CN107194529B - Power distribution network reliability economic benefit analysis method and device based on mining technology - Google Patents
Power distribution network reliability economic benefit analysis method and device based on mining technology Download PDFInfo
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Abstract
本发明提供一种基于文本挖掘技术的配电网可靠性经济效益分析方法，通过确定研究对象，获取研究对象的停电数据信息，抽取停电数据信息关键词，建立与可靠性投资的语义映射表，建立预安排停电预测模型和配电网可靠性投资经济效益分析模型，当出现停电计划时，采用所述配电网可靠性投资经济效益分析模型计算可靠性投资的经济效益。本发明采用了一种关键词提取的数据挖掘方法，可实现对电网缺陷文本的潜在信息挖掘；构建了预安排停电预测模型，从而可根据未来某地区各可靠性提升措施的投资金额预测该预安排停电的停电时户数与缺供电量，且该模型充分考虑预安排停电对可靠性效益的影响，从而可更为准确的评价投资回报率。
The invention provides a method for analyzing the reliability and economic benefits of distribution network based on text mining technology. By determining the research object, acquiring the power failure data information of the research object, extracting the keywords of the power failure data information, and establishing a semantic mapping table with reliability investment, A pre-arranged power outage prediction model and a distribution network reliability investment economic benefit analysis model are established. When a power outage plan occurs, the distribution network reliability investment economic benefit analysis model is used to calculate the economic benefit of the reliability investment. The invention adopts a data mining method of keyword extraction, which can realize the potential information mining of power grid defect text; builds a pre-arranged power failure prediction model, so that the prediction model can be predicted according to the investment amount of each reliability improvement measure in a certain area in the future. The number of households and the lack of power supply when the outage is scheduled, and the model fully considers the impact of prearranged outages on reliability benefits, so that the return on investment can be more accurately evaluated.
Description
Technical Field
The invention relates to the field of power systems, in particular to a method and a device for analyzing reliability and economic benefits of a power distribution network based on an excavation technology.
Background
With the rapid development of national economy, the requirement of users on the reliability of power utilization is higher and higher. The investment and construction and reconstruction of the power distribution network can generate important influence on the reliability level, the power supply quality and the economic operation of the power distribution network of the whole power distribution system.
The traditional power distribution network reliability economic benefit analysis method generally only considers the influence of power distribution network investment on future reliability improvement, and selects the optimal project by adopting methods such as an annual value method, a cost-benefit ratio and the like, so that the maximum reliability benefit can be obtained at the lowest investment cost. However, because the influence of various reliability investment construction periods on the reliability of the power distribution network lacks an effective data source and is difficult to quantitatively analyze, the reliability investment negative benefits of the traditional power distribution network reliability economic benefit analysis model are not considered.
At present, researches on the prediction of the scheduled power failure time are few, and a mature and effective method is lacked. In the reliability management process of a power grid enterprise, information such as the time of a power failure event of the power grid, a power failure area, the number of households in power failure and the like can be recorded, and the information is stored in an information management system in a text form. But the reason for the pre-scheduled blackout event cannot be easily known from the blackout information. Big data technology is the current research focus. Big data includes both structured data and unstructured data, and it is generally considered more difficult to mine unstructured data, such as text, audio, images, and the like. Among them, the Chinese text mining technology is a unique problem at present.
Therefore, the reliability investment economic benefit analysis method is provided and has a high practical value when being applied to a power distribution network reliability investment economic benefit analysis model.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a power distribution network reliability economic benefit analysis method based on an excavation technology. The method comprises the following steps:
I. determining a research object, extracting keywords according to annual power failure data information and establishing a semantic mapping table with a reliability investment project;
II. Acquiring prearranged power failure amount caused by construction of a reliability investment project and reliability investment data of the power supply area;
III, establishing a prearranged power failure prediction model according to prearranged power failure amount and reliable investment data of a power supply area caused by construction of the reliable investment project;
IV, establishing a power distribution network reliability investment economic benefit analysis model;
v: and when a power failure plan occurs, calculating the economic benefit of the reliability investment by adopting the power distribution network reliability investment economic benefit analysis model.
Preferably, the extracting of the power outage data information in step I includes: the first step is to extract keywords of fields and the second step is to discard keywords which have no relationship with the reliability investment items in a word bank.
Preferably, the first step adopts a data mining method for extracting the keywords of the fields; and discarding the keywords which have no relation with the reliability investment project in the word stock according to the meanings of the keywords.
Preferably, the prearranged outage amount caused by the construction of the reliability investment project of the step I comprises a first part of outage data and a second part of outage data.
Preferably, the number of users and the power shortage amount during the power failure of the first part are calculated according to the following formula:
in the formula, TD (1) i The number of households in power failure caused in the construction period of the ith type project in the first part within one year; i is a project reconstruction type; l is the total number of pre-scheduled blackout events, td, due to category i items in the first part of the study area (1) j Pre-arranging the number of power failure time users of a power failure event for a jth piece caused by an ith type item in the first part of the area; EENS (1) i The power shortage caused during the construction period within one year for the ith type project in the first part; n is the total number of prearranged power failure events caused by the ith type item in the first part of the region; eens (1) j And pre-arranging the power shortage amount of the power failure event for the jth part of the first part of the area caused by the ith category item.
Preferably, the number of users and the power shortage amount during the power failure of the second part are calculated according to the following formula:
in the formula, TD (2) i The number of the households in power failure caused in the construction period of the ith type project in the second part within one year; i is a project modification type; m is the total number of pre-scheduled blackout events, td, due to category i items in the first part of the study area (2) j Pre-arranging the number of power failure time users of the power failure event for the jth element of the first part of the area caused by the ith type item; EENS (2) i The power shortage caused during the construction period within one year for the ith type project in the second part; o is the total number of prearranged blackout events caused by the ith category item in the second part of the area, eens (2) j And pre-arranging the power shortage amount of the power failure event for the jth part of the second part of the region caused by the ith category item.
Preferably, the pre-scheduled outage prediction model of step III is calculated according to the following equation:
in the formula, TDP is the power shortage caused by prearranged power failure in the predicted year; ny1 and Ny2 are reliability investment types of the first part and the second part of the predicted annual research area respectively; TD (time division) (1) i 、TD (2) j The power shortage caused by the reliability improvement measures in the first part and the second part in the last year respectively; x i 、Y j The investment amounts of the reliability improvement measures in the first part and the second part respectively in the last year; m i 、N j The investment amount of each reliability improving measure in the first part and the second part is respectively used for predicting the annual reliability.
Preferably, the power distribution network reliability investment economic benefit analysis model in the step IV is calculated according to the following formula:
in the formula, C benefit Investing economic benefit indexes for comprehensive power distribution network reliability; c price The price is the electricity price, and the delta Q is the change value of the power supply shortage in the current year and the last year of investment; i is the power generation ratio coefficient; TDP is a predicted value of power failure and power supply shortage amount prearranged for investment year; c is the total investment of the investment year for improving the reliability of the power distribution network.
An analysis device for reliability, investment and economic benefits of a power distribution network based on mining technology, the device comprising: the data acquisition module, the input module, the data analysis module and the output module are connected in sequence;
the data acquisition module is used for acquiring annual power failure data information of a power supply area;
the input module is used for transmitting annual power failure data information of the power supply area, which is acquired by the acquisition unit, to the data analysis unit;
the data analysis module comprises a data classification unit, a data association unit, a data prediction unit and a data analysis unit;
the data classification unit is used for classifying annual power failure data information; the data association unit is used for extracting keywords from the annual power failure data information to establish a semantic mapping table with a reliability investment project; the data prediction unit establishes a pre-arrangement power failure prediction model according to pre-arrangement power failure amount and power supply area reliability investment data caused by the construction of the reliability investment project to obtain a predicted value of the pre-arrangement power failure power supply shortage amount; the data analysis unit establishes a power distribution network reliability investment economic benefit analysis model according to prearranged power failure and fault power failure, and calculates to obtain a power distribution network reliability investment economic benefit value;
and the data output module judges to obtain the benefits generated by the power distribution network investment according to the received power distribution network reliability investment economic benefit value.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method adopts a data mining method for extracting the keywords, can realize potential information mining of the power grid defect text, is applied to an electric energy quality online monitoring system, establishes a semantic mapping table of the keywords and the reliability investment project, and obtains the incidence relation between the prearranged power failure data and the reliability improvement measures.
(2) The invention constructs a prearranged power failure prediction model based on the incidence relation established by the data mining method, thereby predicting the number of households and the power shortage amount during power failure of the prearranged power failure according to the investment amount of each reliability improving measure in a certain area in the future.
(3) The method applies the constructed pre-arrangement power failure prediction model to the analysis of the economic benefit of the power distribution network reliability investment, and fully considers the influence of the pre-arrangement power failure on the economic benefit of the reliability, thereby evaluating the return on investment more accurately and the like.
Drawings
FIG. 1 is a flow chart of a method for analyzing the reliability, investment and economic benefits of a power distribution network according to the present invention;
FIG. 2 is a schematic diagram of a power grid reliability data storage type and keyword extraction according to the present invention;
FIG. 3 is a semantic mapping diagram of the keywords and reliability investment projects of the present invention.
Detailed Description
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
As shown in the attached figure 1, the invention establishes the incidence relation between the power failure data and the reliability investment data by applying a Chinese text mining method based on the reliability data in the existing information system, applies the incidence relation to the reliability investment economic benefit analysis of the power distribution network, and provides a power distribution network reliability economic benefit analysis method based on a mining technology, which comprises the following steps:
I. determining a research object, extracting keywords according to the power failure data information and establishing a semantic mapping table with the reliability investment project;
taking a power supply area of a certain place and city company as a research object, acquiring power failure data information of the area in a certain year, preprocessing the power failure data, dividing the power failure data into prearranged power failure data and fault power failure data according to the power failure property, and dividing the power failure data into city network power failure data and rural network power failure data according to the power failure occurrence place;
the power grid reliability data are stored in the power quality on-line monitoring system, the storage form is shown in figure 2, the reliability data contents of other fields are in standard format, and the reliability data can be directly used as statistical parameters in reliability statistics.
The power failure property and the power failure area are both structured data, and the reliability data can be preprocessed directly through the two fields. Prearranged outage data can be achieved by screening outage data information 'outage property' fields. The prearranged power failure is formed by seven types of power failures including scheduled maintenance power failure (inside), scheduled construction power failure (inside), temporary maintenance power failure (inside), temporary construction power failure (inside) and user application power failure, power regulation and power limitation. The outage area may be partitioned by screening the "management attributes" field. The field comprises two parts of a main urban network and a rural network, which respectively correspond to two different power failure areas of the urban network and the rural network.
Besides the structured data, some text data have disordered semantic structures and can only be corrected manually, such as a 'remark' field of an electric energy quality online monitoring system, wherein the description of the field on the type of the reliability investment project comprises project information causing the prearranged power failure event, but because the semantic structure of the text is not accurately defined, the information is difficult to directly extract, and special processing needs to be carried out on the text to enable a computer to identify the reliability investment project information of different types.
Establishing a power failure work content basic word bank: keyword extraction is carried out on a 'remark' field of prearranged power failure data through a TF-IDF algorithm, and the word frequency of the keywords is counted to establish a 'power failure work content basic word bank'.
TF-IDF is a statistical method to assess how important a word is for one of a set of documents or a corpus of documents, the importance of a word increasing in proportion to the number of times it appears in a document. By using the algorithm to extract keywords from the item information of the 'remark' field, a 'power failure work content basic word stock' can be established.
Establishing a semantic mapping table of keywords and reliability investment projects: and determining the incidence relation between the basic word bank of the power failure work content and the reliable investment project according to the meanings of the keywords, discarding useless keywords, and establishing a semantic mapping table of the keywords and the reliable investment project.
The projects are classified according to the attributes of the reliability investment projects of the power distribution network, and can be divided into five types of projects including major repair, technical improvement, infrastructure, distribution automation and migration and improvement.
Taking reliability data in a certain year of a certain urban power supply area as an example, a power failure work content basic word bank is established, screened keywords are shown in the attached figure 3, and are mapped to various reliability improving measures for power distribution network construction according to meanings of the keywords, so that project information of the prearranged power failure event can be obtained.
II. Acquiring prearranged outage and power supply area reliability investment data caused by construction of reliability investment projects:
the prearranged power failure amount caused by the construction of the reliability investment project is obtained by matching the power failure event with the project, and the number of users and the power failure amount caused by various reliability improving measures in different investment areas can be obtained by calculating through formulas (1) to (4), wherein the specific calculation formula is as follows:
in the formula (1), TD (1) i The number of the residents in power failure caused in the construction period within one year of the ith project in the urban network is 1, 2, 3, 4 and 5, wherein the i represents overhaul, technical improvement, infrastructure construction, migration improvement and distribution automation projects respectively. L is the total number of prearranged power failure events, td, caused by the ith category item in the urban network of the area (1) j And pre-arranging the number of the power failure time of the power failure event for the jth element of the urban network of the region caused by the ith type item.
In the formula (2), TD (2) i The number of the households in power failure caused in the construction period of the ith type project in the rural power grid within one year is 1, 2, 3, 4 and 5, wherein the values of i respectively represent major repair, technical improvement, capital construction, migration and power distribution automation projects. M is the total number of prearranged power failure events, td, caused by the ith category item in the urban network of the area (2) j And pre-arranging the number of the power failure users of the power failure event for the jth part of the urban network of the region caused by the ith item.
In formula (3), EENS (1) i The method is used for solving the problem of power shortage caused during construction in the ith project in the urban network within one year, wherein the value of i is 1, 2, 3, 4 and 5, and the i respectively represents major repair, technical improvement, capital construction, mobile improvement and distribution automation projects. N is the total number of prearranged power failure events caused by the ith category item in the urban network of the area, eens (1) j And pre-arranging the power shortage amount of the power failure event for the jth part of the urban network of the region caused by the ith item.
In formula (4), EENS (2) i The method is characterized in that the method is the power shortage caused in the construction period of the ith type project in the rural power grid within one year, wherein the value of i is 1, 2, 3, 4 and 5, and the i respectively represents the project of overhaul, technical improvement, capital construction, migration and power distribution automation. O is the total number of prearranged power failure events caused by the ith type item of the rural power grid in the area, eens (2) j And pre-arranging the power shortage amount of the power failure event of the jth part of the rural power grid of the area caused by the ith item.
The reliability investment data of the power supply area is shown in table 1 by a data acquisition questionnaire, and the investment acquisition questionnaire corresponds to the pre-arrangement power failure data caused by the construction of the reliability investment project obtained by calculation.
TABLE 1 investment Collection questionnaire
III, establishing a prearranged power failure prediction model according to the semantic mapping table of the historical data of the scheduled power failure and the reliability investment project:
establishing a pre-arrangement power failure and power shortage amount prediction model through an incidence relation between pre-arrangement power failure historical data and reliability investment:
in the formula (5), the TDP is the power shortage caused by the scheduled power failure in the predicted year; ny1 and Ny2 are reliability investment types of urban networks and rural networks in the region in the forecast year respectively; TD (1) i 、TD (2) j The power supply shortage caused by the reliability improvement measures in the urban network and the rural network in the last year respectively; x i 、Y j The investment amounts of the reliability improvement measures in the urban network and the rural network in the last year are respectively calculated; m is a group of i 、N j And the investment amounts of all reliability improving measures in urban networks and rural networks are respectively predicted.
And IV, establishing a power distribution network reliability investment economic benefit analysis model.
Since the power supply area informs the power-consuming customers of the power failure information in advance in various ways before the scheduled power failure event occurs, the power failure loss of the part is only the power selling loss of the power supply area.
The reliability improvement benefit of the future power distribution network caused by the implementation of the reliability improvement measures can be calculated through the change value of the power shortage amount before and after investment. Because the power shortage of the part is formed by fault power failure, the benefit of the part needs to consider not only the power selling loss of the power supply area, but also the loss of the user, and the GDP method or the power generation ratio method is generally adopted in China at present. The power generation ratio describes economic benefits created by unit electric energy in a certain year and a certain area, is a social measurement of the currency value of the electric energy, and can approximately reflect the average influence of power failure on the whole economy from a macroscopic view. Therefore, the user loss in the comprehensive reliability economic benefit analysis model is subjected to modeling analysis on the user power failure loss by adopting an electrogenesis ratio method.
The concrete formula of the comprehensive power distribution network reliability economic benefit analysis model is as follows:
in formula (6), C benefit Investing economic benefit indexes for comprehensive power distribution network reliability; c price The price is the electricity price, and the delta Q is the change value of the power supply shortage in the current year and the last year of investment; i is the electrogenesis ratio coefficient; TDP is a predicted value of power failure and power supply shortage amount prearranged for investment year; c is the total investment of the investment year for improving the reliability of the power distribution network.
V: when a power failure plan occurs, calculating the economic benefit of the reliability investment by adopting a power distribution network reliability investment economic benefit analysis model:
it is easy to see that if the established reliability investment economic benefit value of the power distribution network is greater than 1, the investment of the power distribution network in the region brings benefits, and the greater the value, the more remarkable the benefits are.
Distribution network reliability economic benefits analytical equipment based on excavation technique, the device includes: the data acquisition module, the input module, the data analysis module and the output module are connected in sequence;
the data acquisition module is used for acquiring annual power failure data information of a power supply area;
the input module is used for transmitting the annual power failure data information of the power supply area acquired by the acquisition unit to the data analysis unit;
the data analysis module comprises a data classification unit, a data association unit, a data prediction unit and a data analysis unit;
the data classification unit is used for classifying the annual power failure data information; the data association unit is used for extracting keywords from annual power failure data information to establish a semantic mapping table with the reliability investment project; the data prediction unit establishes a pre-arrangement power failure prediction model according to the semantic mapping table to obtain a predicted value of pre-arrangement power failure and power shortage amount; the data analysis unit establishes a power distribution network reliability investment economic benefit analysis model according to prearranged power failure and fault power failure, and calculates to obtain a power distribution network reliability investment economic benefit value;
and the data output module judges to obtain the benefits generated by the investment of the power distribution network according to the received reliability investment economic benefit value 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention are included in the scope of the claims of the present invention as filed.
Claims (5)
1. The method for analyzing the reliability and economic benefits of the power distribution network based on the mining technology is characterized by comprising the following steps:
I. determining a research object, extracting keywords according to annual power failure data information and establishing a semantic mapping table with a reliability investment project;
II. Acquiring prearranged power outage and power supply area reliability investment data caused by the construction of the reliability investment project;
the prearranged power failure amount caused by the construction of the reliability investment project in the step II comprises a first part of power failure data and a second part of power failure data;
the number of users and the power shortage amount during the power failure of the first part are calculated according to the following formula:
in the formula, TD (1) i The number of the households in power failure caused in the construction period of the ith type project in the first part within one year; i is a project reconstruction type; l is the total number of pre-scheduled blackout events, td, due to the i-th category item in the first part of the research area (1) j Pre-arranging the number of power failure time users of the power failure event for the jth element of the first part of the area caused by the ith type item; EENS (1) i The power shortage caused during the construction period within one year for the ith type project in the first part; n is the total number of prearranged power failure events caused by the ith type item in the first part of the region; eens (1) j Pre-scheduling the power shortage amount of the power failure event for the jth part of the first part of the area caused by the ith category item;
the number of users and the power shortage amount during the power failure of the second part are calculated according to the following formula:
in the formula, TD (2) i The number of households in power failure caused in the construction period of the ith type project in the second part within one year; i is a project reconstruction type; m is the total number of pre-scheduled blackout events, td, due to category i items in the first part of the study area (2) j Pre-arranging the number of power failure time users of the power failure event for the jth element of the first part of the area caused by the ith type item; EENS (2) i The power shortage caused during the construction period within one year for the ith item in the second part is calculated; o is the total number of prearranged power failure events caused by the ith category item in the second part of the area, eens (2) j Prearranged for blackout events for jth pieces of the second part of said area due to class i itemsThe power supply is insufficient;
III, establishing a prearranged power failure prediction model according to prearranged power failure amount and reliable investment data of a power supply area caused by construction of the reliable investment project;
the pre-scheduled outage prediction model of step III is calculated as follows:
in the formula, TDP is the power shortage caused by prearranged power failure in the predicted year; ny1 and Ny2 are reliability investment types of the first part and the second part of the predicted annual research area respectively; TD (1) i 、TD (2) j The power shortage caused by the reliability improvement measures in the first part and the second part respectively in the last year; x i 、Y j The investment amounts of the reliability improvement measures in the first part and the second part respectively in the last year; m i 、N j The investment amount of each reliability improvement measure in the first part and the second part is respectively used for predicting the annual reliability;
IV, establishing a power distribution network reliability investment economic benefit analysis model;
the power distribution network reliability investment economic benefit analysis model of the step IV is calculated according to the following formula:
in the formula, C benefit Investing economic benefit indexes for comprehensive power distribution network reliability; c price The delta Q is the change value of the power supply shortage in the current year and the last year of investment; i is the power generation ratio coefficient; TDP is a predicted value of power failure and power supply shortage amount prearranged for investment year; c is the total investment amount of the investment year for improving the reliability of the power distribution network;
v: and when a power failure plan occurs, calculating the economic benefit of the reliability investment by adopting the power distribution network reliability investment economic benefit analysis model.
2. The method for analyzing reliability and economic benefits of the power distribution network according to claim 1, wherein the extracting of the outage data information in the step I comprises: the first step is to extract keywords of fields and the second step is to discard keywords which have no relationship with the reliability investment items in a word bank.
3. The analysis method for the reliability and the economic benefit of the power distribution network according to claim 2, characterized in that the first step adopts a data mining method for extracting the keywords of the fields; and discarding the keywords which have no relation with the reliability investment project in the word bank according to the meanings of the keywords.
4. The method for analyzing reliability and economic benefits of the power distribution network according to claim 1, wherein the first part is urban network: and the second part is powered off to be a rural power grid.
5. The device for analyzing the reliability and economic benefits of the power distribution network based on the mining technology as claimed in any one of claims 1 to 4, wherein the device comprises: the data acquisition module, the input module, the data analysis module and the output module are connected in sequence;
the data acquisition module is used for acquiring annual power failure data information of a power supply area;
the input module is used for transmitting the annual power failure data information of the power supply area acquired by the acquisition module to the data analysis module;
the data analysis module comprises a data classification unit, a data association unit, a data prediction unit and a data analysis unit;
the data classification unit is used for classifying annual power failure data information; the data association unit is used for extracting keywords from the annual power failure data information to establish a semantic mapping table with a reliability investment project; the data prediction unit establishes a prearranged power failure prediction model according to prearranged power failure amount caused by the construction of the reliability investment project and reliability investment data of a power supply area to obtain a prearranged power failure power shortage predicted value; the data analysis unit establishes a power distribution network reliability investment economic benefit analysis model according to prearranged power failure and fault power failure, and calculates to obtain a power distribution network reliability investment economic benefit value;
and the data output module judges to obtain the benefits generated by the investment of the power distribution network according to the received reliability investment economic benefit value of the power distribution network.
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