CN110197310A - A kind of electric charging station Optimization Scheduling based on load margin domain - Google Patents
A kind of electric charging station Optimization Scheduling based on load margin domain Download PDFInfo
- Publication number
- CN110197310A CN110197310A CN201910498114.6A CN201910498114A CN110197310A CN 110197310 A CN110197310 A CN 110197310A CN 201910498114 A CN201910498114 A CN 201910498114A CN 110197310 A CN110197310 A CN 110197310A
- Authority
- CN
- China
- Prior art keywords
- days
- load
- charging station
- electric
- 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
- 238000005457 optimization Methods 0.000 title claims abstract description 30
- 239000002245 particle Substances 0.000 claims abstract description 52
- 238000000034 method Methods 0.000 claims abstract description 26
- 230000008602 contraction Effects 0.000 claims abstract description 20
- 230000005611 electricity Effects 0.000 claims description 55
- 241001269238 Data Species 0.000 claims description 33
- 230000036642 wellbeing Effects 0.000 claims description 30
- 230000008859 change Effects 0.000 claims description 10
- 238000012804 iterative process Methods 0.000 claims description 5
- 235000006508 Nelumbo nucifera Nutrition 0.000 claims description 4
- 240000002853 Nelumbo nucifera Species 0.000 claims description 4
- 235000006510 Nelumbo pentapetala Nutrition 0.000 claims description 4
- 230000005684 electric field Effects 0.000 claims description 3
- 238000010348 incorporation Methods 0.000 claims description 3
- 238000007599 discharging Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 abstract description 12
- 230000008901 benefit Effects 0.000 abstract description 10
- 230000000694 effects Effects 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000000996 additive effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 244000144992 flock Species 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 239000005431 greenhouse gas Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Primary Health Care (AREA)
- Evolutionary Computation (AREA)
- Public Health (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Water Supply & Treatment (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Educational Administration (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention provides a kind of electric charging station Optimization Scheduling based on load margin domain.The dispatching method includes the following steps: first, establish the economic load dispatching model of electric charging station, and the constraint condition of economic load dispatching model is determined based on load margin domain, according to constraint condition, using the particle swarm optimization algorithm economic model for having contraction factor, optimal electric charging scheduling strategy is obtained, electric charging is scheduled.The present invention obtains the optimal solution of economic model by particle swarm algorithm, to improve the economic benefit of electric charging station in the process of running, and the constraint condition of economic load dispatching model is determined based on load margin domain, it is solved according to constraint condition, ensure that the stability of electric charging station in the process of running.
Description
Technical field
The present invention relates to electric charging station management and running field, in particular to a kind of electric charging station based on load margin domain is excellent
Change dispatching method.
Background technique
In nowadays traffic, electric car has become a very important role, and then the electricity of electric car in the market
Energy supply is also fast-developing therewith, and there are two types of universal electric energy methods of supplying now: the wind power plant being incorporated into the power networks generates electricity and leads to
Cross power grid power supply.However due to people's lives tempo increase, user demand happens change, produced " change electricity in recent years
Stand " charged form.
New-energy electric vehicle (electric vehicle, EV) because have high energy conservation, low emission, clean and environmental protection spy
Property, become and reduces one of greenhouse gases and effective solution route of energy deficiency.Therefore it at home and abroad receives in recent years
It is extensive to pay attention to that Development of EV has even more been increased to the strategic heights of national development with development energetically, many countries.
Electric car starts gradually to replace traditional fuel-engined vehicle as a kind of vehicles, and many scholars and mechanism regard electric car
For the direction of auto industry and future city transport development, but how the scheduling to electric charging, transported with improving electric charging station
Economic benefit and stability during row become a technical problem urgently to be resolved.
Summary of the invention
The object of the present invention is to provide a kind of electric charging station Optimization Schedulings based on load margin domain, are changed with improving to fill
The economic benefit and stability of power station in the process of running.
To achieve the above object, the present invention provides following schemes:
The present invention provides a kind of electric charging station Optimization Scheduling based on load margin domain, and the dispatching method includes such as
Lower step:
Establish the economic load dispatching model of electric charging station；
Based on load margin domain, the constraint condition of economic load dispatching model is determined；
It is obtained according to the constraint condition using economic model described in the particle swarm optimization algorithm with contraction factor
Optimal electric charging station scheduling strategy；
Based on the optimal electric charging station scheduling strategy, the scheduling of electric charging station is carried out.
Optionally, the economic load dispatching model for establishing charging station, specifically includes:
Establish charging station economic well-being of workers and staff model:
Wherein, F11For charging station economic well-being of workers and staff, T is to dispatch total period, CsgIt (t) is sale of electricity of the t period charging station to power grid
Electricity price；Psg(t) power of the t period charging station to power grid sale of electricity is indicated；σt1For the sale of electricity period；CchaWhen (i, t) is i-th EVt
The charging electricity price of section；Pcha(i, t) is the charge power of i-th EVt period；σt2For charge period；Cbg(t) indicate that the t period fills
Purchase electricity price of the power station from power grid；PbgIt (t) is power of the t period charging station from power grid power purchase, kW；σt3For block of purchase electricity；
Cdischa(i, t) is the electric discharge electricity price of i-th EVt period；Pdischa(i, t) is the discharge power of i-th EVt period；σt4To put
The electric period；
Establish electrical changing station economic well-being of workers and staff model: F12=f1+f2；
Wherein, F12For electrical changing station economic well-being of workers and staff, f1The clothes for changing and collecting when electricity service to EV user are provided every time for electrical changing station
Business expense,N2For user's electrical changing station battery pack ready for use
Number；Crent(j, t) is lease expenses of the jth group EV on-vehicle battery in time period t；Cservice(j, t) is that jth group EV leases battery
In the Additional Services expense of time period t；σt5For the total degree of lease service within one day；f2It is current by user's vehicle for electrical changing station
Electricity carries out the income of charging,CsaleIndicate that EV changes electrical zero price；SOC
(j, t) is the quantity of electric charge percentage of battery when the jth platform EV battery pack t period needing to change electricity demanding；SjFor jth platform EV battery pack
Rated capacity, kW；
Establish wind power plant economic well-being of workers and staff model:
Wherein, F13For wind power plant economic well-being of workers and staff；N3For the number of wind power plant；P (k, t) indicates k-th of wind power plant in the t period
Generated output；CwindFor wind-powered electricity generation rate for incorporation into the power network；Pact(k, t) is the practical power output of k-th of wind power plant t period；σt6Indicate be
Wind-powered electricity generation is surfed the Internet the period；Ppre(k, t) is the prediction power output of i-th of wind power plant t period；CbcThe punishment expense of power output is reduced for wind power plant
Use coefficient；
According to the charging station economic well-being of workers and staff model, the electrical changing station economic well-being of workers and staff model and wind power plant economic well-being of workers and staff mould
Type establishes the economic load dispatching model maxF of charging station1=F11+F12+F13。
Optionally, described to be based on load margin domain, it determines the constraint condition of economic load dispatching model, specifically includes:
Determine the constraint condition of EV charge power: 0≤Pcha(i,t)≤Pchamax(i, t), wherein Pchamax(i, t) is i-th
Maximum charge power of the platform EV in the t period；
Determine the constraint condition of EV discharge power: 0≤Pdischa(i,t)≤Pdischamax(i, t), wherein Pdischamax(i,
It t) is maximum discharge power of i-th EV in the t period
Determine the constraint condition of charging station power purchase power: 0≤Pbg(t)≤Pbgmax(t), wherein Pbgmax(t) it is filled for the t period
Electrical changing station maximum purchase of electricity
Determine the constraint condition of charging station sale of electricity power: 0≤Psg(t)≤Psgmax(t), wherein Psgmax(t) it is filled for the t period
Electrical changing station maximum electricity sales amount；
Determine the constraint condition for changing electric battery charge levels: SOCmin≤SOC(j,t)≤SOCmax, wherein SOCminAnd SOCmax
Respectively indicate the maximum value and minimum value for changing battery charge levels；
Based on load margin domain, the constraint condition of electric charging station general power is determined:Wherein, Pmin(d) and
Pmax(d) maximum value and minimum value of the d days electric charging station general powers are respectively indicated；
Determine Power Output for Wind Power Field constraint condition: 0≤Pact(k,t)≤Ppre(k,t)≤PN(k), wherein PNIt (k) is wind
The installed capacity of electric field k.
Optionally, described to be based on load margin domain, it determines the constraint condition for changing electric battery charge levels, specifically includes:
The M-1 days load lower limit P are calculated according to the M-1 days historical datasmin(M-1), upper load limit Pmax(M-1) and
Electric charging load margin domain Pλ(M-1)；The M days load lower limit P are calculated according to the M days historical datasmin(M), upper load limit
Pmax(M) and electric charging load margin domain Pλ(M)；
According to the M-1 days load lower limits and electric charging load margin domain, formula P (M-1)=P is utilizedmin(M-1)+Pλ
(M-1), the load margin value P (M-1) for calculating the M-1 days, according to the M days load lower limit Pmin(M) and electric charging load margin
Domain Pλ(M), formula P (M)=P is utilizedmin(M)+Pλ(M), the M days load margin value P (M) are calculated；
Judge whether the M days load margin values are greater than the M-1 days load margin values, obtains the first judging result；
If the load margin value that first judging result is the M days is not more than the M-1 days load margin values, utilize
Formula Pmax(M+1)=Pmin(M)+2K1*Pλ(M) valley-fill calculating was carried out to the M days load margin values, obtains bearing for the M+1 days
Lotus upper limit Pmax(M+1), and P is enabledmin(M+1)=Pmin(M) the M+1 days load lower limit P are obtainedmin(M+1)；Wherein, K1To fill out
Valley system number；
If the load margin value that first judging result is the M days is greater than the M-1 days load margin values, public affairs are utilized
Formula Pmin(M+1)=Pmax(M)-2K2*Pλ(M) peak clipping calculating was carried out to the M-1 days load margin values, obtains bearing for the M+1 days
Lotus lower limit Pmin(M+1), and P is enabledmax(M+1)=Pmax(M), the M+1 days upper load limit P are obtainedmax(M+1)；Wherein, K2To cut
Peak coefficient；
Judge whether M+1 is less than d, obtains the second judging result；
If second judging result, which is less than d for M+1, utilizes public affairs according to the M+1 days upper load limits and load lower limit
FormulaThe M+1 days load margin value P (M+1) are calculated, and
The numerical value of M is increased by 1, return step " judges whether the M days load margin values are greater than the M-1 days load margin values, obtains
First judging result "；
If second judging result is that M+1 is not less than d, the M+1 days upper load limits and load lower limit are set respectively
It is set to the maximum value and minimum value of the d days electric charging station general powers.
Optionally, the historical data according to the M-1 days determines the M-1 days load lower limit Pmin(M-1), on load
Limit Pmax(M-1) and electric charging load margin domain Pλ(M-1), it specifically includes:
According to the M-1 days historical datas, formula is utilized
Determine the M-1 days charging load lower limit Pchamin(M-1)；Wherein, Pcha(i, M-1, t) indicates the M-1 days t periods the
The charge power of i platform EV；
According to the M-1 days historical datas, formula is utilized
Determine the M-1 days electric discharge load lower limit Pchangemin(M-1)；Wherein, Pchange(j, M-1, t) indicates the M-1 days t time
Section jth group EV on-vehicle battery changes electrical power；
According to the M-1 days historical datas, formula is utilized
Determine the M-1 days charging upper load limit Pchamax(M-1)；
According to the M-1 days historical datas, formula is utilized
Determine the M-1 days electric discharge upper load limit Pchangemax(M-1)；
According to the M-1 days charging load lower limits and the M-1 days electric discharge load lower limits, formula P is utilizedmin(M-1)=
Pchamin(M-1)+Pchangemin(M-1), the M days load lower limit P are calculatedmin(M-1)；
According to the M-1 days charging upper load limits and the M-1 days electric discharge upper load limits, formula P is utilizedmax(M-1)=
Pchamax(M-1)+Pchangemax(M-1), the M-1 days upper load limit P are calculatedmax(M-1)；
According to the M-1 days upper load limits and the M-1 days load lower limits, formula is utilizedCalculate the M-1 days electric charging load margin domain Pλ(M-1)。
Optionally, the historical data according to the M-1 days calculates the M-1 days load lower limit Pmin(M-1), on load
Limit Pmax(M-1) and electric charging load margin domain Pλ(M-1), before further include:
Enable the first the number of iterations s=0；
Load criterion according to the M-s days historical datas, calculating the M-s days is poor；
The load criterion difference for judging the M-s days obtains third judging result whether in pre-set interval；
If the third judging result indicates the M-s days load criterion differences not in pre-set interval, by the first iteration
The numerical value of number increases by 1, return step " poor according to the load criterion of the M-s days historical datas, calculating the M-s days "；
If the third judging result indicated the M-s days load criterion differences in pre-set interval, by the M days history
Data replace with the M-s days historical datas；
Enable secondary iteration number l=0；
Load criterion according to the M-s-l days historical datas, calculating the M-s-l days is poor；
The load criterion difference for judging the M-s-l days obtains the 4th judging result whether in pre-set interval；
If the 4th judging result indicates that not in pre-set interval, second is changed for the M-s-l days load criterion differences
The numerical value of generation number increases by 1, and return step " according to the M-s-l days historical datas, calculates the M-s-l days load criterions
Difference "；
If the 4th judging result indicated the M-s-l days load criterion differences in pre-set interval, by the M-1 days
Historical data replaces with the M-s-l days historical datas.
Optionally, the constraint condition of the determining electric charging station general power further includes later
Based on Credibility Theory, uncertain equivalent form is converted by the constraint condition of electric charging station general power are as follows:
Or
Wherein, γ is confidence level, (pt1,pt2,pt3,pt4) it is EV predicted load, (pw1,pw2,pw3,pw4) it is grid-connected
Run the power output predicted value of wind power plant, (θ1,θ2,θ3,θ4) indicate proportionality coefficient, Pchange(j, t) is changing for jth EVt period
Electric battery charge levels, Pwind(k, t) is the wind power output of k-th of wind power plant t period, and w indicates w-th of wind power plant, ΩwIndicate institute
There is wind power plant set.
Optionally, described according to the constraint condition, it is passed through using described in the particle swarm optimization algorithm with contraction factor
Help model, obtains optimal electric charging station scheduling strategy, specifically includes:
According to constraint condition, speed, position and the fitness function value and population of each particle in population are initialized
Individual optimal value and group's optimal value；
Utilize the speed more new formula v (n) for having contraction factorm+1=φ { v (n)m+c1r1[pbestm-X(n)m]+c2r2
[gbest-X(n)m], update the speed of each particle；Wherein, φ is contraction factor,
c1And c2Respectively indicate the first Studying factors and the second Studying factors；v(t)m+1With v (n)mIt indicates the m+1 times iteration and changes for the m times
The speed of n-th of particle in generation, X (n)mIndicate the position of n-th of particle of the m times iteration；pbestmIndicate the m times iteration
Individual optimal value；Gbest indicates group's optimal value；r1And r2The first random number and second respectively in [0,1] range is random
Number.
Utilize location update formula X (n)m+1=X (n)m+v(n)m+1, update the position of each particle, wherein X (n)m+1Table
Show the position of n-th of particle of the m+1 times iteration；
According to the speed of each particle and position, the economic model is calculated, calculates the fitness function of each particle；
Individual optimal value is set by the maximum particle of the fitness function value, judges whether the individual optimal value is big
Group's optimal value in last iterative process, obtains the 5th judging result；
If the individual optimal value is greater than group's optimal value in last iterative process, the individual optimal value is set
It is set to group's optimal value；
Repeatedly whether third generation number is less than preset threshold for judgement, obtains the 6th judging result；
If the 6th judging result indicates that third the number of iterations is less than preset threshold, third the number of iterations increases by 1, returns
It returns step and " utilizes the speed more new formula v (n) for having contraction factorm+1=φ { v (n)m+c1r1[pbestm-X(n)m]+c2r2
[gbest-X(n)m], update the speed of each particle "；
If the 6th judging result indicates that third the number of iterations is not less than the preset threshold, by group's optimal value
Position is set as optimal electric charging station scheduling strategy.
The specific embodiment provided according to the present invention, the invention discloses following technical effects:
The present invention provides a kind of electric charging station Optimization Scheduling based on load margin domain.The dispatching method includes such as
Lower step: firstly, establishing the economic load dispatching model of electric charging station, and the constraint of economic load dispatching model is determined based on load margin domain
Condition is obtained optimal filling and is changed according to constraint condition using the particle swarm optimization algorithm economic model for having contraction factor
Electric scheduling strategy, is scheduled electric charging.The present invention obtains the optimal solution of economic model by particle swarm algorithm, is filled with improving
The economic benefit of electrical changing station in the process of running, and determine based on load margin domain the constraint condition of economic load dispatching model, according to
Constraint condition is solved, and ensure that the stability of electric charging station in the process of running.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is a kind of flow chart of the electric charging station Optimization Scheduling based on load margin domain provided by the invention；
Fig. 2 is the composition figure of electric charging station provided by the invention；
Fig. 3 is the flow chart of the constraint condition of determining electric charging station general power provided by the invention；
Fig. 4 is the credibility distribution figure of fuzzy variable provided by the invention；
Fig. 5 is the process provided by the invention using economic model described in the particle swarm optimization algorithm with contraction factor
Figure；
Fig. 6 is EV charge and discharge electricity price curve graph provided by the invention；
Fig. 7 is that wind-powered electricity generation provided by the invention predicts power curve figure
Fig. 8 is the economic benefit distribution map that the scheduling strategy provided by the invention for not considering load margin domain obtains；
Fig. 9 is the economic benefit distribution map that the scheduling strategy provided by the invention for considering load margin domain obtains；
Figure 10 is economic index curve graph provided by the invention.
Specific embodiment
The object of the present invention is to provide a kind of electric charging station Optimization Schedulings based on load margin domain, are changed with improving to fill
The economic benefit and stability of power station in the process of running.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Mode is applied to be described in further detail invention.
There are two aspect uncertain factors in invention: one is charging EV load, the second is changing electric battery charge levels.This is
It is as power supply role, but EV user's vehicle when EV user uses vehicle because electric car is as load role in charging
Be again it is random and it is difficult to predict, so the stability problem of both loads have it is to be solved.
The present invention puts forward that a kind of ' load margin domain ' index " charging EV load margin domain " and " changes electric battery charge
Amount nargin domain " describes above two uncertain load.By the load service condition of one day, the load margin of next day is extrapolated
Domain, and consider special circumstances, it is solved using load variance.In a model next day will be further constrained using load margin domain
Load, realize that economy is optimal, while additionally aiding peak load shifting.So the uncertain problem to these two aspects is analyzed
It is of great significance.
Fig. 1 is a kind of flow chart of the electric charging station Optimization Scheduling based on load margin domain provided by the invention, such as
Shown in Fig. 1, the dispatching method includes the following steps:
Step 101, the economic load dispatching model of electric charging station is established.
Consider that the electric charging station of wind power integration establishes " wind-net-vehicle load " mould using EV as network system electric terminal
Type, as shown in Fig. 2, comprising be incorporated into the power networks wind power plant, to EV carry out United Dispatching network system；It is uncertain containing two parts
The EV of load.It include: the load that the charging EV of vehicle charging is carried out to power battery in three parts EV；And it is completed on charging rack
The quantity of electric charge for changing battery of rapidly replacing battery.
The economic load dispatching model that electric charging station is established described in step 101, specifically includes:
Establish charging station economic well-being of workers and staff model:Its
In, F11For charging station economic well-being of workers and staff, T is to dispatch total period, CsgIt (t) is sale of electricity electricity price of the t period charging station to power grid；Psg(t)
Indicate power of the t period charging station to power grid sale of electricity；σt1For the sale of electricity period；Ccha(i, t) is the charging electricity of i-th EVt period
Valence；Pcha(i, t) is the charge power of i-th EVt period；σt2For charge period；Cbg(t) indicate t period charging station from power grid
Purchase electricity price；PbgIt (t) is power of the t period charging station from power grid power purchase, kW；σt3For block of purchase electricity；Cdischa(i, t) is the
The electric discharge electricity price of i platform EVt period；Pdischa(i, t) is the discharge power of i-th EVt period；σt4For the period of discharging.
Establish electrical changing station economic well-being of workers and staff model: F12=f1+f2；Wherein, F12For electrical changing station economic well-being of workers and staff, f1It is every for electrical changing station
The service fee collected when electricity service to EV user is changed in secondary offer,
N2For user's electrical changing station battery pack number ready for use；Crent(j, t) is lease expenses of the jth group EV on-vehicle battery in time period t；
Cservice(j, t) is that jth group EV leases battery in the Additional Services expense of time period t；σt5For within one day lease service it is total
Number；f2The income of charging is carried out by user's vehicle current electric quantity for electrical changing station,CsaleIndicate that EV changes electrical zero price；SOC (j, t) is jth platform EV battery pack t
The quantity of electric charge percentage of battery when period needs to change electricity demanding；SjFor the rated capacity of jth platform EV battery pack.
Establish wind power plant economic well-being of workers and staff model:
Wherein, F13For wind power plant economic well-being of workers and staff；N3For the number of wind power plant；P (k, t) indicates k-th of wind power plant in the hair of t period
Electrical power；CwindFor wind-powered electricity generation rate for incorporation into the power network；Pact(k, t) is the practical power output of k-th of wind power plant t period；σt6What is indicated is wind-powered electricity generation
It surfs the Internet the period；Ppre(k, t) is the prediction power output of i-th of wind power plant t period；CbcThe rejection penalty system of power output is reduced for wind power plant
Number.
According to the charging station economic well-being of workers and staff model, the electrical changing station economic well-being of workers and staff model and wind power plant economic well-being of workers and staff mould
Type establishes the economic load dispatching model maxF of charging station1=F11+F12+F13。
Step 102, it is based on load margin domain, determines the constraint condition of economic load dispatching model.
Determine the constraint condition of EV charge power: 0≤Pcha(i,t)≤Pchamax(i, t), wherein Pchamax(i, t) is i-th
Maximum charge power of the platform EV in the t period；Determine the constraint condition of EV discharge power: 0≤Pdischa(i,t)≤Pdischamax(i,
T), wherein Pdischamax(i, t) is maximum discharge power of i-th EV in the t period；Determine the constraint item of charging station power purchase power
Part: 0≤Pbg(t)≤Pbgmax(t), wherein PbgmaxIt (t) is t period electric charging station maximum purchase of electricity；Determine charging station sale of electricity function
The constraint condition of rate: 0≤Psg(t)≤Psgmax(t), wherein PsgmaxIt (t) is t period electric charging station maximum electricity sales amount；Determine wind
Electric field output power constraint condition: 0≤Pact(k,t)≤Ppre(k,t)≤PN(k), wherein PN(k) hold for the installation of wind power plant k
Amount；Determine the constraint condition for changing electric battery charge levels: SOCmin≤SOC(j,t)≤SOCmax, wherein SOCminAnd SOCmaxRespectively
The maximum value and minimum value of battery charge levels are changed in expression；
Based on load margin domain, the constraint condition of electric charging station general power is determined:
Wherein, Pmin(d) and Pmax(d) maximum value and minimum value of the d-1 days electric charging station general powers are respectively indicated.
Load margin domain index: it for " the charging electric automobile load " in " wind-net-vehicle load " model and " changes electric
Uncertain propose " the charging EV load margin domain " and " changing electric battery charge levels nargin domain " two of two kinds of loads of the pond quantity of electric charge "
Index, to detect daily load variations.
As shown in figure 3, being based on load margin domain, determines the constraint condition of electric charging station general power, specifically includes:
Firstly, input historical data, and nargin domain is calculated according to historical data, it is calculated according to the M-1 days historical datas
The M-1 days load lower limit Pmin(M-1), upper load limit Pmax(M-1) and electric charging load margin domain Pλ(M-1)；According to the M days
Historical data calculate the M days load lower limit Pmin(M), upper load limit Pmax(M) and electric charging load margin domain Pλ(M)。
Wherein, the M-1 days load lower limit P were calculated according to the M-1 days historical datasmin(M-1), upper load limit Pmax
(M-1) and electric charging load margin domain Pλ(M-1), it specifically includes, according to the M-1 days historical datas, utilizes formulaDetermine the M-1 days charging load lower limit Pchamin(M-
1)；Wherein, Pcha(i, M-1, t) indicates the charge power of the M-1 days i-th EV of t period, specifically, according to M-1
It historical data, utilizes formulaDetermine M-1
It electric discharge load lower limit Pchangemin(M-1)；Wherein, Pchange(j, M-1, t) indicates the M-1 days t period jth group EV
On-vehicle battery changes electrical power；According to the M-1 days historical datas, formula is utilizedDetermine the M-1 days charging upper load limit Pchamax(M-
1)；According to the M-1 days historical datas, formula is utilizedDetermine the M-1 days electric discharge upper load limits
Pchangemax(M-1)；According to the M-1 days charging load lower limits and the M-1 days electric discharge load lower limits, formula P is utilizedmin(M-
1)=Pchamin(M-1)+Pchangemin(M-1), the M days load lower limit P are calculatedmin(M-1)；According to the M-1 days charging loads
The upper limit and the M-1 days electric discharge upper load limits, utilize formula Pmax(M-1)=Pchamax(M-1)+Pchangemax(M-1), the is calculated
M-1 days upper load limit Pmax(M-1)；According to the M-1 days upper load limits and the M-1 days load lower limits, formula is utilizedCalculate the M-1 days electric charging load margin domain Pλ(M-1).According to M
It historical data calculates the M days load lower limit Pmin(M), upper load limit Pmax(M) and electric charging load margin domain Pλ(M)
Method is same as mentioned above, no longer burdensome herein.
Then, the load lower limit according to the M-1 days and electric charging load margin domain utilize formula P (M-1)=Pmin(M-1)
+Pλ(M-1), the load margin value P (M-1) for calculating the M-1 days, according to the M days load lower limit Pmin(M) and electric charging load
Nargin domain Pλ(M), formula P (M)=P is utilizedmin(M)+Pλ(M), the M days load margin value P (M) are calculated.
Then, judge whether the M days load margin values are greater than the M-1 days load margin values, obtain the first judgement knot
Fruit；If the load margin value that first judging result is the M days is not more than the M-1 days load margin values, formula is utilized
Pmax(M+1)=Pmin(M)+2K1*Pλ(M) valley-fill calculating was carried out to the M days load margin values, obtained on the M+1 days loads
Limit Pmax(M+1), and P is enabledmin(M+1)=Pmin(M) the M+1 days load lower limit P are obtainedmin(M+1)；Wherein, K1For valley-fill system
Number；If the load margin value that first judging result is the M days is greater than the M-1 days load margin values, formula P is utilizedmin
(M+1)=Pmax(M)-2K2*Pλ(M) peak clipping calculating was carried out to the M-1 days load margin values, obtains the M+1 days load lower limits
Pmin(M+1), and P is enabledmax(M+1)=Pmax(M), the M+1 days upper load limit P are obtainedmax(M+1)；Wherein, K2For peak clipping system
Number.
Finally, judging whether M+1 is less than d, the second judging result is obtained；Step 307, if second judging result is M+
1, which is less than d, utilizes formula then according to the M+1 days upper load limits and load lower limitCalculate the M+1 days load margin value P (M+1), and by M
Numerical value increase by 1, return step " judges whether the M days load margin values are greater than the M-1 days load margin values, obtains the
One judging result "；If second judging result is that M+1 is not less than d, by the M+1 days upper load limits and load lower limit point
It is not set as the maximum value and minimum value of the d days electric charging station general powers.
Due to cannot be guaranteed two kinds of uncertain loads of " wind-net-vehicle load " Optimal Operation Model daily all in normal
Range inevitably has certain fortuitous events and occurs.Therefore, in order to ensure that the reasonability of load margin domain prediction, will be retouched with standard deviation sigma
The dispersion degree for stating one day load data, when electric charging station encounters fortuitous event, the easy mistake of load (quantity of electric charge) standard deviation
Greatly, that is, illustrate that load peak-valley difference is excessive, belong to bad data, make rejecting processing.If the load of proxima luce (prox. luc) is not in zone of reasonableness
Within, then the proxima luce (prox. luc) of proxima luce (prox. luc) is taken, and so on, until reasonable value, then carry out the scheduling of next day.It specifically includes:
Enable the first the number of iterations s=0；Load criterion according to the M-s days historical datas, calculating the M-s days is poor；Judgement
The M-s days load criterion differences obtain third judging result whether in pre-set interval；If the third judging result indicates the
The numerical value of first the number of iterations is then increased by 1, return step is " according to M- not in pre-set interval by M-s days load criterion differences
The load criterion of s days historical datas, calculating the M-s days is poor "；If the third judging result indicates the M-s days load marks
The M days historical datas are then replaced with the M-s days historical datas in pre-set interval by quasi- difference；Enable secondary iteration number l=
0；Load criterion according to the M-s-l days historical datas, calculating the M-s-l days is poor；Judge that the M-s-l days load criterions are poor
Whether in pre-set interval, the 4th judging result is obtained；If the 4th judging result indicates that the M-s-l days load criterions are poor
Not in pre-set interval, then the numerical value of secondary iteration number is increased by 1, return step " according to the M-s-l days historical datas,
The load criterion for calculating the M-s-l days is poor "；If the 4th judging result indicates the M-s-l days load criterion differences default
In section, then the M-1 days historical datas are replaced with to the M-s-l days historical datas.
Credibility Theory is to study a new branch of mathematics of blooming quantitative law, and establish in strict axiom
Change on basis, credibility measure can be defined, fuzzy variable, subordinating degree function, credibility distribution, phase may further be drawn
The key concepts such as prestige value, variance.
Credibility measure: the credibility measure of axiomatic definition is exactly to meet normality, monotonicity, self-duality on domain
With the set function of maximization four axioms of additive property.
In order to guarantee that set function Cr { A } there are people intuitively it is expected the certain mathematical properties having, it may be considered that as follows
Five axioms:
Axiom 1:Cr { Θ }=1；
Axiom 2:Cr is to be increased monotonically, it may be assumed that whenWhen have Cr { A }≤Cr { B }；
Axiom 3:Cr is self dual, it may be assumed that has Cr { A }+Cr { A to arbitrary A ∈ P (Θ)c}=1；
Axiom 4: meet Cr { A to anyi{ the A of }≤0.5iThere is Cr { ∪iAi∧ 0.5=supiCr{Ai}；
Axiom 5: Θ is setkIt is nonempty set, CrkMeet preceding four axioms respectively on it, k=1,2 ... n, and Θ=
Θ1×Θ2×…×Θn, then to each (θ1,θ2,…,θn) ∈ Θ establishment
Meet:
Cr{(θ1,θ2,…θn)=Cr1{θ1}∧Cr2{θ2}∧…Crn{θn}
In summary five theorems, then can define: if set function Cr meets preceding four axioms, be referred to as credible survey
Degree.
Fuzzy Chance Constraint: the Fuzzy Chance Constrained Programming Model based on Credibility Theory may be expressed as:
minf(X)
s.t.Cr(gj(X, ξ)≤0) >=γ j=1,2 ... n
In formula, Cr { } indicates the credibility that { } occurs, and γ indicates preset confidence level, gj(X, ξ)≤0 is about
Beam condition, wherein X is decision variable, and ξ indicates fuzzy variable.
Uncertain variables and its credibility distribution: uncertain variables namely fuzzy variable in the present invention are wind power plant
Power output and daily load prediction value.The power output of wind power plant can be by Trapezoid Fuzzy Number (Pw1,Pw2,Pw3,Pw4) indicate, daily load prediction
Value can use Trapezoid Fuzzy NumberIndicate, then the credibility distribution figure of fuzzy variable as shown in Figure 4 and distribution function
Are as follows:
In formula,It can be obtained according to prediction data, i=t or i=w.If prediction data isAnd proportionality coefficient is set as θk, it can be obtained by data:
Due to containing the wind power plant that is incorporated into the power networks in electric charging station, it is therefore desirable to consider the uncertain problem of wind power output, together
When also to consider the uncertain problem of EV load prediction.Therefore the present invention is solved the two uncertainties and is asked using Credibility Theory
Topic carries out Uncertainty Management to the constraint condition, is established respectively for two processes of peak load shifting based on credibility measure
Inequality constraints condition:
In formula, Cr indicates credibility measure；μ is fuzzy variable；Pact(i, t) is the power output of i-th of wind power plant t period；γ
For confidence level.
Sharpening equivalent processes: in fuzzy chance planning process, most important step is exactly the place of fuzzy restriction condition
Reason.Constraint condition is subjected to sharpening processing.
If function gj(X, ξ) has following form:
gj(X, ξ)=h1(X)ξ1+h2(X)ξ2+...+hm(X)ξm+h0(X) (40)
In formula, ξkIt is expressed as trapezoidal fuzzy variable (ξk1,ξk2,ξk3,ξk4), k=1,2 ..., m m ∈ R, ξk1-ξk4It indicates to be subordinate to
Category degree parameter.
Construct two functions:
Above-mentionedNeed to meet following formula condition:
WhenWhen,WhenWhen,
To the practical problem of formula (38), formula (39) description, greater than 0.5, therefore confidence level generally requires
Cr { g (X, ξ)≤0 } >=γ is just of equal value are as follows:
For ease of calculation, EV load and wind-powered electricity generation load are further processed.According to formula (38), formula (39), EV load is pre-
Shown in measured value such as formula (45), the predicted value of wind power output is such as shown in (46):
Formula (45) and (46) are brought into formula (44), and spread out merging similar terms, it is negative that formula (38) conversion is obtained EV
Lotus, the wind power plant that is incorporated into the power networks power output do not know equivalent form:
Similarly, (39) can be equivalent to as shown in formula (48):
W indicates w-th of wind power plant, ΩwIt indicates all wind power plant set, during summation, enables w respectively1And w2Equal to institute
There is wind power plant set omegawIn w sum.
Step 103, according to the constraint condition, using economic mould described in the particle swarm optimization algorithm with contraction factor
Type obtains optimal electric charging station scheduling strategy.
Particle swarm optimization algorithm is that a kind of simulate during flock of birds is looked for food migrates the intelligent algorithm with clustering behavior.In conjunction with this
Text, EV particle find optimal solution, have many advantages, such as that fast convergence rate, algorithm are easily realized from RANDOM SOLUTION, iteration.
As shown in figure 5, according to the constraint condition described in step 103, using the particle swarm optimization algorithm with contraction factor
The economic model is solved, optimal electric charging station scheduling strategy is obtained, specifically includes:
According to constraint condition, speed, position and the fitness function value and population of each particle in population are initialized
Individual optimal value and group's optimal value.Each particle is according to specifying constraint given initial value, by the electric power of each EV
As control variable, will be divided within one day T (T=24) it is a when discontinuity surface, generating primary group population quantity is 50, and each grain
Son is T × (N1+N2The matrix of) × 2, T are 24 hours one day:
Formula X above1Indicate the initialization particle of a certain period charging some day, Pcha1,1With Pchange1,1It is expressed as first
The charge power of the 1st EV of a period and first EV battery pack change electrical power.
Utilize the speed more new formula v (n) for having contraction factorm+1=φ { v (n)m+c1r1[pbestm-X(n)m]+c2r2
[gbest-X(n)m], update the speed of each particle；Wherein, φ is contraction factor,
c1And c2Respectively indicate the first Studying factors and the second Studying factors；v(t)m+1With v (n)mIt indicates the m+1 times iteration and changes for the m times
The speed of n-th of particle in generation, X (n)mIndicate the position of n-th of particle of the m times iteration；pbestmIndicate the m times iteration
Individual optimal value；Gbest indicates group's optimal value.In speed more new formula, Studying factors c1And c2Particle is respectively represented certainly
The influence of body posterior infromation and other particle posterior infromations to particle running track.As Studying factors c1When larger, particle can be made
It hovers in subrange too much；And Studying factors c2When larger, and particle Premature Convergence can be made to local minimum.In order to
The flying speed for efficiently controlling particle makes algorithm reach the balance of global detection and part exploitation between the two, and the present invention is in speed
Contraction factor is added in degree more new formula；r1And r2For the uniform random number in [0,1] range.
Utilize location update formula X (n)m+1=X (n)m+v(n)m+1, update the position of each particle.
According to the speed of each particle and position, the economic model is calculated, calculates the fitness function of each particle.
Individual optimal value is set by the maximum particle of the fitness function value, judges whether the individual optimal value is big
Group's optimal value in last iterative process, obtains the 5th judging result.
If the individual optimal value is greater than group's optimal value in last iterative process, the individual optimal value is set
It is set to group's optimal value.
Repeatedly whether third generation number is less than preset threshold for judgement, obtains the 6th judging result.
If the 6th judging result indicates that third the number of iterations is less than preset threshold, third the number of iterations increases by 1, returns
It returns step and " utilizes the speed more new formula v (n) for having contraction factorm+1=φ { v (n)m+c1r1[pbestm-X(n)m]+c2r2
[gbest-X(n)m], update the speed of each particle ".
If the 6th judging result indicates that third the number of iterations is not less than the preset threshold, by group's optimal value
Position is set as optimal electric charging station scheduling strategy.
Step 104, based on the optimal electric charging station scheduling strategy, the scheduling of electric charging station is carried out.
In order to verify the validity of the electric charging station Optimization Scheduling of the invention based on load margin domain, to the present invention
Method has carried out simulating, verifying:
Based on above-mentioned model, a certain EV electric charging station share 1000 EV, 400 change battery and need to dispatch, it is grid-connected
Run four wind power plants: assuming that each EV battery capacity having the same and charge-discharge electric power, the wherein battery capacity of each EV
For 12KWh, charge power 3KW, discharge power 1.5KW, SOCNmin=20%, SOCNmax=90%, every change electric installation often into
Row once changes electrically operated, will pay 10 yuan of the battery owner of lease expenses, 10 meta-service expenses.It is set altogether in the electric charging station
There are 80 charging piles, the charge power of every charging pile is adjustable within the scope of 50kW, wherein each wind power plant being incorporated into the power networks is total
Capacity is 40MW (doubly-fed wind turbine that 20 single-machine capacities are 2MW).EV charge and discharge electricity price curve is as shown in attached drawing 6, wind
Electricity prediction power curve is as shown in attached drawing 7.
To further discuss influence of the electric charging station to " wind-net-vehicle load " model economy, and emphasize load margin
The importance in domain, set forth herein following two categories models to compare and analyze, and the results are shown in attached figure 8 shown in attached drawing 9:
Table 1 does not consider that the model result in load margin domain compares
| Model | Economic well-being of workers and staff (104 yuan) | Joint load variance (10^8kW2) |
| 1. considering charging station | 3.2978 | 1.1814 |
| 2. considering electrical changing station | 32.8220 | 1.1803 |
| 3. considering electric charging station | 33.1990 | 0.3771 |
Table 2 considers that the model result in load margin domain compares
| Model | Economic well-being of workers and staff (104 yuan) | Joint load variance (10^8kW2) |
| 4. considering charging station | 6.3573 | 0.1417 |
| 5. considering electrical changing station | 32.9920 | 0.1084 |
| 6. considering electric charging station | 33.6990 | 0.1115 |
In conclusion by the comparative analyses of 6 models it can be concluded that, load margin domain being introduced into and considers electrical changing station
When electric charging station model, it is slightly above on 2.9% basis of electrical changing station in joint load variance and still maintains highest economic well-being of workers and staff,
About 33.7 ten thousand yuan.
And consider the EV electric charging station optimum results under different confidence levels.Economic index curve is as shown in Fig. 10,
It can be concluded that confidence level takes between 0.8-0.9, economy is best, about 33.70 ten thousand yuan.
The specific embodiment provided according to the present invention, the invention discloses following technical effects:
The present invention provides a kind of electric charging station Optimization Scheduling based on load margin domain.The dispatching method includes such as
Lower step: firstly, establishing the economic load dispatching model of electric charging station, and the constraint of economic load dispatching model is determined based on load margin domain
Condition is obtained optimal filling and is changed according to constraint condition using the particle swarm optimization algorithm economic model for having contraction factor
Electric scheduling strategy, is scheduled electric charging.The present invention obtains the optimal solution of economic model by particle swarm algorithm, is filled with improving
The economic benefit of electrical changing station in the process of running, and determine based on load margin domain the constraint condition of economic load dispatching model, according to
Constraint condition is solved, and ensure that the stability of electric charging station in the process of running.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.For system disclosed in embodiment
For, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is said referring to method part
It is bright.
Specific examples are used herein to describe the principles and implementation manners of the present invention, the explanation of above embodiments
Method and its core concept of the invention are merely used to help understand, described embodiment is only that a part of the invention is real
Example is applied, instead of all the embodiments, based on the embodiments of the present invention, those of ordinary skill in the art are not making creation
Property labour under the premise of every other embodiment obtained, shall fall within the protection scope of the present invention.
Claims (8)
1. a kind of electric charging station Optimization Scheduling based on load margin domain, which is characterized in that the dispatching method includes such as
Lower step:
Establish the economic load dispatching model of electric charging station；
Based on load margin domain, the constraint condition of economic load dispatching model is determined；
It is obtained optimal according to the constraint condition using economic model described in the particle swarm optimization algorithm with contraction factor
Electric charging station scheduling strategy；
Based on the optimal electric charging station scheduling strategy, the scheduling of electric charging station is carried out.
2. the electric charging station Optimization Scheduling according to claim 1 based on load margin domain, which is characterized in that described
The economic load dispatching model for establishing charging station, specifically includes:
Establish charging station economic well-being of workers and staff model:
Wherein, F11For charging station economic well-being of workers and staff, T is to dispatch total period, CsgIt (t) is sale of electricity electricity price of the t period charging station to power grid；
Psg(t) power of the t period charging station to power grid sale of electricity is indicated；σt1For the sale of electricity period；Ccha(i, t) is filling for i-th EVt period
Electricity price；Pcha(i, t) is the charge power of i-th EVt period；σt2For charge period；Cbg(t) indicate t period charging station from
The purchase electricity price of power grid；PbgIt (t) is power of the t period charging station from power grid power purchase, kW；σt3For block of purchase electricity；Cdischa(i,t)
For the electric discharge electricity price of i-th EVt period；Pdischa(i, t) is the discharge power of i-th EVt period；σt4For the period of discharging；
Establish electrical changing station economic well-being of workers and staff model: F12=f1+f2；
Wherein, F12For electrical changing station economic well-being of workers and staff, f1The service charge changed and collected when electricity service to EV user is provided every time for electrical changing station
With,N2For user's electrical changing station battery pack number ready for use；
Crent(j, t) is lease expenses of the jth group EV on-vehicle battery in time period t；Cservice(j, t) be jth group EV lease battery when
Between section t Additional Services expense；σt5For the total degree of lease service within one day；f2User's vehicle current electric quantity is pressed for electrical changing station
The income of charging is carried out,CsaleIndicate that EV changes electrical zero price；SOC(j,t)
The quantity of electric charge percentage of battery when needing to change electricity demanding for the jth platform EV battery pack t period；SjFor the specified of jth platform EV battery pack
Capacity, kW；
Establish wind power plant economic well-being of workers and staff model:
Wherein, F13For wind power plant economic well-being of workers and staff；N3For the number of wind power plant；P (k, t) indicates k-th of wind power plant in the hair of t period
Electrical power；CwindFor wind-powered electricity generation rate for incorporation into the power network；Pact(k, t) is the practical power output of k-th of wind power plant t period；σt6What is indicated is wind-powered electricity generation
It surfs the Internet the period；Ppre(k, t) is the prediction power output of i-th of wind power plant t period；CbcThe rejection penalty system of power output is reduced for wind power plant
Number；
According to the charging station economic well-being of workers and staff model, the electrical changing station economic well-being of workers and staff model and wind power plant economic well-being of workers and staff model, build
The economic load dispatching model max F of vertical charging station1=F11+F12+F13。
3. the electric charging station Optimization Scheduling according to claim 2 based on load margin domain, which is characterized in that described
Based on load margin domain, determines the constraint condition of economic load dispatching model, specifically includes:
Determine the constraint condition of EV charge power: 0≤Pcha(i,t)≤Pchamax(i, t), wherein Pchamax(i, t) is i-th EV
In the maximum charge power of t period；
Determine the constraint condition of EV discharge power: 0≤Pdischa(i,t)≤Pdischamax(i, t), wherein Pdischamax(i, t) is
Maximum discharge power of i-th EV in the t period
Determine the constraint condition of charging station power purchase power: 0≤Pbg(t)≤Pbgmax(t), wherein PbgmaxIt (t) is t period electric charging
Maximum of standing purchase of electricity
Determine the constraint condition of charging station sale of electricity power: 0≤Psg(t)≤Psgmax(t), wherein PsgmaxIt (t) is t period electric charging
Maximum of standing electricity sales amount；
Determine the constraint condition for changing electric battery charge levels: SOCmin≤SOC(j,t)≤SOCmax, wherein SOCminAnd SOCmaxRespectively
The maximum value and minimum value of battery charge levels are changed in expression；
Based on load margin domain, the constraint condition of electric charging station general power is determined:
Wherein, Pmin(d) and Pmax(d) maximum value and minimum value of the d days electric charging station general powers are respectively indicated；
Determine Power Output for Wind Power Field constraint condition: 0≤Pact(k,t)≤Ppre(k,t)≤PN(k), wherein PNIt (k) is wind power plant
The installed capacity of k.
4. the electric charging station Optimization Scheduling according to claim 3 based on load margin domain, which is characterized in that described
Based on load margin domain, determines the constraint condition for changing electric battery charge levels, specifically includes:
The M-1 days load lower limit P are calculated according to the M-1 days historical datasmin(M-1), upper load limit Pmax(M-1) He Chonghuan
Electric load nargin domain Pλ(M-1)；The M days load lower limit P are calculated according to the M days historical datasmin(M), upper load limit Pmax
(M) and electric charging load margin domain Pλ(M)；
According to the M-1 days load lower limits and electric charging load margin domain, formula P (M-1)=P is utilizedmin(M-1)+Pλ(M-1),
The load margin value P (M-1) for calculating the M-1 days, according to the M days load lower limit Pmin(M) and electric charging load margin domain Pλ
(M), formula P (M)=P is utilizedmin(M)+Pλ(M), the M days load margin value P (M) are calculated；
Judge whether the M days load margin values are greater than the M-1 days load margin values, obtains the first judging result；
If the load margin value that first judging result is the M days is not more than the M-1 days load margin values, formula is utilized
Pmax(M+1)=Pmin(M)+2K1*Pλ(M) valley-fill calculating was carried out to the M days load margin values, obtained on the M+1 days loads
Limit Pmax(M+1), and P is enabledmin(M+1)=Pmin(M) the M+1 days load lower limit P are obtainedmin(M+1)；Wherein, K1For valley-fill system
Number；
If the load margin value that first judging result is the M days is greater than the M-1 days load margin values, formula is utilized
Pmin(M+1)=Pmax(M)-2K2*Pλ(M) peak clipping calculating was carried out to the M-1 days load margin values, obtains the M+1 days loads
Lower limit Pmin(M+1), and P is enabledmax(M+1)=Pmax(M), the M+1 days upper load limit P are obtainedmax(M+1)；Wherein, K2For peak clipping
Coefficient；
Judge whether M+1 is less than d, obtains the second judging result；
If second judging result, which is less than d for M+1, utilizes formula according to the M+1 days upper load limits and load lower limitCalculate the M+1 days load margin value P (M+1), and by M
Numerical value increase by 1, return step " judges whether the M days load margin values are greater than the M-1 days load margin values, obtains the
One judging result ", wherein K indicates load margin coefficient；
If second judging result is that M+1 is not less than d, the M+1 days upper load limits and load lower limit are respectively set to
The maximum value and minimum value of d days electric charging station general powers.
5. the electric charging station Optimization Scheduling according to claim 4 based on load margin domain, which is characterized in that institute
It states and determines the M-1 days load lower limit P according to the M-1 days historical datasmin(M-1), upper load limit Pmax(M-1) and electric charging
Load margin domain Pλ(M-1), it specifically includes:
According to the M-1 days historical datas, formula is utilized
Determine the M-1 days charging load lower limit Pchamin(M-1)；Wherein, Pcha(i, M-1, t) indicates the M-1 days t periods the
The charge power of i platform EV；
According to the M-1 days historical datas, formula is utilized
Determine the M-1 days electric discharge load lower limit Pchangemin(M-1)；Wherein, Pchange(j, M-1, t) indicates the M-1 days t time
Section jth group EV on-vehicle battery changes electrical power；
According to the M-1 days historical datas, formula is utilized
Determine the M-1 days charging upper load limit Pchamax(M-1)；
According to the M-1 days historical datas, formula is utilized
Determine the M-1 days electric discharge upper load limit Pchangemax(M-1)；
According to the M-1 days charging load lower limits and the M-1 days electric discharge load lower limits, formula P is utilizedmin(M-1)=Pchamin
(M-1)+Pchangemin(M-1), the M days load lower limit P are calculatedmin(M-1)；
According to the M-1 days charging upper load limits and the M-1 days electric discharge upper load limits, formula P is utilizedmax(M-1)=Pchamax
(M-1)+Pchangemax(M-1), the M-1 days upper load limit P are calculatedmax(M-1)；
According to the M-1 days upper load limits and the M-1 days load lower limits, formula is utilized
Calculate the M-1 days electric charging load margin domain Pλ(M-1)。
6. the electric charging station Optimization Scheduling according to claim 4 based on load margin domain, which is characterized in that described
The M-1 days load lower limit P are calculated according to the M-1 days historical datasmin(M-1), upper load limit Pmax(M-1) and electric charging is negative
Lotus nargin domain Pλ(M-1), before further include:
Enable the first the number of iterations s=0；
Load criterion according to the M-s days historical datas, calculating the M-s days is poor；
The load criterion difference for judging the M-s days obtains third judging result whether in pre-set interval；
If the third judging result indicates the M-s days load criterion differences not in pre-set interval, by the first the number of iterations
Numerical value increase by 1, return step " according to the M-s days historical datas, the load criterion of calculating the M-s days was poor "；
If the third judging result indicated the M-s days load criterion differences in pre-set interval, by the M days historical datas
Replace with the M-s days historical datas；
Enable secondary iteration number l=0；
Load criterion according to the M-s-l days historical datas, calculating the M-s-l days is poor；
The load criterion difference for judging the M-s-l days obtains the 4th judging result whether in pre-set interval；
If the 4th judging result indicates the M-s-l days load criterion differences not in pre-set interval, by secondary iteration time
Several numerical value increases by 1, return step " poor according to the load criterion of the M-s-l days historical datas, calculating the M-s-l days "；
If the 4th judging result indicated the M-s-l days load criterion differences in pre-set interval, by the M-1 days history
Data replace with the M-s-l days historical datas.
7. the electric charging station Optimization Scheduling according to claim 4 based on load margin domain, which is characterized in that described
The constraint condition for determining electric charging station general power further includes later
Based on Credibility Theory, uncertain equivalent form is converted by the constraint condition of electric charging station general power are as follows:
Or
Wherein, γ is confidence level, (pt1,pt2,pt3,pt4) it is EV predicted load, (pw1,pw2,pw3,pw4) it is the wind that is incorporated into the power networks
The power output predicted value of electric field, (θ1,θ2,θ3,θ4) indicate proportionality coefficient, Pchange(j, t) changes electric battery for the jth EVt period
The quantity of electric charge, Pwind(k, t) is the wind power output of k-th of wind power plant t period, and w indicates w-th of wind power plant, ΩwIndicate all wind-powered electricity generations
Field set.
8. the electric charging station Optimization Scheduling according to claim 1 based on load margin domain, which is characterized in that described
Optimal fill is obtained using economic model described in the particle swarm optimization algorithm with contraction factor according to the constraint condition
Electrical changing station scheduling strategy, specifically includes:
According to constraint condition, of speed, position and the fitness function value and population of each particle in population is initialized
Body optimal value and group's optimal value；
Utilize the speed more new formula v (n) for having contraction factorm+1=φ { v (n)m+c1r1[pbestm-X(n)m]+c2r2
[gbest-X(n)m], update the speed of each particle；Wherein, φ is contraction factor,
c1And c2Respectively indicate the first Studying factors and the second Studying factors；v(t)m+1With v (n)mIt indicates the m+1 times iteration and changes for the m times
The speed of n-th of particle in generation, X (n)mIndicate the position of n-th of particle of the m times iteration；pbestmIndicate the m times iteration
Individual optimal value；Gbest indicates group's optimal value；r1And r2The first random number and second respectively in [0,1] range is random
Number.
Utilize location update formula X (n)m+1=X (n)m+v(n)m+1, update the position of each particle, wherein X (n)m+1Indicate m
The position of n-th of particle of+1 iteration；
According to the speed of each particle and position, the economic model is calculated, calculates the fitness function of each particle；
Individual optimal value is set by the maximum particle of the fitness function value, judges whether the individual optimal value is greater than
Group's optimal value during an iteration, obtains the 5th judging result；
If the individual optimal value is greater than group's optimal value in last iterative process, set the individual optimal value to
Group's optimal value；
Repeatedly whether third generation number is less than preset threshold for judgement, obtains the 6th judging result；
If the 6th judging result indicates that third the number of iterations is less than preset threshold, third the number of iterations increases by 1, returns to step
Suddenly " the speed more new formula v (n) for having contraction factor is utilizedm+1=φ { v (n)m+c1r1[pbestm-X(n)m]+c2r2
[gbest-X(n)m], update the speed of each particle "；
If the 6th judging result indicates that third the number of iterations is not less than the preset threshold, by the position of group's optimal value
It is set as optimal electric charging station scheduling strategy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910498114.6A CN110197310B (en) | 2019-06-10 | 2019-06-10 | Charging and battery replacing station optimal scheduling method based on load margin domain |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910498114.6A CN110197310B (en) | 2019-06-10 | 2019-06-10 | Charging and battery replacing station optimal scheduling method based on load margin domain |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110197310A true CN110197310A (en) | 2019-09-03 |
| CN110197310B CN110197310B (en) | 2021-04-30 |
Family
ID=67754337
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910498114.6A Active CN110197310B (en) | 2019-06-10 | 2019-06-10 | Charging and battery replacing station optimal scheduling method based on load margin domain |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110197310B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110570043A (en) * | 2019-09-16 | 2019-12-13 | 燕山大学 | scheduling method and system for wind-network-station-vehicle system |
| CN111740411A (en) * | 2020-06-22 | 2020-10-02 | 东北电力大学 | BSS-containing microgrid combined system optimization scheduling method considering backlog penalty mechanism |
| CN111856185A (en) * | 2020-07-23 | 2020-10-30 | 上海电机学院 | Embedded system and method for monitoring and improving electric energy quality of wind turbine generator |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130179135A1 (en) * | 2012-01-10 | 2013-07-11 | General Electric Company | Systems and methods for electric vehicle mobility modeling |
| CN105976065A (en) * | 2016-05-20 | 2016-09-28 | 燕山大学 | Environment economic dispatching two-level dispatching solution method including centralized charging stations |
| CN106327014A (en) * | 2016-08-24 | 2017-01-11 | 上海电机学院 | Scheduling optimization method for electric power system having wind power plant |
| CN107330564A (en) * | 2017-07-18 | 2017-11-07 | 国家电网公司 | A kind of electric automobile Optimal Operation Model based on the double yardsticks of space-time |
| CN109559035A (en) * | 2018-11-29 | 2019-04-02 | 国网(苏州)城市能源研究院有限责任公司 | A kind of urban power distribution network bi-level programming method considering flexibility |
| CN109861208A (en) * | 2019-01-07 | 2019-06-07 | 南京工程学院 | A kind of grid-connected Optimization Scheduling of electric car based on two stages pretreatment strategy |
-
2019
- 2019-06-10 CN CN201910498114.6A patent/CN110197310B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130179135A1 (en) * | 2012-01-10 | 2013-07-11 | General Electric Company | Systems and methods for electric vehicle mobility modeling |
| CN105976065A (en) * | 2016-05-20 | 2016-09-28 | 燕山大学 | Environment economic dispatching two-level dispatching solution method including centralized charging stations |
| CN106327014A (en) * | 2016-08-24 | 2017-01-11 | 上海电机学院 | Scheduling optimization method for electric power system having wind power plant |
| CN107330564A (en) * | 2017-07-18 | 2017-11-07 | 国家电网公司 | A kind of electric automobile Optimal Operation Model based on the double yardsticks of space-time |
| CN109559035A (en) * | 2018-11-29 | 2019-04-02 | 国网(苏州)城市能源研究院有限责任公司 | A kind of urban power distribution network bi-level programming method considering flexibility |
| CN109861208A (en) * | 2019-01-07 | 2019-06-07 | 南京工程学院 | A kind of grid-connected Optimization Scheduling of electric car based on two stages pretreatment strategy |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110570043A (en) * | 2019-09-16 | 2019-12-13 | 燕山大学 | scheduling method and system for wind-network-station-vehicle system |
| CN110570043B (en) * | 2019-09-16 | 2022-03-11 | 燕山大学 | Scheduling method and system for wind-network-station-vehicle system |
| CN111740411A (en) * | 2020-06-22 | 2020-10-02 | 东北电力大学 | BSS-containing microgrid combined system optimization scheduling method considering backlog penalty mechanism |
| CN111740411B (en) * | 2020-06-22 | 2021-08-17 | 东北电力大学 | BSS-containing microgrid combined system optimization scheduling method considering backlog penalty mechanism |
| CN111856185A (en) * | 2020-07-23 | 2020-10-30 | 上海电机学院 | Embedded system and method for monitoring and improving electric energy quality of wind turbine generator |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110197310B (en) | 2021-04-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Marzband et al. | Framework for smart transactive energy in home-microgrids considering coalition formation and demand side management | |
| Bozorgavari et al. | Two-stage hybrid stochastic/robust optimal coordination of distributed battery storage planning and flexible energy management in smart distribution network | |
| Mousavi et al. | A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas | |
| Soares et al. | A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads | |
| Singh et al. | Distributed power generation planning for distribution networks using electric vehicles: Systematic attention to challenges and opportunities | |
| Ghofrani et al. | Optimal charging/discharging of grid-enabled electric vehicles for predictability enhancement of PV generation | |
| CN110197310A (en) | A kind of electric charging station Optimization Scheduling based on load margin domain | |
| Gholinejad et al. | Hierarchical energy management system for home-energy-hubs considering plug-in electric vehicles | |
| CN110033142A (en) | Consider the electric charging station Optimized Operation strategy of negative rules | |
| Egbue et al. | Multi-agent approach to modeling and simulation of microgrid operation with vehicle-to-grid system | |
| CN113326467B (en) | Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties | |
| Momen et al. | Load restoration and energy management of a microgrid with distributed energy resources and electric vehicles participation under a two-stage stochastic framework | |
| He et al. | Optimal integration of Vehicle to Building (V2B) and Building to Vehicle (B2V) technologies for commercial buildings | |
| CN105207207B (en) | Micro-grid system dispatching method under isolated network state based on energy management | |
| Marasciuolo et al. | Chance-constrained calculation of the reserve service provided by EV charging station clusters in energy communities | |
| Bhuvaneswari et al. | Online economic environmental optimization of a microgrid using an improved fast evolutionary programming technique | |
| Tian et al. | Network security-aware charging of electric vehicles | |
| Qing et al. | Integrated optimal planning of distribution network with geographical-zone-restricted renewable energy sources | |
| Moravej et al. | Optimal planning of microgrid using multi criteria decision analysis | |
| CN115392785A (en) | Power distribution network congestion scheduling method considering congestion resistance | |
| CN115395547A (en) | Flexible energy storage system optimal configuration method based on whole county photovoltaic propulsion | |
| CN113937811A (en) | Optimal scheduling method for multi-energy coupling power distribution system | |
| Sasaki et al. | Dynamic economic load dispatch with emergency demand response for microgrid system operation | |
| Li et al. | Further discussions on sufficient conditions for exact relaxation of complementarity constraints for storage-concerned economic dispatch | |
| Zhang et al. | Stochastic scheduling of power system in the presence of electric vehicle and renewable sources considering security and economic indexes using an improved mutation particle swarm optimization (M-PSO) algorithm |
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 |