CN103501001A - Load curve alternating injection-based intelligent power distribution network scheduling system and method - Google Patents
Load curve alternating injection-based intelligent power distribution network scheduling system and method Download PDFInfo
- Publication number
- CN103501001A CN103501001A CN201310466722.1A CN201310466722A CN103501001A CN 103501001 A CN103501001 A CN 103501001A CN 201310466722 A CN201310466722 A CN 201310466722A CN 103501001 A CN103501001 A CN 103501001A
- Authority
- CN
- China
- Prior art keywords
- load curve
- power distribution
- load
- distribution network
- time period
- 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.)
- Granted
Links
Images
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a load curve alternating injection-based intelligent power distribution network scheduling system and a load curve alternating injection-based intelligent power distribution network scheduling method. According to the method, when an operation mode is generated, an adaptive power supply scheme is adopted for loads of different time intervals in consideration of difference of a workday load curve and a rest-day load curve, the reliability of power supply and the adaptability to load are improved, the network loss is reduced as much as possible, and the economy of power supply is improved. The generation of the operation mode is more flexible compared with the original strategy, and the utilization rate of power distribution equipment is practically improved.
Description
Technical field
The present invention relates to intelligent distribution network dispatching patcher and the method alternately injected based on a kind of load curve, belong to the intelligent grid field.
Background technology
Power distribution network is the intermediate link connected between power consumer and power transmission network, and the safety and economic operation of power distribution network directly affects fail safe, reliability and the economy of whole electric power system.Along with the raising of the increasing rapidly of electricity needs, power reguirements, the scale of power distribution network is more and more huger, wiring becomes increasingly complex, and realizes the Optimized Operation of power distribution network, and operational mode is carried out to the routine work that rational adjustment will become the dispatcher.
At present, two kinds of load levels are mainly considered in the establishment of power distribution network normal operating mode plan, i.e. general load level and meet kurtosis summer load level, so, form general supplying charge case and met kurtosis summer power supply plan.Generally, the operational mode of power distribution network does not adjust, meet kurtosis before the summer at first the ruuning situation to the previous year analyzed, heavy, the difficult circuit shifted of high spot reviews load, at balanced load, guarantee on many power supplys customer power supply basis, formulate adjustment scheme and the track remodelling scheme of operational mode.For important multiple power supplies user, require from two different power supply points lead-in wires, and to require higher level transformer station be not from same power supply, to the different ditches of the customer requirements 10kV inlet wire cable of particular importance, the different bars of overhead wire.Generally, do not change normal operational mode after load variations yet.
Can find out that this scheduling strategy exists some shortcomings.Because working day and festivals or holidays (comprising two-day weekend and other country's legal festivals and holidays) are also a kind of normal periods of change, its load has different Changing Patterns, therefore from the economy aspect of operation of power networks, considers these two time periods are separately discussed.
Take certain power distribution network as example, and wherein a feeder line at certain workaday load curve as shown in Figure 1.From 0 o'clock until 17: 30, load, always in reduced levels, changes quite mild; After this, load starts to increase fast, until 21 load peaks that occur a day, load value reaches 0.34, and load value starts to descend afterwards, until be down to 0.22 24 the time.At the load curve of certain festivals or holidays as shown in Figure 2, from 0 until about 10 loads in reduced levels; This afterload starts to increase, and increases to more than 0.25, and keep the long period on this level, until 14 points, this afterload reduces again, but amplitude is little; About 15: 45, load starts again to rise, and reaches top in a day about 19: 30, and load value reaches 0.35, and keeps the long period on this level, until 22: 45 loads start to descend.Can find out that daytime on working day load level is lower, change gently, and, on next peak, namely 17 30/afterloads rise rapidly, and reach peak of power consumption at 21, and the peak-valley difference of a day is relatively large.Increase working day and load to compare on daytime festivals or holidays, occur the little peak of electricity consumption in a period of time therein, the electricity consumption top appears at 19: 30, and the time that power load starts to increase is compared and shifts to an earlier date to some extent working day.
Existing power distribution network scheduling strategy has only been considered ordinary circumstance and has been met kurtosis summer two kinds of load levels and Changing Pattern is formulated normal operating mode, does not consider the difference of load at different time sections internal loading rule and load curve.The variation of the inadaptable load of such scheduling strategy, caused the waste of electric energy, reduced power supplying efficiency, will be unfavorable for the economical operation of electrical network.Therefore need a kind of new scheduling strategy, this factor of difference by load at different time sections internal loading rule and load curve takes into account, to improve the economy of electrical network.
Summary of the invention
Goal of the invention: the present invention proposes intelligent distribution network dispatching patcher and the method alternately injected based on load curve, by distinguishing the load curve of working day and festivals or holidays, the better power distribution network operational mode of selection adaptation.
Technical scheme: the technical solution used in the present invention is the intelligent distribution network dispatching method alternately injected based on load curve, comprises the following steps:
1) electrical power distribution automatization system collection distribution transformer load data and busbar voltage Data Concurrent are given the load curve maker, by the load curve maker, use existing Forecasting Methodology to predict the load data of every day next month;
2) the load curve maker will be divided into time period on working day and time period festivals or holidays each week of next month, and calculate each load data mean value constantly of each distribution transforming in each time period, then the average load curve that carries out curve fitting and obtain each each distribution transforming of time period, finally by working day average load curve and the average load curve festivals or holidays staggered network reconfiguration system that is injected into successively;
3) network reconfiguration system carries out dynamic restructuring based on multi-objective particle swarm algorithm after receiving each distribution transforming average load curve of injection, selects a kind of operational mode of via net loss minimum, until do not receive the average load curve of injection.
As a modification of the present invention, described step 2) in, the time period comprises n days (n=5 or 2), contains m moment point (m=24 or 96) every day, according to the following formula each moment point in the time period is averaging:
P wherein
xibe the load data of x days i moment point, i=1,2,3 ... m.
As of the present invention another improve, also comprise that operational mode that step 4) obtains according to step 3) implements the operation of power distribution network successively.
The intelligent distribution network dispatching patcher of alternately injecting based on load curve, by voltage transformer, current transformer, electrical power distribution automatization system, load curve maker and network reconfiguration system form, power distribution network information on load and information of voltage that wherein electrical power distribution automatization system received current instrument transformer and voltage transformer are sent, and send to the load curve maker, generate distribution transforming average load curve by the load curve maker, then send to network reconfiguration system, after drawing optimum operating mode by the network reconfiguration system computing, transfer to electrical power distribution automatization system and form concrete power distribution network operation.
Beneficial effect: the present invention is when the generating run mode, both considered working day load curve and day off load curve difference, load in different time sections is taked to the power supply plan adapted, reliability and the adaptability to loading of power supply have been improved, reduce as far as possible again via net loss, improved the economy of power supply.Make the generation of operational mode compare original strategy more flexible, and conscientiously improved the utilance of controller switching equipment.
The accompanying drawing explanation
Fig. 1 is the load chart on working day;
Fig. 2 is load charts festivals or holidays;
The workflow diagram that Fig. 3 is dispatching method in the present invention;
The structural representation that Fig. 4 is dispatching patcher in the present invention.
Embodiment
Below in conjunction with the drawings and specific embodiments, further illustrate the present invention, should understand these embodiment only is not used in and limits the scope of the invention for the present invention is described, after having read the present invention, those skilled in the art all fall within the application's claims limited range to the modification of various equivalents of the present invention.
As shown in Figure 1, at first electrical power distribution automatization system gathers distribution transformer load data and busbar voltage data, and send to the load curve maker, then by load curve maker basis, had now the load forecasting method of linear regression, the load data of predicting every day next month.
Within one month, comprised for four weeks, seven days weeks were divided into to time period on working day and time period festivals or holidays, wherein working day, the time period was comprised of Mon-Fri, and time period festivals or holidays is by forming on Saturday and Sunday.To be divided into accordingly 4 time periods on working day and 4 time periods festivals or holidays next month, totally 8 time periods.Make each time period comprise n days (n=5 or 2), and, to the load data discretization of every day, obtain the load of m moment point (m=24 or 96) in a day.
The load of each moment point in each time period can be averaging, and the definition average load of i moment point in a period of time is p
i,
P wherein
xibe the load data of x days i moment point, i=1,2,3 ... m.
So just can calculate the average load of 24 or 96 moment point in the time period.The average load of these 24 or 96 moment point is fitted within on a curve, just obtained the average load curve in this time period.According to aforesaid method, 8 time periods difference digital simulations to next month, just obtained 8 groups of average load curves.
Working day the average load curve and festivals or holidays the average load curve staggeredly successively inject following network reconfiguration system.So-called staggered injection the successively is exactly according to average load curve-festivals or holidays average load curve-working day average load curve-festivals or holidays average load curve-working day average load curve-festivals or holidays average load curve-working day average load curve-average load curve festivals or holidays injection network reconfiguration system so sequentially on working day.
The average load curve that network reconfiguration system injects each group, all adopt multi-objective particle swarm algorithm to carry out dynamic restructuring, selects a kind of operational mode of via net loss minimum, so select altogether 8 kinds of operational modes next month.Just can implement power distribution network according to resulting 8 kinds of operational modes has operated.
The intelligent distribution network dispatching patcher of alternately injecting based on load curve, comprise voltage transformer, current transformer, electrical power distribution automatization system, load curve maker and network reconfiguration system.Current transformer and voltage transformer obtain information on load and the bus information of of that month every day from power distribution network, send to electrical power distribution automatization system, and electrical power distribution automatization system sends to the load curve maker after receiving information.In the load curve maker, the information on load of of that month every day carried out to linear regression, and predict the load of every day next month.By front described, within one month, comprised for four weeks, seven days weeks were divided into to time period on working day and time period festivals or holidays, wherein working day, the time period was comprised of Mon-Fri, and time period festivals or holidays is by forming on Saturday and Sunday.To be divided into accordingly 4 time periods on working day and 4 time periods festivals or holidays next month, totally 8 time periods.The load curve maker can be averaging each moment load in each time period of next month:
P in above formula
xibe the load data of x days i moment point, i=1,2,3 ... m, n=2 or 5.
After completing above-mentioned calculating, the load curve maker generates 8 groups of average load curves according to above-mentioned data, and sends to network reconfiguration system.
To receive working day the average load curve and festivals or holidays the average load curve staggered carry out network reconfiguration, according to working day the average load curve-festivals or holidays average load curve-working day average load curve-such order of average load curve festivals or holidays carry out successively network reconfiguration.By network reconfiguration, select a kind of operational mode of via net loss minimum to every group of average load curve, so select altogether 8 kinds of operational modes next month.Finally these 8 kinds of operational modes are sent to electrical power distribution automatization system to form concrete power distribution network operation by it.
Claims (4)
1. the intelligent distribution network dispatching method alternately injected based on load curve, is characterized in that, comprises the following steps:
1) electrical power distribution automatization system collection distribution transformer load data and busbar voltage Data Concurrent are given the load curve maker, by the load curve maker, use existing Forecasting Methodology to predict the load data of every day next month;
2) the load curve maker will be divided into time period on working day and time period festivals or holidays each week of next month, and calculate each load data mean value constantly of each distribution transforming in each time period, then the average load curve that carries out curve fitting and obtain each each distribution transforming of time period, finally by working day average load curve and the average load curve festivals or holidays staggered network reconfiguration system that is injected into successively;
3) network reconfiguration system carries out dynamic restructuring based on multi-objective particle swarm algorithm after receiving each distribution transforming average load curve of injection, selects a kind of operational mode of via net loss minimum, until do not receive the average load curve of injection.
2. by the intelligent distribution network dispatching method alternately injected based on load curve claimed in claim 1, it is characterized in that, described step 2) in, the time period comprises n days (n=5 or 2), contain m moment point (m=24 or 96) every day, according to the following formula each moment point in the time period be averaging:
P wherein
xibe the load data of x days i moment point, i=1,2,3 ... m.
3. by the intelligent distribution network dispatching method alternately injected based on load curve claimed in claim 1, it is characterized in that, also comprise that operational mode that step 4) obtains according to step 3) implements the operation of power distribution network successively.
4. a power distribution network dispatching patcher that realizes the described dispatching method of any one in claims 1 to 3, it is characterized in that, by voltage transformer, current transformer, electrical power distribution automatization system, load curve maker and network reconfiguration system form, power distribution network information on load and information of voltage that wherein electrical power distribution automatization system received current instrument transformer and voltage transformer are sent, and send to the load curve maker, generate distribution transforming average load curve by the load curve maker, then send to network reconfiguration system, after drawing optimum operating mode by the network reconfiguration system computing, transfer to electrical power distribution automatization system and form concrete power distribution network operation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310466722.1A CN103501001B (en) | 2013-10-09 | 2013-10-09 | Load curve alternating injection-based intelligent power distribution network scheduling system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310466722.1A CN103501001B (en) | 2013-10-09 | 2013-10-09 | Load curve alternating injection-based intelligent power distribution network scheduling system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103501001A true CN103501001A (en) | 2014-01-08 |
| CN103501001B CN103501001B (en) | 2015-06-03 |
Family
ID=49866184
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310466722.1A Active CN103501001B (en) | 2013-10-09 | 2013-10-09 | Load curve alternating injection-based intelligent power distribution network scheduling system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103501001B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200276A (en) * | 2014-07-14 | 2014-12-10 | 河海大学 | Intelligent power distribution network reconstructing method based on characteristic load injection |
| CN104505860A (en) * | 2014-12-30 | 2015-04-08 | 中国海洋石油总公司 | Maritime smart power grid load analyzing and dispatching system |
| CN105279596A (en) * | 2014-09-01 | 2016-01-27 | 国家电网公司 | Intelligent power-limiting power regulating method |
| CN105701717A (en) * | 2015-11-11 | 2016-06-22 | 东南大学 | Power distribution network interactive solution programming method based on improved genetic algorithm |
| CN106849109A (en) * | 2017-03-15 | 2017-06-13 | 国网江苏省电力公司连云港供电公司 | A kind of urban distribution network load control method accessed for scale charging pile |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012127595A1 (en) * | 2011-03-18 | 2012-09-27 | 富士通株式会社 | Power-leveling control apparatus, power leveling power-storage apparatus, power-leveling control method, and leveling program |
| CN103036231A (en) * | 2012-12-07 | 2013-04-10 | 温州电力局 | Forecasting method, device, and upper computer of power load |
| CN103151805A (en) * | 2013-03-28 | 2013-06-12 | 武汉大学 | Method for optimizing and configuring power supply of grid-connection-mode microgrid |
| CN103311942A (en) * | 2013-03-18 | 2013-09-18 | 国家电网公司 | Control method of battery energy storage system for peak clipping and valley filling in distribution network |
-
2013
- 2013-10-09 CN CN201310466722.1A patent/CN103501001B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012127595A1 (en) * | 2011-03-18 | 2012-09-27 | 富士通株式会社 | Power-leveling control apparatus, power leveling power-storage apparatus, power-leveling control method, and leveling program |
| CN103036231A (en) * | 2012-12-07 | 2013-04-10 | 温州电力局 | Forecasting method, device, and upper computer of power load |
| CN103311942A (en) * | 2013-03-18 | 2013-09-18 | 国家电网公司 | Control method of battery energy storage system for peak clipping and valley filling in distribution network |
| CN103151805A (en) * | 2013-03-28 | 2013-06-12 | 武汉大学 | Method for optimizing and configuring power supply of grid-connection-mode microgrid |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200276A (en) * | 2014-07-14 | 2014-12-10 | 河海大学 | Intelligent power distribution network reconstructing method based on characteristic load injection |
| CN104200276B (en) * | 2014-07-14 | 2017-12-05 | 河海大学 | A kind of intelligent distribution network reconstructing method of feature based load injection |
| CN105279596A (en) * | 2014-09-01 | 2016-01-27 | 国家电网公司 | Intelligent power-limiting power regulating method |
| CN104505860A (en) * | 2014-12-30 | 2015-04-08 | 中国海洋石油总公司 | Maritime smart power grid load analyzing and dispatching system |
| CN104505860B (en) * | 2014-12-30 | 2016-07-13 | 中国海洋石油总公司 | A kind of marine intelligent grid load Analysis dispatching patcher |
| CN105701717A (en) * | 2015-11-11 | 2016-06-22 | 东南大学 | Power distribution network interactive solution programming method based on improved genetic algorithm |
| CN105701717B (en) * | 2015-11-11 | 2020-06-26 | 东南大学 | Power distribution network interaction scheme compilation method based on improved genetic algorithm |
| CN106849109A (en) * | 2017-03-15 | 2017-06-13 | 国网江苏省电力公司连云港供电公司 | A kind of urban distribution network load control method accessed for scale charging pile |
| CN106849109B (en) * | 2017-03-15 | 2019-06-25 | 国网江苏省电力公司连云港供电公司 | A kind of urban distribution network load control method for the access of scale charging pile |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103501001B (en) | 2015-06-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | Active power regulation for large-scale wind farms through an efficient power plant model of electric vehicles | |
| Kavousi-Fard et al. | Efficient integration of plug-in electric vehicles via reconfigurable microgrids | |
| Hanemann et al. | Effects of electric vehicle charging strategies on the German power system | |
| Xu et al. | Adequacy and economy analysis of distribution systems integrated with electric energy storage and renewable energy resources | |
| US8886362B2 (en) | Integrated distribution system optimization | |
| CN103501001B (en) | Load curve alternating injection-based intelligent power distribution network scheduling system and method | |
| Romo et al. | Power quality of actual grids with plug-in electric vehicles in presence of renewables and micro-grids | |
| Gazijahani et al. | Joint energy and reserve scheduling of renewable powered microgrids accommodating price responsive demand by scenario: a risk-based augmented epsilon-constraint approach | |
| Yan et al. | Robust optimization-based energy storage operation for system congestion management | |
| CN102509167B (en) | Photovoltaic power generation and energy management system based on virtual power plant | |
| CN106992544A (en) | Microgrid energy real-time management control method | |
| Chen et al. | Evaluation of advantages of an energy storage system using recycled EV batteries | |
| Kiaee et al. | Utilisation of alkaline electrolysers in existing distribution networks to increase the amount of integrated wind capacity | |
| Sheikhi et al. | Distributed generation penetration impact on distribution networks loss | |
| Mahesh et al. | Optimal sizing of photovoltaic/wind/battery hybrid renewable energy system including electric vehicles using improved search space reduction algorithm | |
| Ireshika et al. | Autonomous demand side management of electric vehicles in a distribution grid | |
| CN104268668A (en) | Low-carbon optimized dispatching method for closed loop | |
| Yuvaraj et al. | A comprehensive review and analysis of the allocation of electric vehicle charging stations in distribution networks | |
| Denysiuk et al. | Optimization of consumption of primary fuel in local electricity systems using diesel generators | |
| CN105608635A (en) | Economical scheduling system and method for regional power grid based on peak and valley electricity prices | |
| Georgiev et al. | Optimized power flow control of smart grids with electric vehicles and DER | |
| Golla et al. | Optimal placement of charging station and distributed generator along with scheduling in distribution system using arithmetic optimization algorithm | |
| Infante et al. | Multistakeholder planning and operational strategy for electric vehicle battery swapping stations | |
| Luna et al. | Generation and demand scheduling for a grid-connected hybrid microgrid considering price-based incentives | |
| Celli et al. | Reference scenarios for Active Distribution System according to ATLANTIDE project planning models |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C41 | Transfer of patent application or patent right or utility model | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20150311 Address after: 211100 Nanjing City, Jiangning Province, West Road, Buddha District, No. 8 Applicant after: HOHAI University Applicant after: State Grid Corporation of China Applicant after: JIANGSU NANJING POWER SUPPLY Co. Applicant after: JIANGSU ELECTRIC POWER Co. Address before: 211100 Nanjing City, Jiangning Province, West Road, Buddha District, No. 8 Applicant before: HOHAI University Applicant before: NANJING HEHAI TECHNOLOGY Ltd. Applicant before: State Grid Corporation of China Applicant before: JIANGSU NANJING POWER SUPPLY Co. Applicant before: JIANGSU ELECTRIC POWER Co. |
|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |