CN108694478A - A kind of sale of electricity risk indicator computational methods considering power quantity predicting deviation - Google Patents
A kind of sale of electricity risk indicator computational methods considering power quantity predicting deviation Download PDFInfo
- Publication number
- CN108694478A CN108694478A CN201810746933.3A CN201810746933A CN108694478A CN 108694478 A CN108694478 A CN 108694478A CN 201810746933 A CN201810746933 A CN 201810746933A CN 108694478 A CN108694478 A CN 108694478A
- Authority
- CN
- China
- Prior art keywords
- electricity
- sale
- deviation
- load prediction
- risk
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000005611 electricity Effects 0.000 title claims abstract description 230
- 238000000205 computational method Methods 0.000 title claims abstract description 8
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000005070 sampling Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000012954 risk control Methods 0.000 abstract description 2
- 238000000034 method Methods 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 230000007774 longterm Effects 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 2
- 238000012502 risk assessment Methods 0.000 description 2
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 230000006735 deficit Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 238000013277 forecasting method Methods 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to electricity market fields, more particularly to a kind of sale of electricity risk indicator computational methods considering power quantity predicting deviation, the present invention is based on the history electricity consumptions of client to carry out load prediction and calculated load prediction deviation, the distribution probability figure of load prediction deviation is extracted according to load prediction deviation, and the foundation of electricity sales amount is signed in this, as sale of electricity company and electricity power enterprise, finally using the economic loss of sale of electricity company as sale of electricity risk indicator, calculate sale of electricity company sale of electricity risk of loss caused by power quantity predicting deviation, its traffic-operating period is analyzed for sale of electricity company, it promotes risk control capability and provides important reference.
Description
Technical field
The present invention relates to electricity market fields, and in particular to a kind of sale of electricity risk indicator calculating considering power quantity predicting deviation
Method.
Background technology
After sale of electricity side is decontroled, sale of electricity company acts as the function served as bridge of Generation Side and electricity consumption side in electricity market, from hair
Electric enterprise goes out clear electricity price purchase electricity with some, and to customer sales in a manner of retail.Due to electricity commodity can not mass storage,
And relation between supply and demand must moment balance will lead to sale of electricity company if deviation occur excessive for the practical electricity consumption of client and Contract generation
The purchase of electricity of signature and practical electricity sales amount deviation.If the practical electricity consumption of client is less than the examination gold that prediction electricity will cause great number
Volume;If the practical electricity consumption of client is more than prediction electricity, will be settled accounts with the listed power price of great number with electricity power enterprise.
Currently, since China sale of electricity side is decontroled still in pilot phase, electricity transaction is mainly carried out by power grid enterprises and is united
The pattern for purchasing state monopoly for marketing and Direct Purchase of Electric Energy by Large Users realizes that power grid enterprises can realize power generation on demand by control centre, and power purchase is not present
Deviation caused by amount and practical electricity sales amount deviation is examined.After sale of electricity side is decontroled, sale of electricity company needs the electricity consumption of fully analysis client
Feature, Accurate Prediction power user consumption, and in this, as the foundation for signing power purchase side contract, realize migration efficiency optimization.
Since sale of electricity side is decontroled, the time is short, and domestic sale of electricity company sale of electricity risk is also in research initial stage, to sale of electricity company
Risk assessment there is no ripe theoretical result and invention.About in the existing achievement in research of sale of electricity risk, mostly made with power grid enterprises
Consider certain constraintss using prospective earnings as optimization aim for research object, establish integrated decision-making mixed with risk assessment it is whole
Number Optimized model;Other researchs assess client to seek top-tier customer as target, by subjective weighting method, objective weighted model
Index of correlation, by selecting top-tier customer to reduce the operations risks of sale of electricity company.
And there is difference substantially, first, the marketing that power grid enterprises pass through the state monopoly for purchase and marketing with power grid enterprises in sale of electricity company
Pattern monopolizes the sale of electricity side resource in addition to straight power purchase large user, and sale of electricity company will face keen competition;Secondly, power grid enterprise
Industry is huge in occupation of the most important resource of electric system, and the experience for having long term power to market.Therefore, it is looked forward to for power grid
The achievement in research of industry sale of electricity risk is not suitable for the sale of electricity company after sale of electricity side opens mostly.
In under the pattern of long-term spot market, the power purchase side business and sale of electricity side business of sale of electricity company will all change,
Power purchase side sale of electricity company by autonomous negotiating, concentrate bid etc. modes electricity power enterprise with it is a certain go out clear electricity price sign power purchase and close
Together;Sale of electricity side sale of electricity company generally signs sale of electricity contract with the price less than government's listed power price with client.According to existing rule
Fixed, sale of electricity company power purchase typically occurs in before sale of electricity, it is therefore necessary to purchase risk is signed according to power quantity predicting and electricity power enterprise,
But the deviation of the practical electricity consumption in sale of electricity side will lead to sale of electricity corporate profit reduction even loss.And there is not a kind of science also at present
The method that is calculated of the risk indicator to sale of electricity company.
Invention content
To solve the above-mentioned problems, the present invention provides a kind of sale of electricity risk indicator calculating sides considering power quantity predicting deviation
Method, specific technical solution are as follows:
It is a kind of to consider that the sale of electricity risk indicator computational methods of power quantity predicting deviation include the following steps:
(1) the history electricity consumption and payment for collecting electricity marketization transacting customer record;Obtaining it, electricity consumption is gone through month by month
History data;
(2) load prediction is carried out to electricity marketization transacting customer;
(3) analysis load prediction result refines electricity demand forecasting deviation profile probability graph;
(4) it is arranged using the economic loss of sale of electricity company as sale of electricity risk indicator and is sold according to the calculating of electricity demand forecasting deviation
Risk index assesses sale of electricity risk of loss.
Preferably, the step (3) specifically includes following steps:
(1) it chooses n times load prediction results to be analyzed, if the client of ith load prediction predicts electricity consumption and reality
Electricity consumption is respectively Wpi,Wri, then predict that the following formula of ordered series of numbers that electricity consumption and practical electricity consumption are constituted is 1. shown:
Wherein, N is load prediction results sample number;
(2) calculated load predicts relative deviation:
If ith load prediction relative deviation is vi, then ith load prediction relative deviation viCalculation formula it is as follows:
Wherein i is frequency in sampling;
(3) load prediction relative deviation v is obtained according to the n times prediction result of selectioniDistribution probability curve, when sampling time
When number i → ∞, the distribution probability curve of load prediction relative deviation is close to normal distyribution function, shown in following formula 3:
μ is load prediction relative deviation viExpectation;σ is load prediction relative deviation viStandard deviation;σ2For load prediction
Relative deviation viVariance.
Preferably, the step (4) specifically includes following steps:
(1) sale of electricity risk indicator is set:
1) risk of loss of the practical electricity consumption of client less than prediction electricity consumption:
2) risk of loss of the practical electricity consumption of client higher than prediction electricity consumption:
(2) sale of electricity risk indicator is calculated:
If the listed power price of local government's setting is other Pc, the insufficient examination electricity price of electricity consumption is P0;Sale of electricity company is looked forward to from power generation
The electricity price of asking the appearance of of industry power purchase is Pb, signature electricity sales amount is WP;It is P that sale of electricity company signs sale of electricity unit price with clients;
Using the economic loss of sale of electricity company as sale of electricity risk indicator, the sale of electricity caused by power quantity predicting deviation of sale of electricity company
Risk of loss can indicate as follows:
1) E is set1It is less than the risk of loss of prediction electricity consumption for the practical electricity consumption of client, then E1Calculation formula be
2) E is set2It is higher than the risk of loss of prediction electricity consumption for the practical electricity consumption of client, then E2Calculation formula be:
Wherein, viFor load prediction relative deviation, f (vi) it is load prediction relative deviation viDistribution probability curvilinear function;
(3) sale of electricity risk of loss is assessed:
If EtotalFor sale of electricity company sale of electricity risk of loss caused by load prediction deviation, then EtotalCalculation formula such as
Under:
Etotal=E1+E2 ⑥。
Beneficial effects of the present invention are:The present invention provides a kind of sale of electricity risk indicator calculating considering power quantity predicting deviation
Method, the history electricity consumption based on client carry out load prediction and calculated load prediction deviation, are refined according to load prediction deviation
Go out the distribution probability figure of load prediction deviation, and sign the foundation of electricity sales amount in this, as sale of electricity company and electricity power enterprise, finally
Using the economic loss of sale of electricity company as sale of electricity risk indicator, sale of electricity company sale of electricity wind caused by power quantity predicting deviation is calculated
Danger loss analyzes its traffic-operating period for sale of electricity company, promotion risk control capability provides important reference.
Description of the drawings
Fig. 1 is that the load prediction relative deviation of 100 load prediction samples in the embodiment of the present invention is distributed scatter plot;
Fig. 2 is that the load prediction of 100 times, 200 times, 500 times load prediction samples in the embodiment of the present invention is relatively inclined
Difference cloth probability curve diagram.
Specific implementation mode
In order to better understand the present invention, the invention will be further described in the following with reference to the drawings and specific embodiments:
It is a kind of to consider that the sale of electricity risk indicator computational methods of power quantity predicting deviation include the following steps:
1, the history electricity consumption and payment record of electricity marketization transacting customer are collected;Obtain the history of its electricity consumption month by month
Data;Before sale of electricity side is decontroled, normal client information about power can generally be obtained by power grid enterprises, for directly being purchased from electricity power enterprise
The large user of electricity generally has uses electrographic recording in detail.
2, load prediction is carried out to electricity marketization transacting customer;It can be by being born based on BP neural network model
Lotus is predicted, since the load forecasting method is the prior art, repeats no more herein.
3, analysis load prediction result refines electricity demand forecasting deviation profile probability graph;
(1) it chooses n times load prediction results to be analyzed, if the client of ith load prediction predicts electricity consumption and reality
Electricity consumption is respectively Wpi,Wri, then predict that the following formula of ordered series of numbers that electricity consumption and practical electricity consumption are constituted is 1. shown:
Wherein, N is load prediction results sample number;
(2) calculated load predicts relative deviation:
If ith load prediction relative deviation is vi, then ith load prediction relative deviation viCalculation formula it is as follows:
Wherein i is frequency in sampling;
(3) 100 load prediction samples are randomly selected, load prediction relative deviation v is obtainediDistribution situation such as Fig. 1 institutes
Show, when load prediction sample is enough, i.e., when N is sufficiently large, event occur frequency function only have with overall distribution density it is micro-
Small difference, so as to be used as probability density.In order to reduce calculation amount, herein to randomly selecting 100 times, 200 times
It is analyzed with 500 electricity demand forecasting results, obtains three kinds of situation load prediction relative deviation viDistribution probability curve such as
Shown in Fig. 2.As shown in Figure 2, load prediction relative deviation v is obtained according to the n times prediction result of selectioniDistribution probability curve,
As frequency in sampling i → ∞, the distribution probability curve of load prediction relative deviation is close to normal distyribution function, 3 institute of following formula
Show:
μ is load prediction relative deviation viExpectation;σ is load prediction relative deviation viStandard deviation;σ2For load prediction
Relative deviation viVariance.In practical applications, due to client's load prediction data library Finite Samples, in addition if frequency in sampling
Too senior general reduces computational efficiency, and general 500 load prediction samples of extraction can reach computational accuracy requirement.
4, it is arranged using the economic loss of sale of electricity company as sale of electricity risk indicator and is sold according to the calculating of electricity demand forecasting deviation
Risk index is assessed sale of electricity risk of loss, is as follows:
(1) sale of electricity risk indicator is set:
In under the pattern of long-term spot market, sale of electricity company is closed with electricity power enterprise with the electricity price signature power purchase of a certain determination
Together, and clear purchase of electricity;Sale of electricity side sale of electricity company generally signs sale of electricity contract with the price less than government's listed power price with client,
General indefinite electricity sales amount.If sale of electricity company is exactly equal to total with the practical electricity consumption of client with the electricity sales amount that electricity power enterprise signs
Amount is then not present caused by electricity deviation and loses risk.However, since customer electricity is there are significant fluctuation and randomness,
Sale of electricity company is hardly possible to accomplish that purchase of electricity and practical electricity sales amount are essentially equal, therefore there are the following two kinds types for sale of electricity company
Loss risk:
1) risk of loss of the practical electricity consumption of client less than prediction electricity consumption:
Sale of electricity company signs purchase risk using load prediction results as according to electricity power enterprise, if the practical electricity consumption of client
Less than prediction electricity consumption, differential section electricity will bear the examination electricity price of statutory regulation, sale of electricity company made to suffer a loss;
2) risk of loss of the practical electricity consumption of client higher than prediction electricity consumption:
Sale of electricity company makes concessions to client on the basis of listed power price using the local listed power price that government puts into effect as benchmark;
If the practical electricity consumption total amount of client, higher than prediction electricity consumption, sale of electricity company needs to exceed portion with listed power price and electricity power enterprise's clearing
Divide electricity, directly results in the sale of electricity company deficit interest concessions amount of money;
(2) sale of electricity risk indicator is calculated:
If the listed power price of local government's setting is other Pc, the insufficient examination electricity price of electricity consumption is P0;Sale of electricity company is looked forward to from power generation
The electricity price of asking the appearance of of industry power purchase is Pb, signature electricity sales amount is WP;It is P that sale of electricity company signs sale of electricity unit price with clients;
Using the economic loss of sale of electricity company as sale of electricity risk indicator, the sale of electricity caused by power quantity predicting deviation of sale of electricity company
Risk of loss can indicate as follows:
1) E is set1It is less than the risk of loss of prediction electricity consumption for the practical electricity consumption of client, then E1Calculation formula be
2) E is set2It is higher than the risk of loss of prediction electricity consumption for the practical electricity consumption of client, then E2Calculation formula be:
Wherein, viFor load prediction relative deviation, f (vi) it is load prediction relative deviation viDistribution probability curvilinear function;
(3) sale of electricity risk of loss is assessed:
If EtotalFor sale of electricity company sale of electricity risk of loss caused by load prediction deviation, then EtotalCalculation formula such as
Under:
Etotal=E1+E2 ⑥。
The present invention is not limited to above-described specific implementation mode, and the foregoing is merely the preferable case study on implementation of the present invention
, be not intended to limit the invention, all within the spirits and principles of the present invention made by any modification, equivalent replacement and change
Into etc., it should all be included in the protection scope of the present invention.
Claims (3)
1. a kind of sale of electricity risk indicator computational methods considering power quantity predicting deviation, it is characterised in that:Include the following steps:
(1) the history electricity consumption and payment for collecting electricity marketization transacting customer record;Obtain the history number of its electricity consumption month by month
According to;
(2) load prediction is carried out to electricity marketization transacting customer;
(3) analysis load prediction result refines electricity demand forecasting deviation profile probability graph;
(4) it is arranged using the economic loss of sale of electricity company as sale of electricity risk indicator and sale of electricity wind is calculated according to electricity demand forecasting deviation
Dangerous index assesses sale of electricity risk of loss.
2. a kind of sale of electricity risk indicator computational methods considering power quantity predicting deviation according to claim 1, feature exist
In:The step (3) specifically includes following steps:
(1) it chooses n times load prediction results to be analyzed, if the client of ith load prediction predicts electricity consumption and practical electricity consumption
Amount is respectively Wpi,Wri, then predict that the following formula of ordered series of numbers that electricity consumption and practical electricity consumption are constituted is 1. shown:
Wherein, N is load prediction results sample number;
(2) calculated load predicts relative deviation:
If ith load prediction relative deviation is vi, then ith load prediction relative deviation viCalculation formula it is as follows:
Wherein i is frequency in sampling;
(3) load prediction relative deviation v is obtained according to the n times prediction result of selectioniDistribution probability curve, when frequency in sampling i →
When ∞, the distribution probability curve of load prediction relative deviation is close to normal distyribution function, shown in following formula 3:
μ is load prediction relative deviation viExpectation;σ is load prediction relative deviation viStandard deviation;σ2It is opposite for load prediction
Deviation viVariance.
3. a kind of sale of electricity risk indicator computational methods considering power quantity predicting deviation according to claim 1, feature exist
In:The step (4) specifically includes following steps:
(1) sale of electricity risk indicator is set:
1) risk of loss of the practical electricity consumption of client less than prediction electricity consumption:
2) risk of loss of the practical electricity consumption of client higher than prediction electricity consumption:
(2) sale of electricity risk indicator is calculated:
If the listed power price of local government's setting is other Pc, the insufficient examination electricity price of electricity consumption is P0;Sale of electricity company purchases from electricity power enterprise
The electricity price of asking the appearance of of electricity is Pb, signature electricity sales amount is WP;It is P that sale of electricity company signs sale of electricity unit price with clients;
Using the economic loss of sale of electricity company as sale of electricity risk indicator, sale of electricity company sale of electricity risk caused by power quantity predicting deviation
Loss can indicate as follows:
1) E is set1It is less than the risk of loss of prediction electricity consumption for the practical electricity consumption of client, then E1Calculation formula be
2) E is set2It is higher than the risk of loss of prediction electricity consumption for the practical electricity consumption of client, then E2Calculation formula be:
Wherein, viFor load prediction relative deviation, f (vi) it is load prediction relative deviation viDistribution probability curvilinear function;
(3) sale of electricity risk of loss is assessed:
If EtotalFor sale of electricity company sale of electricity risk of loss caused by load prediction deviation, then EtotalCalculation formula it is as follows:
Etotal=E1+E2 ⑥。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810746933.3A CN108694478A (en) | 2018-07-09 | 2018-07-09 | A kind of sale of electricity risk indicator computational methods considering power quantity predicting deviation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810746933.3A CN108694478A (en) | 2018-07-09 | 2018-07-09 | A kind of sale of electricity risk indicator computational methods considering power quantity predicting deviation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108694478A true CN108694478A (en) | 2018-10-23 |
Family
ID=63850448
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810746933.3A Pending CN108694478A (en) | 2018-07-09 | 2018-07-09 | A kind of sale of electricity risk indicator computational methods considering power quantity predicting deviation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108694478A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111080017A (en) * | 2019-12-20 | 2020-04-28 | 河南中睿和能源科技有限公司 | Electricity selling business integrated service system with electric quantity prediction function |
| CN111563766A (en) * | 2020-04-24 | 2020-08-21 | 广东卓维网络有限公司 | Electricity quantity deviation control system for electricity selling company |
| CN112036737A (en) * | 2020-08-28 | 2020-12-04 | 京东方科技集团股份有限公司 | Method and device for calculating regional electric quantity deviation |
| CN112906931A (en) * | 2019-12-04 | 2021-06-04 | 国网电力科学研究院有限公司 | Method and system for predicting short-term load of power selling company in electric power spot market |
| CN113706336A (en) * | 2021-09-01 | 2021-11-26 | 知能汇融(北京)咨询有限公司 | Risk assessment method and device, computer equipment and storage medium |
| CN113762225A (en) * | 2021-11-09 | 2021-12-07 | 博兴兴业精细化工产业发展有限公司 | Automatic monitoring alarm system for chemical workshop |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103489044A (en) * | 2013-09-26 | 2014-01-01 | 华东交通大学 | Smart-grid-orientated bidding power generation risk control method |
| CN106408186A (en) * | 2016-09-13 | 2017-02-15 | 国网福建省电力有限公司 | Method of evaluating a variety of market power purchase risks of provincial power grid containing wind power |
| CN106803145A (en) * | 2016-12-22 | 2017-06-06 | 国网重庆市电力公司电力科学研究院 | Grid company implements risk analysis and the bypassing method of Direct Purchase of Electric Energy by Large Users project |
| CN107085774A (en) * | 2017-05-16 | 2017-08-22 | 新奥泛能网络科技股份有限公司 | A kind of sale of electricity service platform method of evaluating performance and device |
-
2018
- 2018-07-09 CN CN201810746933.3A patent/CN108694478A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103489044A (en) * | 2013-09-26 | 2014-01-01 | 华东交通大学 | Smart-grid-orientated bidding power generation risk control method |
| CN106408186A (en) * | 2016-09-13 | 2017-02-15 | 国网福建省电力有限公司 | Method of evaluating a variety of market power purchase risks of provincial power grid containing wind power |
| CN106803145A (en) * | 2016-12-22 | 2017-06-06 | 国网重庆市电力公司电力科学研究院 | Grid company implements risk analysis and the bypassing method of Direct Purchase of Electric Energy by Large Users project |
| CN107085774A (en) * | 2017-05-16 | 2017-08-22 | 新奥泛能网络科技股份有限公司 | A kind of sale of electricity service platform method of evaluating performance and device |
Non-Patent Citations (1)
| Title |
|---|
| 方泉: "电力市场下电网公司的售电风险分析", 《中国优秀博硕士学位论文全文数据库(硕士)经济与管理科学辑》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112906931A (en) * | 2019-12-04 | 2021-06-04 | 国网电力科学研究院有限公司 | Method and system for predicting short-term load of power selling company in electric power spot market |
| CN111080017A (en) * | 2019-12-20 | 2020-04-28 | 河南中睿和能源科技有限公司 | Electricity selling business integrated service system with electric quantity prediction function |
| CN111563766A (en) * | 2020-04-24 | 2020-08-21 | 广东卓维网络有限公司 | Electricity quantity deviation control system for electricity selling company |
| CN112036737A (en) * | 2020-08-28 | 2020-12-04 | 京东方科技集团股份有限公司 | Method and device for calculating regional electric quantity deviation |
| CN112036737B (en) * | 2020-08-28 | 2024-01-09 | 京东方科技集团股份有限公司 | Method and device for calculating regional electric quantity deviation |
| CN113706336A (en) * | 2021-09-01 | 2021-11-26 | 知能汇融(北京)咨询有限公司 | Risk assessment method and device, computer equipment and storage medium |
| CN113706336B (en) * | 2021-09-01 | 2024-02-02 | 知能汇融(北京)咨询有限公司 | Risk assessment method, risk assessment device, computer equipment and storage medium |
| CN113762225A (en) * | 2021-11-09 | 2021-12-07 | 博兴兴业精细化工产业发展有限公司 | Automatic monitoring alarm system for chemical workshop |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108694478A (en) | A kind of sale of electricity risk indicator computational methods considering power quantity predicting deviation | |
| Seifert et al. | Identifying excesses and deficits in Chinese industrial productivity (1953–1990): a weighted data envelopment analysis approach | |
| CN111754116B (en) | Credit evaluation method and device based on label portrait technology | |
| CA2578085A1 (en) | System and method for flexible spread participation | |
| CN109961368A (en) | Data processing method and device based on machine learning | |
| Zhang et al. | Statistical arbitrage mining for display advertising | |
| Strang | Applied financial non-linear programming models for decision making | |
| JP2024512076A (en) | Asset class backed tokenization platform | |
| US9558490B2 (en) | Systems and methods for predicting a merchant's change of acquirer | |
| Fröwis et al. | The operational cost of Ethereum airdrops | |
| Makkonen et al. | Multi‐criteria decision support in the liberalized energy market | |
| JP2002304443A (en) | Electric power sales contract assisting device | |
| Ramachandra et al. | Design and simulation of loan approval prediction model using AWS platform | |
| SEİFODDİNİ | A multi-criteria approach to rating Metaverse games | |
| JP2014006578A (en) | Marketplace risk prediction device, marketplace risk prediction method, and marketplace risk prediction program | |
| KR102100322B1 (en) | Apparatus and method of trading management for demand response resource real-time | |
| US20090070187A1 (en) | Energy merchant exchange | |
| Macallan et al. | Assessing the risk to inflation from inflation expectations | |
| US20230162220A1 (en) | Ict-based inter-company network marketing system and operating method thereof | |
| CN113706215A (en) | Electric power credit investigation pricing method based on game | |
| Farouq et al. | Real exchange rate and economic growth: The interacting role of financial development in Nigeria | |
| JP2003296558A (en) | Method, device and program for optimally distributing business investment and portable recording medium | |
| Thygerson | Modeling branch profitability | |
| JP6121017B2 (en) | Financial institution management support system and program | |
| Sutopo et al. | Optimizing Supply Chain Collaboration Based on Agreement Buyer-Supplier Relationship with Network Design Problem |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181023 |