CN107017668A - A kind of virtual plant power regulating method based on wind energy and luminous energy - Google Patents

A kind of virtual plant power regulating method based on wind energy and luminous energy Download PDF

Info

Publication number
CN107017668A
CN107017668A CN201710167321.4A CN201710167321A CN107017668A CN 107017668 A CN107017668 A CN 107017668A CN 201710167321 A CN201710167321 A CN 201710167321A CN 107017668 A CN107017668 A CN 107017668A
Authority
CN
China
Prior art keywords
power
plant
wind
output
curve
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
Application number
CN201710167321.4A
Other languages
Chinese (zh)
Inventor
周建其
钱伟杰
郁家麟
金烨
曾建梁
孙峰
郑琦
张代红
褚明华
沈海华
刘伟
朱晶亮
李春
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiaxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Original Assignee
Jiaxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Jiaxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd filed Critical Jiaxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Priority to CN201710167321.4A priority Critical patent/CN107017668A/en
Publication of CN107017668A publication Critical patent/CN107017668A/en
Pending legal-status Critical Current

Links

Classifications

    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/382
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/50Photovoltaic [PV] energy
    • Y02E10/56Power conversion systems, e.g. maximum power point trackers
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/70Wind energy
    • Y02E10/76Power conversion electric or electronic aspects

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a kind of virtual plant power regulating method based on wind energy and luminous energy, comprise the following steps:Obtain the power curve of wind power plant and photovoltaic generation factory;Calculate fluctuation degree C1 of the power curve of wind power plant in prediction period;Calculate fluctuation degree C2 of the power curve of photovoltaic generation factory in prediction period;Obtain the average demand power P in prediction period;Wind power plant's power output and photovoltaic generation factory power output are calculated according to C1, C2 and P;According to the power output for calculating each power plant of gained adjustment.This programme is in the case where meeting average demand power, and the power curve fluctuation degree according to each power plant calculates most gentle power output, so that total output reaches that most steadily, power network fluctuation is minimum, the impact brought because of generated output change is also minimum.This programme is applied to new energy control field.

Description

A kind of virtual plant power regulating method based on wind energy and luminous energy
Technical field
The present invention relates to New-energy power system field, more particularly, to a kind of virtual plant work(based on wind energy and luminous energy Rate adjusting method.
Background technology
Virtual plant refers to distributed power source, controllable burden and energy storage device in power network by distributed power management system A virtual controllable aggregate is aggregated into, the operation and scheduling of power network is participated in, coordinated between intelligent grid and distributed power source Contradiction, fully excavates value and benefit that distributed energy is brought by power network and user.
In the pure isolated area powered by virtual plant, the power output for suitably allocating each power plant is balance cost and receipts The important step of benefit.
State Intellectual Property Office of the People's Republic of China disclosed entitled on 07 02nd, 2014《A kind of virtual plant Stratified random Optimization Scheduling》Patent document (publication number:CN103903066A), it is initially set up including upper-level virtual electricity Two layers of coordination optimization scheduling model of virtual plant of factory's layer and lower floor's micro-capacitance sensor layer, wherein each micro-capacitance sensor Optimal Operation Model of lower floor For Chance-constrained Model, probability distribution of being exerted oneself under uncontrollable micro battery separate state is described using empirical distribution function in model, And set up joint probability distribution model according to uncontrollable micro battery joint histogram selection Copula functions.Then it is flat using sampling Virtual plant is coordinated and optimized scheduling model for two layers and is converted into individual layer deterministic models and asks by equal approximation method and KKT optimality conditions Solution, optimal scheduling is carried out to virtual plant.This scheme can take into account the coordinated operation between multiple micro-capacitance sensors in virtual plant;Using general Rate distribution can take into full account uncontrollable micro battery randomness with correlation to Optimal Scheduling with Copula correlation analysis Influence, can be achieved virtual plant and coordinates random optimization scheduling, but can not realize the demand in each station output and area Balance adjustment between power.
The content of the invention
The present invention mainly solve shortage present in prior art to station output and regional demand power it Between adjusting method technical problem there is provided a kind of stability is good, precision is high, impact it is small virtual based on wind energy and luminous energy Power plant's power regulating method.
The present invention is mainly what is be addressed by following technical proposals for above-mentioned technical problem:One kind based on wind energy and The virtual plant power regulating method of luminous energy, comprises the following steps:
S001, the power curve for obtaining wind power plant and photovoltaic generation factory;
S002, fluctuation degree C1 of the power curve in prediction period for calculating wind power plant;Calculate photovoltaic generation factory Fluctuation degree C2 of the power curve in prediction period;
S003, the average demand power P obtained in prediction period;
S004, foundation C1, C2 and P calculate wind power plant's power output and photovoltaic generation factory power output;
S005, the power output according to each power plant of calculating gained adjustment;
The fluctuation degree is the variance or standard deviation of power curve each sampled point in prediction period, and sample rate F is by following Formula is determined:
In formula, C1 and C2 use last time to calculate degree of fluctuation, F0 on the basis of sample rate on the basis of income value, F0, C ' And C ' is preset value.
The virtual plant of this programme includes wind power plant and photovoltaic generation factory, and the output in three class power plants imports total power network Then for being used with electric unit.The power curve in each power plant is the anaplasia at any time obtained according to weather condition and history generated energy The generated output curve of change, is premeasuring.The change severe degree of fluctuation degree response curve.To reduce impact, changed power is got over It is gentle better.This programme is in the case where meeting average demand power, and the power curve fluctuation degree according to each power plant is calculated most Gentle power output, so that total output reaches most steady, power network fluctuation minimum, brings because generated output change Impact is also minimum.
This programme dynamically adjusts sample rate F according to fluctuation degree, and sample rate is higher when fluctuation degree is larger, and fluctuation degree is adopted when smaller Sample rate is relatively low, system is had highest control efficiency.
Preferably, the step S004 is specially:
Wind-force mean power P1 in prediction period is calculated according to the power curve of wind power plant;According to photovoltaic generation factory Power curve calculate prediction period in photovoltaic mean power P2;The power output weighted value in each power plant is calculated, formula is such as Under:
NP1+kP2=P
In formula, n is the power output weighted value of wind power plant, and k is the power output weighted value of photovoltaic generation factory;
The power output of wind power plant is nP1, and the power output of photovoltaic generation factory is kP2.
Above-mentioned flow can make the output of all power plants always and average demand power-balance, and smoothness highest.
Preferably, when any one in n and k is more than 1, then setting the weighted value more than 1 as 1, increase other one Individual weighted value is until meet formula nP1+kP2=P.
It is the most important condition to ensure that output is not less than average demand power, and then carries out the adjustment of smoothness.
Preferably, the prediction period refers to the duration in T seconds from current time, T is prediction duration.
A length of setting value, is determined by staff according to actual conditions during prediction.
Preferably, every duration T1, recalculating each power plant fluctuation degree and power plant being exported according to result of calculation Power is adjusted, and T1 is obtained by below equation:
In formula, C1 and C2 are to be spaced degree of fluctuation on the basis of duration, C ' on the basis of last time calculates income value, T0.
This programme also dynamically adjusts time delay interval duration T1 according to fluctuation degree, and interval duration is shorter when fluctuation degree is larger, ripple Interval duration is longer when dynamic degree is smaller, system is had highest control efficiency.
The substantial effect that the present invention is brought is total generated electric power output is reached balance with demand, and with most Good smoothness, fluctuation is gentle, small to power network and electrical equipment impact, reduces the life-span of cost, extension power network and equipment.
Brief description of the drawings
Fig. 1 is a kind of flow chart of the present invention.
Embodiment
Below by embodiment, and with reference to accompanying drawing, technical scheme is described in further detail.
Embodiment:A kind of virtual plant power regulating method based on wind energy and luminous energy of the present embodiment, as shown in figure 1, Comprise the following steps:
S001, the power curve for obtaining wind power plant and photovoltaic generation factory;
S002, fluctuation degree C1 of the power curve in prediction period for calculating wind power plant;Calculate photovoltaic generation factory Fluctuation degree C2 of the power curve in prediction period;
S003, the average demand power P obtained in prediction period;
S004, foundation C1, C2 and P calculate wind power plant's power output and photovoltaic generation factory power output;
S005, the power output according to each power plant of calculating gained adjustment;
The fluctuation degree is the variance or standard deviation of power curve each sampled point in prediction period, and sample rate F is by following Formula is determined:
In formula, C1 and C2 use last time to calculate degree of fluctuation, F0 on the basis of sample rate on the basis of income value, F0, C ' And C ' is preset value.
The virtual plant of this programme includes wind power plant and photovoltaic generation factory, and the output in two class power plants imports total power network Then for being used with electric unit.The power curve in each power plant is the anaplasia at any time obtained according to weather condition and history generated energy The generated output curve of change, is premeasuring.The change severe degree of fluctuation degree response curve.To reduce impact, changed power is got over It is gentle better.This programme is in the case where meeting average demand power, and the power curve fluctuation degree according to each power plant is calculated most Gentle power output, so that total output reaches most steady, power network fluctuation minimum, brings because generated output change Impact is also minimum.
This programme dynamically adjusts sample rate F according to fluctuation degree, and sample rate is higher when fluctuation degree is larger, and fluctuation degree is adopted when smaller Sample rate is relatively low, system is had highest control efficiency.
The step S004 is specially:
Wind-force mean power P1 in prediction period is calculated according to the power curve of wind power plant;According to photovoltaic generation factory Power curve calculate prediction period in photovoltaic mean power P2;The power output weighted value in each power plant is calculated, formula is such as Under:
NP1+kP2=P
In formula, n is the power output weighted value of wind power plant, and k is the power output weighted value of photovoltaic generation factory;
The power output of wind power plant is nP1, and the power output of photovoltaic generation factory is kP2.
Above-mentioned flow can make the output of all power plants always and average demand power-balance, and smoothness highest.
When any one in n and k is more than 1, then the weighted value more than 1 is set as 1, increases another weighted value straight To meeting formula nP1+kP2=P.
It is the most important condition to ensure that output is not less than average demand power, and then carries out the adjustment of smoothness.
The prediction period refers to the duration in T seconds from current time, and T is prediction duration.
A length of setting value, is determined by staff according to actual conditions during prediction.
Every duration T1, recalculate each power plant fluctuation degree and station output is adjusted according to result of calculation Whole, T1 is obtained by below equation:
In formula, C1 and C2 are to be spaced degree of fluctuation on the basis of duration, C ' on the basis of last time calculates income value, T0.
This programme also dynamically adjusts time delay interval duration T1 according to fluctuation degree, and interval duration is shorter when fluctuation degree is larger, ripple Interval duration is longer when dynamic degree is smaller, system is had highest control efficiency.
Average demand power P is calculated in the following manner:
Local GDP data over the years and locality GDP target datas that S101, acquisition virtual plant are powered;Obtain virtual The local electric power data and curves over the years that power plant is powered;
S102, calculating power system electricity consumption coefficient of elasticity:
Et={ (Pt+1-Pt)/Pt}/{(gt+1-gt)/gt};
EtPower system electricity consumption coefficient of elasticity, P during for preceding ttFor average use of the t before locality within the correspondence period Electrical power, gtFor the GDP data of t before locality;GDP data then use newest local GDP target datas;
S103, pass through power system electricity consumption coefficient of elasticity formula calculate average demand power P:
S is effective age, is preset value, can be adjusted by staff, and the more high then s of precision of demand is bigger;It is right It should the period be the period over the years corresponding with prediction period, that is, be currently needed for the period and period pair over the years calculated Should.Average demand power P is linked up with local GDP, and GDP is more high, and then required power is also higher.
The present invention makes total generated electric power output reach balance with demand, and with best smoothness, fluctuation is gentle, It is small to power network and electrical equipment impact, reduce the life-span of cost, extension power network and equipment.
Specific embodiment described herein is only to spirit explanation for example of the invention.Technology neck belonging to of the invention The technical staff in domain can be made various modifications or supplement to described specific embodiment or be replaced using similar mode Generation, but without departing from the spiritual of the present invention or surmount scope defined in appended claims.
Although more having used the terms such as power curve, fluctuation degree, average demand power herein, it is not precluded from using The possibility of other terms.It is used for the purpose of more easily describing and explaining the essence of the present invention using these terms;Them Any additional limitation is construed to all to disagree with spirit of the present invention.

Claims (5)

1. a kind of virtual plant power regulating method based on wind energy and luminous energy, it is characterised in that comprise the following steps:
S001, the power curve for obtaining wind power plant and photovoltaic generation factory;
S002, fluctuation degree C1 of the power curve in prediction period for calculating wind power plant;Calculate the power of photovoltaic generation factory Fluctuation degree C2 of the curve in prediction period;
S003, the average demand power P obtained in prediction period;
S004, foundation C1, C2 and P calculate wind power plant's power output and photovoltaic generation factory power output;
S005, the power output according to each power plant of calculating gained adjustment;
The fluctuation degree is the variance or standard deviation of power curve each sampled point in prediction period, and sample rate F is by below equation It is determined that:
In formula, C1 and C2 use last time to calculate degree of fluctuation on the basis of sample rate on the basis of income value, F0, C '.
2. a kind of virtual plant power regulating method based on wind energy and luminous energy according to claim 1, it is characterised in that The step S004 is specially:
Wind-force mean power P1 in prediction period is calculated according to the power curve of wind power plant;According to the work(of photovoltaic generation factory Rate curve calculates the photovoltaic mean power P2 in prediction period;The power output weighted value in each power plant is calculated, formula is as follows:
NP1+kP2=P
In formula, n is the power output weighted value of wind power plant, and k is the power output weighted value of photovoltaic generation factory;
The power output of wind power plant is nP1, and the power output of photovoltaic generation factory is kP2.
3. a kind of virtual plant power regulating method based on wind energy and luminous energy according to claim 2, it is characterised in that When any one in n and k is more than 1, then the weighted value more than 1 is set as 1, increase another weighted value until meeting public Formula nP1+kP2=P.
4. a kind of virtual plant power regulating method based on wind energy and luminous energy according to claim 1 or 2 or 3, it is special Levy and be, the prediction period refers to the duration in T seconds from current time, T is prediction duration.
5. a kind of virtual plant power regulating method based on wind energy and luminous energy according to claim 1 or 2 or 3, it is special Levy and be, every duration T1, recalculate each power plant fluctuation degree and station output is adjusted according to result of calculation Whole, T1 is obtained by below equation:
In formula, C1 and C2 use last time to be spaced degree of fluctuation on the basis of duration, C ' on the basis of calculating income value, T0.
CN201710167321.4A 2017-03-20 2017-03-20 A kind of virtual plant power regulating method based on wind energy and luminous energy Pending CN107017668A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710167321.4A CN107017668A (en) 2017-03-20 2017-03-20 A kind of virtual plant power regulating method based on wind energy and luminous energy

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710167321.4A CN107017668A (en) 2017-03-20 2017-03-20 A kind of virtual plant power regulating method based on wind energy and luminous energy

Publications (1)

Publication Number Publication Date
CN107017668A true CN107017668A (en) 2017-08-04

Family

ID=59440627

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710167321.4A Pending CN107017668A (en) 2017-03-20 2017-03-20 A kind of virtual plant power regulating method based on wind energy and luminous energy

Country Status (1)

Country Link
CN (1) CN107017668A (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012131867A1 (en) * 2011-03-28 2012-10-04 株式会社日立製作所 Demand/supply schedule control system for low-voltage grid, and demand/supply schedule control method for low-voltage grid
CN103199747A (en) * 2013-04-07 2013-07-10 华北电力大学 Method for using battery energy storage system to smooth power of photovoltaic power generation system
CN103269091A (en) * 2013-06-06 2013-08-28 山东电力工程咨询院有限公司 Wind-solar energy storage capacity configuring method based on wind-solar energy average output curve
CN103595068A (en) * 2013-11-13 2014-02-19 国家电网公司 Control method for stabilizing wind and light output power fluctuation through hybrid energy storage system
CN104732349A (en) * 2015-03-30 2015-06-24 国家电网公司 Power network planning method
CN105389634A (en) * 2015-12-01 2016-03-09 广东智造能源科技研究有限公司 Combined short-term wind power prediction system and method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012131867A1 (en) * 2011-03-28 2012-10-04 株式会社日立製作所 Demand/supply schedule control system for low-voltage grid, and demand/supply schedule control method for low-voltage grid
CN103199747A (en) * 2013-04-07 2013-07-10 华北电力大学 Method for using battery energy storage system to smooth power of photovoltaic power generation system
CN103269091A (en) * 2013-06-06 2013-08-28 山东电力工程咨询院有限公司 Wind-solar energy storage capacity configuring method based on wind-solar energy average output curve
CN103595068A (en) * 2013-11-13 2014-02-19 国家电网公司 Control method for stabilizing wind and light output power fluctuation through hybrid energy storage system
CN104732349A (en) * 2015-03-30 2015-06-24 国家电网公司 Power network planning method
CN105389634A (en) * 2015-12-01 2016-03-09 广东智造能源科技研究有限公司 Combined short-term wind power prediction system and method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
黄昕颖等: "基于投资组合的虚拟电厂多电源容量配置", 《电力系统自动化》 *

Similar Documents

Publication Publication Date Title
CN107528345B (en) Multi-time-scale network source load and storage coordination control method
CN105375479B (en) A kind of distributed energy energy management method based on Model Predictive Control
CN102694391B (en) Day-ahead optimal scheduling method for wind-solar storage integrated power generation system
CN105680474B (en) Control method for restraining rapid power change of photovoltaic power station through energy storage
CN105159093B (en) Microgrid energy Optimal Control System and its design method based on model adaptation
CN112186802A (en) Multi-time scale rolling scheduling method and system for dynamic economic scheduling
CN106972550A (en) A kind of virtual plant power regulating method based on tide energy and luminous energy
CN104636824A (en) Distributed photovoltaic siting and sizing method for determining minimum permeability
Ramadan et al. Optimal resilient facade thermal photovoltaic clustering allocation for microgrid enhanced voltage profile
CN106300365B (en) A kind of static voltage stability control method based on air conditioner load
CN103916071B (en) A kind of uniform output intelligent control system of wind light mutual complementing power generation and method
CN108321916B (en) Base station with energy cooperation function and energy cooperation method
CN107069832A (en) A kind of virtual plant power regulating method based on tide energy and wind energy
CN110474348A (en) A kind of peak regulating method and device of power distribution network
CN109978277A (en) Region online load prediction technique and device in photovoltaic power generation
CN112311078B (en) Solar load adjusting method and device based on information fusion
CN104778507B (en) A kind of building intelligent electricity consumption strategy acquisition methods based on APSO algorithm
CN103763761B (en) The processing method of solar energy base station energy supply
CN107609690A (en) A kind of method of load active management decision optimization
CN104713189B (en) The control method and PV air-conditioner system of PV air-conditioner system
CN109904865B (en) Intelligent peak-valley load balance management and control main system of high-voltage distribution network
CN106972545A (en) A kind of virtual plant power regulating method
CN107017668A (en) A kind of virtual plant power regulating method based on wind energy and luminous energy
CN107016493A (en) The method that virtual plant is automatically adjusted
CN116169700A (en) Power distribution network energy storage configuration model and solving method thereof

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: 20170804