CN106650986A - Method and device for forecasting regional maximum loads of power distribution network - Google Patents
Method and device for forecasting regional maximum loads of power distribution network Download PDFInfo
- Publication number
- CN106650986A CN106650986A CN201610820707.6A CN201610820707A CN106650986A CN 106650986 A CN106650986 A CN 106650986A CN 201610820707 A CN201610820707 A CN 201610820707A CN 106650986 A CN106650986 A CN 106650986A
- Authority
- CN
- China
- Prior art keywords
- load
- type
- peak
- nature
- white
- 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
- 238000000034 method Methods 0.000 title claims abstract description 25
- 230000005611 electricity Effects 0.000 claims description 35
- 238000012937 correction Methods 0.000 claims description 8
- 238000004519 manufacturing process Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 abstract description 2
- 238000013461 design Methods 0.000 abstract description 2
- 229920006395 saturated elastomer Polymers 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 239000000686 essence Substances 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000000844 transformation 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
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a method and a device for forecasting the regional maximum loads of a power distribution network. According to the technical scheme of the invention, the white and black peak maximum load superimposition maximum-seeking method is conducted. That means, the maximum loads of two peak periods in a region in the day and at night are respectively calculated. According to the calculation result, a maximum value is obtained as a planned and calculated maximum load value for the above region. Based on the method and the device for forecasting the regional maximum loads of the power distribution network, the standard and scientific regional maximum loads of the power distribution network are provided. Therefore, the unified compilation of modular, standardized and typical design schemes for the power distribution network is facilitated, wherein the power region classification, the saturated load density, the load maturity and other grid features are adopted as applicability signs.
Description
Technical field
The invention belongs to technical field of power systems, more particularly to a kind of distribution web area peak load Forecasting Methodology and dress
Put.
Background technology
The " three high " need for electricity that urban power distribution network is presented in recent years the high quality of power supply, high reliability and high load capacity density is special
Levy, requirements at the higher level are proposed to urban electric power supply.
At present, with history self-assembling formation and random continuous extension urban power distribution network：
1. municipal a large amount of transformations and strict limitation and restriction of the landscaping to power channel are faced；
2. it must is fulfilled for the newly-increased need for electricity of newly-built business and residential quarters.
Continue to continue to use and traditionally develop and the way of construction distribution line with load, random big, efficiency is low, adaptability
Difference, redundancy is little, repeats transformation, builds many, urban development has been not suitable with, in awkward, dilemma.And root
Originally electric power enterprise internal stiffening pipes reason cannot be met, it is ensured that personal safety, power grid security and high reliability are powered the realization of target,
And the needs of quickening distribution science and technology new equipment application.
Therefore, city power distribution gridding planing method is taken at present, i.e., with plot need for electricity to be oriented to, carry out system
Change load prediction, while according to the Planning Standard of power supply area differentiation, so that power grid is elementary cell, by medium voltage distribution network
It is built into the planing method of target net.And described gridding planning, it is necessary to systematization load prediction as foundation, could shape
Into including including rack, site, passage, communication, automation, protection and informationization, towards the distribution for () electricity reliability
The overall planning method of net programme.
Therefore a kind of standard, the region peak load Forecasting Methodology of science are needed badly.
The content of the invention
In view of this, the present invention proposes a kind of distribution web area peak load Forecasting Methodology and device, it is intended to solve distribution
The systematization load prediction problem in net gridding region.
To reach above-mentioned purpose, what the technical scheme of the invention was realized in：
A kind of distribution web area peak load Forecasting Methodology, it is characterised in that：Employ white black peak peak load superposition
Most method is sought, i.e., is calculated the region peak load of two peak periods of daytime and evening respectively, and its maximum is taken according to result and make
For the planning calculating peak load value of one's respective area；
Computing formula is：Pmax=MAX (P is white, and P is black).
Further,
P is white=∑ (the white i of Psumi*K) (i=1,5, i are " continuing load nature of electricity consumed type ")
P is black=∑ (the black i of Psumi*K) (i=1,5, i are " continuing load nature of electricity consumed type ")
Psumi=∑s (Pj) (j=0, n, j are " to continue load nature of electricity consumed type " in region for the plot of i)；
Pj=plot j distant view load values；
The white i of K：I continues load nature of electricity consumed type daytime period load correction factor；
The black i of K：I continues load nature of electricity consumed type load correction factor the period in the evening.
Wherein, it is described continue load nature of electricity consumed type include continuous type of production, commerce services type, public service-oriented, residential,
The big class of routine work type 5.
Further, each " white black period corrected parameter " for continuing load nature of electricity consumed type be as in the table below：
Each " the white black period corrected parameter for continuing load nature of electricity consumed type of table 1
Further, the computing formula of the distant view load value is：
P=load density target * block areas (not containing large user) * plot ratio * demand factor * simultaneity factors+∑ P large user
M (m=0, n, m are intramassif large user's sequence number).
Another aspect of the present invention, a kind of distribution web area peak load Forecasting Methodology device, including：
Region peak load calculates module, comparison module, for calculating the area of two peak periods of daytime and evening respectively
Domain peak load, and its maximum is taken as the planning calculating peak load value of one's respective area according to result.
Further, the region peak load calculates module includes Psum modules and correction factor module, distant view load
Value computing module, for calculating the load sum of lasting load nature of electricity consumed type.
Relative to prior art, the distribution web area peak load Forecasting Methodology and device of the present invention, using the teaching of the invention it is possible to provide standard,
The region peak load prediction of science, is conducive to unified organizational system with territorial classification of powering, saturation loading density, load maturity etc.
Grid search-engine is applicability mark, modular, standardized typical distribution net design.
Description of the drawings
The accompanying drawing for constituting the part of the present invention is used for providing a further understanding of the present invention, the schematic reality of the present invention
Apply example and its illustrate, for explaining the present invention, not constituting inappropriate limitation of the present invention.In the accompanying drawings：
Fig. 1 is embodiment of the present invention typical case's daily load indicatrix (continuous type of production)；
Fig. 2 is embodiment of the present invention typical case's daily load indicatrix (commerce services type)；
Fig. 3 is embodiment of the present invention typical case's daily load indicatrix (public service-oriented)；
Fig. 4 is embodiment of the present invention typical case's daily load indicatrix (residential)；
Fig. 5 is embodiment of the present invention typical case's daily load indicatrix (routine work type).
Specific embodiment
It should be noted that in the case where not conflicting, the feature in embodiments of the invention and embodiment can be mutual
Combination.
Below with reference to the accompanying drawings and in conjunction with the embodiments describing the present invention in detail.
Classified according to based model for load duration load nature of electricity consumed, electric load can be divided into：Continuous type of production (two, three class industrial lands),
Commerce services type (containing lands used such as commercial facility, culture, physical culture, universities and colleges), public service-oriented (health care, transport hub
Land used), residential (residential estate), routine work type (other lands used outside above-mentioned 4 class) 5 big class, load characteristic such as Fig. 1-Fig. 5
It is shown.
Each load peak period for continuing load nature of electricity consumed type is as shown in the table：
It is all kinds of to use the typical Value Types load peak period
| Sequence number | Continue load nature of electricity consumed | The load peak period | Remarks (generally corresponding land character) |
| 1 | Continuous type of production | 0～23 | 2nd, three class industrial land |
| 2 | Commerce services type | 9～22 | Containing lands used such as commercial facility, culture, physical culture, universities and colleges |
| 3 | Public service-oriented | 9～20 | The lands used such as health care, transport hub |
| 4 | Residential | 18～21 | Residential estate |
| 5 | Routine work type | 10～17 | Other lands used outside above-mentioned 4 class |
From upper table it can be clearly seen that " residential " has good electricity consumption mistake with " routine work type " plot electricity consumption
Peak phenomenon.Meanwhile, when can be seen that " residential " peak from corresponding load characteristic curve, " routine work type " load only has
The 40% of peak load；And during " routine work type " peak, " residential " load only has the 20% of peak load.
1st, region peak load Forecasting Methodology
According to load curve indicatrix, the load peak in a region must be occurred when day or night two is big
One of section, therefore, with reference to the needs that planning is calculated with peak load, when we can respectively calculate two peaks of daytime and evening
The region peak load of section, and its maximum is taken as the planning calculating peak load value of one's respective area according to result.
" most method is sought in white black peak peak load superposition " specific formula for calculation is as follows：
Pmax=MAX (P is white, and P is black)
Wherein：
P is white=∑ (the white i of Psumi*K) (i=1,5, i are " continuing load nature of electricity consumed type ")
P is black=∑ (the black i of Psumi*K) (i=1,5, i are " continuing load nature of electricity consumed type ")
Psumi=∑s (Pj) (j=0, n, j are " to continue load nature of electricity consumed type " in region for the plot of i)；
Pj=plot j distant view load values；
The white i of K：I continues load nature of electricity consumed type daytime period load correction factor；
The black i of K：I continues load nature of electricity consumed type load correction factor the period in the evening.
According to typical load indicatrix, each " white black period corrected parameter " for continuing load nature of electricity consumed type is such as following table institute
State：
Each " white black period corrected parameter " for continuing load nature of electricity consumed type
| Sequence number | Continue load nature of electricity consumed | K is white | K is black |
| 1 | Continuous type of production | 1 | 0.9 |
| 2 | Commerce services type | 0.8 | 1 |
| 3 | Public service-oriented | 1 | 0.6 |
| 4 | Residential | 0.2 | 1 |
| 5 | Routine work type | 1 | 0.4 |
2nd, distant view distributed load Forecasting Methodology
(total rule) are advised in detail according to land used, and chooses the load density target and simultaneity factor of respective horizontal and be calculated distant view point
Cloth load prediction, collects and obtains the future load forecast result in the region, while individually being considered large user in region.
The computing formula for having the plot distant view load of planned land use is：
P=load density target * block areas (not containing large user) * plot ratio * demand factor * simultaneity factors+∑ P large user
I (i=0, n, i are intramassif large user's sequence number).
Presently preferred embodiments of the present invention is the foregoing is only, not to limit the present invention, all essences in the present invention
Within god and principle, any modification, equivalent substitution and improvements made etc. should be included within the scope of the present invention.
Claims (6)
1. a kind of distribution web area peak load Forecasting Methodology, it is characterised in that：Employ white black peak peak load superposition to ask
Most method, i.e., calculate respectively the region peak load of two peak periods of daytime and evening, and takes its maximum conduct according to result
The planning calculating peak load value of one's respective area；
Computing formula is：Pmax=MAX (P is white, and P is black).
2. a kind of distribution web area peak load Forecasting Methodology according to claim 1, it is characterised in that：
P is white=∑ (the white i of Psumi*K) (i=1,5, i are " continuing load nature of electricity consumed type ")
P is black=∑ (the black i of Psumi*K) (i=1,5, i are " continuing load nature of electricity consumed type ")
Psumi=∑s (Pj) (j=0, n, j are " to continue load nature of electricity consumed type " in region for the plot of i)；
Pj=plot j distant view load values；
The white i of K：I continues load nature of electricity consumed type daytime period load correction factor；
The black i of K：I continues load nature of electricity consumed type load correction factor the period in the evening.
Wherein, the load nature of electricity consumed type of continuing includes continuous type of production, commerce services type, public service-oriented, residential, daily
The big class of work type 5.
3. a kind of distribution web area peak load Forecasting Methodology according to claim 2, it is characterised in that：It is each to continue electricity consumption
" the white black period corrected parameter " of property type is as in the table below：
Each " the white black period corrected parameter for continuing load nature of electricity consumed type of table 1
4. a kind of distribution web area peak load Forecasting Methodology according to claim 2, it is characterised in that：The distant view is born
The computing formula of charge values is：
P=load density target * block areas (not containing large user) * plot ratio * demand factor * simultaneity factors+∑ P large user m (m
=0, n, m are intramassif large user's sequence number).
5. a kind of distribution web area peak load Forecasting Methodology device, it is characterised in that include：
Region peak load calculates module, comparison module, for calculate two peak periods of daytime and evening respectively region most
Big load, and its maximum is taken as the planning calculating peak load value of one's respective area according to result.
6. a kind of distribution web area peak load Forecasting Methodology device according to claim 5, it is characterised in that the area
Domain peak load calculates module includes Psum modules and correction factor module, distant view load value computing module, continues for calculating
The load sum of load nature of electricity consumed type.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610820707.6A CN106650986A (en) | 2016-09-13 | 2016-09-13 | Method and device for forecasting regional maximum loads of power distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610820707.6A CN106650986A (en) | 2016-09-13 | 2016-09-13 | Method and device for forecasting regional maximum loads of power distribution network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106650986A true CN106650986A (en) | 2017-05-10 |
Family
ID=58851849
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610820707.6A Pending CN106650986A (en) | 2016-09-13 | 2016-09-13 | Method and device for forecasting regional maximum loads of power distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106650986A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108345954A (en) * | 2017-11-16 | 2018-07-31 | 北京四方继保自动化股份有限公司 | Distribution network line short-term load forecasting method and device |
| CN109559250A (en) * | 2018-12-10 | 2019-04-02 | 国网浙江省电力有限公司 | A kind of city power distribution net gridding planing method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1828645A (en) * | 2006-04-07 | 2006-09-06 | 上海电力学院 | Town power distribution network simultaneity factor load prediction method |
| CN102402726A (en) * | 2011-11-04 | 2012-04-04 | 中国电力科学研究院 | Method for predicting electric quantity of large-scale distribution network based on regional load analysis |
| CN105389625A (en) * | 2015-10-27 | 2016-03-09 | 福建奥通迈胜电力科技有限公司 | Active distribution network ultra-short term load prediction method |
-
2016
- 2016-09-13 CN CN201610820707.6A patent/CN106650986A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1828645A (en) * | 2006-04-07 | 2006-09-06 | 上海电力学院 | Town power distribution network simultaneity factor load prediction method |
| CN102402726A (en) * | 2011-11-04 | 2012-04-04 | 中国电力科学研究院 | Method for predicting electric quantity of large-scale distribution network based on regional load analysis |
| CN105389625A (en) * | 2015-10-27 | 2016-03-09 | 福建奥通迈胜电力科技有限公司 | Active distribution network ultra-short term load prediction method |
Non-Patent Citations (1)
| Title |
|---|
| 张鹏超: "基于单位建筑面积负荷指标法的城市饱和负荷预测研究", 《湖北电力》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108345954A (en) * | 2017-11-16 | 2018-07-31 | 北京四方继保自动化股份有限公司 | Distribution network line short-term load forecasting method and device |
| CN109559250A (en) * | 2018-12-10 | 2019-04-02 | 国网浙江省电力有限公司 | A kind of city power distribution net gridding planing method |
| CN109559250B (en) * | 2018-12-10 | 2020-12-01 | 国网浙江省电力有限公司 | Grid planning method for urban power distribution network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Unver | Southeastern Anatolia Project (GAP) | |
| Scognamiglio | ‘Photovoltaic landscapes’: Design and assessment. A critical review for a new transdisciplinary design vision | |
| Tegou et al. | Environmental management framework for wind farm siting: Methodology and case study | |
| Okedu et al. | Renewable energy in Nigeria: The challenges and opportunities in mountainous and riverine regions | |
| Kwon et al. | A superstructure model of an isolated power supply system using renewable energy: Development and application to Jeju Island, Korea | |
| Dey et al. | Demand‐side management based optimal scheduling of distributed generators for clean and economic operation of a microgrid system | |
| Rabe et al. | Sustainable regional energy planning: The case of hydro | |
| Sedghamiz et al. | A game theory approach for conjunctive use optimization model based on virtual water concept | |
| CN108711878A (en) | Consider the photovoltaic plant polymorphic type component capacity collocation method of power producing characteristics | |
| Zhu et al. | Optimization of photovoltaic provision in a three-dimensional city using real-time electricity demand | |
| CN106650986A (en) | Method and device for forecasting regional maximum loads of power distribution network | |
| Zhao et al. | Analysis of food‐energy‐water nexus based on competitive uses of stream flows of BeiChuan River in eastern QingHai‐Tibet Plateau, China | |
| Medvedeva et al. | Application of Green Technologies in Irrigation | |
| Choi et al. | Sustainable agricultural growth for rural development in Asia: a review | |
| Laoharojanaphand et al. | Optimal power scheduling of hydropower with co-located floating solar and wind generation using stochastic weight tradeoff chaotic particle swarm optimization | |
| Vajjhala et al. | Green corridors: Linking interregional transmission expansion and renewable energy policies | |
| Bardis et al. | Simulation and Evaluation of a Hybrid Renewable Energy System for Supplying a Desalination Unit on the Island of Lipsi, Greece | |
| Qamili et al. | Evaluation and Integration of Photovoltaic (PV) Systems in Albanian Energy Landscape | |
| Gupta et al. | Environmental and technoeconomic aspects of distributed generation | |
| Mutanga et al. | Cities as green economy drivers | |
| Aytemiz | The optimal joint provision of water for irrigation and hydropower in the Euphrates River: the case of conflict between Turkey and Syria | |
| Ariyaningsih et al. | Urban Metabolism—An Approach for Enhancing Resilience | |
| McCoy et al. | Community Solar: Strategies and Implementation for Sustainability | |
| Mesonada et al. | Land, water and energy: The crossing of governance | |
| Ugwoke et al. | The Integrated Rural Renewable and Sustainable Energy Planning Framework for Low-Income Countries |
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 |
Application publication date: 20170510 |
|
| RJ01 | Rejection of invention patent application after publication |