CN114493367A - Power supply reliability assessment method considering differentiated user load probability characteristics - Google Patents
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Abstract
The invention provides a power supply reliability evaluation method considering the load probability characteristics of differentiated users, which comprises the following steps: firstly, carrying out clustering load grading of area differentiation user types based on a k-means clustering method, and taking specific load points contained in a certain level of load as the probability of the level of load occurring in one year; then, a failure mode consequence analysis method is applied to calculate the power supply reliability evaluation indexes under each level of load level one by one; and finally, carrying out convolution calculation on the load probability of each level and the power supply reliability calculation result under the load of each level to obtain the power supply reliability calculation result of the load probability characteristic. The reliability indexes of the network are respectively calculated under each level of load level, the probability of the occurrence of different load levels is taken as the probability of the occurrence of the power supply reliability of the level, the reliability indexes under the annual load curve are further obtained, and the accuracy of reliability evaluation and analysis is improved.
Description
Technical Field
The invention belongs to the technical field of power supply reliability evaluation methods, and particularly relates to a power supply reliability evaluation method considering the load probability characteristics of differentiated users.
Background
Under the new trend of power system innovation, a power grid enterprise regresses enterprise attributes, higher requirements are provided for quality improvement and efficiency improvement of power grid enterprise assets, and the attention degree to power grid investment efficiency and asset benefits needs to be further improved in a power grid planning decision link.
At present, the planning of the urban power distribution network mostly uses the maximum load moment as a planning reference, and uses the standard of meeting the N-1 standard under the load condition as a standard to formulate a new construction and transformation scheme of the power distribution network, and the above standard and method greatly improve the investment cost of the whole planning, and on one hand, the requirement of differentiation of power supply reliability is disconnected, so that the equipment standby redundancy of a low-reliability area is realized; on the other hand, the rigidity meets the construction scheme of 'N-1' according to the instantaneous maximum load, the probability characteristics of load and fault are ignored, the asset utilization efficiency is further reduced, and the method is not suitable for the current economic and social development stage and the transformation requirement of a power grid enterprise.
Therefore, the control standard of the planning bearing capacity of the power distribution network, which considers the differentiated load probability characteristics and the power supply reliability probability characteristics of different users, is necessary to guide the scientific formulation of the power distribution network planning scheme under the new situation of power system reformation, balance the economic and reliability requirements and realize the double improvement of safety benefits.
Disclosure of Invention
In order to solve the technical problem, the invention provides a power supply reliability evaluation method considering the probability characteristics of differentiated user loads, which comprises the following steps:
the method comprises the following steps: firstly, carrying out clustering load grading of area differentiation user types based on a k-means clustering method, and taking specific load points contained in a certain level of load as the probability of the level of load occurring in one year;
step two: then, a failure mode consequence analysis method is applied to calculate the power supply reliability evaluation indexes under each level of load level one by one;
step three: and finally, carrying out convolution calculation on the load probability of each level and the power supply reliability calculation result under the load of each level to obtain the power supply reliability calculation result of the load probability characteristic.
Further, the step of carrying out clustering load grading of the region-differentiated user types based on the k-means clustering method in the step one comprises the following steps:
step 1: selecting a load series k, and randomly selecting k load points as an initial clustering center;
step 2: according to the distance from each load point to each cluster center, merging the load point closest to the load level;
and step 3: recalculating the center of each load level, namely the average value of each load point in the load level;
and 4, step 4: and repeating the steps 2 and 3 until the square error criterion function is stabilized at the minimum value.
Further, the load series k selected in the step 1 is combined with an elbow method and a contour coefficient method to judge the best k value of the clustering effect.
Further, in the step one, the specific load points included in a certain level of load are used as the probability of the load occurring in one year, and a user load multi-level horizontal discrete probability distribution model corresponding to a load curve of a certain user type is established according to the load curve.
Compared with the prior art, the invention has the beneficial effects that:
the traditional reliability evaluation method is to select typical load values, such as the maximum value and the minimum value, to respectively carry out load flow calculation and reliability calculation to obtain the indexes of the system load in a typical state interval, and the accuracy of reliability evaluation and analysis cannot be ensured.
Drawings
Fig. 1 is a schematic diagram of the grading effect in the case of different grading numbers of loads in the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
the embodiment is as follows:
the invention provides a power supply reliability evaluation method considering the probability characteristics of differential user loads, which comprises the following steps of firstly carrying out clustering load grading of regional differential user types based on a k-means clustering method:
the Euclidean distance is used as an evaluation index of similarity, namely the closer the distance of 2 samples is, the greater the similarity is, and the specific process is as follows:
step 1: selecting a load series k, and randomly selecting k load points as an initial clustering center;
step 2: according to the distance from each load point to each cluster center, merging the load point closest to the load level;
and step 3: recalculating the center of each load level, namely the average value of each load point in the load level;
and 4, step 4: and repeating the steps 2 and 3 until the square error criterion function is stabilized at the minimum value.
As shown in the attached figure 1, SPSS statistical analysis software is applied to the research, and the load grading calculation is carried out on the basis of a k-means algorithm according to different load type curves.
(2) Determination of the optimal grading number:
considering that the load grading number based on the k-means clustering algorithm, namely the k value, is set by subjective experience, if the grading number is selected improperly, the clustering merging effect is greatly reduced, and meanwhile, the reliability calculation result is influenced. In order to better reflect the influence of different actual change trends of the load on the selection of the grading number k, the invention adopts the mutual combination of an elbow method and a contour coefficient method to judge the best k value of the clustering effect,
1) contour coefficient method
The core index of the method is a contour coefficient, and the contour coefficient of a certain sample point Xi is defined as follows:
wherein, ajIs XiAverage distance from other samples in the same cluster, called degree of agglomeration, bjIs XiThe average distance to all samples in the nearest cluster, called the degree of separation, and the definition of the nearest cluster is:
wherein p is a certain cluster CkThe sample of (1). Ready to use XiAfter the average distance of all samples to a certain cluster is used as the distance for measuring the distance from the point to the cluster, the distance X is selectediThe closest cluster is taken as the closest cluster.
And obtaining an average contour coefficient by averaging after calculating the contour coefficients of all the samples, wherein the value range of the average contour coefficient is [ -1,1], the closer the distance of the samples in the clusters is, the farther the distance of the samples among the clusters is, the larger the average contour coefficient is, and the better the clustering effect is.
2) Elbow method
The core index of the elbow method is SSE, and the calculation formula is as follows:
wherein, CiIs the ith cluster, p is CiSample point of (1), miIs CiOf center of mass, i.e. CiThe mean value of all samples in the cluster, SSE, is the clustering error of all samples, and represents how good the clustering effect is.
The core idea of the elbow method is as follows: with the increase of the cluster number k, the sample division is finer, the aggregation degree of each cluster is gradually increased, then the error square sum SSE is naturally gradually reduced, and when k is smaller than the true cluster number, since the increase of k greatly increases the aggregation degree of each cluster, the reduction range of SSE is large, and when k reaches the true cluster number, the return of the aggregation degree obtained by increasing k is rapidly reduced, so the reduction range of SSE is rapidly reduced, and then the reduction range tends to be gentle along with the continuous increase of the k value, that is, the relation graph of SSE and k is the shape of an elbow, and the k value corresponding to the elbow is the true cluster number of data.
The clustering effect under different clustering numbers can not be comprehensively evaluated sometimes by only using the contour coefficient method or only using the elbow method, sometimes when the contour coefficient obtains the maximum value, the SSE still has a larger value or the SSE curve does not reach the inflection point, and at this time, the k value near the inflection point of the SSE curve and having a larger contour coefficient is taken as the optimal clustering number.
After a grading result is obtained, a user load multi-level horizontal discrete probability distribution model corresponding to a load curve of a certain user type is established according to the load curve, namely, the specific load point number contained in a certain level of load can be used as the probability of the load in the level of load in one year:
wherein alpha isiIs the probability of the i-th order load value, Num [ i ]]The number of load points included in the ith level load clustering result.
Then, a failure mode consequence analysis method is applied to calculate the power supply reliability evaluation indexes under each level of load level one by one, taking ASAI as an example:
wherein, ASAIiFor average power supply availability, SAIDI, at level i load leveliThe average outage duration of the system at the i-th level of load.
And finally, performing convolution calculation on the calculation results of the power supply reliability of each level of load probability and each level of load to obtain the calculation results of the power supply reliability considering the load probability characteristics:
and obtaining a power supply reliability calculation result of the load probability characteristic.
The technical solutions of the present invention or similar technical solutions designed by those skilled in the art based on the teachings of the technical solutions of the present invention are all within the scope of the present invention.
Claims (4)
1. A power supply reliability assessment method considering differentiated user load probability characteristics is characterized by comprising the following steps:
the method comprises the following steps: firstly, carrying out clustering load grading of area differentiation user types based on a k-means clustering method, and taking specific load points contained in a certain level of load as the probability of the level of load occurring in one year;
step two: then, a failure mode consequence analysis method is applied to calculate the power supply reliability evaluation indexes under each level of load level one by one;
step three: and finally, carrying out convolution calculation on the load probability of each level and the power supply reliability calculation result under the load of each level to obtain the power supply reliability calculation result of the load probability characteristic.
2. The method for evaluating the reliability of power supply considering the probability characteristics of the load of the differentiated users according to claim 1, wherein the step of performing the clustering load classification of the area-differentiated user types based on the k-means clustering method in the first step comprises the following steps:
step 1: selecting a load series k, and randomly selecting k load points as an initial clustering center;
step 2: according to the distance from each load point to each cluster center, merging the load point closest to the load level;
and step 3: recalculating the center of each load level, namely the average value of each load point in the load level;
and 4, step 4: and repeating the steps 2 and 3 until the square error criterion function is stabilized at the minimum value.
3. The method for evaluating the reliability of power supply considering the load probability characteristics of the differentiated users as claimed in claim 2, wherein the load series k selected in the step 1 is combined with an elbow method and a contour coefficient method to judge the k value with the best clustering effect.
4. The method according to claim 1, wherein in the first step, the specific load points included in a certain level of load are used as the probability of the load occurring in the certain level of load in a year, and a multi-level discrete probability distribution model of the user load corresponding to a load curve of a certain user type is established according to the load curve.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115994714A (en) * | 2023-03-22 | 2023-04-21 | 江苏金寓信息科技有限公司 | IDC machine room lithium battery efficiency evaluation method based on big data statistics |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115994714A (en) * | 2023-03-22 | 2023-04-21 | 江苏金寓信息科技有限公司 | IDC machine room lithium battery efficiency evaluation method based on big data statistics |
| CN115994714B (en) * | 2023-03-22 | 2023-05-19 | 江苏金寓信息科技有限公司 | IDC machine room lithium battery efficiency evaluation method based on big data statistics |
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