CN107069783B  Heat storage electric boiler merges energystorage system optimal control method  Google Patents
Heat storage electric boiler merges energystorage system optimal control method Download PDFInfo
 Publication number
 CN107069783B CN107069783B CN201710031302.9A CN201710031302A CN107069783B CN 107069783 B CN107069783 B CN 107069783B CN 201710031302 A CN201710031302 A CN 201710031302A CN 107069783 B CN107069783 B CN 107069783B
 Authority
 CN
 China
 Prior art keywords
 abandonment
 electric boiler
 particle
 heat storage
 energy
 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.)
 Active
Links
 238000005338 heat storage Methods 0.000 title claims abstract description 50
 238000004146 energy storage Methods 0.000 title claims abstract description 45
 239000002245 particle Substances 0.000 claims abstract description 42
 230000004927 fusion Effects 0.000 claims abstract description 18
 230000005611 electricity Effects 0.000 claims description 39
 238000005457 optimization Methods 0.000 claims description 7
 230000000875 corresponding Effects 0.000 claims description 4
 238000011156 evaluation Methods 0.000 claims description 4
 238000005516 engineering process Methods 0.000 abstract description 4
 241000196324 Embryophyta Species 0.000 description 8
 238000009825 accumulation Methods 0.000 description 6
 230000029087 digestion Effects 0.000 description 4
 238000010438 heat treatment Methods 0.000 description 3
 238000000034 method Methods 0.000 description 3
 238000004458 analytical method Methods 0.000 description 2
 238000006243 chemical reaction Methods 0.000 description 2
 238000005485 electric heating Methods 0.000 description 2
 238000004088 simulation Methods 0.000 description 2
 230000005619 thermoelectricity Effects 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
 230000001276 controlling effect Effects 0.000 description 1
 230000000694 effects Effects 0.000 description 1
 238000004070 electrodeposition Methods 0.000 description 1
 238000003379 elimination reaction Methods 0.000 description 1
 230000000051 modifying Effects 0.000 description 1
 238000010248 power generation Methods 0.000 description 1
 230000001105 regulatory Effects 0.000 description 1
Classifications

 H—ELECTRICITY
 H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
 H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
 H02J3/00—Circuit arrangements for ac mains or ac distribution networks
 H02J3/28—Arrangements for balancing of the load in a network by storage of energy

 H—ELECTRICITY
 H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
 H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
 H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
 H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

 Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSSSECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSSREFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
 Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
 Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
 Y02E10/00—Energy generation through renewable energy sources
 Y02E10/70—Wind energy
 Y02E10/76—Power conversion electric or electronic aspects
Abstract
A kind of heat storage electric boiler fusion energystorage system optimal control method, belongs to power system and automation technology.The purpose of the present invention is by when one heat storage electric boiler of design merge the model of energystorage system and realize that the heat storage electric boiler for the optimum control for merging energystorage system to heat storage electric boiler merges energystorage system optimal control method.Step of the invention is: establishing the mathematical model of heat storage electric boiler fusion energystorage system optimal control, obtains the wind power prediction information of wind power plant, and then obtains abandonment power prediction information, the abandonment power that the mathematical model obtained using step 1 and step 2 are obtained；Consider the constraint condition that step 1 is mentioned, hybrid system is optimized using particle swarm algorithm.The present invention can be integrated in the system of heat storage electric boiler fusion energy storage hybrid system control centre, realize the optimal control to whole system, take into account consumption abandonment maximization and electrode boiler adjusts number adjusting and minimizes, realize the operation of hybrid system economic stability.
Description
Technical field
The invention belongs to power system and automation technologies.
Background technique
China's Wind Power Generation Industry is quickly grown, and installed capacity of winddriven power occupies first place in the world, but windpowered electricity generation abandonment outstanding problem at present, especially
It is that power grid " three Norths " area, Flexible Power Grid based on thermoelectricity are poor.During heating in winter, northern area thermoelectricity unit
" electricity determining by heat " operation, it is largescale " abandonment " that the downward peak modulation capacity deficiency of unit further results in the night dip period.How
Consumption windpowered electricity generation has become one of the critical issue for restricting the development of China's windpower electricity generation.For the wind electricity digestion for solving the problems, such as China,
Country has put into effect a series of measures, clearly proposes to attempt to promote abandonment electric heating, promotes conversion of the electric load to thermic load.
In addition, consumption abandonment electric power is assisted also to receive extensive attention using energy storage technology.
Existing literature to heat storage electric boiler improve wind power plant windpowered electricity generation onsite elimination economy and scheduling controlling technology into
More indepth study is gone, it was demonstrated that utilize the application prospect of heat storage electric boiler technology consumption abandonment.Also there is document proposition
Hybrid system is constituted using energy storage fusion heat storage electric boiler, is that target carries out simulation analysis using the Income Maximum of system, demonstrate,proves
The economic feasibility of hybrid system is illustrated.But heat storage electric boiler tracking abandonment changed power is not accounted in existing literature
Regulating power problem.The electrode of currently used heat storage electric boiler may be implemented power and continuously adjust, but it adjusts speed
Degree and adjusting number are restricted by electrode mechanical part.Quickly, frequently power regulation will seriously damage heat storage electric boiler
Service life.Energy storage mixed heat accumulation formula grill pan furnace system how is controlled, while maximizing its consumption windpowered electricity generation, reduces its Machinery Ministry
The mobile number of part extends service life of equipment in turn, becomes research hotspot at present.
Summary of the invention
The purpose of the present invention is merge the model of energystorage system by one heat storage electric boiler of design to realize to accumulation of heat
The heat storage electric boiler that formula electric boiler merges the optimum control of energystorage system merges energystorage system optimal control method.
Step of the invention is:
Step 1, the mathematical model for establishing heat storage electric boiler fusion energystorage system optimal control:
Step 101: establish hybrid system consumption abandonment electricity index:
(1)
W _{ s } ^{ t }FortThe abandonment electricity that period wind power plant is dissolved using heat storage electric boiler and electrochemical energy storage；
Step 102: it establishes heat storage electric boiler and adjusts number index:
(2)
Tap^{t}For heat storage electric boiler the t period power gear；
Step 103: according to the difference of dimension between each objective function, subordinating degree function is constructed respectively to each target,
It is translated into the satisfaction to optimum results, corresponding function are as follows:
(3)
(4)
To be up to value when target with abandonment consumption；To adjust the minimum target of number with boiler gear
When value；To dissolve the acceptable flexible value of abandonment；The acceptable flexible value of number is adjusted for boiler gear；
Abandonment total electricity is dissolved for hybrid system；Number is always adjusted for heat storage electric boiler；
Step 104: establish the evaluation function of heat storage electric boiler fusion each index of energystorage system:
(5)
k_{1}、k_{2}For the weight coefficient of each section, and meet；To dissolve abandonment subordinating degree function；To adjust number subordinating degree function；
Step 105: abovementioned various indexs being carried out using the constraint condition of heat storage electric boiler fusion energystorage system optimization
Limit constraint；
Step 2: obtaining the wind power prediction information of wind power plant, and then obtain abandonment power prediction information；
Step 3: the abandonment power that the mathematical model and step 2 obtained using step 1 is obtained；Consider what step 1 was mentioned
Constraint condition optimizes hybrid system using particle swarm algorithm:
Step 301 initializes population, and the population is made of multiple particles, and the value of each particle is limiting model
It is randomly generated in enclosing；
The objective function of step 1 mathematical model is imported algorithm by step 302, as the objective function of algorithm, steps for importing
Abandonment power prediction information obtained in 2, as abandonment constraint qualification condition；
Step 303 starts iteration, calculates the comprehensive fitness degree of each particle in population, does not meet the grain of constraint condition
Son is punished according to penalty function, takes the maximum particle of comprehensive fitness degree compared with current optimal particle, enable comprehensive fitness degree compared with
Big particle is standard optimal particle；
After step 304 is iterated update to particle each in population according to following formula, return step 303；
(15)
(16)
K is the number of iterations；W is the inertia weight factor；、For the random number between 0 ~ 1；、For Studying factors
(accelerated factor)；It isThe speed of a particle at the kth iteration；It isA particle position at the kth iteration
It sets；The optimal solution found for particle itself；The optimal solution found in group for particle；
When step 304 the number of iterations reaches maximum value, iteration terminates, when obtaining two objective satisfaction degree maximum of hybrid system
Optimal control mode.
The present invention adjusts the minimum objective function of number with wind electricity digestion maximum and electrode boiler, considers abandonment constraint, function
Rate Constraints of Equilibrium, equipment state constraint, the constraint of heat supply contract and boiler power constraint, realize and merge to heat storage electric boiler
The optimum control of energystorage system.It can be integrated in the system of heat storage electric boiler fusion energy storage hybrid system control centre, realize
Optimal control to whole system, takes into account consumption abandonment maximization and electrode boiler adjusts number and adjusts minimum, realizes mixing
Systematic economy stable operation.
Detailed description of the invention
Fig. 1 is process flow chart of the invention；
Fig. 2 is the abandonment power curve that this example uses；
Fig. 3 is the corresponding subordinating degree function of two targets；
Fig. 4 is heat storage electric boiler operation power gear after optimization；
Fig. 5 is heataccumulator tank day part quantity of heat storage；
Fig. 6 is energystorage battery day part operation power；
Fig. 7 is energystorage battery day part reserve of electricity；
Fig. 8 is the control method consumption abandonment signal that this method proposes.
Specific embodiment
Step of the invention is:
Step 1, the mathematical model for establishing heat storage electric boiler fusion energystorage system optimal control:
Step 101: establish hybrid system consumption abandonment electricity index:
(1)
W _{ s } ^{ t }FortThe abandonment electricity that period wind power plant is dissolved using heat storage electric boiler and electrochemical energy storage.
Step 102: it establishes heat storage electric boiler and adjusts number index:
(2)
Tap^{t}For heat storage electric boiler the t period power gear.Following Examples is by 30MW electrode boiler according to every 5MW mono
A power gear is divided into 7 gears from 0 to 30MW.
Step 103: according to the difference of dimension between each objective function, subordinating degree function is constructed respectively to each target,
It is translated into the satisfaction to optimum results, it is intended under the premise of meeting institute's Prescribed Properties, so that comprehensive satisfaction reaches
To maximum.Corresponding function are as follows:
(3)
(4)
To be up to value when target with abandonment consumption；To adjust the minimum target of number with boiler gear
When value；To dissolve the acceptable flexible value of abandonment；The acceptable flexible value of number is adjusted for boiler gear；
Abandonment total electricity is dissolved for hybrid system；Number is always adjusted for heat storage electric boiler.As shown in Figure 3.
Step 104: establish the evaluation function of heat storage electric boiler fusion each index of energystorage system:
(5)
k_{1}、k_{2}For the weight coefficient of each section, and meet；To dissolve abandonment subordinating degree function；To adjust number subordinating degree function.
Step 105: abovementioned various indexs being carried out using the constraint condition of heat storage electric boiler fusion energystorage system optimization
Limit constraint.Specifically include abandonment constraint, powerbalance constraint, equipment state constraint, boiler power constraint and heat supply contract about
Beam.
The constraint condition of the heat storage electric boiler fusion energystorage system optimization are as follows:
The constraint condition for establishing heat storage electric boiler fusion energystorage system optimization, specifically includes abandonment constraint, powerbalance
Constraint, equipment state constraint, boiler power constraint and the constraint of heat supply contract.
Wherein, abandonment constraint condition are as follows:
(6)
WhereinW _{ qf } ^{ t }For t period abandonment electricity,W _{ s } ^{ t }Dissolving abandonment electricity for system can be further represented as
(7)
W _{ g } ^{ t }FortIt is used to heat electric boiler directly to the electricity of pipe network heat supply in period；W _{ qi } ^{ t }FortPeriod is used to heat grill pan
Furnace is the electricity of heataccumulator tank heat accumulation；W _{ ci } ^{ t }FortThe electricity of period electrochemical energy storage charging.
Powerbalance constraint are as follows:
(8)
(9)
Q _{ c } ^{ t }FortThe quantity of heat storage of period heataccumulator tank；Q _{ qi } ^{ t }FortThe heat that period boiler is stored in heataccumulator tank；Q _{ qo } ^{ t }FortPeriod stores
The heat of hot tank release；W _{ c } ^{ t }FortThe electricity of period electrochemical energy storage；W _{ ci } ^{ t }FortPeriod electrochemical energy storage charge capacity；W_{co} ^{t}Fort
Period electrochemical energy storage discharge electricity amount.
Energy storage and heat accumulation equipment state constraint are as follows:
(10)
(11)
Q _{ max }For heataccumulator tank maximum quantity of heat storage,Q _{ min }For heataccumulator tank minimum quantity of heat storage；SOC_{min}、SOC_{max}Respectively indicate charged shape
The maxima and minima of state generally takes " 0.2 ", " 0.8 ".In following exampleQ _{ max }For 300GJ,Q _{ min }It is 0.
The constraint of heat supply contract are as follows:
(12)
tThe heat that moment provides to heat supply companyQ _{ x } ^{ t }It can be further represented as
(13)
Q _{ x.min }For the minimum heating load of heat supply contract；For electric heating conversion coefficient coefficient, unit GJ/MWh； W _{ co } ^{ t }Fort
The electricity of period electrochemical energy storage electric discharge.In following exampleTake 3.597.
Electric boiler power constraint are as follows:
(14)
P _{ h } ^{ t }FortPeriod electric boiler runs power, should be less than the maximum power of electric boilerP _{ hmax }.In following exampleP _{ h } ^{ t }It takes
30MW。
Step 2: obtaining the wind power prediction information of wind power plant, and then obtain abandonment power prediction information；Wind power plant cluster
Control system obtains 15 minutes grade active power output information of each wind power plant in wind power plant cluster by wind power prediction system, considers negative
Lotus demand and schedule obtain 96 points of whole day of abandonment predictive information.
This example randomly selects one day abandonment electricity in wind factory heat supply midterm and abandonment prediction electricity is replaced to carry out simulation analysis,
As shown in Figure 2.
Step 3: the abandonment power that the mathematical model and step 2 obtained using step 1 is obtained；Consider what step 1 was mentioned
Constraint condition optimizes hybrid system using particle swarm algorithm.
To system with wind electricity digestion maximum, boiler adjusts the minimum target of number and carries out single object optimization, obtains in monocular
Hybrid system running boundary condition in the case of mark determines、、、, value, and then determine heat accumulating type grill pan
Evaluation function this example of furnace fusion each index of energystorage system takes " 506.25,2,303.155,43 " respectively.
Step 301 initializes population, and the population is made of multiple particles, and the value of each particle is limiting model
It is randomly generated in enclosing.
The objective function of step 1 mathematical model is imported algorithm by step 302, as the objective function of algorithm, in step 1
Algorithm is written in the constraint condition of consideration in the form of penalty function, when particle does not meet constraint condition in iterative process, by penalizing letter
Number is punished；Abandonment power prediction information obtained in steps for importing 2, as abandonment constraint qualification condition.
Step 303 starts iteration, calculates the comprehensive fitness degree of each particle in population, does not meet the grain of constraint condition
Son is punished according to penalty function, takes the maximum particle of comprehensive fitness degree compared with current optimal particle, enable comprehensive fitness degree compared with
Big particle is standard optimal particle.
After step 304 is iterated update to particle each in population according to following formula, return step 303；
(15)
(16)
K is the number of iterations；W is the inertia weight factor；、For the random number between 0 ~ 1；、For Studying factors
(accelerated factor), following Examples takes 2；It isThe speed of a particle at the kth iteration；It isA particle is in kth
Position when secondary iteration；The optimal solution found for particle itself；The optimal solution found in group for particle.
When step 304 the number of iterations reaches maximum value, iteration terminates, when obtaining two objective satisfaction degree maximum of hybrid system
Optimal control mode.Each equipment period power and state are as shown in figs. 47.Fig. 8 is that hybrid system dissolves windpowered electricity generation signal
Scheme, dash area is that system dissolves abandonment electricity in figure.
Fig. 4Fig. 5 is respectively the power gear of electric boiler and the heat of heataccumulator tank.Electric boiler according to setting 7 gears with
The operation of track windpowered electricity generation, whole day are adjusted electrode 26 times altogether, with boiler realtime tracking windpowered electricity generation, are adjusted electrode position mode in real time and are compared, greatly
Big reduce adjusts number, effectively extends the service life of equipment.A part of direct heating of heat that boiler generates, another part
It is stored in heataccumulator tank.This example, which sets unit time thermic load, cannot be below 60GJ to convert being electrical power being 16.68MWh.Work as boiler
Underpower is to meet " the 3141,4356 " period in minimum thermic load such as Fig. 8, and heataccumulator tank discharges heat, at this time heataccumulator tank
Interior quantity of heat storage decline；When boiler power can satisfy thermic load such as " the 110,5665 " period, heataccumulator tank heat accumulation, heat accumulation in tank
Amount increases.
Fig. 6Fig. 7 is respectively energystorage battery operation power and energystorage battery reserve of electricity.When boiler operatiopn power is higher than abandonment
Power such as " 1,14,20 " period energystorage battery power is less than 0, the deficiency of battery discharge supplement electricity under the premise of meeting SOC,
Reserve of electricity decline；Such as " 4,6, the 10,21 " period when abandonment power is greater than boiler operatiopn power, energy storage power are greater than 0, meet
Battery charges under the premise of SOC, and reserve of electricity rises.
Fig. 8 gives the coordinating and optimizing control method tracking abandonment using heat storage electric boiler proposed in this paper fusion energy storage
The case where dissolving windpowered electricity generation.After accessing hybrid system, dash area is that hybrid system dissolves the total 465.78MWh of abandonment, wind electricity digestion
Ability and heat storage electric boiler traditional control method (22:0005:00 is run, remaining time shuts down by heataccumulator tank heat release heat supply)
Compared to being obviously improved.
Claims (1)
1. a kind of heat storage electric boiler merges energystorage system optimal control method, it is characterised in that:
Step 1, the mathematical model for establishing heat storage electric boiler fusion energystorage system optimal control:
Step 101: establish hybrid system consumption abandonment electricity index:
W_{s} ^{t}The abandonment electricity dissolved for t period wind power plant using heat storage electric boiler and electrochemical energy storage；
Step 102: it establishes heat storage electric boiler and adjusts number index:
Tap^{t}For heat storage electric boiler the t period power gear；
Step 103: according to the difference of dimension between each objective function, subordinating degree function being constructed respectively to each target, by it
It is converted into the satisfaction to optimum results, corresponding function are as follows:
K_{max}To be up to value when target with abandonment consumption；P_{min}To adjust value when the minimum target of number with boiler gear；δ_{1}
To dissolve the acceptable flexible value of abandonment；δ_{2}The acceptable flexible value of number is adjusted for boiler gear；F (k) disappears for hybrid system
Receive abandonment total electricity；F (p) is that heat storage electric boiler always adjusts number；
Step 104: establish the evaluation function of heat storage electric boiler fusion each index of energystorage system:
μ_{max}=k_{1}μ(k)+k_{2}μ(p) (5)
k_{1}、k_{2}For the weight coefficient of each section, and meet k_{1}+k_{2}=1；μ (k) is consumption abandonment subordinating degree function；μ (p) is to adjust
Number subordinating degree function；
Step 105: constraint is defined using the constraint condition of heat storage electric boiler fusion energystorage system optimization；
Step 2: obtaining the wind power prediction information of wind power plant, and then obtain abandonment power prediction information；
Step 3: the abandonment power that the mathematical model and step 2 obtained using step 1 is obtained；Consider the constraint that step 1 is mentioned
Condition optimizes hybrid system using particle swarm algorithm:
Step 301 initializes population, and the population is made of multiple particles, and the value of each particle is limiting in range
It is randomly generated；
The objective function of step 1 mathematical model is imported algorithm by step 302, as the objective function of algorithm, in steps for importing 2
Obtained abandonment power prediction information, as abandonment constraint qualification condition；
Step 303 starts iteration, calculates the comprehensive fitness degree of each particle in population, do not meet the particle of constraint condition according to
It is punished according to penalty function, takes the maximum particle of comprehensive fitness degree compared with current optimal particle, enable comprehensive fitness degree biggish
Particle is standard optimal particle；
After step 304 is iterated update to particle each in population according to following formula, return step 303；
K is the number of iterations；W is the inertia weight factor；r_{1}、r_{2}For the random number between 0~1；c_{1}、c_{2}For Studying factors；It is
The speed of i particle at the kth iteration；For ith of particle position at the kth iteration；p_{best}It is found for particle itself
Optimal solution；g_{best}The optimal solution found in group for particle；
When step 304 the number of iterations reaches maximum value, iteration terminates, when obtaining two objective satisfaction degree maximum of hybrid system most
Excellent control mode.
Priority Applications (1)
Application Number  Priority Date  Filing Date  Title 

CN201710031302.9A CN107069783B (en)  20170117  20170117  Heat storage electric boiler merges energystorage system optimal control method 
Applications Claiming Priority (1)
Application Number  Priority Date  Filing Date  Title 

CN201710031302.9A CN107069783B (en)  20170117  20170117  Heat storage electric boiler merges energystorage system optimal control method 
Publications (2)
Publication Number  Publication Date 

CN107069783A CN107069783A (en)  20170818 
CN107069783B true CN107069783B (en)  20191101 
Family
ID=59598532
Family Applications (1)
Application Number  Title  Priority Date  Filing Date 

CN201710031302.9A Active CN107069783B (en)  20170117  20170117  Heat storage electric boiler merges energystorage system optimal control method 
Country Status (1)
Country  Link 

CN (1)  CN107069783B (en) 
Families Citing this family (5)
Publication number  Priority date  Publication date  Assignee  Title 

CN108153940B (en) *  20171207  20210430  东北电力大学  OPLC thermal circuit model modeling method based on superposition principle 
CN108964014B (en) *  20180524  20211130  国网浙江省电力有限公司  Optimization method of thermoelectric hybrid energy system 
CN109242340B (en) *  20180929  20210827  国网辽宁省电力有限公司电力科学研究院  Monitoring data evaluation system and evaluation method for heat storage electric boiler digestion system 
CN111697615A (en) *  20190315  20200922  新奥数能科技有限公司  Method and device for determining abandoned wind absorption and gear adjustment times 
CN110347039B (en) *  20190709  20210326  东北大学  Combined heat and power optimization method considering user satisfaction and electric boiler participating peak regulation 
Citations (6)
Publication number  Priority date  Publication date  Assignee  Title 

CN102368620A (en) *  20111028  20120307  浙江大学  Windenergy/ solarenergy/ storage/ oceancurrentenergy newenergy isolated network stabilization operation integration control system and method thereof 
CN102611118A (en) *  20120314  20120725  清华大学  Method for comprehensively controlling reactive voltage of wind farm with imported prediction method 
CN102856925A (en) *  20120903  20130102  北京科诺伟业科技有限公司  Comprehensive power distribution method for wind power plant 
CN103401264A (en) *  20130618  20131120  国家电网公司  Overload adjusting method of power transmission network 
WO2016005048A1 (en) *  20140707  20160114  LichtBlick SE  System and method for determining the suitability of a plurality of electrical producers and consumers which are operated in a network as a virtual power plant for providing control power 
CN106058942A (en) *  20160803  20161026  长沙理工大学  Energy hub optimizing model taking wind power nondeterminacy into consideration and including power to gas and CCHP 

2017
 20170117 CN CN201710031302.9A patent/CN107069783B/en active Active
Patent Citations (6)
Publication number  Priority date  Publication date  Assignee  Title 

CN102368620A (en) *  20111028  20120307  浙江大学  Windenergy/ solarenergy/ storage/ oceancurrentenergy newenergy isolated network stabilization operation integration control system and method thereof 
CN102611118A (en) *  20120314  20120725  清华大学  Method for comprehensively controlling reactive voltage of wind farm with imported prediction method 
CN102856925A (en) *  20120903  20130102  北京科诺伟业科技有限公司  Comprehensive power distribution method for wind power plant 
CN103401264A (en) *  20130618  20131120  国家电网公司  Overload adjusting method of power transmission network 
WO2016005048A1 (en) *  20140707  20160114  LichtBlick SE  System and method for determining the suitability of a plurality of electrical producers and consumers which are operated in a network as a virtual power plant for providing control power 
CN106058942A (en) *  20160803  20161026  长沙理工大学  Energy hub optimizing model taking wind power nondeterminacy into consideration and including power to gas and CCHP 
NonPatent Citations (1)
Title 

蓄热式电锅炉融合储能的风电消纳优化控制;王鹤等;《分布式能源》;20161031;第1卷(第2期);全文 * 
Also Published As
Publication number  Publication date 

CN107069783A (en)  20170818 
Similar Documents
Publication  Publication Date  Title 

CN107069783B (en)  Heat storage electric boiler merges energystorage system optimal control method  
CN103793758B (en)  Multiobjective optimization scheduling method for electric vehicle charging station including photovoltaic power generation system  
CN102522776B (en)  Method for improving wind power tracking capability on planned output by energy storage system  
CN103296682B (en)  A kind of multiple space and time scales progressive become excellent load scheduling Model Design method  
CN106451566B (en)  multisource coordination control method for island intelligent microgrid  
CN105162149B (en)  Generation schedule output method is tracked based on the lightpreserved system that fuzzy selfadaption is adjusted  
CN106160091B (en)  Promote the electric automobile charging station charge and discharge dispatching method of regenerative resource consumption  
CN106408131A (en)  Photovoltaic microgrid multitarget scheduling method based on demandside management  
CN108808659A (en)  The coordination optimization of wind electricity digestion integrated energy system controls and economic evaluation method  
Tang et al.  Optimal operation method for microgrid with wind/PV/diesel generator/battery and desalination  
CN111400641A (en)  Dayahead optimal scheduling method for comprehensive energy system containing heat accumulation type electric heating  
CN104901338A (en)  Island isolated microgrid energy control method  
CN106786505B (en)  A kind of dispersion charging pile coordinated dispatching method based on neighborhood information  
Wang et al.  Improved PSObased energy management of standalone microgrid under twotime scale  
Huang et al.  Optimal design of an island microgrid with considering scheduling optimization  
CN111786422B (en)  Realtime optimization scheduling method for participating in upperlayer power grid by micropower grid based on BP neural network  
CN110098623B (en)  Prosumer unit control method based on intelligent load  
CN111917137A (en)  Regulation and control method for multiple distributed energy sources in regional power grid  
CN109861292A (en)  One kind improving clean energy resource based on multipleenergysource energystorage system and dissolves method  
CN107086579B (en)  It is a kind of based on the air conditioner user of echo effect to the response method of Spot Price  
CN106300443B (en)  A kind of three for reducing abandonment layer cogeneration microgrid energy control method  
CN109002947A (en)  A kind of region multienergy system thermoelectricity schedule model method  
CN108764554A (en)  A kind of robust Optimal methods that guiding electric vehicle orderly charges  
Zhang et al.  Energy storage technology in power grid and its configuration optimization method  
CN208564861U (en)  A kind of complementary coupled electricity generation system 
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  
GR01  Patent grant  
GR01  Patent grant 