CN106684916A - Operation optimization method of grid-connected photovoltaic system with storage battery - Google Patents
Operation optimization method of grid-connected photovoltaic system with storage battery Download PDFInfo
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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
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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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- 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
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- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to an operation optimization method of a grid-connected photovoltaic system with a storage battery. The method comprises the following steps of (1) averagely dividing a to-be-optimized cycle into N time frames; (2) building a global linear programming model for the whole optimized cycle, wherein the global linear programming model comprises a target function and a constraint function and the target function takes the minimum system operation cost within the optimized cycle as a target value; and (3) determining an optimal solution by adopting a simplex method aiming at the target function and the constraint function to obtain operation parameters of the system in the N time frames. Compared with the prior art, the operation optimization method has the advantages of a high optimization speed, high efficiency and a reliable optimization structure.
Description
Technical field
The present invention relates to a kind of grid-connected photovoltaic system running optimizatin method, more particularly, to a kind of grid-connected light with accumulator
Volt running Optimization method.
Background technology
With petering out for traditional energy, human society more and more applies regenerative resource, and photovoltaic generation is which
In one kind.In order to use resource, regenerative resource typically all to generate electricity by the EIAJ that can obtain as far as possible, not with can
Modulability.In order to improve the runnability of photovoltaic system, accumulator is housed in some photovoltaic systems so as to which external characteristics includes defeated
Go out power controllable within the specific limits.
Grid-connected photovoltaic system with accumulator system includes user's distribution 2, photovoltaic generation unit 3, accumulator 4 and load 5,
Each several part mutual relation and its Fig. 1 is seen with the relation between public electric wire net 1.Photovoltaic generation unit 3 converts solar energy into electric energy,
Electric energy gives load 5, accumulator 4 or public electric wire net 1 via user's distribution 2.The peak power that photovoltaic generation unit 3 can be exported
There is relation with photovoltaic cell itself performance (material, area, clean-up performance), sunshine (intensity, angle), environment (temperature), and
Intensity of sunshine and temperature with day as mechanical periodicity, therefore, the peak power that 3 unit of photovoltaic generation unit can be exported also with day is
Mechanical periodicity, exerts oneself and concentrates 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, will also continue to support the operation of various emerging distributed generation system and micro-grid systems,
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
Reduction is sent out, power supply cost, increases the efficiency of the whole society.Therefore time-of-use tariffs or Spot Price are generally carried out in electrical network, at peak
When electricity price it is higher, during paddy, electricity price is relatively low.Accumulator 4 generally arranges multigroup as the energy storage instrument in system, and per group storage 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 carry out reducing operating cost in addition to operation of power networks quality index is met, also, obtain most
Big income is purpose running optimizatin.In all of optimized algorithm, in Genetic Algorithms Theory, range of application is most wide, can almost solve
The Optimized model of any feature.But as the algorithm is similar to enumerative technique, when control variable number increases, amount of calculation is exponentially
Go up, thus affect the practical effect of the method.
The content of the invention
The purpose of the present invention be exactly in order to overcome defect that above-mentioned prior art is present and provide it is a kind of with accumulator simultaneously
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 accumulator, the method comprise the steps：
(1) cycle to be optimized is equally divided into into N number of period；
(2) global linear programming model is set up to whole optimization cycle, described global linear programming model includes target
Function and constraint function, wherein described object function is with the minimum desired value of system operation cost in optimization cycle；
(3) optimal solution is asked using simplex method for object function and constraint function, the operation for system being obtained in N number of period is joined
Number.
In step (2), object function is：
Wherein, F is system operation cost in optimization cycle, and i represents i-th period, and j represents jth group storage battery, and M is to be
Accumulator battery number in system, Pgrid+.iFor mean power from system in the i-th period to electrical network power purchase, Pgrid-.iFor system in the i-th period
To the mean power of electrical network sale of electricity, fgrid+.iFor electricity price from system in the i-th period to electrical network power purchase, fgrid-.iIn the i-th period to be
The electricity price united to electrical network 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 jth group storage battery i-th period average discharge power, Psb-.i.jIt is jth group storage battery 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 is
The duration of each period.
Depreciable cost f of accumulatordFor：
Wherein, Q is discharge and recharge total in accumulator life cycle, CcostFor the initial outlay cost of accumulator.
In step (2), constraint function includes：
(a) through-put power constraint function：
Wherein, i represents i-th period, Pgrid+.iFor mean power from system in the i-th period to electrical network power purchase, Pgrid-.iFor
Mean power of the system to electrical network sale of electricity, P in i-th periodgmax+.iFor peak power from the i-th period system to electrical network power purchase,
Pgmax-.iFor peak power from the i-th period system to electrical network sale of electricity；
(b) power-balance constraint function：
Wherein, PPV.iFor the active predicted mean vote of exerting oneself of the i-th period photovoltaic generation unit, PL.iIt is active negative for the i-th period
Lotus power prediction meansigma methodss, Psb+.i.jFor jth group storage battery i-th period average discharge power, Psb-.i.jStore for jth group
Average charge power of the battery i-th period, j represent jth group storage battery, and M is accumulator battery 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；
The residual capacity constraint function of (d) accumulator：
SOCmin.j≤SOCi.j≤SOCmax.j, i=1,2,3 ... N, j=1,2,3 ... M,
SOCi.jFor jth i-th period of group storage battery when battery remaining power, SOCmin.jFor the minimum of jth group storage battery
Residual capacity, SOCmax.jFor the greatest residual capacity of jth group storage battery；
(e) accumulator constant constraint functions of SOC in the whole optimization cycle：
Wherein, Δ t is the duration of each period.
Battery remaining power SOC during jth i-th period of group storage batteryi.jSpecially：
Wherein, SOC0.jJth group storage battery initial residual capacity, P when starting for whole optimization cyclesb+.k.jStore for jth group
Average discharge power of the battery k-th period, Psb-.k.jFor jth group storage battery k-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 be each period when
It is long.
Operational factor of the system that step (3) is tried to achieve in N number of period includes：Each period system is to the average of electrical network power purchase
Power, mean power from each period system to electrical network sale of electricity, per group storage battery each period average discharge power and
Per group storage battery in the average charge power of each period.
Compared with prior art, the invention has the advantages that：
(1) present invention is improved to traditional Universal Model so as to set up global linear programming model so that the model
Can be by seeking optimal solution using simplex method, solving speed is fast, drastically increases solution efficiency；
(2) present invention variable in the object function and constraint function in traditional Universal Model is carried out it is necessary disassemble,
System will be split as with the power that interacts of electrical network in i-th period：Mean power of the system to electrical network power purchase in i-th period
Pgrid+.i, mean power P of the system to electrical network sale of electricity in the i-th periodgrid-.i, by the discharge and recharge 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 periodsb+.i.j, jth group storage battery is
The average charge power P of i periodsb-.i.j, so that becoming constraint equation by two decision variables that same physical quantitiess split out
Basic variable and nonbasic variable, the linear correlation even if its coefficient column vector is tried one's best, with ensure two interrelated decision variables one by one
Be set to 0, just optimal solution can be sought using solution efficiency height, the simplex method of global scope optimizing.
Description of the drawings
Fig. 1 is the structural representation of the grid-connected photovoltaic system with accumulator；
Fig. 2 is the FB(flow block) of the grid-connected photovoltaic system running optimizatin method with accumulator of the invention；
Fig. 3 is real-time example photovoltaic, load and electric price parameter curve chart；
Fig. 4 is the two group storage battery discharge and recharge schemes and residual capacity change under tou power price；
Fig. 5 is the two group storage battery discharge and recharge schemes and residual capacity change under Spot Price；
Fig. 6 is linear model of the present invention and nonlinear model run time comparison diagram under different periods number；
Fig. 7 is linear model of the present invention and nonlinear model run time comparison diagram under different batteries group number.
In figure, 1 is public electric wire net, and 2 is user's distribution, and 3 is photovoltaic generation unit, and 4 is accumulator, and 5 is 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 accumulator, the memory capacity of accumulator constantly will be reduced, until can not use.The present invention according to
According to the research of French country's solar energy research institute (National Solar Energy Institute, INES) energy storage laboratory,
By the discharge and recharge in the initial outlay cost (present invention puts aside capital use cost) of accumulator and accumulator life cycle
The ratio of total amount, as coefficient of depreciation, depreciable cost during its charge and discharge is：
In formula：fdFor accumulator coefficient of depreciation (unit/kWh)；Q is discharge and recharge (kWh) total in accumulator life cycle；
CcostFor the initial outlay cost (unit) of accumulator.
Pervasive initial optimization model, the model are piecewise linear model, are nonlinear in general, can adopt non-
Linear algorithm is solved.Because the variable number and dimension of model it is more, so solution efficiency can be affected using non-linear class algorithm.This
Piecewise linear model by decoupling control variable, is rewritten as global linear model on the basis of initial optimization model by invention,
And then using solving speed it is fast, the Simplex Algorithm for LP of globally optimal solution can be solved solved, drastically increase
Solution efficiency.
(1) pervasive initial optimization model and its problem of presence：
A. object function：
It is optimized with the minimum desired value of full-time operating cost of user's light-preserved system.Its cost includes that light is stored up
Grid-connected system interacts expense, the depreciable cost of accumulator and maintenance cost with the electric energy of power distribution network.Therefore, its object function is such as
Under：
In formula：N is the when hop count of optimization cycle segmentation, group numbers of the M for accumulator, 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 from system in the i-th period to electrical network power purchase, fgrid-.i
For electricity price from system in the i-th period to electrical network sale of electricity, Pgrid.iFor the i-th period when average between system and electrical network interact power,
Work as Pgrid.iDuring ＞ 0, custom system to electrical network power purchase, now fgrid+.iMore than 0, fgrid-.i=0, work as Pgrid.iDuring ＜ 0, user system
Unite to electrical network sale of electricity, now fgrid+.i=0, fgrid-.ifgrid-.iMore than 0.
In formula (2), Δ SOCi.jT residual capacity variable quantity that () is jth group storage battery within i-th period.
In formula：Psb.i.jFor the charge-discharge electric power of the i-th period of jth group storage battery, with battery discharging as positive direction, η+.jFor
Efficiency when jth group storage battery discharges, η-.jEfficiency when charging for jth group storage battery；Work as Psb.i.jDuring ＞ 0, i.e., accumulator is put
Electricity, now η+.jMore than 0, η-.j=0；Work as Psb.i.jDuring ＜ 0, i.e., accumulator charges, now 1/ η+.j=0, η-.jMore than 0.
B. constraints：
1. through-put power constraint：
For custom system with electrical network, light storage grid-connected system interacts power with external power grid also bound：
-Pgmax-.i≤Pgrid.i≤Pgmax+.i, i=1,2,3...N (4)
In formula：Pgmax+.iFor peak power from the i-th period system to electrical network power purchase, Pgmax-.iIt is the i-th period system to electrical network
The peak 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 predicted mean vote of exerting oneself of the i-th period photovoltaic generation unit, PL.iIt is active negative for the i-th period
Lotus power prediction meansigma methodss,
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 peak power that jth group storage battery charges, Psbmax+.jFor the maximum of jth group storage battery electric discharge
Power.
4. residual capacity (State of Charge, the SOC) constraint of accumulator：
The depth of discharge of jth group storage battery cannot be below its least residue capacity SOCmin.j, charge no more than accumulator
Greatest residual capacity SOCmax.j。
SOCmin.j≤SOCi.j≤SOCmax.j, i=1,2,3 ... N, j=1,2,3 ... M (7)
Wherein, for battery remaining power SOC during jth i-th period of group storage batteryijYing You：
In formula：SOC0.jJth group storage battery initial residual capacity when starting for an optimization cycle,
5. the SOC per group storage battery is constant in an optimization cycle whole story：
In institute's Prescribed Properties, 4., 5. there is period coupling in constraints, and 2. constraints have different performance electric power storage
Coupling between the group of pond.
There is absolute value sign in the object function of the model on remaining battery capacity, and accumulator cell charging and discharging efficiency is not
Identical, purchase sale of electricity valency is also differed, so be not linear model from terms of stricti jurise, but piecewise linear model.If one
Individual optimization cycle is divided into N number of period, and the accumulator of M group different performances is had in whole system, then in whole optimization cycle
There is MN decision variable.So if properly not selecting optimized algorithm, then Searching efficiency will be very low, affect the reality of optimization method
Border uses.
Because accumulator cell charging and discharging power will not be carried out in the same period simultaneously, so by accumulator cell charging and discharging in some documents
This physical quantity of power represented with two decision variables, i.e. charge power and discharge power, but due to same accumulator this
It is a physical quantity that two decision variables are actual, therefore necessarily has one for 0 in the two decision variables of same period, so and increasing
Add a Nonlinear Constraints, i.e., same accumulator is equal to 0 in the product of the charge power and discharge power of same period,
The Non-Linear Programming of a decision variable L=2MN is finally solved, therefore solution amount of calculation is larger during optimizing, it is longer to solve the time.
(2) the linearizing basic ideas of initial model：
The present invention disassembles the charge-discharge electric power of accumulator for two decision variables, and will interact power with public electric wire net
Also disassemble as two variables, row write aforementioned initial optimization model again.Except for the difference that, same battery is not explicitly adopted in the present invention
The product of the charge-discharge electric power of group same period is 0 this constraints, but by appropriate rewriting initial optimization model, is made
Two decision variables (herein referred as interrelated decision variable) split out by same physical quantitiess always occur in constraints in pairs,
And make its coefficient column vector try one's best linear correlation, in order to seek optimal solution using the simplex method of linear programming model, and ensure
One of two interrelated decision variables one are set to 0.
Linear programming is essentially convex programming, its feasible zone be convex set (for the model that there is L decision variable, this
Individual convex set has L summit), and its excellent solution must be certain summit of feasible zone.Solution of linear programming --- simplex method, from one
Individual initial feasible summit is set out, and by computing on the table that carries out by rule, looks for more excellent summit, until going out by rule judgment
Again there is no more excellent summit, then the numerical value of the decision variable of that last vertex correspondence is optimal solution.Therefore which is excellent
The scheme searched during change will not be exponentially increased by decision variable number, when decision variable is more, simplex algorithm efficiency
It is substantially better than other algorithms.With compared using nonlinear algorithm because amount of calculation is less, solution efficiency is higher, though consider
Period remains to efficiently find optimal solution when coupling.
In initial optimization model, in object function, the variable quantity of remaining battery capacity carries absolute value sign, and
Accumulator cell charging and discharging power and accumulator cell charging and discharging efficiency are differed, and purchase sale of electricity valency is also differed, so above-mentioned initial optimization mould
Type is not linear model truly, but piecewise linear model.
The present invention by the variable in initial optimization model carry out it is necessary disassemble, and note rewrite constraints expressed
To meet the linear programming model of certain specification, make to be become the base of constraint equation by two decision variables that same physical quantitiess split out
Variable and nonbasic variable, the linear correlation even if its coefficient column vector is tried one's best, to ensure that one of two interrelated decision variables one are set to
0, just optimal solution can be sought using solution efficiency height, the simplex method of global scope optimizing
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
Light storage custom system is also not necessarily equal with sale of electricity electricity price to the purchase electricity price of electrical network, needs for light to store up grid-connected system and external power grid
Interaction power Pgrid.iSplit into two variables：System is to electrical network sale of electricity power Pgrid-.i, system is to electrical network power purchase power Pgrid+.i,
By the charge-discharge electric power P of accumulatorsb.i.jSplit into two variables：Accumulator charge power Psb-.i.j, battery discharging power
Psb+.i.j。
Therefore a kind of grid-connected photovoltaic system running optimizatin method with accumulator of the present invention, is illustrated in figure 2 the method
FB(flow block), comprises the steps：
(1) cycle to be optimized is equally divided into into N number of period；
(2) global linear programming model is set up to whole optimization cycle, described global linear programming model includes target
Function and constraint function, wherein described object function is with the minimum desired value of system operation cost in optimization cycle；
(3) optimal solution is asked using simplex method for object function and constraint function, the operation for system being obtained in N number of period is joined
Number.
In step (2), object function is：
Wherein, F is system operation cost in optimization cycle, and i represents i-th period, and j represents jth group storage battery, and M is to be
Accumulator battery number in system, Pgrid+.iFor mean power from system in the i-th period to electrical network power purchase, Pgrid-.iFor system in the i-th period
To the mean power of electrical network sale of electricity, fgrid+.iFor electricity price from system in the i-th period to electrical network power purchase, fgrid-.iIn the i-th period to be
The electricity price united to electrical network 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 jth group storage battery i-th period average discharge power, Psb-.i.jIt is jth group storage battery 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 is
The duration of each period.
Depreciable cost f of accumulatordFor：
Wherein, Q is discharge and recharge total in accumulator life cycle, CcostFor the initial outlay cost of accumulator.
In step (2), constraint function includes：
(a) through-put power constraint function：
Wherein, i represents i-th period, Pgrid+.iFor mean power from system in the i-th period to electrical network power purchase, Pgrid-.iFor
Mean power of the system to electrical network sale of electricity, P in i-th periodgmax+.iFor peak power from the i-th period system to electrical network power purchase,
Pgmax-.iFor peak power from the i-th period system to electrical network sale of electricity；
(b) power-balance constraint function：
Wherein, PPV.iFor the active predicted mean vote of exerting oneself of the i-th period photovoltaic generation unit, PL.iIt is active negative for the i-th period
Lotus power prediction meansigma methodss, Psb+.i.jFor jth group storage battery i-th period average discharge power, Psb-.i.jStore for jth group
Average charge power of the battery i-th period, j represent jth group storage battery, and M is accumulator battery 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；
The residual capacity constraint function of (d) accumulator：
SOCmin.j≤SOCi.j≤SOCmax.j, i=1,2,3 ... N, j=1,2,3 ... M,
SOCi.jFor jth i-th period of group storage battery when battery remaining power, SOCmin.jFor the minimum of jth group storage battery
Residual capacity, SOCmax.jFor the greatest residual capacity of jth group storage battery；
(e) accumulator constant constraint functions of SOC in the whole optimization cycle：
Wherein, Δ t is the duration of each period.
Battery remaining power SOC during jth i-th period of group storage batteryi.jSpecially：
Wherein, SOC0.jJth group storage battery initial residual capacity, P when starting for whole optimization cyclesb+.k.jStore for jth group
Average discharge power of the battery k-th period, Psb-.k.jFor jth group storage battery k-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 be each period when
It is long.
Operational factor of the system that step (3) is tried to achieve in N number of period includes：Each period system is to the average of electrical network power purchase
Power, mean power from each period system to electrical network sale of electricity, per group storage battery each period average discharge power and
Per group storage battery in the average charge power of each period.
Optimized model is programmed using Matlab softwares, and calls simplex method to be solved.Timesharing is respectively adopted
Electricity price example and Spot Price example are verified to the above-mentioned model that improves and optimizates.Initial data is as follows with result.The present embodiment
It is middle using 24 hours one day as an optimization cycle, and 24 hourly averages are divided into into 24 periods, each period is 1 hour.
(1) tou power price example
Choose two groups of different accumulator batteries of parameter, such as table 1.The tou power price of 24 periods, paddy will be divided within one day simultaneously
Period is 00:00—08:00、11:00—13:00 and 22:00—24:00, section is 09 at ordinary times:00—12:00、14:00—19:
00 and 21:00—22:00, the peak period is 19:00—21:00, 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 set forth herein Optimized model, MATLAB environment run, draw two group storage batteries filling in each period
Discharge power scheme.
Fig. 4 is the optimum results obtained with overall situation linear programming model of the invention, and left axle represents accumulator cell charging and discharging power,
The charging indicated above of its zero line, following presentation electric discharge, right axle represent the residual capacity of accumulator.Analysis is compared for convenience,
Electricity price trend is also depict in the figure.As can be seen that as electricity price experienced peak valley twice in one day, two group storage batteries also experience
Discharge and recharge twice, the paddy period of the period correspondence purchase sale of electricity valency that accumulator charges, peak period of electric discharge correspondence purchase sale of electricity valency.And
And second electric discharge of accumulator is carried out without secondary high electricity price after charging at once, but wait until that the higher period is put
Electricity, to obtain bigger income, this is because this model considers period coupling, the optimal discharge and recharge scheme of accumulator is to one
The result of the global optimization of complete charging-discharging cycle.Couple discounting for the period, optimization is sequentially in time one by one
Period is carried out successively, the output of past period, as the input of optimization of next period, carries out not considering during a certain period decision-making
In optimization cycle, the electricity price of future time period changes the impact to this period Optimal Decision-making, can cause second of accumulator to discharge not
The peak period is occurred in necessarily, the income of system differs.
Meanwhile, the discharge and recharge scheme of accumulator meets each constraints, including the SOC of accumulator a discharge and recharge week
Variable quantity sum 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, two group storage batteries are taken real-time
Emulated under electricity price.
Spot Price trend is put in 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 that 0-1 periods, 5-7 periods, 10-11,16-17 period electricity price
It is low, battery charging；4-5 periods, 7-8 periods, 9-10 periods, 13-14 periods, 18-19 periods electricity price are high, and accumulator battery is put
Electricity；1-4 periods, 8-9 periods, 11-13 periods, though electricity price is higher, immediately period, have a higher electricity price, therefore accumulator battery
Remain unchanged after the previous period charges, discharge to during higher electricity price；14-16 periods, 19-24 periods, accumulator battery are remaining
Capacity reaches minima, neither charges nor discharges.Two group storage batteries in one cycle, have passed through many as can be seen from Figure 5
Secondary discharge and recharge, and, the discharge and recharge period of two group storage batteries is corresponding.Meanwhile, also it is seen that, when electricity price is than relatively low
When, two group storage batteries charge, but are not to begin to electric discharge in the next period, but a certain in one piece of subsequent region
Discharged during individual high electricity price, and then the interests of system are maximized in whole optimization cycle, when having fully demonstrated consideration
The benefit of section coupling.
In order to verify the solution efficiency and precision of carried 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 doing from certain document
Method, is to ensure that one of two variables disassembled are zero, and it is zero that two variables are multiplied, and then ensures two variables disassembled
One of one be set to zero.Increase constraints：
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.
When a., hop count is different
Under Spot Price, 24 periods may be divided into incessantly within one day, be divided within one day 24,48,96 periods below, it is right
Than the solution time of two kinds of algorithms.Two groups of different accumulator batteries of parameter are taken, is emulated under matlab environment, solve the time
As shown in fig. 6, target function value is as shown in table 2.
Target function value (the unit of 2 different periods Linear Model with Side of table and nonlinear model：Unit)
| 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, the object function that the nonlinear model obtained under the different periods is obtained with this model
Value approximately equal, and less using target function value after this model, i.e. more superiority.From in run time, with the period
Number is continuously increased, and the required run time of this model also less approximately can be ignored in increase, but incrementss, and nonlinear model
Simulation time incrementss corresponding to type it is obvious that its growth pattern is presented exponential increase trend, during from 48 periods to 96
The time of section is even more and uprushes to 665s.Tens times of time phase difference even hundred times needed for two kinds of model correspondences.With when hop count
Increase, this gap also constantly expanding, therefore for identical accumulator battery number, different tou power prices when hop count and
Speech, the arithmetic speed of this model are significantly faster than that nonlinear model.
B. organize number different
Under the Spot Price environment of 96 periods, take the different accumulator battery of parameters, compare nonlinear model with
The run time and optimum results of this model, run time is as shown in fig. 7, optimum results are as shown in table 3.
Target function value (the unit of 3 96 period of table different batteries group number linear model and nonlinear model：Unit)
| 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 present invention
The row time is still little, can approximately ignore.Run time corresponding to nonlinear model is in 3 group storage batteries and amount added above
Seldom, but still remain the trend of growth.Even thousand of times of the order of magnitude difference hundred times of two model correspondence times.For
For optimum results, target function value approximately equal, for minimum optimization, the result obtained by linear programming is more defined
Really.
As can be seen here, no matter from the time of calculating or precision, the global linear programming model of the present invention can for this model
The economical operation scheduling of user's light-preserved system is applied to accurately and quickly.
Claims (6)
1. a kind of grid-connected photovoltaic system running optimizatin method with accumulator, it is characterised in that the method comprises the steps：
(1) cycle to be optimized is equally divided into into N number of period；
(2) global linear programming model is set up to whole optimization cycle, described global linear programming model includes object function
And constraint function, wherein described object function is with the minimum desired value of system operation cost in optimization cycle；
(3) optimal solution is asked using simplex method for object function and constraint function, obtains operational factor of the system in N number of period.
2. a kind of grid-connected photovoltaic system running optimizatin method with accumulator according to claim 1, it is characterised in that step
Suddenly in (2), object function is：
Wherein, F is system operation cost in optimization cycle, and i represents i-th period, and j represents jth group storage battery, and M is in system
Accumulator battery number, Pgrid+.iFor mean power from system in the i-th period to electrical network power purchase, Pgrid-.iIt is system in the i-th period to electricity
The mean power of net sale of electricity, fgrid+.iFor electricity price from system in the i-th period to electrical network power purchase, fgrid-.iFor system in the i-th period to
The electricity price of electrical network 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 period, Psb-.i.jFor jth group storage battery 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 each period
Duration.
3. a kind of grid-connected photovoltaic system running optimizatin method with accumulator according to claim 2, it is characterised in that store
Depreciable cost f of batterydFor：
Wherein, Q is discharge and recharge total in accumulator life cycle, CcostFor the initial outlay cost of accumulator.
4. a kind of grid-connected photovoltaic system running optimizatin method with accumulator according to claim 1, it is characterised in that step
Suddenly in (2), constraint function includes：
(a) through-put power constraint function：
Wherein, i represents i-th period, Pgrid+.iFor mean power from system in the i-th period to electrical network power purchase, Pgrid-.iFor i-th
Mean power of the system to electrical network sale of electricity, P in periodgmax+.iFor peak power from the i-th period system to electrical network power purchase, Pgmax-.i
For peak power from the i-th period system to electrical network sale of electricity；
(b) power-balance constraint function：
Wherein, PPV.iFor the active predicted mean vote of exerting oneself of the i-th period photovoltaic generation unit, PL.iFor the i-th period burden with power work(
Rate predicted mean vote, Psb+.i.jFor jth group storage battery i-th period average discharge power, Psb-.i.jFor jth group storage battery
In the average charge power of i-th period, j represents jth group storage battery, and M is accumulator battery 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；
The residual capacity constraint function of (d) accumulator：
SOCmin.j≤SOCi.j≤SOCmax.j, i=1,2,3 ... N, j=1,2,3 ... M,
SOCi.jFor jth i-th period of group storage battery when battery remaining power, SOCmin.jFor the least residue of jth group storage battery
Capacity, SOCmax.jFor the greatest residual capacity of jth group storage battery；
(e) accumulator constant constraint functions of SOC in the whole optimization cycle：
Wherein, Δ t is the duration of each period.
5. a kind of grid-connected photovoltaic system running optimizatin method with accumulator according to claim 4, it is characterised in that
Battery remaining power SOC during j i-th period of group storage batteryi.jSpecially：
Wherein, SOC0.jJth group storage battery initial residual capacity, P when starting for whole optimization cyclesb+.k.jFor jth group storage battery
In the average discharge power of k-th period, Psb-.k.jFor jth group storage battery k-th period average charge power, 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.
6. a kind of grid-connected photovoltaic system running optimizatin method with accumulator according to claim 1, it is characterised in that step
Suddenly operational factor of the system that (3) try to achieve in N number of period includes：Mean power from each period system to electrical network power purchase, each
Period system is to the mean power of electrical network sale of electricity, often average discharge power and every group storage battery of the group storage battery in each period
In the average charge power of each period.
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