CN106712120B - Alternating current-direct current mixing micro-capacitance sensor optimizing operation method based on leader-followers games model - Google Patents
Alternating current-direct current mixing micro-capacitance sensor optimizing operation method based on leader-followers games model Download PDFInfo
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- 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
- H02J5/00—Circuit arrangements for transfer of electric power between ac networks and dc networks
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- 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
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- 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
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H02J3/383—
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- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- Y02E10/00—Energy generation through renewable energy sources
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- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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Abstract
A kind of alternating current-direct current mixing micro-capacitance sensor optimizing operation method based on leader-followers games model: acquisition micro-capacitance sensor region intensity of illumination, temperature, the meteorological data of cloud amount, photovoltaic power generation quantity historical data in micro-capacitance sensor is counted, next day photovoltaic cell power output in alternating current-direct current mixing micro-capacitance sensor is predicted using the prediction technique based on canonical trend；Bi-directional inverter, flow controller two sides transmission power data in alternating current-direct current mixing micro-capacitance sensor are acquired, and establish the mathematical model of various equipment in alternating current-direct current mixing micro-capacitance sensor；It was divided into multiple scheduling slots for one day, photovoltaic operator and grid company the leader-followers games model mutually coordinated in each scheduling slot interests are established, leader-followers games model includes the photovoltaic utilization rate model of photovoltaic operator benefit and the alternating current-direct current mixing micro-capacitance sensor network loss model for representing grid company minimization of loss；Using the order Oscillating particle swarm algorithm of asynchronous variation Studying factors, the global search performance of algorithm is improved.Applicability of the present invention is wide, and precision of prediction is higher.
Description
Technical field
The present invention relates to multi-target optimum operation methods in a kind of alternating current-direct current mixing micro-capacitance sensor.It is based on more particularly to one kind
The alternating current-direct current mixing micro-capacitance sensor optimizing operation method of leader-followers games model.
Background technique
A large amount of consumption of fossil energy bring serious environmental disruption in conventional electric power system, are based on this problem, new energy
Source generation mode is used widely.However, power output often exists since new energy is usually influenced by a variety of weather conditions
Very great fluctuation process.Its uncontrollability even reliably brings adverse effect to power grid power supply with safe operation.Microgrid is a variety of by integrating
Distributed generation resource and load become controllable power domain, to improve uncontrollable generation of electricity by new energy consumption rate.Therefore microgrid
Extensive concern of the technology by researchers at home and abroad.
Electric load can be divided into AC load and DC load according to its power supply mode.It is negative due to being exchanged in traditional load
Lotus accounting is very big, and the research of microgrid focuses primarily upon the research of AC microgrids.Due to current HVDC Transmission Technology more at
It is ripe, DC load increasing, very big energy will be brought to damage by inverter access AC microgrids all direct currents, power supply
Consumption, while a large amount of power electronic equipment access can also introduce a large amount of harmonic waves to power grid, be based on this background, alternating current-direct current mixes micro- electricity
Net has very big Research Prospects and application value.
In order to promote electric system market-oriented reform, China currently gradually decontrols sale of electricity side, to use for electric power
Family provides flexile electric energy service.New energy operator can equally become independent sale of electricity company in micro-capacitance sensor.With light
For lying prostrate operator, by the lower-cost photovoltaic power generation electric energy acquisition income of user's sale into microgrid, in order to maximum
Change income, the target pursued is to try to all consumptions that photovoltaic is contributed, i.e. maximization photovoltaic utilization rate.The study found that pursuing
The network loss that micro-capacitance sensor is influenced whether during maximizing photovoltaic utilization rate, that is, lead to grid company benefit damage.Such case
Under, in order to pursue respective operational objective, there are interest games with grid company for photovoltaic operator, it is therefore necessary to ask for this
Topic optimizes scheduling research.
Summary of the invention
The technical problem to be solved by the invention is to provide one kind can make the reliable and stable operation of micro-capacitance sensor, meets simultaneously
The alternating current-direct current mixing micro-capacitance sensor optimizing operation method based on leader-followers games model of surplus internet access request.
The technical scheme adopted by the invention is that: a kind of alternating current-direct current mixing micro-capacitance sensor optimization fortune based on leader-followers games model
Row method, includes the following steps:
1) micro-capacitance sensor region intensity of illumination, temperature, the meteorological data of cloud amount are acquired, photovoltaic power generation in micro-capacitance sensor is counted
Historical data is measured, next day photovoltaic cell power output in alternating current-direct current mixing micro-capacitance sensor is carried out using the prediction technique based on canonical trend
Prediction；The historical load data of exchanging area and DC area is counted respectively, next day AC load and DC load is carried out respectively pre-
It surveys；
2) bi-directional inverter, flow controller two sides transmission power data in alternating current-direct current mixing micro-capacitance sensor are acquired,
The mathematical model of various equipment in alternating current-direct current mixing micro-capacitance sensor is established using acquired data；
3) it was divided into multiple scheduling slots for one day, establishes photovoltaic operator and grid company in each scheduling slot interests phase
The leader-followers games model mutually coordinated, leader-followers games model include photovoltaic utilization rate model and the generation of photovoltaic operator benefit
The alternating current-direct current mixing micro-capacitance sensor network loss model of table grid company minimization of loss；
4) using the order Oscillating particle swarm algorithm of asynchronous variation Studying factors, the global search performance of algorithm is improved, from
And avoid precocious phenomenon.
Based on the prediction technique of canonical trend described in step 1), be on the basis of certain known period power to future when
Section power predicted, the mathematic(al) representation of the prediction technique based on canonical trend are as follows:
Ppre(t+i)=Ppre(t)+ΔPpre(t+i)=Ppre(t)(1+P′typ(t))；
Wherein, PpreIt (t+i) is the generated power forecasting value of the t+i period；PpreIt (t) is the practical hair of t-th of period
Electric power value；ΔPpreIt (t+i) is the t+i period with respect to the prediction power changing value of t-th of period；ΔPpreIt (t+i) is t
The change rate of a period to the t+i period, P'(t) it is to be obtained based on canonical trend, canonical trend is by with similar
Power calculation in the date of Meteorological Characteristics obtains.
Power in the date with similar Meteorological Characteristics is obtained by various as follows:
The similarity relation formula of day to be predicted and historical data:
Wherein, σjFor the incidence coefficient of jth day and day to be predicted, σjValue is greater than setting limit value σ0Think and day to be predicted
Belong to same similar day；ρ is resolution ratio, takes 0.5；xjFor the characterization jth day Meteorological Characteristics of the meteorological data composition counted
Vector, indicate are as follows:
xj=[xj(1),xj(2),…,xj(m)]
The incidence coefficient for calculating day to be predicted and historical data filters out and is greater than association limit value σ0Sample data, obtain K
The canonical trend P ' of similar day is calculated after a similar day by following formulatyp(t)
The mathematical model of various equipment in alternating current-direct current mixing micro-capacitance sensor described in step 2), comprising:
(1) the energy-storage battery service life
Wherein NlifeFor the cycle life of energy-storage battery；DOD is depth of discharge；NaFor depth of discharge highest power；aiFor
DOD i-th power coefficient, aiBy being fitted to obtain to measurement data；
Energy-storage battery should meet the constraint condition of power-balance, state-of-charge bound in use:
DOD (t)=1-SOC (t)
SOCmin≤SOC(t)≤SOCmax
SOC (t) is t-th of period energy-storage battery state-of-charge；For energy-storage battery capacity；PSBIt (t) is the t period
The charge-discharge electric power of energy-storage battery；Δ t is the time span of a period；The depth of discharge of DOD (t) expression energy-storage battery；
SOCmax、SOCminThe respectively upper lower limit value of state-of-charge；
(2) cost model of energy-storage battery
Including life consumption cost C0With energy loss cost C1Two parts
Wherein, CinitFor the installation cost of energy-storage battery, CgFor external power grid electricity price, η is the energy efficiency of energy-storage battery；
(3) the overall cost model of energy-storage battery:
(4) flow controller and bi-directional inverter power transmission model
Wherein,Indicate t-th of period equipmentEfficiency of transmission；riFor transimission power Pi(t) coefficient；PCS indicates tide
Stream controller, ILC indicate bi-directional inverter；
(5) transformer efficiency of transmission model
In formula: SNFor transformer capacity；β indicates load factor,For power factor, p0For under transformer voltage rating
No-load loss, pkNShort circuit loss when for rated current.
Leader-followers games model described in step 3) are as follows:
Wherein, PPV,kIt (t) is the practical power output of k-th of photovoltaic cell,For predicting for k-th photovoltaic cell
Power；SSBIt (t) is carrying capacity of the energy-storage battery in the t period；μchFor charging mark, the μ when energy storage is in charged statechIt is 1,
It is 0 when in other states；μdisFor electric discharge mark, the μ when energy storage is in discharge conditiondisIt is 1, is 0 when being in other states；For energy storage charge power,For energy storage discharge power；ηchFor energy storage charge efficiency, ηdisIt discharges and imitates for energy storage
Rate；Pgrid(t) power is exchanged with bulk power grid for alternating current-direct current micro-capacitance sensor；PPVIt (t) is photovoltaic output power；ηPV,iFor photovoltaic inverter
Power transmission efficiency；PACL,j(t) j-th of exchanging area load power is indicated；PILCFor bi-directional inverter transimission power；ΗILCIt is double
To converter power efficiency of transmission；PETIt (t) is electric power electric transformer transimission power；ΗET(t) electric power electric transformer power
Efficiency of transmission；ΗSBCFor the converter power efficiency of transmission being connect with energy-storage battery；PDCL,mFor m-th of DC area load power；For bi-directional inverter transmission capacity；For flow controller transmission capacity；γPVIndicate photovoltaic utilization rate, f is electricity
Net network loss and energy storage life consumption and energy loss are discounted function, γPVIt is calculate by the following formula:
In formula, T is the when number of segment in dispatching cycle；S is the photovoltaic cell quantity of dispersion installation；PPV,k(t) with
Respectively k-th of photovoltaic cell scheduling power output is contributed with prediction；
F is calculate by the following formula:
F=fl+fbat
fbatEconomic loss caused by being lost for energy-storage battery；CinitFor the mounting cost of energy-storage battery；flIt is mixed for alternating current-direct current
Close micro-capacitance sensor network loss；Pi,kIndicate the power of the i-th class flow controller or bi-directional inverter k transmission；ηi,kIndicate the i-th class trend control
Device processed or bi-directional inverter k efficiency of transmission；N is the type of power converter；mkFor kth class power conversion in mixing micro-capacitance sensor
The quantity of equipment.
It is comprised the following steps described in step 4) using the order Oscillating particle swarm algorithm of asynchronous variation Studying factors:
(1) it initializes, inputs scale, variable number, inertia weight, maximum flying speed, the greatest iteration time of population
The parameter of several, each Reactive-power control equipment and initially go out force vector；
(2) position of current each particle is set as individual extreme point xpbest, calculate the adaptive value δ of each particlefit=
Ft(x), the optimal solution F that minimum adaptive value is current as group is takenbest, and remember that the position of the corresponding particle of minimum adaptive value is complete
Office extreme point xgbest, set primary iteration frequency nitIt is 1；It is evolved using speed v of the following formula to particle:
vit+1=wvit+c1,itr1[xit-(1+ξ1)xit+ξ1xit-1]+c2,itr2[xpbest-(1+ξ2)xit+ξ2xit-1]
It evolves according to the following formula to the position x of particle simultaneously:
xit+1=xit+vit+1
In formula, c1,itAnd c2,itFor Studying factors, in the order Oscillating particle swarm algorithm of asynchronous variation Studying factors, often
C in secondary iteration1,itAnd c2,itIt calculates according to the following formula,
Wherein c1,iniFor c1,itIteration initial value, c1,finFor c1,itIteration final value, c2,iniFor c2,itIteration it is initial
Value, c2,finFor c2,itIteration final value, it indicate current iteration number, itmaxFor maximum number of iterations.
ξ1And ξ2It is random number, if current iteration number is less than the 1/2, ξ of maximum number of iterations1And ξ2Meet:
If current iteration number is less than the 1/2, ξ of maximum number of iterations1And ξ2It should meet:
(3) judge whether current the number of iterations meets maximum number of iterations, export calculated result if meeting, otherwise set
Determine the number of iterations nit=nit+1；
(4) position and speed of more new particle, and update Reactive-power control equipment power output variable；
(5) judge whether the state of all particles in population meets each inequality constraints condition in leader-followers games model,
Retain particle position if meeting, otherwise takes the corresponding particle position limit value of inequality constraints condition；
(6) adaptive value for calculating current each particle, saves globally optimal solution Fbest, global optimum position xgbestAnd individual
Optimal location xpbest, and go to (3) step.
Alternating current-direct current mixing micro-capacitance sensor optimizing operation method based on leader-followers games model of the invention, has the advantages that
1. the mutually coordinated leader-followers games model of photovoltaic operator and grid company in alternating current-direct current mixing micro-capacitance sensor is established,
Model includes the agent model for the maximization photovoltaic utilization rate for representing photovoltaic operator interests and represents grid company target most
The slave body Model of smallization alternating current-direct current mixing micro-capacitance sensor Network Loss Rate, the model meet objective both sides and are actually needed and open up in mathematical terms
It is existing, therefore reasonable scheduling scheme more more objective than traditional scheduler mode can be provided.
2. a kind of load based on canonical trend, generation of electricity by new energy power forecasting method are proposed, it is pre- with existing load
Survey method is compared, and this method principle is relatively simple, and applicability is wide, and with higher using row, precision of prediction is higher, is suitable for
Prediction data is provided for alternating current-direct current mixing micro-capacitance sensor optimization.
3. be easily trapped into precocious disadvantage for conventional particle group algorithm, to particle swarm algorithm using change Studying factors into
Row improves, and has obtained a kind of order Oscillating particle swarm algorithm of asynchronous variation Studying factors with more excellent global search performance.
Detailed description of the invention
Fig. 1 is alternating current-direct current mixing micro-capacitance sensor demonstration project structure chart；
Fig. 2 is the load prediction test curve that the method for the present invention obtains；
Fig. 3 is the load prediction error testing curve that the method for the present invention obtains；
Fig. 4 is the photovoltaic power generation output forecasting curve that the method for the present invention obtains；
Fig. 5 is photovoltaic power generation output forecasting error testing；
Fig. 6 is the demonstration project energy-storage battery depth of discharge and Life Relation that the method for the prior art obtains；
Fig. 7 is the flow controller efficiency measurement data that the method for the prior art obtains；
Fig. 8 is the 100kWPCS efficiency function matched curve that the method for the prior art obtains；
Fig. 9 is the 250kWPCS efficiency function matched curve that the method for the prior art obtains；
Figure 10 is the commutator transformer efficiency function matched curve that the method for the prior art obtains；
Figure 11 is the demonstration project load and photovoltaic power generation output forecasting that the method for the present invention obtains；
Figure 12 is transimission power curve at the PCC of the method for the present invention acquisition；
Figure 13 is the curve of output of the commutator transformer that the method for the present invention obtains and three flow controllers；
Figure 14 is the flow controller transimission power curve that the method for the present invention obtains；
Figure 15 is the flow battery and energy-storage battery group power curve that the method for the present invention obtains；
Figure 16 is the flow battery and energy-storage battery group remaining capacity that the method for the present invention obtains；
Figure 17 is the different capacity range converter start and stop situation that the method for the present invention obtains.
Specific embodiment
The alternating current-direct current mixing micro-capacitance sensor of the invention based on leader-followers games model is optimized below with reference to embodiment and attached drawing
Operation method is described in detail.
Alternating current-direct current mixing micro-capacitance sensor optimizing operation method based on leader-followers games model of the invention, which is characterized in that packet
Include following steps:
1) micro-capacitance sensor region intensity of illumination, temperature, the meteorological data of cloud amount are acquired, photovoltaic power generation in micro-capacitance sensor is counted
Historical data is measured, next day photovoltaic cell power output in alternating current-direct current mixing micro-capacitance sensor is carried out using the prediction technique based on canonical trend
Prediction；The historical load data of exchanging area and DC area is counted respectively, next day AC load and DC load is carried out respectively pre-
It surveys；
The prediction technique based on canonical trend is function on the basis of certain known period power to future time period
Rate predicted, the mathematic(al) representation of the prediction technique based on canonical trend are as follows:
Ppre(t+i)=Ppre(t)+ΔPpre(t+i)=Ppre(t)(1+P′typ(t))；
Wherein, PpreIt (t+i) is the generated power forecasting value of the t+i period；PpreIt (t) is the practical hair of t-th of period
Electric power value；ΔPpreIt (t+i) is the t+i period with respect to the prediction power changing value of t-th of period；ΔPpreIt (t+i) is t
The change rate of a period to the t+i period, P'(t) it is to be obtained based on canonical trend, canonical trend is by with similar
Power calculation in the date of Meteorological Characteristics obtains.
Power in the date with similar Meteorological Characteristics is obtained by various as follows:
The similarity relation formula of day to be predicted and historical data:
Wherein, σjFor the incidence coefficient of jth day and day to be predicted, σjValue is greater than setting limit value σ0Think and day to be predicted
Belong to same similar day；ρ is resolution ratio, takes 0.5；xjFor the characterization jth day Meteorological Characteristics of the meteorological data composition counted
Vector, indicate are as follows:
xj=[xj(1),xj(2),…,xj(m)]
The incidence coefficient for calculating day to be predicted and historical data filters out and is greater than association limit value σ0Sample data, obtain K
The canonical trend P ' of similar day is calculated after a similar day by following formulatyp(t)
2) bi-directional inverter, flow controller two sides transmission power data in alternating current-direct current mixing micro-capacitance sensor are acquired,
The mathematical model of various equipment in alternating current-direct current mixing micro-capacitance sensor is established using acquired data；
The mathematical model of various equipment in the alternating current-direct current mixing micro-capacitance sensor, comprising:
(1) the energy-storage battery service life
Wherein NlifeFor the cycle life of energy-storage battery；DOD is depth of discharge；NaFor depth of discharge highest power；aiFor
DOD i-th power coefficient, aiBy being fitted to obtain to measurement data；
Energy-storage battery should meet the constraint condition of power-balance, state-of-charge bound in use:
DOD (t)=1-SOC (t)
SOCmin≤SOC(t)≤SOCmax
SOC (t) is t-th of period energy-storage battery state-of-charge；For energy-storage battery capacity；PSBIt (t) is the t period
The charge-discharge electric power of energy-storage battery；Δ t is the time span of a period；The depth of discharge of DOD (t) expression energy-storage battery；
SOCmax、SOCminThe respectively upper lower limit value of state-of-charge；
(2) cost model of energy-storage battery
Including life consumption cost C0With energy loss cost C1Two parts
Wherein, CinitFor the installation cost of energy-storage battery, CgFor external power grid electricity price, η is the energy efficiency of energy-storage battery；
(3) the overall cost model of energy-storage battery:
(4) flow controller and bi-directional inverter power transmission model
It is found according to flow controller, the measurement analysis of bi-directional inverter input-output power, efficiency of transmission and transmission function
Rate has functional relation, can be fitted with polynomial function.And quadratic function can satisfy engineering precision demand.
Wherein,Indicate t-th of period equipmentEfficiency of transmission；riFor transimission power Pi(t) coefficient；PCS indicates tide
Stream controller, ILC indicate bi-directional inverter；
(5) transformer efficiency of transmission model
In formula: SNFor transformer capacity；β indicates load factor,For power factor, p0For under transformer voltage rating
No-load loss, pkNShort circuit loss when for rated current.
3) it was divided into multiple scheduling slots for one day, establishes photovoltaic operator and grid company in each scheduling slot interests phase
The leader-followers games model mutually coordinated, leader-followers games model include photovoltaic utilization rate model and the generation of photovoltaic operator benefit
The alternating current-direct current mixing micro-capacitance sensor network loss model of table grid company minimization of loss；
The leader-followers games model are as follows:
Wherein, PPV,kIt (t) is the practical power output of k-th of photovoltaic cell,For predicting for k-th photovoltaic cell
Power；SSBIt (t) is carrying capacity of the energy-storage battery in the t period；μchFor charging mark, the μ when energy storage is in charged statechIt is 1,
It is 0 when in other states；μdisFor electric discharge mark, the μ when energy storage is in discharge conditiondisIt is 1, is 0 when being in other states；For energy storage charge power,For energy storage discharge power；ηchFor energy storage charge efficiency, ηdisIt discharges and imitates for energy storage
Rate；Pgrid(t) power is exchanged with bulk power grid for alternating current-direct current micro-capacitance sensor；PPVIt (t) is photovoltaic output power；ηPV,iFor photovoltaic inverter
Power transmission efficiency；PACL,j(t) j-th of exchanging area load power is indicated；PILCFor bi-directional inverter transimission power；ΗILCIt is double
To converter power efficiency of transmission；PETIt (t) is electric power electric transformer transimission power；ΗET(t) electric power electric transformer power
Efficiency of transmission；ΗSBCFor the converter power efficiency of transmission being connect with energy-storage battery；PDCL,mFor m-th of DC area load power；For bi-directional inverter transmission capacity；For flow controller transmission capacity；γPVIndicate photovoltaic utilization rate, f is electricity
Net network loss and energy storage life consumption and energy loss are discounted function, γPVIt is calculate by the following formula:
In formula, T is the when number of segment in dispatching cycle；S is the photovoltaic cell quantity of dispersion installation；PPV,k(t) with
Respectively k-th of photovoltaic cell scheduling power output is contributed with prediction；
F is calculate by the following formula:
F=fl+fbat
fbatEconomic loss caused by being lost for energy-storage battery；CinitFor the mounting cost of energy-storage battery；flIt is mixed for alternating current-direct current
Close micro-capacitance sensor network loss；Pi,kIndicate the power of the i-th class flow controller or bi-directional inverter k transmission；ηi,kIndicate the i-th class trend control
Device processed or bi-directional inverter k efficiency of transmission；N is the type of power converter；mkFor kth class power conversion in mixing micro-capacitance sensor
The quantity of equipment.
4) using the order Oscillating particle swarm algorithm of asynchronous variation Studying factors, the global search of particle swarm algorithm is improved
Can, to avoid precocious phenomenon.It comprises the following steps:
(1) it initializes, inputs scale, variable number, inertia weight, maximum flying speed, the greatest iteration time of population
The parameter of several, each Reactive-power control equipment and initially go out force vector；
(2) position of current each particle is set as individual extreme point xpbest, calculate the adaptive value δ of each particlefit=
Ft(x), the optimal solution F that minimum adaptive value is current as group is takenbest, and remember that the position of the corresponding particle of minimum adaptive value is complete
Office extreme point xgbest, set primary iteration frequency nitIt is 1；It is evolved using speed v of the following formula to particle:
vit+1=wvit+c1,itr1[xit-(1+ξ1)xit+ξ1xit-1]+c2,itr2[xpbest-(1+ξ2)xit+ξ2xit-1]
It evolves according to the following formula to the position x of particle simultaneously:
xit+1=xit+vit+1
In formula, c1,itAnd c2,itFor Studying factors, in the order Oscillating particle swarm algorithm of asynchronous variation Studying factors, often
C in secondary iteration1,itAnd c2,itIt calculates according to the following formula,
Wherein c1,iniFor c1,itIteration initial value, c1,finFor c1,itIteration final value, c2,iniFor c2,itIteration it is initial
Value, c2,finFor c2,itIteration final value, it indicate current iteration number, itmaxFor maximum number of iterations.
ξ1And ξ2It is random number, if current iteration number is less than the 1/2, ξ of maximum number of iterations1And ξ2Meet:
If current iteration number is less than the 1/2, ξ of maximum number of iterations1And ξ2It should meet:
(3) judge whether current the number of iterations meets maximum number of iterations, export calculated result if meeting, otherwise set
Determine the number of iterations nit=nit+1；
(4) position and speed of more new particle, and update Reactive-power control equipment power output variable；
(5) judge whether the state of all particles in population meets each inequality constraints condition in leader-followers games model,
Retain particle position if meeting, otherwise takes the corresponding particle position limit value of inequality constraints condition；
(6) adaptive value for calculating current each particle, saves globally optimal solution Fbest, global optimum position xgbestAnd individual
Optimal location xpbest, and go to (3) step.
Specific example is given below:
By taking a certain alternating current-direct current mixing micro-capacitance sensor demonstration project in Zhejiang area as an example, using prediction proposed by the present invention and modeling
Method optimizes scheduling.The photovoltaic cell for being 2MW comprising capacity in the demonstration project, total capacity are the energy-storage battery of 1MWh
Group, peak power output 250kW；Connected between exchanging area and DC area by the flow controller that 4 power are 250kW
It connects, it is as shown in Figure 1 to simplify structure.
Photovoltaic and load power output are predicted in accordance with the following steps first.Prediction result is as shown in figure 11.
A) 20 days a few days ago to be predicted meteorological and load datas are read；
B) historical data is calculated according to meteorological data and predicts the related coefficient of day；
C) related coefficient highest 5 days are selected as similar day；
D) it rejects bad data and restores；
E) 1~96 period of similar day load median is sought as representative value；
F) adjacent time interval load changing rate is calculated；
G) period power to be predicted is calculated according to change rate obtained by formula.
By analyzing bi-directional inverter, flow controller and electric power electric transformer two sides transimission power, obtain
The function curve of two kinds of equipment transimission powers and efficiency, as shown in Figure 7 to 10.
For the correctness for verifying established model, verified in demonstration project structure chart.Asynchronous variation study because
During the order Oscillating particle swarm algorithm of son is realized, by repeatedly testing, final population is taken as 50, Studying factors c1, c2
2 are taken as, inertial factor takes 0.6, and flying speed takes 0.8, and maximum number of iterations is 2000 times, and chaos step number takes 10.And it uses
The order Oscillating particle swarm algorithm optimization algorithm of the asynchronous variation Studying factors proposed is solved.Optimize operation result such as Figure 12
To shown in Figure 16.
On the power distribution problems of three flow controllers and electric power electric transformer, there is no simply put down power
It distributes, but proposes two kinds of flow controller power distribution methods: is another one is the power distribution for minimizing change of current loss
Kind is the power distribution method for taking into account power loss and sharing control.
Minimum target is lost with transducing power, while being conveyed watt level sequence, carries out local optimum, distribution knot
Fruit such as Figure 17.Operation advantage in this way includes:
1) three flow controller power losses are minimum.From figure 8, it is seen that flow controller operational efficiency is not with defeated
Power is sent to increase and be increased monotonically, but there are an efficiency peak dots.Reasonable distribution flow controller power can be such that operation damages
It consumes minimum.
2) each flow controller variation is relatively small, and life consumption is lower.Fix the conveying function of three flow controllers
After rate size order, it will be assigned to three flow controllers in an orderly manner in the conveying instruction of different periods power, rather than divide at random
Match.When avoiding whole transmission power in this way and changing greatly, same flow controller transmission power value of adjacent time interval is by most
Become the generation of minimum or opposite situation greatly.
To take into account the commutation mode flowed with change of current loss for target, trend is selected according to the different range of change of current general power
The start and stop number of units of controller and electric power electric transformer, is flowed between the converter of unlatching.Operation is run in this way
Advantage be it is easy to control, the change of current loss it is lower.By analyzing converter various combination, the equipment of different capacity range
Start and stop situation is as shown in figure 17.Concrete outcome is listed by table 1.
Table 1 is the same as power bracket converter start and stop situation
| Power bracket/kW | Electric power electric transformer opens number of units | Flow controller opens number of units |
| 0-117 | 1 | 0 |
| 117-250 | 0 | 1 |
| 250-500 | 0 | 2 |
| 500-750 | 0 | 3 |
| 750-1000 | 1 | 3 |
Optimized Operation time interval takes 1h, i.e., was divided into 24 scheduling slots from Fig. 4-11 it is found that about in 11:00 for one day
~13:00 period photovoltaic power output is greater than load, and other times photovoltaic office is less than load, and as seen from Figure 12, alternating current-direct current mixing is micro-
Power grid can be to outer net transmission power.During photovoltaic power output is greater than load, Figure 15 can be seen that the period energy storage and be in charging shape
State, as can be seen from Figure 16, energy-storage battery remaining capacity rise.Therefore, it can be seen that the Optimized Operation scheme mentioned can make
The reliable and stable operation of micro-capacitance sensor, while meeting the requirement of surplus online.
Claims (4)
1. a kind of alternating current-direct current mixing micro-capacitance sensor optimizing operation method based on leader-followers games model, which is characterized in that including as follows
Step:
1) micro-capacitance sensor region intensity of illumination, temperature, the meteorological data of cloud amount are acquired, photovoltaic power generation quantity in micro-capacitance sensor is counted and goes through
History data carry out in advance next day photovoltaic cell power output in alternating current-direct current mixing micro-capacitance sensor using the prediction technique based on canonical trend
It surveys；The historical load data for counting exchanging area and DC area respectively, respectively predicts next day AC load and DC load；
2) bi-directional inverter, flow controller two sides transmission power data in alternating current-direct current mixing micro-capacitance sensor are acquired, are utilized
Acquired data establish the mathematical model of various equipment in alternating current-direct current mixing micro-capacitance sensor；
3) it was divided into multiple scheduling slots for one day, establishes photovoltaic operator and grid company and mutually assisted in each scheduling slot interests
The leader-followers games model of tune, leader-followers games model include the photovoltaic utilization rate model of photovoltaic operator benefit and represent electricity
The alternating current-direct current mixing micro-capacitance sensor network loss model that net companies losses minimize；
The leader-followers games model are as follows:
Wherein, PPV,kIt (t) is the practical power output of k-th of photovoltaic cell,For the prediction power output of k-th of photovoltaic cell；SSB
It (t) is carrying capacity of the energy-storage battery in the t period；μchFor charging mark, the μ when energy storage is in charged statechIt is 1, is in it
It is 0 when his state；μdisFor electric discharge mark, the μ when energy storage is in discharge conditiondisIt is 1, is 0 when being in other states；For energy storage charge power,For energy storage discharge power；ηchFor energy storage charge efficiency, ηdisIt discharges and imitates for energy storage
Rate；Pgrid(t) power is exchanged with bulk power grid for alternating current-direct current micro-capacitance sensor；PPVIt (t) is photovoltaic output power；ηPV,iFor photovoltaic inverter
Power transmission efficiency；PACL,j(t) j-th of exchanging area load power is indicated；PILCFor bi-directional inverter transimission power；ΗILCIt is double
To converter power efficiency of transmission；PETIt (t) is electric power electric transformer transimission power；ΗET(t) electric power electric transformer power
Efficiency of transmission；ΗSBCFor the converter power efficiency of transmission being connect with energy-storage battery；PDCL,mFor m-th of DC area load power；For bi-directional inverter transmission capacity；For flow controller transmission capacity；γPVIndicate photovoltaic utilization rate, f is electricity
Net network loss and energy storage life consumption and energy loss are discounted function, γPVIt is calculate by the following formula:
In formula, T is the when number of segment in dispatching cycle；S is the photovoltaic cell quantity of dispersion installation；PPV,k(t) withRespectively
It contributes for k-th of photovoltaic cell scheduling power output with prediction；
F is calculate by the following formula:
F=fl+fbat
fbatEconomic loss caused by being lost for energy-storage battery；CinitFor the mounting cost of energy-storage battery；flIt is mixed for alternating current-direct current micro-
Grid net loss；Pi,kIndicate the power of the i-th class flow controller or bi-directional inverter k transmission；ηi,kIndicate the i-th class flow controller
Or bi-directional inverter k efficiency of transmission；N is the type of power converter；mkFor kth class power converter in mixing micro-capacitance sensor
Quantity；
4) using the order Oscillating particle swarm algorithm of asynchronous variation Studying factors, the global search performance of algorithm is improved, to keep away
Exempt from precocious phenomenon；
The order Oscillating particle swarm algorithm using asynchronous variation Studying factors comprises the following steps:
(1) it initializes, inputs the scale of population, variable number, inertia weight, maximum flying speed, maximum number of iterations, each
The parameter of a Reactive-power control equipment and initial force vector out；
(2) position of current each particle is set as individual extreme point xpbest, calculate the adaptive value δ of each particlefit=Ft
(x), the optimal solution F that minimum adaptive value is current as group is takenbest, and remember the position of the corresponding particle of minimum adaptive value for the overall situation
Extreme point xgbest, set primary iteration frequency nitIt is 1；It is evolved using speed v of the following formula to particle:
vit+1=wvit+c1,itr1[xit-(1+ξ1)xit+ξ1xit-1]+c2,itr2[xpbest-(1+ξ2)xit+ξ2xit-1]
It evolves according to the following formula to the position x of particle simultaneously:
xit+1=xit+vit+1
In formula, c1,itAnd c2,itIt changes every time in the order Oscillating particle swarm algorithm of asynchronous variation Studying factors for Studying factors
C in generation1,itAnd c2,itIt calculates according to the following formula,
Wherein c1,iniFor c1,itIteration initial value, c1,finFor c1,itIteration final value, c2,iniFor c2,itIteration initial value,
c2,finFor c2,itIteration final value, it indicate current iteration number, itmaxFor maximum number of iterations；
ξ1And ξ2It is random number, if current iteration number is less than the 1/2, ξ of maximum number of iterations1And ξ2Meet:
If current iteration number is greater than the 1/2, ξ of maximum number of iterations1And ξ2It should meet:
(3) judge whether current the number of iterations meets maximum number of iterations, export calculated result if meeting, otherwise setting changes
For frequency nit=nit+1；
(4) position and speed of more new particle, and update Reactive-power control equipment power output variable；
(5) judge whether the state of all particles in population meets each inequality constraints condition in leader-followers games model, if full
It is sufficient then retain particle position, otherwise take the corresponding particle position limit value of inequality constraints condition；
(6) adaptive value for calculating current each particle, saves globally optimal solution Fbest, global optimum position xgbestIt is optimal with individual
Position xpbest, and go to (3) step.
2. the alternating current-direct current mixing micro-capacitance sensor optimizing operation method according to claim 1 based on leader-followers games model, special
Sign is, based on the prediction technique of canonical trend described in step 1), be on the basis of certain known period power to future when
Section power predicted, the mathematic(al) representation of the prediction technique based on canonical trend are as follows:
Ppre(t+i)=Ppre(t)+ΔPpre(t+i)=Ppre(t)(1+P'typ(t))；
Wherein, PpreIt (t+i) is the generated power forecasting value of the t+i period；PpreIt (t) is the actual power function of t-th of period
Rate value；ΔPpreIt (t+i) is the t+i period with respect to the prediction power changing value of t-th of period；P'typIt (t) is t-th of period
To the change rate of the t+i period, P'typIt (t) is to be obtained based on canonical trend, canonical trend is by with similar meteorology
Power calculation in the date of feature obtains.
3. the alternating current-direct current mixing micro-capacitance sensor optimizing operation method according to claim 2 based on leader-followers games model, special
Sign is that the power in the date with similar Meteorological Characteristics is obtained by various as follows:
The similarity relation formula of day to be predicted and historical data:
Wherein, σjFor the incidence coefficient of jth day and day to be predicted, σjValue is greater than setting limit value σ0Think to belong to day to be predicted
Same similar day；ρ is resolution ratio, takes 0.5；xjFor counted meteorological data composition characterization jth day Meteorological Characteristics to
Amount indicates are as follows:
xj=[xj(1),xj(2),…,xj(m)]
The incidence coefficient for calculating day to be predicted and historical data filters out and is greater than setting limit value σ0Sample data, obtain K phase
Like the canonical trend P' for calculating similar day after day by following formulatyp(t)
4. the alternating current-direct current mixing micro-capacitance sensor optimizing operation method according to claim 1 based on leader-followers games model, special
Sign is, the mathematical model of various equipment in alternating current-direct current mixing micro-capacitance sensor described in step 2), comprising:
(1) the energy-storage battery service life
Wherein NlifeFor the cycle life of energy-storage battery；DOD is depth of discharge；NaFor depth of discharge highest power；aiFor DOD i-th
Power coefficient, aiBy being fitted to obtain to measurement data；
Energy-storage battery should meet the constraint condition of power-balance, state-of-charge bound in use:
DOD (t)=1-SOC (t)
SOCmin≤SOC(t)≤SOCmax
SOC (t) is t-th of period energy-storage battery state-of-charge；For energy-storage battery capacity；PSBIt (t) is t period energy storage electricity
The charge-discharge electric power in pond；Δ t is the time span of a period；The depth of discharge of DOD (t) expression energy-storage battery；SOCmax、
SOCminThe respectively upper lower limit value of state-of-charge；
(2) cost model of energy-storage battery
Including life consumption cost C0With energy loss cost C1Two parts
Wherein, CinitFor the installation cost of energy-storage battery, CgFor external power grid electricity price, η is the energy efficiency of energy-storage battery；
(3) the overall cost model of energy-storage battery:
(4) flow controller and bi-directional inverter power transmission model
Wherein,Indicate the efficiency of transmission of t-th of period equipment θ；riFor transimission power Pi(t) coefficient；PCS indicates power flowcontrol
Device, ILC indicate bi-directional inverter；
(5) transformer efficiency of transmission model
In formula: SNFor transformer capacity；β indicates load factor,For power factor, p0For the zero load under transformer voltage rating
Loss, pkNShort circuit loss when for rated current.
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