CN114447967A - Battery energy storage configuration method and configuration terminal of optical storage charging station and storage medium - Google Patents
Battery energy storage configuration method and configuration terminal of optical storage charging station and storage medium Download PDFInfo
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- H—ELECTRICITY
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
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- 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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in 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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- 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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
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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
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- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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Abstract
The invention is suitable for the technical field of power grids, and provides a battery energy storage configuration method, a configuration terminal and a storage medium for an optical storage charging station, wherein the method comprises the following steps: acquiring charging load data and photovoltaic power generation data of a light storage charging station; determining a target function and a constraint condition according to the charging load data and the photovoltaic power generation data, and establishing an energy storage optimization configuration model according to the target function and the constraint condition; and solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme. According to the invention, an energy storage optimal configuration model is established according to the charging coincidence data and the photovoltaic power generation data, an optimal configuration scheme is obtained by performing optimal solution, various factors are comprehensively considered, the configuration scheme is more reasonable, the advantages of the optical storage charging station can be effectively exerted, and the optical storage charging station can better participate in power grid regulation and control.
Description
Technical Field
The invention belongs to the technical field of power grids, and particularly relates to a battery energy storage configuration method, a configuration terminal and a storage medium for an optical storage charging station.
Background
As a new type of electric vehicle, the electric vehicle has a large charging load in the entire area. Particularly, the electric bus is a first soldier and a main force for the development of new energy automobiles, the charging load power of the electric bus charging station has large fluctuation and high peak power, and the requirements on a distribution transformer and line capacity are high. Therefore, the photovoltaic and energy storage system is configured in the electric bus charging station to form the optical storage charging station, so that the impact of the peak charging load on the power distribution network is reduced, and the photovoltaic power generation power in the station can be consumed in the daytime.
The prior art lacks an optimization method for battery energy storage configuration of the optical energy storage charging station, and cannot effectively exert the advantages of the optical energy storage charging station, so that the optical energy storage charging station can better participate in power grid regulation and control.
Disclosure of Invention
In view of this, embodiments of the present invention provide a battery energy storage configuration method, a configuration terminal, and a storage medium for an optical storage charging station, so as to solve the problem that the optical storage charging station cannot effectively participate in power grid regulation and control due to lack of planning for battery energy storage configuration of the optical storage charging station in the prior art.
A first aspect of an embodiment of the present invention provides a battery energy storage configuration method for an optical energy storage charging station, including:
acquiring charging load data and photovoltaic power generation data of a light storage charging station;
determining a target function and a constraint condition according to the charging load data and the photovoltaic power generation data, and establishing an energy storage optimization configuration model according to the target function and the constraint condition;
and solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme.
A second aspect of the embodiments of the present invention provides a battery energy storage configuration device for an optical storage charging station, including:
the parameter acquisition module is used for acquiring charging load data and photovoltaic power generation data of the optical storage charging station;
the model establishing module is used for determining a target function and a constraint condition according to the charging load data and the photovoltaic power generation data and establishing an energy storage optimization configuration model according to the target function and the constraint condition;
and the model solving module is used for solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme.
A third aspect of the embodiments of the present invention provides a configuration terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the battery energy storage configuration method for an optical storage charging station according to the first aspect of the embodiments of the present invention.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the battery energy storage configuration method for an optical storage charging station according to the first aspect of the embodiments of the present invention are implemented.
The embodiment of the invention provides a battery energy storage configuration method, a configuration terminal and a storage medium for an optical storage charging station, wherein the method comprises the following steps: acquiring charging load data and photovoltaic power generation data of a light storage charging station; determining a target function and a constraint condition according to the charging load data and the photovoltaic power generation data, and establishing an energy storage optimization configuration model according to the target function and the constraint condition; and solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme. According to the embodiment of the invention, the energy storage optimal configuration model is established according to the charging coincidence data and the photovoltaic power generation data, the optimal configuration scheme is obtained by performing optimal solution, various factors are comprehensively considered, the configuration scheme is more reasonable, the advantages of the optical storage charging station can be effectively exerted, and the optical storage charging station can better participate in power grid regulation and control.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart illustrating an implementation of a battery energy storage configuration method of an optical storage charging station according to an embodiment of the present invention;
fig. 2 is a charging load curve of the optical storage charging station according to the embodiment of the present invention;
fig. 3 is a fitness iteration trend in the process of solving a model by using a chicken flock algorithm according to the embodiment of the present invention;
FIG. 4 is an objective function scan curve of a model provided by an embodiment of the invention;
fig. 5 is a fitness iteration trend graph in the process of solving a model by using a chicken flock algorithm after reducing the electricity price difference according to the embodiment of the present invention;
FIG. 6 is a graph of an objective function scan of a model after reduction of power price difference provided by an embodiment of the present invention;
fig. 7 is a schematic diagram of a battery energy storage configuration device of an optical energy storage charging station according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a configuration terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Referring to fig. 1, an embodiment of the present invention provides a battery energy storage configuration method for an optical storage charging station, including:
s101: acquiring charging load data and photovoltaic power generation data of a light storage charging station;
s102: determining a target function and a constraint condition according to the charging load data and the photovoltaic power generation data, and establishing an energy storage optimization configuration model according to the target function and the constraint condition;
s103: and solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme.
According to the embodiment of the invention, the energy storage optimal configuration model is established according to the charging coincidence data and the photovoltaic power generation data, the optimal configuration scheme is obtained by performing optimal solution, various factors are comprehensively considered, the configuration scheme is more reasonable, the advantages of the optical storage charging station can be effectively exerted, and the optical storage charging station can better participate in power grid regulation and control.
Wherein, before obtaining the charging load data, further comprising: the method comprises the steps of obtaining Yuan-Yuan charging load data, eliminating data with zero electricity consumption and fault display states of charging states, screening effective data, and obtaining the charging load data after preprocessing (the effective data are data including charging pile numbers, transaction electricity quantities, charging starting time, charging ending time and photovoltaic power generation power). And according to the preprocessed charging load data, calculating a load charging load value, processing the load charging load value into a daily load value at a time interval of 15min so as to obtain a daily load curve, and simultaneously processing the photovoltaic power generation power into a power value at a time interval of 15min so as to obtain the charging load data.
In some embodiments, the objective function may be calculated as:
wherein F is the total cost, FPSCFor converting the light storage charging station into the daily sum of costs, MLAs a penalty factor, ω1And omega2Is a weight coefficient; fBATFor the service life of the battery energy storage systemCost of purchase amounting to 1 charge-discharge cycle per day in the life, FOMConverting the battery energy storage system operation to daily cost, FDEPFor the profit of the electricity price difference, FPVAnd the benefits brought by the photovoltaic electric energy price are increased by absorbing the photovoltaic power generation electric quantity for a battery energy storage system. The battery energy storage system is a system for storing electric energy in the optical storage charging station and comprises a plurality of energy storage batteries and other accessories.
In some embodiments, the cost of purchase F of the optical storage charging station is reduced to 1 charging and discharging cycle per day in the service lifeBATThe calculation formula of (c) may be:
FBAT＝(FES+cBATSBAT)/Tlife
differential electricity price gain FDEPThe calculation formula of (c) may be:
income F brought by photovoltaic power price increased by battery energy storage system absorbing photovoltaic power generation electric quantityPVThe calculation formula of (c) may be:
FPV＝EPV(ce-cPV)
penalty factor MLThe calculation formula of (c) may be:
wherein, FESFor the cost of the battery energy storage system equipment, cBATFor the cost per unit capacity of the energy storage cell, SBATFor the total capacity, T, of the battery energy storage systemlifeThe expected cycle life of the energy storage battery; c. Ce,tFor real-time electricity prices, PBC,tReal-time charging power, P, for energy storage batteriesBD,tReal-time discharging power of the energy storage battery, and T is charging time; ePVElectric energy for photovoltaic generation, ceTo average electricity price, cPVThe photovoltaic grid-connected electricity price is obtained; pLmaxFor power supply lineMaximum load power, PmaxThe actual total load of the charging station after the battery is charged is maximum.
In some embodiments, the optical storage charging station operation and maintenance is converted to a daily cost FOMThe calculation formula of (c) may be:
FOM＝rcBATSBAT/Tlife
or the like, or, alternatively,
wherein r is the operation and maintenance cost converted to daily cBATFor the cost per unit capacity of the energy storage cell, SBATFor the total capacity, T, of the battery energy storage systemlifeThe expected cycle life of the energy storage battery; c. CWS,tGuarantee operation and maintenance cost, T, for unit capacity of photovoltaic and energy storage systemOWTo provide the maintenance life.
In general, can adopt
And determining the cost of converting the operation and maintenance of the optical storage charging station to daily cost. To simplify the calculation, the above formula can also be simplified to FOM＝rcBATSBAT/Tlife. Both of the above equations can be used to determine FOM。
In the embodiment of the invention, factors such as the increment uncertainty of the load, the fluctuation of the power grid electricity price, the capacity attenuation of the energy storage battery, the price reduction of the energy storage battery and the like are comprehensively considered, and the target function of a single target problem is obtained by taking the margin of the reduced load peak power relative to the power supply line capacity as a penalty function according to the fixed investment and maintenance cost of the energy storage battery, the depreciation cost of the battery life, the charge cost and the discharge cost, so that the target function is more comprehensive.
In some embodiments, the constraints may include: the method comprises the following steps of power supply line power capacity constraint, energy storage battery chargeable and dischargeable depth constraint and energy storage battery chargeable and dischargeable multiplying power constraint.
The optical storage charging station needs to cope with severe fluctuation of load, has enough large power throughput capacity, and compensates for short-time peak power of charging pile load or absorbs excessive generating power of a photovoltaic module. The maximum power allowed by the power supply line may be referenced to the rated capacity of the distribution transformer.
In some embodiments, the supply line power capacity constraint may be:
the battery energy storage system has the function of stabilizing the fluctuation of power and the lowest capacity S of the energy storage batteryBAT_minThe minimum measurement cannot be lower than the maximum accumulated electric energy value in the daily average load fluctuation curve when the power supply capacity is exceeded or the electric energy is reversely transmitted, namely: the chargeable and dischargeable capacity constraint of the energy storage battery can be as follows:
the chargeable and dischargeable depth constraint of the energy storage battery can be as follows:
SOCmin≤SOCt≤SOCmax
Pb,t＞0，SOCt＝SOCt-1-Pb,tΔT/(ηdSBAT)
Pb,t<0，SOCt＝SOCt-1-Pb,tΔTηc/SBAT
the charge and discharge multiplying power refers to the multiplying power of the charge and discharge current relative to the Ah capacity of the energy storage battery. The allowable charge-discharge rates of various types of energy storage batteries are different, and excessive charge-discharge rates are generally not recommended for lithium iron phosphate batteries. Meanwhile, the allowable charging and discharging multiplying power of the energy storage battery in different SOC states is also different, but the fixed multiplying power can be adopted as a constraint condition when the capacity of the energy storage battery is configured. Here, the capacity of the energy storage battery is expressed in kWh units, so the charge-discharge rate is also expressed in the rate of the battery capacity, that is, the constraint of the chargeable-dischargeable rate of the energy storage battery may be:
-βbcSBAT<Pb,t<βbdSBAT
wherein, Pbat_minIs the lowest power, lambda, of the battery energy storage systembatIn order to be a margin factor,
for the highest peak power, the peak power is,
is the lowest peak power, Pin_maxFor supplying maximum power, P, in the forward directionout_maxMaximum power is transmitted for the reverse direction; sBAT_minFor minimum capacity of energy storage cell, Pol,tFor the load power exceeding the maximum power P of the forward power supplyin_maxThe cumulative number of electric energy per day is the number of degrees of overload electric energy; pt -The integral of the power in the backward feeding is the photovoltaic power generation electric energy degree which needs to be accommodated by the energy storage battery in one day; t is a charging period; SOCtFor the actual state of charge, SOC, of the energy storage cell at time tminIs the minimum state of charge limit, SOC, of the batterymaxIs the maximum state of charge limit, SOC, of the batteryt-1To be Pb,tThe output power of the battery energy storage system at the moment T, delta T is the charging and discharging sampling time, etadFor the discharge efficiency of the energy storage cell, etacFor the charging efficiency of the energy storage cell, SBATThe total capacity of the battery energy storage system; beta is abcTo charge rate, betabdThe discharge rate is shown.
In some embodiments, SOCminCan be 20%, SOCmaxMay be 80%.
The practically allowable charging and discharging space of the energy storage battery is about 60% of the rated capacity.
Considering charging station load growth uncertainty, population growth and the constant development of electric vehicles increase the power demand of charging stations. Therefore, the load increase factor is considered when optimizing the battery energy storage configuration of the optical storage charging station, and the load curve of each year is obtained by multiplying the initial load curve by the load increase coefficient.
In some embodiments, S103 may include:
s1031: and solving the energy storage optimization configuration model by adopting a chicken flock algorithm to obtain a target energy storage configuration scheme.
In the embodiment of the invention, both the objective function and the constraint condition are complex, so that the conventional optimization solving method is difficult to solve the energy storage optimization configuration model, and therefore, the improved competitive particle swarm optimization algorithm (chicken swarm optimization algorithm) is adopted to solve the energy storage optimization configuration model, the correctness and the precision are high, and the actual application requirements can be met.
The chicken flock algorithm comprises the following specific steps:
1. let the foraging space be D-dimension, and the total number of individuals contained in the cluster be CnumThe number of cocks is CrnumThe number of hens is ChnumThe number of cubs is Cpnum。
Foraging behavior of chicken flocks was as follows:
1) foraging behavior of the rooster; the position of the ith cock after the mth foraging in the jth dimension space is
The position after the (m + 1) th foraging is as follows:
k＝[1,Crnum],k≠i
wherein rand (0, sigma)2) Is a Gaussian distribution with a mean of 0 and a standard deviation of σ2，fi，fkThe fitness of the ith cock and the kth cock is shown, and epsilon is a constant.
2) Foraging behavior of hens; the ith henThe position after the m-th foraging in the j-dimensional space is
The position after the (m + 1) th foraging is as follows:
wherein rand is [0,1 ]]R is the ith hen mate, s is any cock except the r cock, fiFitness of ith hen, fsThe fitness of the r-th cock and the s-th cock is shown.
3) Foraging behavior of the young; the position of the ith cub after the mth foraging in the jth dimension space is
The position after the (m + 1) th foraging is as follows:
wherein t is the hen followed by the ith cub, and conf is the following coefficient of the cub following the hen.
2. And initializing parameters. The initial configuration algorithm parameters mainly comprise the size of the chicken flock, the iteration times, the updating frequency of the population relation, the dimension of the individual position, the proportion of the cocks and the hens in the chicken flock and the like.
3. The chicken flock is initialized. The chicken groups are sorted and graded according to the fitness value, wherein the cock is the previous individual, the cubs are the last individual, and the rest are hens. Dividing chicken groups into groups according to the number of the cocks, randomly distributing the hens into the groups, and determining the partnership of the cocks and the hens. Randomly selecting a hen, randomly leading cubs, and determining the maternal-child relationship of the cubs of the hen.
4. The algorithm iteration is started, whether the grouping needs to be updated or not is judged, whether the relation among the chicken flocks needs to be updated or not is judged, and the relation among the chicken flocks and the chicken flocks is updated if the relation is needed; otherwise, the positions of the cock, the hen and the cub are respectively updated one by one according to respective position updating strategies, and meanwhile, the fitness value of the updated positions is calculated.
5. The individual location is updated. And comparing the fitness value of the new position with the fitness value of the original position, updating the individual position if the fitness value of the new position is small, and keeping the original position unchanged if the fitness value of the new position is not small.
6. Stopping iteration after the maximum iteration times are reached, and outputting the optimal solution, otherwise returning to the step 3, and carrying out circular iteration for searching.
The above method is described in detail with reference to specific examples.
Fig. 2 shows a charging load curve of an optical storage charging station. Fig. 2 is a graph of the mean load, the maximum load envelope, the minimum load envelope and the standard deviation of the load of the light storage charging station in 12 months of a year. Assuming that the power supply capacity of the 500kVA distribution transformer is 400kW, the average load and the highest load both exceed the upper limit of the power supply capacity, and the lowest load causes the photovoltaic power generation backfeed.
From the formula of the objective function
Battery energy storage system cost FBATMainly comprises two parts of a current transformation control device, a battery and a management system thereof, and the operation and maintenance cost FOMFor simple calculation, the system cost is also hooked. The major cost components of the battery energy storage system are shown in table 1.
TABLE 1 Battery energy storage System Primary cost component
| Cost of battery energy storage system | Price interval | Reduced price |
| Variable flow control equipment | 300-600 yuan/kW | About 0.123/kW |
| Lithium iron phosphate battery and BMS | 700-2000 yuan/kWh | About 0.60 yuan/kWh |
| Cost of operation and maintenance | The cost of the battery is 10% | About 0.06 yuan/kWh |
The cycle life of the lithium iron phosphate battery is calculated according to 2000 times, and the life of the converter equipment is calculated according to 10 years.
As can be seen from table 1, the cost of the battery energy storage system is mainly determined by the cost of the battery, and the cost of the battery conversion is determined by the cycle life of the battery. The profitability of a battery energy storage system is very sensitive to peak-to-valley electricity price differences.
The time-of-use electricity price of the power grid is different according to different regions, different electricity utilization capacities, different time, different seasons and different regional characteristics. Here, the time-of-use electricity rate example table shown in table 2 is used as a basis for calculating the electricity rate difference value.
TABLE 2 time-of-use electricity price example table
| Time period name | Time interval | Electric degreePrice of electricity |
| Peak section | 8:00-11:00 18:00-23:00 | 1.015 |
| Flat section | 7:00-8:00 11:00-18:00 | 0.6262 |
| Millet section | 23:00-7:00 | 0.3271 |
The photovoltaic electricity price policy is continuously adjusted, in 2020, the instruction prices of newly added centralized photovoltaic power stations in class I-III resource areas which are brought into the national financial subsidy range are respectively reduced to 0.35 yuan (including tax, the same below), 0.4 yuan and 0.49 yuan per kilowatt hour, and the full-power-generation subsidy standard of the industrial and commercial distributed photovoltaic power generation project adopting the 'self-generation and surplus internet-surfing' mode is reduced to 0.05 yuan per kilowatt hour. The example relates to an industrial and commercial distributed photovoltaic power generation project with a photovoltaic power generation project positioned in a 'spontaneous self-use and surplus Internet surfing' mode, and the Internet surfing electricity price is 0.3644 RMB/kWh of the power grid desulfurization coal benchmarking electricity price.
The improved competitive particle swarm optimization algorithm is compiled by using Python language, and a scale factor and the like are introduced to improve the traditional competitive particle swarm optimization algorithm, so that the convergence speed and the convergence precision of the particle swarm optimization algorithm are improved. The improved competitive particle swarm optimization algorithm has good convergence performance through testing, for example, refer to fig. 3.
In order to test the correctness and the accuracy of the improved competitive particle swarm optimization algorithm for searching the objective function solution provided by the embodiment of the invention, the input quantity of the linear increase of the battery capacity is used for testing, and the scanning curve shown in fig. 4 is obtained. The vicinity of the pole was scanned with high precision to obtain the comparison results shown in table 3.
TABLE 3 COMPARATIVE CALCULATION TABLE FOR SOLUTION-ALGORITHM
| Battery energy storage configuration capacity | Optimized value of objective function | |
| Chicken flock algorithm result | 577.1380019481085 | -1570.2070590960825 |
| Scanning results | 577.138 | -1570.207059096082 |
As can be seen from table 3, the algorithm used in the present invention almost completely coincides with the result of high-precision scanning, thereby proving the accuracy and high precision of the improved competitive particle swarm optimization algorithm (chicken swarm optimization algorithm).
According to the calculation result, the chargeable and dischargeable capacity of the energy storage battery is selected to be 600kWh by considering a proper margin. According to the constraint requirement of the charging and discharging depth of the battery, the nominal capacity of the energy storage battery is as follows:
SBAT＝S/0.6＝1000kWh
in order to test the sensitivity of the capacity optimization method to the peak-to-valley electricity price difference, the peak-to-valley electricity price difference is reduced by 0.1 yuan, and optimization calculation is performed again, and the result is shown in table 4:
TABLE 4 comparison table of solving method after reduction of electricity price difference
| Energy storage optimized configuration capacity | Optimized value of objective function | |
| Particle swarm algorithm results | 355.137994265637 | -922.8714512016086 |
| Scanning results | 355.13799 | -922.871451201608 |
As can be seen from table 4 in conjunction with fig. 5 and 6, the solution result of the particle swarm algorithm on the model after price reduction and the optimal result obtained by scanning are also basically consistent with the scanning result, but the minimum value of the objective function is-922.871 (the negative sign indicates profit), which is much lower than the value of-1570.207 before price reduction, indicating that the profitability of the energy storage system is greatly reduced.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Referring to fig. 7, the embodiment of the present invention further provides a battery energy storage configuration device for an optical storage charging station, including:
the parameter acquisition module 21 is configured to acquire charging load data and photovoltaic power generation data of the optical storage charging station;
the model establishing module 22 is used for determining a target function and a constraint condition according to the charging load data and the photovoltaic power generation data, and establishing an energy storage optimization configuration model according to the target function and the constraint condition;
and the model solving module 23 is used for solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme.
In some embodiments, the objective function may be calculated as:
wherein F is the total cost, FPSCFor converting the light storage charging station into the daily sum of costs, MLAs a penalty factor, ω1And omega2Is a weight coefficient; fBATFor the acquisition cost of the battery energy storage system reduced to 1 charge-discharge cycle per day in the service life, FOMConverting the battery energy storage system operation to daily cost, FDEPFor the profit of the electricity price difference, FPVAnd the benefits brought by the photovoltaic electric energy price are increased by absorbing the photovoltaic power generation electric quantity for a battery energy storage system.
In some embodiments, the cost of purchase F of the optical storage charging station is reduced to 1 charging and discharging cycle per day in the service lifeBATThe formula of (c) may be:
FBAT＝(FES+cBATSBAT)/Tlife
differential electricity price gain FDEPThe calculation formula of (c) may be:
income F brought by photovoltaic power price increased by battery energy storage system absorbing photovoltaic power generation electric quantityPVThe calculation formula of (c) may be:
FPV＝EPV(ce-cPV)
penalty factor MLThe calculation formula of (c) may be:
wherein, FESFor the cost of the battery energy storage system equipment, cBATFor the cost per unit capacity of the energy storage cell, SBATFor the total capacity, T, of the battery energy storage systemlifeThe expected cycle life of the energy storage battery; c. Ce,tFor real-time electricity prices, PBC,tReal time charging power, P, for energy storage batteriesBD,tReal-time discharging power of the energy storage battery, and T is charging time; ePVElectric energy for photovoltaic generation, ceTo average electricity price, cPVThe photovoltaic grid-connected electricity price is obtained; pLmaxFor maximum load power of the supply line, PmaxAnd the actual total load of the charging station after the battery is added with energy storage is maximum.
In some embodiments, the optical storage charging station operation and maintenance is converted to a daily cost FOMThe calculation formula of (c) may be:
FOM＝rcBATSBAT/Tlife
or the like, or, alternatively,
wherein r is the operation and maintenance cost converted to daily cBATFor the cost per unit capacity of the energy storage cell, SBATFor the total capacity, T, of the battery energy storage systemlifeThe expected cycle life of the energy storage battery; c. CWS,tGuarantee operation and maintenance cost, T, for unit capacity of photovoltaic and energy storage systemOWTo provide the maintenance life.
In some embodiments, the constraints may include: the method comprises the following steps of power supply line power capacity constraint, energy storage battery chargeable and dischargeable depth constraint and energy storage battery chargeable and dischargeable multiplying power constraint.
In some embodiments, the supply line power capacity constraint may be:
the chargeable and dischargeable capacity constraint of the energy storage battery can be as follows:
the chargeable and dischargeable depth constraint of the energy storage battery can be as follows:
SOCmin≤SOCt≤SOCmax
Pb,t＞0，SOCt＝SOCt-1-Pb,tΔT/(ηdSBAT)
Pb,t<0，SOCt＝SOCt-1-Pb,tΔTηc/SBAT
the constraint of the chargeable and dischargeable multiplying power of the energy storage battery can be as follows:
-βbcSBAT<Pb,t<βbdSBAT
wherein, Pbat_minIs the lowest power, lambda, of the battery energy storage systembatIn order to be a margin factor,
for the highest peak power, the peak power is,
is the lowest peak power, Pin_maxMaximum power for forward supply, Pout_maxMaximum power is transmitted for reverse direction;
SBAT_minfor minimum capacity of energy storage cell, Pol,tFor the load power exceeding the maximum power P of the forward power supplyin_maxPart of (A), Pt -The power in the reverse feeding is shown, and T is a charging period; SOCtFor the actual state of charge, SOC, of the energy storage cell at time tminIs the minimum state of charge limit, SOC, of the batterymaxIs the maximum state of charge limit, SOC, of the batteryt-1To be Pb,tThe output power of the battery energy storage system at the time T, and delta T is the charging and dischargingSample time, ηdFor the discharge efficiency of the energy storage cell, etacFor the charging efficiency of the energy storage cell, SBATThe total capacity of the battery energy storage system; beta is abcTo charge rate, betabdThe discharge rate is shown.
In some embodiments, model solving module 23 may include:
and the optimizing unit 231 is used for solving the energy storage optimization configuration model by adopting a chicken flock algorithm to obtain a target energy storage configuration scheme.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the foregoing division of each functional unit and module is merely used for illustration, and in practical applications, the foregoing function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the configuration terminal is divided into different functional units or modules to perform all or part of the above-described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the above-mentioned apparatus may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Fig. 8 is a schematic block diagram of a configuration terminal according to an embodiment of the present invention. As shown in fig. 8, the configuration terminal 4 of this embodiment includes: one or more processors 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processors 40. The processor 40 executes the computer program 42 to implement the steps in the above-mentioned embodiments of the battery energy storage configuration method of the optical storage charging station, such as the steps S101 to S103 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functions of the modules/units in the above-described embodiment of the optical charging station battery energy storage configuration device, such as the functions of the modules 21 to 23 shown in fig. 7.
Illustratively, the computer program 42 may be divided into one or more modules/units, which are stored in the memory 41 and executed by the processor 40 to accomplish the present application. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 42 in the configuration terminal 4. For example, the computer program 42 may be divided into a parameter acquisition module 21, a model building module 22 and a model solving module 23.
The parameter acquisition module 21 is configured to acquire charging load data and photovoltaic power generation data of the optical storage charging station;
the model establishing module 22 is used for determining a target function and a constraint condition according to the charging load data and the photovoltaic power generation data, and establishing an energy storage optimization configuration model according to the target function and the constraint condition;
and the model solving module 23 is used for solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme.
Other modules or units are not described in detail herein.
The configuration terminal 4 includes, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 8 is only one example of a configuration terminal and does not constitute a limitation of the configuration terminal 4, and may include more or less components than those shown, or combine certain components, or different components, for example, the configuration terminal 4 may further include input devices, output devices, network access devices, buses, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the configuration terminal, such as a hard disk or a memory of the configuration terminal. The memory 41 may also be an external storage device of the configuration terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the configuration terminal. Further, the memory 41 may also include both an internal storage unit of the configuration terminal and an external storage device. The memory 41 is used for storing computer programs 42 and other programs and data needed for configuring the terminal. The memory 41 may also be used to temporarily store data that has been output or is to be output.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed configuration terminal and method can be implemented in other manners. For example, the above-described embodiments of the configuration terminal are merely illustrative, and for example, a module or a unit may be divided into only one logic function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the embodiments described above may be implemented by a computer program, which is stored in a computer readable storage medium and used by a processor to implement the steps of the embodiments of the methods described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may include any suitable increase or decrease as required by legislation and patent practice in the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A battery energy storage configuration method for an optical energy storage charging station is characterized by comprising the following steps:
acquiring charging load data and photovoltaic power generation data of a light storage charging station;
determining an objective function and a constraint condition according to the charging load data and the photovoltaic power generation data, and establishing an energy storage optimization configuration model according to the objective function and the constraint condition;
and solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme.
2. The battery energy storage configuration method for an optical storage charging station according to claim 1, wherein the calculation formula of the objective function is:
wherein F is the total cost, FPSCFor converting the light storage charging station into the daily sum of costs, MLAs a penalty factor, ω1And omega2Is a weight coefficient; fBATFor the acquisition cost of the battery energy storage system reduced to 1 charge-discharge cycle per day in the service life, FOMConverting the battery energy storage system operation to daily cost, FDEPFor the profit of the electricity price difference, FPVAnd the benefits brought by the price of photovoltaic electric energy are increased by absorbing the electric quantity of photovoltaic power generation for a battery energy storage system.
3. The optical storage charging station battery energy storage configuration method of claim 2,
the purchase cost F of the optical storage charging station is reduced to 1 charge-discharge cycle per day in the service lifeBATThe calculation formula of (2) is as follows:
FBAT＝(FES+cBATSBAT)/Tlife
the electricity price difference gain FDEPThe calculation formula of (2) is as follows:
income F brought by photovoltaic power generation electric quantity absorbed by battery energy storage system and photovoltaic electric energy pricePVThe calculation formula of (2) is as follows:
FPV＝EPV(ce-cPV)
the penalty factor MLThe calculation formula of (2) is as follows:
wherein, FESFor the cost of the battery energy storage system equipment, cBATFor the cost per unit capacity of the energy storage cell, SBATFor the total capacity, T, of the battery energy storage systemlifeThe expected cycle life of the energy storage battery; c. Ce,tFor real-time electricity prices, PBC,tReal-time charging power, P, for energy storage batteriesBD,tReal-time discharging power of the energy storage battery, and T is charging time; ePVElectric energy for photovoltaic generation, ceTo average electricity price, cPVThe photovoltaic grid-connected electricity price is obtained; pLmaxFor maximum load power of the supply line, PmaxThe actual total load of the charging station after the battery is charged is maximum.
4. The method of claim 3, wherein the operation and maintenance of the optical storage charging station is reduced to per-cellDaily cost FOMThe calculation formula of (2) is as follows:
FOM＝rcBATSBAT/Tlife
or the like, or, alternatively,
wherein r is the operation and maintenance cost converted to daily cBATFor the cost per unit capacity of the energy storage cell, SBATFor the total capacity, T, of the battery energy storage systemlifeThe expected cycle life of the energy storage battery; c. CWS,tGuarantee operation and maintenance cost, T, for unit capacity of photovoltaic and energy storage systemOWTo provide the maintenance life.
5. The optical storage charging station battery energy storage configuration method of claim 1, wherein the constraint condition comprises: the method comprises the following steps of power supply line power capacity constraint, energy storage battery chargeable and dischargeable depth constraint and energy storage battery chargeable and dischargeable multiplying power constraint.
6. The optical storage charging station battery energy storage configuration method of claim 5,
the power supply line power capacity constraint is as follows:
the chargeable and dischargeable capacity constraint of the energy storage battery is as follows:
the chargeable and dischargeable depth constraint of the energy storage battery is as follows:
SOCmin≤SOCt≤SOCmax
Pb,t＞0，SOCt＝SOCt-1-Pb,tΔT/(ηdSBAT)
Pb,t<0，SOCt＝SOCt-1-Pb,tΔTηc/SBAT
the energy storage battery can charge and discharge multiplying power restraint is as follows:
-βbcSBAT<Pb,t<βbdSBAT
wherein, Pbat_minIs the lowest power, lambda, of the battery energy storage systembatIn order to be a margin factor,
for the highest peak power, the peak power is,
is the lowest peak power, Pin_maxMaximum power for forward supply, Pout_maxMaximum power is transmitted for reverse direction; sBAT_minFor minimum capacity of energy storage cell, Pol,tFor the load power exceeding the maximum power P of the forward power supplyin_maxPart of (A), Pt -The power in the reverse feeding is shown, and T is a charging period; SOCtFor the actual state of charge, SOC, of the energy storage cell at time tminIs the minimum state of charge limit, SOC, of the batterymaxIs the maximum state of charge limit, SOC, of the batteryt-1To be Pb,tThe output power of the battery energy storage system at the moment T, delta T is the charging and discharging sampling time, etadFor the discharge efficiency of the energy storage cell, etacFor the charging efficiency of the energy storage cell, SBATThe total capacity of the battery energy storage system; beta is abcTo charge rate, betabdThe discharge rate is shown.
7. The battery energy storage configuration method of an optical energy storage charging station according to claim 1, wherein solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme comprises:
and solving the energy storage optimization configuration model by adopting a chicken flock algorithm to obtain a target energy storage configuration scheme.
8. A battery energy storage configuration device for an optical storage charging station, comprising:
the parameter acquisition module is used for acquiring charging load data and photovoltaic power generation data of the optical storage charging station;
the model establishing module is used for determining a target function and a constraint condition according to the charging load data and the photovoltaic power generation data and establishing an energy storage optimization configuration model according to the target function and the constraint condition;
and the model solving module is used for solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme.
9. A configuration terminal comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor when executing said computer program realizes the steps of the method for configuring battery energy storage of an optical storage charging station according to any of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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| CN114997544A (en) * | 2022-08-04 | 2022-09-02 | 北京理工大学 | Method and system for optimizing and configuring capacity of hydrogen optical storage charging station |
| CN115514005A (en) * | 2022-10-12 | 2022-12-23 | 北京双登慧峰聚能科技有限公司 | Economical evaluation method and device for photovoltaic power station energy storage system configuration |
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| CN114997544A (en) * | 2022-08-04 | 2022-09-02 | 北京理工大学 | Method and system for optimizing and configuring capacity of hydrogen optical storage charging station |
| CN115514005A (en) * | 2022-10-12 | 2022-12-23 | 北京双登慧峰聚能科技有限公司 | Economical evaluation method and device for photovoltaic power station energy storage system configuration |
| CN115514005B (en) * | 2022-10-12 | 2023-10-27 | 北京双登慧峰聚能科技有限公司 | Economical evaluation method and device for energy storage system configuration of photovoltaic power station |
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