CN106684916B - A kind of grid-connected photovoltaic system running optimizatin method with battery - Google Patents
A kind of grid-connected photovoltaic system running optimizatin method with battery Download PDFInfo
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Classifications
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- H02J3/383—
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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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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B10/00—Integration of renewable energy sources in buildings
- Y02B10/10—Photovoltaic [PV]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
Abstract
The grid-connected photovoltaic system running optimizatin method with battery that the present invention relates to a kind of, this method comprises the following steps: (1) period to be optimized being equally divided into N number of period；(2) global linear programming model is established to entire optimizing cycle, the global linear programming model includes objective function and constraint function, wherein the objective function is with the minimum target value of system operation cost in optimizing cycle；(3) optimal solution is asked using simplex method for objective function and constraint function, obtains system in the operating parameter of N number of period.Compared with prior art, the present invention has optimal speed fast, high-efficient, and optimization structure is reliable.
Description
Technical field
The present invention relates to a kind of grid-connected photovoltaic system running optimizatin methods, more particularly, to a kind of grid-connected light with battery
Lie prostrate running Optimization method.
Background technique
With petering out for traditional energy, human society more and more applies renewable energy, and photovoltaic power generation is it
One of.In order to use resource as far as possible, renewable energy generally all generates electricity by the maximum output that can be obtained, and not having can
Modulability.In order to improve the runnability of photovoltaic system, battery is housed in certain photovoltaic systems, it includes defeated for making its external characteristics
Power is controllable in a certain range out.
Grid-connected photovoltaic system system with battery includes user's distribution 2, photovoltaic generation unit 3, battery 4 and load 5,
Each section correlation and its relationship between public electric wire net 1 are shown in Fig. 1.Photovoltaic generation unit 3 converts solar energy into electric energy,
Electric energy gives load 5, battery 4 or public electric wire net 1 via user's distribution 2.The maximum power that photovoltaic generation unit 3 can export
There is relationship with photovoltaic cell itself performance (material, area, clean-up performance), sunshine (intensity, angle), environment (temperature), and
Intensity of sunshine and temperature are using day as mechanical periodicity, and therefore, the maximum power that 3 unit of photovoltaic generation unit can export also is with day
Mechanical periodicity, power output concentrate on the period of daylight.Public electric wire net 1 will be people's production within following a very long time
The main supply channel of household electricity also will continue to support the operation of various emerging distributed generation systems and micro-grid system,
It is responsible for the heavy responsibility for balancing electrical energy production and consumption in its overlay area at any time.The peak-valley difference for reducing public electric wire net load is conducive to
Hair, power supply cost are reduced, the efficiency of the whole society is increased.Therefore time-of-use tariffs or Spot Price are generally carried out in power grid, at peak
When electricity price it is higher, electricity price is lower when paddy.Battery 4 is as the energy storage tool in system, usual setting multiple groups, and each battery
Performance has differences, and the charge and discharge control for how carrying out each group storage battery also becomes an important process of power grid regulation.
The target of power grid regulation can also carry out obtaining most to reduce operating cost in addition to meeting grid operation quality index
Big income is purpose running optimizatin.In all optimization algorithms, application range is most wide in Genetic Algorithms Theory, can almost solve
The Optimized model of any feature.But since the algorithm is similar to enumerative technique, when controlling variables number increase, calculation amount is exponentially
Go up, thus influences the practical effect of this method.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of with battery and
Net photovoltaic system running optimizatin method.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of grid-connected photovoltaic system running optimizatin method with battery, this method comprises the following steps:
(1) period to be optimized is equally divided into N number of period；
(2) global linear programming model is established to entire optimizing cycle, the global linear programming model includes target
Function and constraint function, wherein the objective function is with the minimum target value of system operation cost in optimizing cycle；
(3) optimal solution is asked using simplex method for objective function and constraint function, the operation for obtaining system in N number of period is joined
Number.
Objective function in step (2) are as follows:
Wherein, F is system operation cost in optimizing cycle, and i indicates i-th of period, and j indicates that jth group storage battery, M are to be
Battery group number, P in systemgrid+.iFor mean power of the system to power grid power purchase, P in the i-th periodgrid-.iFor system in the i-th period
To the mean power of power grid sale of electricity, fgrid+.iFor electricity price of the system to power grid power purchase, f in the i-th periodgrid-.iTo be in the i-th period
The electricity price united to power grid sale of electricity, fd.jFor the depreciable cost of jth group storage battery, fm.jFor the maintenance cost of jth group storage battery,
Psb+.i.jAverage discharge power for jth group storage battery i-th of period, Psb-.i.jIt is jth group storage battery in i-th period
Average charge power, η+.jEfficiency when discharging for jth group storage battery, η-.jEfficiency when charging for jth group storage battery, Δ t are
The duration of each period.
The depreciable cost f of batterydAre as follows:
Wherein, Q is discharge and recharge total in battery life cycle, CcostFor the initial outlay cost of battery.
Constraint function includes: in step (2)
(a) transimission power constraint function:
Wherein, i indicates i-th of period, Pgrid+.iFor mean power of the system to power grid power purchase, P in the i-th periodgrid-.iFor
Mean power of the system to power grid sale of electricity, P in i-th periodgmax+.iIt is the i-th period system to the maximum power of power grid power purchase,
Pgmax-.iIt is the i-th period system to the maximum power of power grid sale of electricity；
(b) power-balance constraint function:
Wherein, PPV.iFor the active power output predicted mean vote of the i-th period photovoltaic generation unit, PL.iIt is active negative for the i-th period
Lotus power prediction average value, Psb+.i.jAverage discharge power for jth group storage battery i-th of period, Psb-.i.jFor jth group storage
Average charge power of the battery i-th of period, j indicate that jth group storage battery, M are battery group number in system；
(c) accumulator cell charging and discharging power constraint function:
Wherein, Psbmax+.jFor the maximum discharge power of jth group storage battery, Psbmax-.jFor the maximum charge of jth group storage battery
Power, η+.jEfficiency when discharging for jth group storage battery, η-.jEfficiency when charging for jth group storage battery；
(d) the residual capacity constraint function of battery:
SOCmin.j≤SOCi.j≤SOCmax.j, i=1,2,3 ... N, j=1,2,3 ... M,
SOCi.jBattery remaining power when for jth i-th of period of group storage battery, SOCmin.jFor the minimum of jth group storage battery
Residual capacity, SOCmax.jFor the greatest residual capacity of jth group storage battery；
(e) battery constant constraint function of SOC in entire optimizing cycle:
Wherein, Δ t is the duration of each period.
Battery remaining power SOC when jth i-th of period of group storage batteryi.jSpecifically:
Wherein, SOC0.jJth group storage battery initial residual capacity, P when starting for entire optimizing cyclesb+.k.jFor jth group storage
Average discharge power of the battery k-th of period, Psb-.k.jAverage charge power for jth group storage battery k-th of period,
η+.jEfficiency when discharging for jth group storage battery, η-.jFor jth group storage battery charging when efficiency, Δ t be each period when
It is long.
Operating parameter of the system that step (3) acquires in N number of period includes: each period system being averaged to power grid power purchase
Power, each period system to the mean power of power grid sale of electricity, each battery each period average discharge power and
Average charge power of each battery in each period.
Compared with prior art, the present invention has the advantage that
(1) present invention improves traditional Universal Model to establish global linear programming model, so that the model
Optimal solution can be sought by using simplex method, solving speed is fast, greatly improves solution efficiency；
(2) present invention carries out necessary dismantling to the variable in the objective function and constraint function in traditional Universal Model,
The interaction power of system in the i-th period and power grid is split are as follows: mean power of the system to power grid power purchase in the i-th period
Pgrid+.i, mean power P of the system to power grid sale of electricity in the i-th periodgrid-.i, by the charge and discharge in the i-th period of jth group storage battery
Power splits into two variables: average discharge power P of the jth group storage battery i-th of periodsb+.i.j, jth group storage battery is
The average charge power P of i periodsb-.i.j, so that split out by same physical quantitiess two decision variables be made to become constraint equation
Basic variable and nonbasic variable, even if its coefficient column vector is linearly related as far as possible, with guarantee two interrelated decision variables one by one
It is set to 0, high, global scope optimizing simplex method can seeks optimal solution using solution efficiency.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of the grid-connected photovoltaic system with battery；
Fig. 2 is the flow diagram of the grid-connected photovoltaic system running optimizatin method of the invention with battery；
Fig. 3 is real-time example photovoltaic, load and electric price parameter curve graph；
Fig. 4 is two group storage battery charge and discharge schemes and residual capacity variation under tou power price；
Fig. 5 is two group storage battery charge and discharge schemes and residual capacity variation under Spot Price；
Fig. 6 is linear model and nonlinear model runing time comparison diagram of the present invention under different periods number；
Fig. 7 is linear model and nonlinear model runing time comparison diagram of the present invention under different batteries group number.
In figure, 1 is public electric wire net, and 2 be user's distribution, and 3 be photovoltaic generation unit, and 4 be battery, and 5 be load.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment
With continuing on for battery, the memory capacity of battery will be reduced constantly, until cannot use.The present invention according to
According to the research in French national energy storage laboratory solar energy research institute (National Solar Energy Institute, INES),
By the charge and discharge in the initial outlay cost (present invention puts aside capital use cost) of battery and battery life cycle
The ratio between total amount, the depreciable cost as coefficient of depreciation, when charge and discharge are as follows:
In formula: fdFor battery coefficient of depreciation (member/kWh)；Q is discharge and recharge (kWh) total in battery life cycle；
CcostFor the initial outlay cost (member) of battery.
Pervasive initial optimization model, the model be piecewise linear model, be in general it is nonlinear, can be used non-
Linear algorithm solves.Because the variable number and dimension of model are more, so will affect solution efficiency using non-linear class algorithm.This
Piecewise linear model, by partition control variable, is rewritten as global linear model on the basis of initial optimization model by invention,
And then Simplex Algorithm for LP that is fast using solving speed, can solving globally optimal solution is solved, and is greatly improved
Solution efficiency.
(1) pervasive initial optimization model and its there are the problem of:
A. objective function:
It is optimized with the minimum target value of full-time operating cost of user's light-preserved system.Its cost includes light storage
Grid-connected system interacts expense, the depreciable cost of battery and maintenance cost with the electric energy of power distribution network.Therefore, objective function is such as
Under:
In formula: N is the when number of segment of optimizing cycle segmentation, and M is the group number of battery, fd.jFor the folding of jth group storage battery
Old cost, fm.jFor the maintenance cost of jth group storage battery, fgrid+.iFor electricity price of the system to power grid power purchase, f in the i-th periodgrid-.i
For electricity price of the system to power grid sale of electricity, P in the i-th periodgrid.iAverage between system and power grid interacts power when for the i-th period,
Work as Pgrid.iWhen > 0, custom system is to power grid power purchase, f at this timegrid+.iGreater than 0, fgrid-.i=0, work as Pgrid.iWhen < 0, user system
It unites to power grid sale of electricity, at this time fgrid+.i=0, fgrid-.ifgrid-.iGreater than 0.
In formula (2), Δ SOCi.jIt (t) is residual capacity variable quantity of the jth group storage battery within i-th of period.
In formula: Psb.i.jFor the charge-discharge electric power of the i-th period of jth group storage battery, it is positive direction with electric power storage tank discharge, η+.jFor
Efficiency when jth group storage battery discharges, η-.jEfficiency when charging for jth group storage battery；Work as Psb.i.jWhen > 0, i.e., battery is put
Electricity, at this time η+.jGreater than 0, η-.j=0；Work as Psb.i.jWhen < 0, i.e., battery charges, at this time 1/ η+.j=0, η-.jGreater than 0.
B. constraint condition:
1. transimission power constrains:
For custom system and power grid, light storage grid-connected system, which interacts power with external power grid, also bound:
-Pgmax-.i≤Pgrid.i≤Pgmax+.i, i=1,2,3...N (4)
In formula: Pgmax+.iMaximum power for from the i-th period system to power grid power purchase, Pgmax-.iIt is the i-th period system to power grid
The maximum power of sale of electricity.
2. power-balance constraint:
For whole system, in arbitrary period, power-balance constraint need to be met:
In formula: PPV.iFor the active power output predicted mean vote of the i-th period photovoltaic generation unit, PL.iIt is active negative for the i-th period
Lotus power prediction average value,
3. accumulator cell charging and discharging power constraint.
-Psbmax-.j≤Psb.i.j≤Psbmax+.j, i=1,2,3 ... N, j=1,2,3 ... M (6)
In formula, Psbmax-.jFor the maximum power of jth group storage battery charging, Psbmax+.jFor the maximum of jth group storage battery electric discharge
Power.
4. the residual capacity (State of Charge, SOC) of battery constrains:
The depth of discharge of jth group storage battery cannot be below its least residue capacity SOCmin.j, charging is no more than battery
Greatest residual capacity SOCmax.j。
SOCmin.j≤SOCi.j≤SOCmax.j, i=1,2,3 ... N, j=1,2,3 ... M (7)
Battery remaining power SOC when wherein, for jth i-th of period of group storage batteryijYing You:
In formula: SOC0.jJth group storage battery initial residual capacity when starting for an optimizing cycle,
5. the SOC of each battery is constant in an optimizing cycle whole story:
In institute's Prescribed Properties, 4., 5. there is period coupling in constraint condition, 2. constraint condition has different performance electric power storage
Coupling between the group of pond.
There are absolute value signs on remaining battery capacity in the objective function of the model, and accumulator cell charging and discharging efficiency is not
Identical, purchase sale of electricity valence is not also identical, so be not linear model in terms of stricti jurise, but piecewise linear model.If one
A optimizing cycle is divided into N number of period, and the battery of M group different performance is shared in whole system, then in entire optimizing cycle
There is MN decision variable.So if not selecting optimization algorithm properly, then Searching efficiency will be very low, influences the reality of optimization method
Border uses.
Because accumulator cell charging and discharging power will not carry out simultaneously in the same period, so by accumulator cell charging and discharging in some documents
This physical quantity of power indicates with two decision variables, i.e. charge power and discharge power, but due to same battery this
Practical two decision variables are a physical quantity, therefore the two decision variables centainly have one for 0, so increase again in the same period
Having added a Nonlinear Constraints, i.e., same battery is equal to 0 in the charge power of same period and the product of discharge power,
The Non-Linear Programming of a decision variable L=2MN is finally solved, therefore solution calculation amount is larger when optimizing, it is longer to solve the time.
(2) basic ideas of initial model linearisation:
The dismantling of the charge-discharge electric power of battery is two decision variables by the present invention, and by the interaction power with public electric wire net
Also dismantling is two variables, and column write aforementioned initial optimization model again.Unlike, same battery is not used in the present invention explicitly
The product of the charge-discharge electric power of group same period is 0 this constraint condition, but by appropriate rewriting initial optimization model, make
Two decision variables (herein referred as interrelated decision variable) split out by same physical quantitiess always occur in pairs in constraint condition,
And keep its coefficient column vector linearly related as far as possible, in order to which the simplex method of application linear programming model seeks optimal solution, and guarantee
One of two interrelated decision variables one are set to 0.
Linear programming is essentially convex programming, feasible zone be convex set (for there are the model of L decision variable, this
A convex set has L vertex), and its excellent solution must be some vertex of feasible zone.Solution of linear programming --- simplex method, from one
A initial feasible vertex is set out, and by operation on the table by rule progress, is looked for more preferably vertex, is judged until by rule
Again there is no more preferably vertex, then the numerical value of the decision variable of that last vertex correspondence is optimal solution.Therefore its is excellent
The scheme searched during changing will not be exponentially increased by decision variable number, when decision variable is more, simplex algorithm efficiency
It is substantially better than other algorithms.It is compared with using nonlinear algorithm, because calculation amount is smaller, solution efficiency is higher, even if considering
Period remains to efficiently find optimal solution when coupling.
In initial optimization model, the variable quantity of remaining battery capacity has absolute value sign in objective function, and
Accumulator cell charging and discharging power and accumulator cell charging and discharging efficiency be not identical, and purchase sale of electricity valence is not also identical, so above-mentioned initial optimization mould
The linear model of type not instead of not truly, piecewise linear model.
Variable in initial optimization model is carried out necessary dismantling by the present invention, and notices that rewriting constraint condition is expressed
To meet the linear programming model centainly standardized, split out by same physical quantitiess two decision variables is made to become the base of constraint equation
Variable and nonbasic variable, even if its coefficient column vector is linearly related as far as possible, to guarantee that one of two interrelated decision variables one are set to
0, high, global scope optimizing simplex method optimal solution can be sought using solution efficiency
In an initial model case, it is contemplated that accumulator cell charging and discharging efficiency acts on charge-discharge electric power with different mathematical expressions, and
The purchase electricity price that light stores up custom system to power grid is also not necessarily equal with sale of electricity electricity price, needs light storing up grid-connected system and external power grid
Interaction power Pgrid.iSplit into two variables: system is to power grid sale of electricity power Pgrid-.i, system is to power grid power purchase power Pgrid+.i,
By the charge-discharge electric power P of batterysb.i.jSplit into two variables: battery charge power Psb-.i.j, battery discharge power
Psb+.i.j。
Therefore a kind of grid-connected photovoltaic system running optimizatin method with battery of the present invention, is illustrated in figure 2 this method
Flow diagram includes the following steps:
(1) period to be optimized is equally divided into N number of period；
(2) global linear programming model is established to entire optimizing cycle, the global linear programming model includes target
Function and constraint function, wherein the objective function is with the minimum target value of system operation cost in optimizing cycle；
(3) optimal solution is asked using simplex method for objective function and constraint function, the operation for obtaining system in N number of period is joined
Number.
Objective function in step (2) are as follows:
Wherein, F is system operation cost in optimizing cycle, and i indicates i-th of period, and j indicates that jth group storage battery, M are to be
Battery group number, P in systemgrid+.iFor mean power of the system to power grid power purchase, P in the i-th periodgrid-.iFor system in the i-th period
To the mean power of power grid sale of electricity, fgrid+.iFor electricity price of the system to power grid power purchase, f in the i-th periodgrid-.iTo be in the i-th period
The electricity price united to power grid sale of electricity, fd.jFor the depreciable cost of jth group storage battery, fm.jFor the maintenance cost of jth group storage battery,
Psb+.i.jAverage discharge power for jth group storage battery i-th of period, Psb-.i.jIt is jth group storage battery in i-th period
Average charge power, η+.jEfficiency when discharging for jth group storage battery, η-.jEfficiency when charging for jth group storage battery, Δ t are
The duration of each period.
The depreciable cost f of batterydAre as follows:
Wherein, Q is discharge and recharge total in battery life cycle, CcostFor the initial outlay cost of battery.
Constraint function includes: in step (2)
(a) transimission power constraint function:
Wherein, i indicates i-th of period, Pgrid+.iFor mean power of the system to power grid power purchase, P in the i-th periodgrid-.iFor
Mean power of the system to power grid sale of electricity, P in i-th periodgmax+.iIt is the i-th period system to the maximum power of power grid power purchase,
Pgmax-.iIt is the i-th period system to the maximum power of power grid sale of electricity；
(b) power-balance constraint function:
Wherein, PPV.iFor the active power output predicted mean vote of the i-th period photovoltaic generation unit, PL.iIt is active negative for the i-th period
Lotus power prediction average value, Psb+.i.jAverage discharge power for jth group storage battery i-th of period, Psb-.i.jFor jth group storage
Average charge power of the battery i-th of period, j indicate that jth group storage battery, M are battery group number in system；
(c) accumulator cell charging and discharging power constraint function:
Wherein, Psbmax+.jFor the maximum discharge power of jth group storage battery, Psbmax-.jFor the maximum charge of jth group storage battery
Power, η+.jEfficiency when discharging for jth group storage battery, η-.jEfficiency when charging for jth group storage battery；
(d) the residual capacity constraint function of battery:
SOCmin.j≤SOCi.j≤SOCmax.j, i=1,2,3 ... N, j=1,2,3 ... M,
SOCi.jBattery remaining power when for jth i-th of period of group storage battery, SOCmin.jFor the minimum of jth group storage battery
Residual capacity, SOCmax.jFor the greatest residual capacity of jth group storage battery；
(e) battery constant constraint function of SOC in entire optimizing cycle:
Wherein, Δ t is the duration of each period.
Battery remaining power SOC when jth i-th of period of group storage batteryi.jSpecifically:
Wherein, SOC0.jJth group storage battery initial residual capacity, P when starting for entire optimizing cyclesb+.k.jFor jth group storage
Average discharge power of the battery k-th of period, Psb-.k.jAverage charge power for jth group storage battery k-th of period,
η+.jEfficiency when discharging for jth group storage battery, η-.jFor jth group storage battery charging when efficiency, Δ t be each period when
It is long.
Operating parameter of the system that step (3) acquires in N number of period includes: each period system being averaged to power grid power purchase
Power, each period system to the mean power of power grid sale of electricity, each battery each period average discharge power and
Average charge power of each battery in each period.
Optimized model is programmed using Matlab software, and simplex method is called to be solved.Timesharing is respectively adopted
Electricity price example and Spot Price example verify the above-mentioned model that improves and optimizates.Initial data is as follows with result.The present embodiment
It is middle to be used as an optimizing cycle for 24 hours one day, and 24 hourly averages are divided into 24 periods, each period is 1 hour.
(1) tou power price example
The different battery group of two groups of parameters is chosen, such as table 1.One day is divided into simultaneously the tou power price of 24 periods, paddy
Period is 00:00-08:00,11:00-13:00 and 22:00-24:00, and usually section is 09:00-12:00,14:00-19:
00 and 21:00-22:00, peak period are 19:00-21:00, and corresponding electricity price, prediction photovoltaic, prediction load are made herein
For given data, as shown in Figure 3.
1 liang of group storage battery parameter of table
According to Optimized model proposed in this paper, is run in MATLAB environment, obtain two group storage batteries filling in each period
Discharge power scheme.
Fig. 4 is the optimum results obtained with global linear programming model of the invention, and left axle indicates accumulator cell charging and discharging power,
The charging indicated above of its zero curve, following presentation electric discharge, right axle indicate the residual capacity of battery.It is analyzed in order to facilitate comparing,
Electricity price trend is also depicted in the figure.As can be seen that two group storage batteries are also undergone as electricity price undergoes peak valley twice in one day
Charge and discharge twice, the paddy period of the period of battery charging corresponding purchase sale of electricity valence, the peak period for the corresponding purchase sale of electricity valence that discharges.And
And time high electricity price of second of electric discharge of battery not after charging carries out at once, but waited until that the higher period is put
Electricity, to obtain bigger income, this is because this model considers period coupling, the best charge and discharge scheme of battery is to one
The result of the global optimization of complete charging-discharging cycle.If not considering that the period couples, optimize sequentially in time one by one
Period successively carries out, the output of past period, as the input of optimization of next period, do not consider when a certain period decision
The electricity price of future time period changes the influence to this period Optimal Decision-making in optimizing cycle, and second of battery can be made to discharge not
The peak period centainly is appeared in, the income of system is different to be surely optimal.
Meanwhile the charge and discharge scheme of battery meets each constraint condition, the SOC including battery is a charge and discharge week
The sum of variable quantity in phase is 0 this constraint.
(2) Spot Price example
This model is applicable not only to tou power price, while being also applied for Spot Price.Equally, take two group storage batteries real-time
It is emulated under electricity price.
Spot Price trend is put into Fig. 5, electricity price is maximum at 5,8,10,14,15,19, is 1.123 yuan/kWh；11
When electricity price it is minimum, be 0.357 yuan/kWh.It can be seen from the figure that 0-1 period, 5-7 period, 10-11,16-17 period electricity price
It is low, battery charging；4-5 period, 7-8 period, 9-10 period, 13-14 period, 18-19 period electricity price are high, and battery group is put
Electricity；1-4 period, 8-9 period, 11-13 period immediately period, have a higher electricity price though electricity price is higher, therefore battery group
It remains unchanged after the charging of previous period, discharges when to higher electricity price；14-16 period, 19-24 period, battery group are remaining
Capacity reaches minimum value, neither charges nor discharges.Two group storage batteries in one cycle, have passed through more as can be seen from Figure 5
Secondary charge and discharge, moreover, the charge and discharge period of two group storage batteries is corresponding.Meanwhile also it is not difficult to find that when electricity price is relatively low
When, the charging of two group storage batteries, but be not to begin to discharge in the next period, but it is a certain in one piece of subsequent region
It discharges when a high electricity price, so that the interests of system maximize in entire optimizing cycle, when having fully demonstrated consideration
The benefit of section coupling.
In order to verify the solution efficiency and precision of mentioned linear programming model, different periods number, different electric power storages are solved respectively
The planning model a few days ago of pond group number, and be compared with Nonlinear programming Model.Nonlinear programming Model selects doing for certain document
Two variable multiplications are zero, and then guarantee two variables of dismantling to guarantee that one of two variables of dismantling are zero by method
One of one be set to zero.Increase constraint condition:
Psb+.i.j×Psb-.i.j=0i=1,2,3 ... N, j=1,2,3 ... M,
Pgrid+.i×Pgrid-.i=0i=1,2,3 ... N.
Number of segment is different when a.
Under Spot Price, 24 periods may be divided within one day incessantly, lower face handle is divided into 24,48,96 periods for one day, right
Than the solution time of two kinds of algorithms.The battery group for taking two groups of parameters different, is emulated under matlab environment, solves the time
As shown in fig. 6, target function value is as shown in table 2.
The target function value (unit: member) of 2 different periods Linear Model with Side of table and nonlinear model
| 24 periods | 48 periods | 96 periods | |
| Linearly | 1575.9 | 1722.3 | 1750.9 |
| It is non-linear | 1580.8 | 1813.1 | 1776.3 |
For optimum results, objective function that the nonlinear model that obtains under the different periods and this model obtain
It is worth approximately equal and smaller using target function value after this model, i.e. more superiority.From runing time, with the period
Number is continuously increased, and the required runing time of this model is also increasing, but incrementss less can approximation ignore, and nonlinear model
Simulation time incrementss corresponding to type are it is obvious that exponential increase trend is presented in its growth pattern, when from 48 periods to 96
The time of section is even more to uprush to 665s.Two kinds of models correspond to tens times of required time phase difference even hundred times.With when number of segment
Increase, this gap also constantly expanding, therefore for identical battery group number, different tou power prices when number of segment and
Speech, the arithmetic speed of this model are significantly faster than that nonlinear model.
B. group number is different
Under the Spot Price environment of 96 periods, take the battery group that parameters are different, compare nonlinear model with
The runing time and optimum results of this model, runing time is as shown in fig. 7, optimum results are as shown in table 3.
The target function value (unit: member) of 3 96 period of table different batteries group number linear model and nonlinear model
| 2 groups | 3 groups | 4 groups | 5 groups | |
| Linearly | 1750.9 | 1706.6 | 1675.4 | 1614.3 |
| It is non-linear | 1776.3 | 1754.3 | 1724.9 | 1728.4 |
As seen from Figure 7, under different batteries group number, the corresponding fortune of global linear programming model (linear) of the invention
The row time is still seldom, and approximation can be ignored.Runing time corresponding to nonlinear model is in 3 group storage batteries and amount added above
Seldom, but still remain the trend of growth.Two models correspond to even thousands of times of the order of magnitude difference hundred times of time.For
For optimum results, target function value is approximately equal, for minimum optimization for, linear programming result obtained more subject to
Really.
It can be seen that no matter this model is from time or precision is calculated, global linear programming model of the invention can
Accurately and quickly dispatched applied to the economical operation of user's light-preserved system.
Claims (5)
1. a kind of grid-connected photovoltaic system running optimizatin method with battery, which is characterized in that this method comprises the following steps:
(1) period to be optimized is equally divided into N number of period；
(2) global linear programming model is established to entire optimizing cycle, the global linear programming model includes objective function
And constraint function, wherein the objective function is with the minimum target value of system operation cost in optimizing cycle；
(3) optimal solution is asked using simplex method for objective function and constraint function, obtains system in the operating parameter of N number of period；
Constraint function includes: in step (2)
(a) transimission power constraint function:
Wherein, i indicates i-th of period, Pgrid+.iFor mean power of the system to power grid power purchase, P in the i-th periodgrid-.iIt is i-th
Mean power of the system to power grid sale of electricity, P in periodgmax+.iMaximum power for from the i-th period system to power grid power purchase, Pgmax-.i
It is the i-th period system to the maximum power of power grid sale of electricity；
(b) power-balance constraint function:
Wherein, PPV.iFor the active power output predicted mean vote of the i-th period photovoltaic generation unit, PL.iFor the i-th period burden with power function
Rate predicted mean vote, Psb+.i.jAverage discharge power for jth group storage battery i-th of period, Psb-.i.jFor jth group storage battery
In the average charge power of i-th of period, j indicates that jth group storage battery, M are battery group number in system；
(c) accumulator cell charging and discharging power constraint function:
Wherein, Psbmax+.jFor the maximum discharge power of jth group storage battery, Psbmax-.jFor the maximum charge power of jth group storage battery,
η+.jEfficiency when discharging for jth group storage battery, η-.jEfficiency when charging for jth group storage battery；
(d) the residual capacity constraint function of battery:
SOCmin.j≤SOCi.j≤SOCmax.j, i=1,2,3 ... N, j=1,2,3 ... M,
SOCi.jBattery remaining power when for jth i-th of period of group storage battery, SOCmin.jFor the least residue of jth group storage battery
Capacity, SOCmax.jFor the greatest residual capacity of jth group storage battery；
(e) battery constant constraint function of SOC in entire optimizing cycle:
Wherein, Δ t is the duration of each period.
2. a kind of grid-connected photovoltaic system running optimizatin method with battery according to claim 1, which is characterized in that step
Suddenly objective function in (2) are as follows:
Wherein, F is system operation cost in optimizing cycle, and i indicates i-th of period, and j indicates that jth group storage battery, M are in system
Battery group number, Pgrid+.iFor mean power of the system to power grid power purchase, P in the i-th periodgrid-.iIt is system in the i-th period to electricity
The mean power of net sale of electricity, fgrid+.iFor electricity price of the system to power grid power purchase, f in the i-th periodgrid-.iFor system in the i-th period to
The electricity price of power grid sale of electricity, fd.jFor the depreciable cost of jth group storage battery, fm.jFor the maintenance cost of jth group storage battery, Psb+.i.jFor
Average discharge power of the jth group storage battery i-th of period, Psb-.i.jFor jth group storage battery i-th of period average charge
Power, η+.jEfficiency when discharging for jth group storage battery, η-.jEfficiency when charging for jth group storage battery, Δ t are each period
Duration.
3. a kind of grid-connected photovoltaic system running optimizatin method with battery according to claim 2, which is characterized in that store
The depreciable cost f of batterydAre as follows:
Wherein, Q is discharge and recharge total in battery life cycle, CcostFor the initial outlay cost of battery.
4. a kind of grid-connected photovoltaic system running optimizatin method with battery according to claim 1, which is characterized in that the
Battery remaining power SOC when j i-th of period of group storage batteryi.jSpecifically:
Wherein, SOC0.jJth group storage battery initial residual capacity, P when starting for entire optimizing cyclesb+.k.jFor jth group storage battery
In the average discharge power of k-th of period, Psb-.k.jAverage charge power for jth group storage battery k-th of period, η+.jFor
Efficiency when jth group storage battery discharges, η-.jEfficiency when charging for jth group storage battery, Δ t are the duration of each period.
5. a kind of grid-connected photovoltaic system running optimizatin method with battery according to claim 1, which is characterized in that step
Suddenly operating parameter of the system that (3) are acquired in N number of period includes: each period system to the mean power, each of power grid power purchase
Period system to the mean power of power grid sale of electricity, each battery each period average discharge power and each battery
In the average charge power of each period.
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