CN114219225A - Power grid investment benefit evaluation system and evaluation method based on multi-source data - Google Patents
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Abstract
The invention discloses a power grid investment benefit evaluation system and method based on multi-source data, relates to the technical field of investment decision of power grid companies, and solves the technical problems that the existing scheme depends on personal experience, and all departments are difficult to coordinate in the implementation process, so that the investment evaluation of power grid projects is unreasonable and the investment return income is poor; the method is provided with an index system module and a data processing module, a success evaluation index system is established according to evaluation indexes, and the income evaluation is carried out on the items to be evaluated by combining the association recognition relationship; the method reasonably evaluates the investment of the power grid project, can accurately feed back the income state of the power grid project, provides data support for the investment of the power grid, and improves the return on investment; according to the method, the load of the measuring area is predicted through the load prediction model, and the load prediction model is based on the fitting function or the artificial intelligent model, so that the calculated amount can be reduced, the load prediction precision is improved, and the accuracy of the method for evaluating the investment benefits of the power grid is further improved.
Description
Technical Field
The invention belongs to the field of investment decision of power grid companies, relates to a processing and fusion technology of multi-source data, and particularly relates to a power grid investment benefit evaluation system and evaluation method based on the multi-source data.
Background
When a company approves a power grid project, quantitative judgment standards are lacked, personal experience is excessively relied on, the screening process is simple and violent, and deviation between investment allocation and actual requirements is easily caused; the power grid company divides and manages the power grid investment from planning, construction and capital transfer to different departments, and the whole process is subjected to various constraints, so that the power grid project is difficult to implement and the return on investment is poor; therefore, a power grid investment benefit evaluation system capable of comprehensively considering various factors to provide reference indexes for power grid investment is needed.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art; therefore, the invention provides a power grid investment benefit evaluation system and method based on multi-source data, which are used for solving the technical problems that the investment evaluation of a power grid project is unreasonable and the investment return income is poor due to the fact that the existing scheme depends on personal experience and all departments are difficult to coordinate in the implementation process.
To achieve the above object, an embodiment according to a first aspect of the present invention provides a power grid investment benefit evaluation system based on multi-source data, including:
an index system module: the system is used for acquiring multi-source data and constructing a performance evaluation index system according to the multi-source data; wherein, the effect evaluation index system is composed of two levels;
a data processing module: the system is used for acquiring index historical data and establishing an association identification relation between project attributes and a success evaluation index system; used for establishing a project reserve library; the method is also used for forecasting the investment requirement and measuring and calculating the investment capacity;
a profit evaluation module: the method is used for evaluating the overall income and the individual income of the power grid project.
Preferably, the establishing of the achievement evaluation index system according to the multi-source data by the index system module comprises:
collecting multi-source data as an index source, and combing the index source to obtain an evaluation index;
combining an analytic hierarchy process with the evaluation indexes to construct a preliminary evaluation index system;
establishing a success evaluation index system by combining the preliminary evaluation index system with an active evaluation method; the active evaluation method comprises relevance analysis and expert scoring.
Preferably, before the performance evaluation index system is constructed, classifying the evaluation indexes includes:
dividing the evaluation index into a forward index, a reverse index and a moderate index;
and fitting each index through a trapezoidal fuzzy membership function to obtain a normalization coefficient, and carrying out non-dimensionalization processing on each index by using the corresponding normalization coefficient.
Preferably, fitting each index includes:
fitting by a half-raised trapezoidal distribution function aiming at the positive indexes;
fitting by a decreasing half trapezoidal distribution function aiming at the reverse index;
and aiming at the moderate indexes, fitting by a trapezoidal distribution function.
Preferably, the data processing module establishes an association identification relationship between the project attribute and the performance evaluation index system, and includes:
acquiring index historical data of the power distribution network of the measuring and calculating region before the current year by combining the effect evaluation index system;
classifying the power grid projects according to the project attributes, and acquiring the total investment of various power grid projects before the current year;
and establishing an association identification relation between the project attribute and the achievement evaluation index system by combining the index historical data.
Preferably, the data processing module screens the power grid projects to obtain the power grid projects to be evaluated, and the power grid projects to be evaluated are integrated to generate a project storage library.
Preferably, the power grid projects in the project reserve library all set corresponding basic information; the basic information includes an item name, an item number, a voltage level, and a meaning tag.
Preferably, the meaning label is used for indicating whether the corresponding power grid project has significance; when the meaning label is 1, the meaning label indicates that the corresponding power grid project has significance, and when the meaning label is 0, the meaning label indicates that the corresponding power grid project does not have significance.
Preferably, the obtaining of the planned invested funds by the data processing module includes:
load prediction is carried out on the measuring and calculating area, and the investment scale is obtained according to the load prediction result;
and multiplying unit investment and investment scale to obtain the planned invested fund.
Preferably, the load prediction for the measurement area includes:
and acquiring the current load influence factor of the measuring area, and bringing the load influence factor into a load prediction model to acquire the corresponding load.
Preferably, the obtaining of the load prediction model includes:
acquiring historical load influence factors and corresponding loads of a measuring area; wherein the historical load impact factors include economic size, electricity level, and population size;
performing principal component analysis on the historical load influence factors to obtain corresponding weight factors;
and establishing a mapping relation between the load and the historical load influence factor and the corresponding weight factor, and marking the mapping relation as a load prediction model.
Preferably, the obtaining of the load prediction model includes:
acquiring historical load influence factors and corresponding loads of a measuring area;
establishing an artificial intelligence model; the artificial intelligence model comprises a deep convolutional neural network and an RBF neural network;
training the artificial intelligence model through the historical load influence factors and the corresponding loads, and marking the trained artificial intelligence model as a load prediction model.
Preferably, the investment capacity estimation is performed by the data processing module, and the method comprises the following steps:
acquiring the total profit amount and the auxiliary amount of a power grid company; wherein the auxiliary amount comprises a bank loan;
and acquiring the investment capacity of the power grid company according to the total profit amount and the auxiliary amount.
Preferably, the data processing module is respectively in communication and/or electrical connection with the index system module and the profit evaluation module; the income evaluation module is in communication connection with the intelligent terminal; the intelligent terminal comprises an intelligent mobile phone, a tablet computer and a notebook computer.
The power grid investment benefit evaluation method based on multi-source data comprises the following steps:
acquiring multi-source data through a data acquisition module, combing the multi-source data to obtain an evaluation index, and carrying out non-dimensionalization processing on the evaluation index;
establishing a preliminary evaluation index system according to the evaluation indexes, and establishing a success evaluation index system by combining an active evaluation method;
establishing an association identification relation between the project attribute and the success evaluation index system through a data processing module; establishing a project reserve library and carrying out investment demand prediction and investment capacity measurement and calculation of a power grid company;
and evaluating the overall income and the individual income of the power grid project to be evaluated in the project reserve library through the income evaluation module.
Compared with the prior art, the invention has the beneficial effects that:
1. the system is provided with an index system module and a data processing module, multi-source data are collected and sorted to obtain evaluation indexes, a success evaluation index system is established according to the evaluation indexes, and then the income evaluation is carried out on the items to be evaluated in an item storage library by combining with the association recognition relation; the method reasonably evaluates the investment of the power grid project, can accurately feed back the income state of the power grid project, provides data support for the investment of the power grid, and improves the return on investment.
2. The evaluation indexes are divided into forward indexes, reverse indexes and moderate indexes, a normalization coefficient is obtained by fitting through a trapezoidal fuzzy membership function, and each index is subjected to dimensionless processing through the normalization coefficient; the dimensions of all evaluation indexes are unified, so that the evaluation is convenient and the evaluation is comparable.
3. According to the method, the load of the measuring area is predicted through the load prediction model, and the load prediction model is based on the fitting function or the artificial intelligent model, so that the calculated amount can be reduced, the load prediction precision is improved, and the accuracy of the method for evaluating the investment benefits of the power grid is further improved.
Drawings
FIG. 1 is a schematic diagram of the working steps of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Along with the large scale of the power distribution network, numerous devices, complex network structure and influence of historical development, the time for popularizing and applying the evaluation work aiming at the power distribution network construction and transformation project is short, and an evaluation system is incomplete; when a power grid company approves a planning project, quantitative judgment standards are lacked, personal experience is excessively depended on in management, the screening process is simple and rough, and deviation between investment allocation and actual requirements is easily caused.
The power grid investment of a power grid company in a development area belongs to different departments for division management from planning, construction and capital transfer, the investment scale is also influenced by the financial affairs of the power grid company, and the effective landing of the unified development strategy of the power grid company is influenced to a certain extent by different investment decision-making key bases of different nodes; in a planning link, the source of project initiation is only based on the technical principle of power grid planning, and is lack of leaders; in the planning link, due to the lack of overall investment strategies, the management focus is often focused on integrating investment scale games after professional ideas; in the adjustment of plan execution, due to the lack of clear strategy execution and target constraint, the adjustment of investment is also only based on the constraint and limitation of scale, and the randomness is large; in the post-evaluation stage, the comprehensive benefit of specific investment target comparative analysis investment is lacked.
Referring to fig. 1, the invention provides a power grid investment benefit evaluation system based on multi-source data, which includes an index system module, a data processing module and a profit evaluation module;
an index system module: the system is used for acquiring multi-source data and constructing a performance evaluation index system according to the multi-source data;
a data processing module: the system is used for acquiring index historical data and establishing an association identification relation between project attributes and a success evaluation index system; used for establishing a project reserve library; the method is also used for forecasting the investment requirement and measuring and calculating the investment capacity;
a profit evaluation module: the method is used for evaluating the overall income and the individual income of the power grid project.
In one embodiment, a success evaluation index system is established through an index system module according to multi-source data, when the index system is established, assessment indexes are selected according to the principles of system, science and practicality, the core evaluation target of power grid investment benefit is tightly surrounded, data are collected from multiple aspects as index sources, corresponding evaluation indexes are combed out, a primary evaluation index system is established according to an analytic hierarchy process, the success evaluation index system is established through modes of power grid project relevance analysis, expert scoring and the like, and the main state and the actual situation of power grid operation are comprehensively, accurately and effectively reflected.
It can be understood that the project attributes include project names, project periods, project specifications and the like, and project attributes corresponding to different types of projects are different, so that the incidence relation between the project attributes and the performance evaluation indexes is established, and the accuracy of power grid revenue evaluation is improved.
In a specific embodiment, according to an evaluation index grading principle, referring to a basic theory of an analytic hierarchy process, a result evaluation index system is composed of two levels; the screening of the evaluation indexes needs to consider the overall investment effect of the power distribution network, the investment effect of a certain type of project and the investment effect of a single project at the same time, and is shown in the following table:
the performance evaluation index system comprises a first-level index and a second-level index, wherein the first-level index is used for evaluating the overall investment benefit of the power grid, and the second-level index is derived according to the indexes and is used for evaluating the investment benefit of annual power grid single projects.
In order to enable the indexes to form the same dimension for evaluation, the evaluation indexes are divided into forward indexes, reverse indexes and moderate indexes, the forward indexes, the reverse indexes and the moderate indexes are respectively fitted by adopting a trapezoidal fuzzy membership function, and each index is subjected to dimensionless processing by utilizing a normalization coefficient.
Specifically, forward indicators, such as 10kV line cabling rate and "N-1" pass rate, may employ a half-raised trapezoidal distribution function; inverse indicators, such as heavy duty line duty ratio, high loss distribution ratio, etc., may employ a reduced half trapezoidal distribution function; moderate indexes, which refer to indexes with moderate values in a certain range, such as power supply radius, can be fitted by adopting a trapezoidal distribution function; through the three modes of processing, the membership degrees corresponding to all the evaluation indexes have uniform dimensions and are positive values, are all in the interval of [0, 1], and have comparability.
In one embodiment, the data processing module establishes an association identification relationship between the project attributes and the performance evaluation index system, and comprises:
acquiring index historical data of a power distribution network in a measuring and calculating area in the past year before the current year by combining a success evaluation index system;
classifying the power grid projects according to the project attributes, and acquiring the total investment of various power grid projects before the current year;
and establishing an association identification relation between the project attribute and the achievement evaluation index system by combining the index historical data.
In a specific embodiment, according to a power distribution network planning effectiveness evaluation index system, acquiring yearly index historical data of a power distribution network in a measuring and calculating area before the current year, classifying power distribution network construction and transformation projects according to project attributes, acquiring yearly investment sum of various attribute projects before the current year, and establishing an association identification relationship between the project attributes and planning effectiveness evaluation indexes.
In one embodiment, the data processing module screens the power grid projects to obtain power grid projects to be evaluated, and the power grid projects to be evaluated are integrated to generate a project storage library.
In a specific embodiment, various projects of power grid construction are screened to form a project storage library to be evaluated and ranked, and basic information required to be provided by each project mainly comprises: the items with special significance are required to be marked in terms of item names, serial numbers, voltage levels, item types, total investment of the items and the like.
In one embodiment, obtaining, by a data processing module, a projected fund comprises:
load prediction is carried out on the measuring and calculating area, and the investment scale is obtained according to the load prediction result;
and multiplying unit investment and investment scale to obtain the planned invested fund.
It should be noted that, when forecasting the investment demand, that is, obtaining the planned investment fund, the load forecasting is the basic work for forecasting the investment demand, and the planned investment fund is obtained by multiplying the unit investment by the investment scale in consideration of historical data, the economic development condition of the region, the electric quantity level and other aspects.
In one embodiment, the load prediction of the reckoning area comprises:
and acquiring the current load influence factor of the measuring area, and bringing the load influence factor into a load prediction model to acquire the corresponding load.
It can be understood that the load prediction needs to be performed in combination with the relevant historical data of the measurement area.
In a specific embodiment, the obtaining of the load prediction model includes:
acquiring historical load influence factors and corresponding loads of a measuring area;
performing principal component analysis on the historical load influence factors to obtain corresponding weight factors;
and establishing a mapping relation between the load and the historical load influence factor and the corresponding weight factor, and marking the mapping relation as a load prediction model.
It can be understood that the mapping relationship between the load and the historical load influence factor and the corresponding weight factor is essentially a function curve fitted according to the weight factor.
As for the historical load influence factor, factors that can affect the load such as the economic scale of the measurement area, the power level, and the like should be included.
In a specific embodiment, acquiring historical load influence factors and corresponding loads of a measuring area;
establishing an artificial intelligence model, training the artificial intelligence model through historical load influence factors and corresponding loads, and marking the trained artificial intelligence model as a load prediction model.
According to the method, the mapping relation between the load and the historical load influence factor is established through the artificial intelligence model with strong nonlinear fitting capacity, the calculated amount can be reduced, the accuracy is improved, and a complex calculation process is avoided.
In one embodiment, the data processing module measures and calculates the investment capacity of a power grid company, and determines the total profit amount by accounting the profit condition of the company; and meanwhile, the annual financial plan of the power grid company is used for determining the future annual loan amount and other fund sources, and finally, the total investment capacity of the power grid company is determined.
It is worth noting that the constraint of the power grid investment capacity is the asset liability rate, and the annual investment capacity of the power grid can be calculated through the constraint, so that the investment scale of the power grid is ensured to be within the economic capacity range of a power grid company; the amount of the power grid company repayment loan is determined by the company's repayment plan, and the constraint on the rate of assets liability may be set to increase by 1 percentage per year, and not more than 80% at the maximum.
In one embodiment, the profit evaluation module evaluates the overall profit and the individual project profit of the power grid project, including:
(1) index weight setting method
The setting of the index weight is an important link of investment benefit evaluation, the analysis of the index weight needs to be carried out by means of a statistical principle, an operation research principle and a comprehensive evaluation theoretical method, by adopting a gray correlation degree analysis method, a Delphi method, an analytic hierarchy process and other subjective weighting methods and a comprehensive weight assignment method combined with a variation coefficient method, an entropy weight method, an artificial neural network and other objective weighting methods, the intervention of human factors can be reduced to the minimum extent, a uniform weight standard can be obtained, and the determination of the investment benefit index weight of a power grid company can be comprehensively and effectively realized.
(2) Evaluating the current situation and target of investment demand of power grid company
Firstly, according to the overall strategic planning and investment target of a power grid company, evaluating the current investment situation of the power grid of each project, comparing the difference between the current investment situation of each project and the investment target of the company, and finally giving the weight of each project on the project group investment demand index (primary index) by a comprehensive weight method.
And secondly, evaluating the investment requirement and the investment capacity degree of the power grid based on actual data of the current situation of each project, such as the increase of electricity sales, the development of customers, the condition of the power grid, available funds, the rate of assets and liabilities and the like. And according to the evaluation result, according to the total investment planning target of the power grid company, the expert marks and determines the respective satisfaction degree state of each project to be invested in the first-level index dimension.
(3) Total investment benefit weight setting
In the process of determining the weights of a plurality of targets of the company investment portfolio, the weight change thought of the existing multi-target weight change decision method is combined, and the weight vector of the index factors is considered to change along with the change of the state values of the index factors, so that the functions of the corresponding index factors in the decision process can be better reflected.
The concept of the variable weight vector is obtained through the comprehensive operation of the constant weight vector and the factor state vector, so that the total investment benefit weight of economic benefit, reliability, development index and the like under different scenes is obtained.
(4) Setting of weight of each evaluation index of individual investment project
According to project construction rules and requirements, in projects meeting the requirements of early condition indexes, the difference between target values and current values of all indexes after the projects are implemented is applied, contribution degrees to economic, safety, strategic and strategic benefits of a power grid after the projects are implemented are compared, and the weight and the score of each evaluation index of the individual investment projects are evaluated by a comprehensive index evaluation method. The principle of the primary screening of the power grid project is that safety and policy are used as main indexes, and project economy and strategic indexes are used as reference indexes.
It can be understood that the overall income and the individual income of the power grid project can be respectively obtained according to the weight of each evaluation index and the specific numerical value of the evaluation index.
In one embodiment, the invention provides a power grid investment benefit evaluation method based on multi-source data, which comprises the following steps:
acquiring multi-source data through a data acquisition module, combing the multi-source data to obtain an evaluation index, and carrying out non-dimensionalization processing on the evaluation index;
establishing a preliminary evaluation index system according to the evaluation indexes, and establishing a success evaluation index system by combining an active evaluation method;
establishing an association identification relation between the project attribute and the success evaluation index system through a data processing module; establishing a project reserve library and carrying out investment demand prediction and investment capacity measurement and calculation of a power grid company;
and evaluating the overall income and the individual income of the power grid project to be evaluated in the project reserve library through the income evaluation module.
The working principle of the invention is as follows:
the data acquisition module is used for acquiring multi-source data, combing the multi-source data to obtain an evaluation index, and carrying out non-dimensionalization processing on the evaluation index.
And establishing a preliminary evaluation index system according to the evaluation indexes, and establishing a success evaluation index system by combining an active evaluation method.
Establishing an association identification relation between the project attribute and the success evaluation index system through a data processing module; and establishing a project reserve library, and predicting the investment demand and measuring and calculating the investment capacity of the power grid company.
And evaluating the overall income and the individual income of the power grid project to be evaluated in the project reserve library through the income evaluation module.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (10)
1. Power grid investment benefit evaluation system based on multisource data, its characterized in that includes:
an index system module: the system is used for acquiring multi-source data and constructing a performance evaluation index system according to the multi-source data; wherein, the effect evaluation index system is composed of two levels;
a data processing module: the system is used for acquiring index historical data and establishing an association identification relation between project attributes and a success evaluation index system; used for establishing a project reserve library; the method is also used for forecasting the investment requirement and measuring and calculating the investment capacity;
a profit evaluation module: the method is used for evaluating the overall income and the individual income of the power grid project.
2. The multi-source data-based power grid investment benefit evaluation system according to claim 1, wherein a performance evaluation index system is established by the index system module according to the multi-source data, and the performance evaluation index system comprises:
collecting multi-source data as an index source, and combing the index source to obtain an evaluation index;
combining an analytic hierarchy process with the evaluation indexes to construct a preliminary evaluation index system;
establishing a success evaluation index system by combining the preliminary evaluation index system with an active evaluation method; the active evaluation method comprises relevance analysis and expert scoring.
3. The multi-source data-based power grid investment benefit evaluation system according to claim 2, wherein before the performance evaluation index system is constructed, the evaluation indexes are classified, and the classification comprises:
dividing the evaluation index into a forward index, a reverse index and a moderate index;
and fitting each index through a trapezoidal fuzzy membership function to obtain a normalization coefficient, and carrying out non-dimensionalization processing on each index by using the corresponding normalization coefficient.
4. The multi-source data-based power grid investment benefit evaluation system according to claim 3, wherein fitting each index comprises:
fitting by a half-raised trapezoidal distribution function aiming at the positive indexes;
fitting by a decreasing half trapezoidal distribution function aiming at the reverse index;
and aiming at the moderate indexes, fitting by a trapezoidal distribution function.
5. The multi-source data-based power grid investment benefit evaluation system according to claim 1, wherein the data processing module establishes an association identification relationship between project attributes and a performance evaluation index system, and comprises:
acquiring index historical data of the power distribution network of the measuring and calculating region before the current year by combining the effect evaluation index system;
classifying the power grid projects according to the project attributes, and acquiring the total investment of various power grid projects before the current year;
and establishing an association identification relation between the project attribute and the achievement evaluation index system by combining the index historical data.
6. The multi-source data-based power grid investment benefit evaluation system according to claim 1, wherein the data processing module screens power grid items to obtain power grid items to be evaluated, and integrates the power grid items to be evaluated to generate a project storage library.
7. The multi-source data-based power grid investment benefit evaluation system according to claim 1, wherein the obtaining of the planned invested funds through the data processing module comprises:
load prediction is carried out on the measuring and calculating area, and the investment scale is obtained according to the load prediction result;
and multiplying unit investment and investment scale to obtain the planned invested fund.
8. The multi-source data-based power grid investment benefit evaluation system according to claim 7, wherein the load prediction of the measurement area comprises:
and acquiring the current load influence factor of the measuring area, and bringing the load influence factor into a load prediction model to acquire the corresponding load.
9. The multi-source data-based power grid investment benefit evaluation system according to claim 8, wherein the obtaining of the load prediction model comprises:
acquiring historical load influence factors and corresponding loads of a measuring area; wherein the historical load impact factors include economic size, electricity level, and population size;
performing principal component analysis on the historical load influence factors to obtain corresponding weight factors;
and establishing a mapping relation between the load and the historical load influence factor and the corresponding weight factor, and marking the mapping relation as a load prediction model.
10. The evaluation method of the multi-source data-based power grid investment benefit evaluation system according to any one of claims 1 to 9, comprising:
acquiring multi-source data through a data acquisition module, combing the multi-source data to obtain an evaluation index, and carrying out non-dimensionalization processing on the evaluation index;
establishing a preliminary evaluation index system according to the evaluation indexes, and establishing a success evaluation index system by combining an active evaluation method;
establishing an association identification relation between the project attribute and the success evaluation index system through a data processing module; establishing a project reserve library and carrying out investment demand prediction and investment capacity measurement and calculation of a power grid company;
and evaluating the overall income and the individual income of the power grid project to be evaluated in the project reserve library through the income evaluation module.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114971389A (en) * | 2022-06-20 | 2022-08-30 | 国网湖南省电力有限公司经济技术研究院 | Digital analysis platform for input-output evaluation based on power grid project of data center station |
| CN117575669A (en) * | 2023-11-03 | 2024-02-20 | 深圳市中联通电子股份有限公司 | Investment income calculation method and system for middle school |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577901A (en) * | 2013-11-22 | 2014-02-12 | 国家电网公司 | Method of intertidal zone wind power for accessing power grid |
| CN107622362A (en) * | 2017-11-01 | 2018-01-23 | 国网重庆市电力公司经济技术研究院 | A kind of evaluation method and device of power distribution network returns of investment |
| CN109583766A (en) * | 2018-12-03 | 2019-04-05 | 国网湖南省电力有限公司 | Decontrol power grid enterprises' ancillary investment decision-making technique under increment distribution business background |
| CN110110924A (en) * | 2019-05-05 | 2019-08-09 | 国网经济技术研究院有限公司 | Power distribution network investment scale optimization method and device and computing equipment |
| CN110648253A (en) * | 2019-09-26 | 2020-01-03 | 国家电网有限公司 | Power distribution network investment allocation method based on historical investment effect |
| CN111967634A (en) * | 2020-05-28 | 2020-11-20 | 国网河南省电力公司经济技术研究院 | Comprehensive evaluation and optimal sorting method and system for investment projects of power distribution network |
-
2021
- 2021-11-24 CN CN202111404880.5A patent/CN114219225A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577901A (en) * | 2013-11-22 | 2014-02-12 | 国家电网公司 | Method of intertidal zone wind power for accessing power grid |
| CN107622362A (en) * | 2017-11-01 | 2018-01-23 | 国网重庆市电力公司经济技术研究院 | A kind of evaluation method and device of power distribution network returns of investment |
| CN109583766A (en) * | 2018-12-03 | 2019-04-05 | 国网湖南省电力有限公司 | Decontrol power grid enterprises' ancillary investment decision-making technique under increment distribution business background |
| CN110110924A (en) * | 2019-05-05 | 2019-08-09 | 国网经济技术研究院有限公司 | Power distribution network investment scale optimization method and device and computing equipment |
| CN110648253A (en) * | 2019-09-26 | 2020-01-03 | 国家电网有限公司 | Power distribution network investment allocation method based on historical investment effect |
| CN111967634A (en) * | 2020-05-28 | 2020-11-20 | 国网河南省电力公司经济技术研究院 | Comprehensive evaluation and optimal sorting method and system for investment projects of power distribution network |
Non-Patent Citations (2)
| Title |
|---|
| 崔巍 等: "电网专项领域投资决策模型", 《电力建设》 * |
| 许晓敏: "基于电力需求和投资能力的复杂电网优化投资决策研究", 《中国博士学位论文全文数据库经济与管理科学辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114971389A (en) * | 2022-06-20 | 2022-08-30 | 国网湖南省电力有限公司经济技术研究院 | Digital analysis platform for input-output evaluation based on power grid project of data center station |
| CN117575669A (en) * | 2023-11-03 | 2024-02-20 | 深圳市中联通电子股份有限公司 | Investment income calculation method and system for middle school |
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