power supply enterprise's task then satisfies the reliability demand of user differentiation under the direction of market.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a method and a system for comprehensively evaluating the reliability of a power distribution network, which provide theoretical support for a power distribution network differentiation planning technique and a power supply restoration method, and improve the adaptability, economy and competitiveness of power distribution network planning in the new era as a whole.
In order to achieve the purpose, the invention adopts the following technical scheme: a comprehensive evaluation method for reliability of a power distribution network comprises the following steps: s1, acquiring basic reliability data; s2, transmitting the obtained basic reliability data to a power distribution network reliability comprehensive evaluation index system, and calculating a main evaluation index of the power distribution network reliability; s3, calculating a power distribution network reliability reference evaluation index based on the reliability electricity price based on the main evaluation index calculation result; and S4, obtaining a power distribution network reliability evaluation result according to the power distribution network reliability main evaluation index, the reference evaluation index and a preset grading standard.
Further, in step S1, power distribution network user reliability data is obtained based on the power distribution internet of things sensing layer, and basic reliability data is obtained after data preprocessing; the data preprocessing comprises the following steps: data cleaning, data integration, data transformation and data reduction.
Further, in step S2, the power distribution network reliability comprehensive evaluation index system includes: the reliability of the power distribution network is mainly evaluated and the reliability of the power distribution network is referred to and evaluated;
the main reliability evaluation indexes of the power distribution network are main reliability evaluation indexes based on power supply capacity and main reliability evaluation indexes based on the number of users;
the power distribution network reliability reference evaluation index is a reliability reference evaluation index based on the reliability electricity price.
Further, the main reliability evaluation indexes of the power distribution network based on the number of users comprise at least one of: the system average power failure frequency, the system average short-time power failure frequency, the system average power failure time, the average power supply availability, the rate of multiple power failure users and the rate of long-time power failure users.
Further, the average system outage frequency comprises at least one of the following indicators: the average power failure frequency of a power supply system user in a statistical period, the average power failure frequency of a system without counting external influence, the average power failure frequency of the system without counting the condition that a system power supply is insufficient and the power is limited, and the average power failure frequency of the system without counting short-time power failure.
Further, the average system outage time comprises at least one of the following indicators: the method comprises the following steps of counting the average power failure hours of a power supply user in a period, counting the average power failure hours of external influences, counting the average power failure hours of the condition that a system power supply is not enough for limiting the power, and counting the average power failure hours of short-time power failure.
Further, the power distribution network reliability main evaluation index based on the power supply capacity comprises at least one of: average system equivalent power failure frequency, average system equivalent power failure time and average power failure and power shortage amount of users.
Further, the reliability reference evaluation index of the power distribution network based on the reliability electricity price comprises at least one of the following indexes:
(1) the user average reliability electricity price increment CARPI is provided with the unit of element/kWh:
in the formula,. DELTA.PjRepresenting the reliability price increment of the j-th power supply user in the statistical period, NTRepresenting the total number of users in the area;
(2) the average system outage time SAIDRRP with reduced reliability electricity price is given by time/household:
wherein SAID represents the average system power outage time, P0On the basis of electricity price, muSAIDA proportionality coefficient corresponding to the average power failure time of the system is represented;
(3) the average power failure frequency SAIFRRP of the system with reduced reliability electricity price is given by the unit of times/family:
wherein SAIF represents the average system power failure frequency, μSAIFA proportional coefficient corresponding to the average power failure frequency of the system is represented;
(4) the average power failure and power shortage AENSRRP of the users with reduced reliability electricity prices has the unit of time/household:
in the formula, AENS represents average power failure power shortage of user, muAENSAnd the proportionality coefficient corresponding to the power shortage amount of the system is shown.
Further, in step S4, the scoring criteria of each index are:
the average power failure frequency SAIFI of the system, the unit time/household scoring standard is as follows: when the SAIFI value is more than or equal to 0.65, the SAIFI value is more than 80 minutes;
the average short-term power failure frequency MAIFI of the system is as follows, and the scoring standard of unit times/family is as follows: when the MAIFI value is more than or equal to 0.25, the MAIFI value is more than 80 points;
the average power failure time SAIDI of the system and the scoring standard of unit h/household are as follows: when the SAIDI value is more than or equal to 0.8, the SAIDI value is more than 80 points;
average power availability ASAI, the scoring criteria in units% are: when the ASAI value is more than 99.99, the ASAI value is more than 80 points;
the ratio CEMSI-n of n blackout users, the scoring criteria in% are: when the CEMSI-n value is more than or equal to 5, the CEMSI-n value is more than 80 minutes;
the ratio CELID-2h of the long-time power failure user, the scoring standard of unit percent is as follows: when the CELID-2h value is more than or equal to 9, the score is more than 80;
average system equivalent outage frequency ASIFI, the unit time/household scoring standard is: when the ASIFI value is more than or equal to 0.7, the ASIFI value is more than 80 points;
average system equivalent blackout time ASIDI, unit h/household scoring standard is: when the ASIDI value is more than or equal to 0.9, the ASIDI value is more than 80 points;
AENS units
The scoring criteria of (a) were: when the value of AENS is more than or equal to 0.8, the value is more than 80 minutes; AENS represents the ratio of AENS to average annual power supply of users, and the AENS score is calculated through AENS;
the average reliability electricity price increment CARPI of the user has the scoring standard of unit/kWh as follows: when the CARPI value is more than or equal to 0.18, the CARPI value is more than 80 points;
the average system power failure time SAIDRRP with reduced reliability electricity price has the scoring standard of unit hour/household as follows: when the SAIDRRP value is more than or equal to 1.2, the SAIDRRP value is more than 80 points;
the average power failure frequency SAIFRRP of the system with reduced reliability electricity price is characterized in that the grading standard of unit time/household is as follows: when the SAIFRRP value is more than or equal to 1.5, the SAIFRRP value is more than 80 minutes;
the average power failure and power shortage quantity AENSRRP of the users with reduced reliability electricity prices has the grading standard of unit times/household as follows: when the AENSRRP value is 1.6 or more, it is 80 points or more.
A comprehensive evaluation system for reliability of a power distribution network comprises: the system comprises a data acquisition module, a main evaluation index calculation module, a reference evaluation index calculation module and an evaluation module;
the data acquisition module is used for acquiring basic reliability data;
the main evaluation index calculation module transmits the obtained basic reliability data to a power distribution network reliability comprehensive evaluation index system and calculates a main evaluation index of the power distribution network reliability;
the reference evaluation index calculation module is used for calculating a power distribution network reliability reference evaluation index based on the reliability electricity price based on the main evaluation index calculation result;
and the evaluation module obtains a power distribution network reliability evaluation result according to the main power distribution network reliability evaluation index, the reference evaluation index and a preset grading standard.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the method can provide data support for quantitative evaluation of the power supply reliability of the power distribution network, and analyzes the correctness and the effectiveness of the provided index system through examples.
2. The invention overcomes the defects of the prior art: in the prior art, only individual index description is carried out on the reliability operation condition of the power distribution network, however, the importance of the operation state index under different scenes, different time and different requirements also has obvious difference, and the establishment of a power distribution network reliability evaluation index system is lacked at present. And the current differential research on the reliability electricity price is not used for describing the reliability of the power distribution network. The method provides theoretical support for a power distribution network differentiation planning technology and a power supply recovery method, and improves the adaptability, the economy and the competitiveness of new-generation power distribution network planning on the whole.
Drawings
FIG. 1 is a flow chart of a comprehensive reliability evaluation method for a power distribution network according to the present invention;
FIG. 2 is a diagram of a comprehensive evaluation index system for reliability of a power distribution network provided by the present invention;
FIG. 3 is a distribution network A, B, C reliability indicator score radar chart provided by the present invention;
fig. 4 is a bar chart of the reliability reference index score of the power distribution network A, B, C provided by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
In order to more intuitively represent the reliability level corresponding to the reliability index value of the power distribution network, the index needs to be scored. The invention provides a comprehensive evaluation method for reliability of a power distribution network, which takes three types of membership functions of a positive index, a negative index and a middle value index as bases and combines expert guidance opinions to determine an index scoring standard. According to the method, data acquired by a power distribution internet of things sensing layer is used as a basis, a power distribution network data preprocessing technology is adopted, power supply demand analysis is carried out from multiple spatial dimensions of users, partitions and transformer areas, and a reliability influence factor extraction technology is researched; then analyzing key factors influencing a power distribution network reliability evaluation index system, emphatically considering the influence of reliability electricity price difference, and researching a power distribution network power supply reliability evaluation index system comprising reliability evaluation main indexes based on the number of users and power supply capacity and reliability evaluation reference indexes based on the reliability electricity price; and finally, researching a calculation method for evaluating the multi-type indexes in the index system, providing data support for quantitative evaluation of power supply reliability of the power distribution network, and analyzing the correctness and effectiveness of the index system by examples.
In a first embodiment of the present invention, as shown in fig. 1, a method for comprehensively evaluating reliability of a power distribution network is provided, which includes the following steps:
s1, acquiring basic reliability data;
acquiring reliability data of a power distribution network user based on a power distribution internet of things sensing layer, and preprocessing the data to obtain basic reliability data with reliability;
s2, transmitting the obtained basic reliability data to a power distribution network reliability comprehensive evaluation index system, and calculating a main evaluation index of the power distribution network reliability;
s3, calculating to obtain a power distribution network reliability reference evaluation index based on the reliability electricity price based on the main evaluation index calculation result;
s4, obtaining a power distribution network reliability evaluation result according to the main power distribution network reliability evaluation index, the reference evaluation index and a preset grading standard, and providing data support for power distribution network power supply regulation and control;
wherein, the preset scoring standard is as follows: determining an index scoring standard by taking three membership functions of a positive index, a negative index and a median index as bases and combining the guidance opinions of a plurality of power experts;
in the above step S1, in the present embodiment, the data preprocessing includes:
clearing data: data is "cleaned up" by filling in missing values, smoothing out noisy data, identifying or deleting outliers, and resolving inconsistencies. The method realizes format standardization, abnormal data removal, error correction and removal of repeated data.
Integrating data: combining and uniformly storing data in a plurality of data sources, and the process of establishing a data warehouse is actually data integration.
③ transforming data: the data is converted into a form suitable for data mining through modes of smooth aggregation, data generalization, normalization and the like.
Data reduction: data mining tends to be very large in data volume, long time is needed for mining analysis on a small amount of data, reduction representation of a data set can be obtained through data reduction, the reduction representation is much smaller, the integrity of the original data is still close to being maintained, and the result is the same as or almost the same as the result before reduction.
In step S2, as shown in fig. 2, the power distribution network reliability comprehensive evaluation index system includes: the reliability of the power distribution network is mainly evaluated according to indexes and is referred to and evaluated according to indexes. The main reliability evaluation indexes of the power distribution network are main reliability evaluation indexes based on power supply capacity and main reliability evaluation indexes based on the number of users; the power distribution network reliability reference evaluation index is a reliability reference evaluation index based on the reliability electricity price.
Specifically, in this embodiment, the main evaluation indexes of the reliability of the power distribution network based on the number of users include:
(1) the System Average Interruption Frequency (SAIFI) is expressed in units of times per household.
The SAIFI includes 4 sub-indicators, depending on the user range considered: SAIFI-1, SAIFI-2, SAIFI-3 and SAIFI-4; wherein:
SAIFI-1 represents the average outage frequency of a power supply system user over a statistical period, and is defined as follows:
SAIFI-1 can be calculated by the following formula:
in the formula, the subscript i represents a power failure event, NiIndicating the number of power outage subscribers per power outage event in a given time period, NTIndicating the total number of users in the area.
SAIFI-2 represents the average system outage frequency without external influences, and is defined as the sum of the numerator of equation (1) minus the number of users that are powered off each time by an external influence.
SAIFI-3 represents the average system outage frequency without counting the system power supply shortage and power limit, and is defined as the sum of the numerator of equation (1) minus the number of users with each system power supply shortage and power limit outage.
SAIFI-4 represents the average system outage frequency without counting short-term outages, and is defined as the sum of the numerator of equation (1) minus the number of users in each short-term outage.
(2) The Average short-term power failure Frequency (MAIFI) of the system is (sub/household).
MAIFI represents the average number of short-term power outages of a power supply system user during a statistical period, and is defined as follows:
MAIFI can be calculated by the following equation
In the formula, IMiIndicating number of short-term power failures, NmiThe number of power outage users per short-term power outage event in the statistical period is shown.
(3) The System Average outage Duration Index (SAIDI) is given in units of h/household.
Similar to SAIDI, SAIDI also includes 4 sub-indices: SAIDI-1, SAIDI-2, SAIDI-3, and SAIDI-4. Wherein:
SAIDI-1 represents the average number of hours of outage for a power supply user over a statistical period, and is defined as follows:
SAIDI-1 can be calculated by the following formula:
in the formula, riFor each blackout event time.
SAIDI-2 represents the average number of hours of power outage without counting external influences;
SAIDI-3 represents the average number of power-off hours without counting the insufficient power-limiting condition of the system power supply;
SAIDI-4 represents the average number of hours of power outage without counting short term power outages.
(4) Average Service Availability Index (ASAI) in%.
ASAI represents the ratio of the number of hours of power available to the user during the statistical period to the number of hours during the statistical period, and is defined as follows:
ASAI can be calculated by
The number of hours per year in the formula is 8760 hours in ordinary years and 8784 hours in leap years.
(5) Ratio of Multiple blackout users (Customers experiential Multiple suspended interception And motion interception Events, CEMSI)n) In units (%).
CEMSInThe proportion of all the users experiencing power failure more than n times in the statistical period is shown, and is defined as follows:
CEMSIncan be calculated by
In the formula, CNT(k>n)The number of users whose number of power failures is greater than n times in the counting period is shown.
(6) The percentage of Long-term outage users (Customers experiential Long Total interference duration, CELID-t) is given in (%).
CELID-t represents the proportion of users with accumulated continuous power-off time longer than n hours in a statistical period, and is defined as follows:
CELID-t can be calculated by the following equation
In the formula, CN(k>n)The number of users whose continuous power failure time is longer than n hours is accumulated in the statistical period.
Specifically, in this embodiment, the main evaluation indexes of the reliability of the power distribution network based on the power supply capacity include:
(1) the Average System equivalent power failure Frequency (ASIFI) is given in units of times.
ASIFI represents the equivalent number of system-wide (all users) blackouts converted (equivalent) from the system's impact on the user blackouts during a statistical period, and is defined as follows
ASIFI can be calculated by the following formula
In the formula, LiIndicating capacity per power outage, LTRepresenting the total capacity of the system power supply.
As can be seen from the above equation, ASIFI may be the same as SAIFI for a power distribution network system containing a larger variety of users.
(2) The Average System equivalent outage time (ASIDI) is in units of hours.
ASIDI represents the equivalent number of hours of blackout of the entire system (all users) converted (equivalent) from the influence of the system on the blackout of the users during the statistical period.
ASIDI can be calculated by the following formula
(3) The Average power failure and power shortage of the user (AENS) is in kWh/household.
AENS represents the average amount of electricity that is lost due to a power outage for each user during a statistical period, and is defined as follows:
AENS can be calculated by the following formula
In the formula, WiIndicating the amount of power that is lost at each power outage.
The power outage power supply amount W can be calculated by the following formula:
W＝KSIT
in the formula, SIThe unit of the capacity is the blackout capacity, namely the sum of the capacities of all users stopped supplying power, and is kVA; t is the power failure duration time or equivalent power failure time, and the unit is h; k is a carrier-to-volume ratio coefficient and needs to be corrected once every 1 month according to the specific conditions of the previous year.
The carrier-to-volume ratio coefficient K can be calculated by the following formula:
in the formula, P is the annual average load of the power supply system (or a certain line, a certain user) in kW; s is the sum of the capacities of annual users of a power supply system (or a certain line and a certain user), and the unit is kVA; it should be noted that P and S refer to the sum of the annual average load of the power supply system and the user capacity of the same voltage class. The annual hours are 8760h in the perennial and 8784h in the leap year.
Specifically, in this embodiment, in order to evaluate the average level of the regional reliability electricity prices and the influence of the reliability electricity prices on the power supply reliability of the power distribution network, the power distribution network reliability reference evaluation index based on the reliability electricity prices includes:
(1) the user Average Reliability Price Increment (CARPI) is in units of (yuan/kWh).
CARPI represents the average reliability electricity price increase of all power supply users during a statistical period, and is defined as follows:
CARPI can be calculated by the following formula
In the formula,. DELTA.PjRepresenting the reliability price increment of the j-th power supply user in the statistical period, NTIndicating the total number of users in the area.
(2) The unit of the System Average outage Duration (SAIDRRP) for reducing the Reliability electricity rate is time/household.
SAIDRRP represents the average system outage time due to the reduction of the utility reliability price during the statistical period, which is defined as follows
SAIDRRP may be calculated by the following formula:
wherein SAID represents the average system power outage time, P0On the basis of electricity price, muSAIDAnd a proportionality coefficient corresponding to the average power failure time of the system is shown.
(3) The unit of the System Average power failure Frequency (System Average Interruption Frequency Reduced by Reliability priority, SAIFRRP) with Reduced Reliability electricity rate is one time per household.
SAIFRRP represents the average frequency of power outages during the statistical period due to the reduction in utility reliability electricity prices, and is defined as follows:
SAIFRRP can be calculated by the following formula:
wherein SAIF represents the average system power failure frequency, μSAIFAnd the proportionality coefficient corresponding to the average power failure frequency of the system is shown.
(4) The unit of the Average power failure and shortage of the users with Reduced Reliability electricity prices is inferior/household (Average Energy Not Supplied Reduced by Reliability Price, AENSRRP).
The AENSRRP represents the amount of power supply shortage due to system outage with reduced application reliability power price during the statistical period, and is defined as follows
The AENSRRP can be calculated by the following formula:
in the formula, AENS represents average power failure power shortage of user, muAENSAnd the proportionality coefficient corresponding to the power shortage amount of the system is shown.
In the step S4, the indexes are scored, and the index scoring standard is determined according to the three membership functions of the positive index, the negative index and the median index, and according to the guidance opinions of the multiple power experts. As the reliability reference index of the power distribution network based on the reliability electricity price is provided for the first time, the finally determined 9 main index scoring standards are shown in the table 1. CEMSI-3 and CELID-2h respectively indicate the number of times of power failure equal to or more than 3 times and the number of users whose power failure time is equal to or more than 2 h. Since AENS is related to the system power supply capacity, in order to avoid the problem that the indexes generated by the difference of the power supply capacities of different power distribution networks are not suitable, the AENS score in the index table 1 is calculated by AENS, and the AENS represents the ratio of AENS to the average annual power supply amount of the user.
TABLE 1 evaluation index scoring standard for reliability of power distribution network
In the embodiment, the data of the distribution network A, B, C in three different areas are used for example analysis, and the basic parameters of the distribution network A, B, C are shown in table 2
TABLE 2 basic parameters of distribution network A, B, C
| Power distribution network | Total number of users | Total capacity of system | Daily power supply | Planning levels |
| A | 834 household | 15.27MVA | 274.86MWh | A+ |
| B | 968 family | 8.74MVA | 136.34MWh | A |
| C | 1099 family | 5.89MVA | 93.53MWh | B |
In this embodiment, the power failure data of three power distribution networks in the same year is selected, wherein part of the power failure data is shown in table 3:
TABLE 3 partial outage data for distribution grid A, B, C for a year
The power failure data is substituted into an index calculation formula to calculate a main reliability evaluation index of the power distribution network based on the power supply capacity and the number of users and a reference evaluation index based on the reliability electricity price, and the result is shown in table 4. In table 4, CEMSI-3 and CELID-2h respectively indicate the number of users having a power outage time of 3 or more and a power outage time of 2 or more.
Table 4 distribution network reliability evaluation index calculation result of distribution network A, B, C
It can be seen that each reliability index of the distribution network A is superior to that of the distribution networks B and C, and the distribution network B has higher power supply reliability than that of the distribution network C, which is consistent with the planning grade sequence of the three distribution networks. And the system average power failure time (SAIDI) and the system average power supply availability index (ASAI) of the power distribution network A, B, C both meet the requirements on power supply reliability of power supply areas with different planning levels in '2016 power distribution network planning and design technical guide' to indicate that the selection and calculation of the reliability index in the index system are basically correct.
According to the scoring standard of the main indexes of the reliability evaluation of the power distribution network, three power distribution network reliability evaluation indexes are scored, and the results are shown in table 5:
TABLE 5 distribution network reliability assessment index scoring results for distribution network A, B, C
The distribution network A, B, C reliability index scoring radar chart of fig. 3 shows three distribution network reliability index scoring result radar charts. As can be seen from the figure, all indexes of the power distribution network a are greater than 90 minutes, the overall reliability is optimal, wherein the MAIFI, which is the average short-term power failure frequency of the system, is relatively low, which indicates that a needs to be further improved in reducing the number of short-term power failures; all indexes of the distribution network B are scored above 85 minutes, the distribution network B has good reliability, but the scores are relatively low in CEMSI-3 user ratio that the power outage times are more than 3, and weak links with more power outage times in the system are seriously solved; most indexes of the power distribution network C are more than 75 minutes, the overall reliability is general, the CELID-2h, namely the score of the user ratio of the power outage time more than 2 hours is less than 70 minutes, which shows that the system reliability is seriously influenced by the long-time continuous power outage, the capabilities of maintenance and fault removal need to be enhanced, in addition, the average power outage times and the average power outage time score of the system are lower than those of the other two power distribution networks, which shows that the power outage needs to be reduced, and the overall reliability of the system is improved.
In this embodiment, the relevant parameters of the reliability electricity price are configured: the base electricity prices are 0.53 yuan/kWh, the reliability electricity price increments are 0.7-0.8 yuan/kWh, and it is assumed that all the industrial and commercial users in the three distribution networks participate in the reliability electricity prices. The calculation is performed by combining the specific situation of the user in the power distribution network A, B, C, the obtained system reliability reference index result is shown as the actual numerical values and the scoring results corresponding to the last four reference indexes in tables 4 and 5, and the numerical value in parentheses in the tables is the percentage of the reduction amount in the index value.
Fig. 4 shows the scoring of the distribution network A, B, C in table 5 under the 4 reliability reference evaluation indexes provided by the present patent. It can be concluded that the reliability of the whole system is improved by the high quality service provided by the power supply company after the partial users in the distribution networks a and B participate in the reliable electricity price. The average reliability electricity price increment of A is higher than that of B because the number of industrial and commercial users in A is more. Correspondingly, the percentage of the system average reliability index of the system A, which is improved due to the application of the reliability electricity price, is higher than that of the system B, which also accords with the characteristic that the system reliability is improved along with the cost investment in a certain range, and shows that the reliability evaluation reference index based on the reliability electricity price provided by the invention is effective and can reflect the influence of the reliability electricity price on the system reliability.
In a second embodiment of the present invention, a system for comprehensively evaluating reliability of a power distribution network is provided, which includes: the system comprises a data acquisition module, a main evaluation index calculation module, a reference evaluation index calculation module and an evaluation module;
the data acquisition module is used for acquiring basic reliability data;
the main evaluation index calculation module is used for transmitting the obtained basic reliability data to a power distribution network reliability comprehensive evaluation index system and calculating a main evaluation index of the power distribution network reliability;
the reference evaluation index calculation module is used for calculating a power distribution network reliability reference evaluation index based on the reliability electricity price based on the main evaluation index calculation result;
and the evaluation module is used for obtaining a reliability evaluation result of the power distribution network according to the evaluation index calculation result and a preset grading standard, and providing data support for power supply regulation and control of the power distribution network.
In conclusion, the method combines the power distribution network reliability data preprocessing and the influence factor extraction technology to obtain basic reliability data with certain credibility; and then substituting the obtained data into a formula to respectively calculate main evaluation indexes of the reliability of the power distribution network based on the power supply capacity and the number of users, calculating a reference evaluation index of the reliability of the power distribution network based on the reliability electricity price according to a related calculation result, and finally obtaining an evaluation result of the reliability of the power distribution network to provide data support for power supply regulation and control of the power distribution network.
As will be appreciated by one skilled in the art, 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-usable 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create 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.
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