CN112003381A - Method and system for configuring capacity of energy storage battery in optical storage charging station - Google Patents

Method and system for configuring capacity of energy storage battery in optical storage charging station Download PDF

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CN112003381A
CN112003381A CN202010762384.6A CN202010762384A CN112003381A CN 112003381 A CN112003381 A CN 112003381A CN 202010762384 A CN202010762384 A CN 202010762384A CN 112003381 A CN112003381 A CN 112003381A
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charging station
charging
optical storage
energy storage
storage battery
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张晶
李香龙
袁瑞铭
王阳
陈振
李涛永
刁晓虹
刘畅
赵思翔
姜振宇
李斌
马澄斌
闫东翔
蒋林洳
张元星
陈天锦
巨汉基
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State Grid Corp of China SGCC
Xuji Group Co Ltd
China Electric Power Research Institute Co Ltd CEPRI
State Grid Jibei Electric Power Co Ltd
State Grid Beijing Electric Power Co Ltd
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State Grid Corp of China SGCC
Xuji Group Co Ltd
China Electric Power Research Institute Co Ltd CEPRI
State Grid Jibei Electric Power Co Ltd
State Grid Beijing Electric Power Co Ltd
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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
    • H02J15/00—Systems for storing electric energy
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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 a method and a system for configuring the capacity of an energy storage battery in an optical storage charging station, wherein the method comprises the following steps: acquiring an average load curve of the light storage charging station in a preset period; determining the optimal configuration capacity of an energy storage battery in the optical storage charging station according to the average load curve of the optical storage charging station in a preset period; and configuring the energy storage battery in the light storage charging station according to the optimal configuration capacity of the energy storage battery in the light storage charging station. The capacity of the energy storage battery in the light storage charging station is configured more reasonably, and the reliability of renewable energy power generation in the light storage charging station is improved to the maximum extent.

Description

Method and system for configuring capacity of energy storage battery in optical storage charging station
Technical Field
The invention relates to the technical field of intelligent power utilization and energy storage, in particular to a method and a system for configuring the capacity of an energy storage battery in an optical storage charging station.
Background
Electric vehicles attract more and more attention along with the energy crisis and environmental pollution problems, and electric vehicles with energy-saving and environment-friendly characteristics have become the development direction of the global automobile industry.
The electric automobile is popularized to enter the daily life of people, the charging problem of the electric automobile needs to be solved, and if the electric automobile is directly connected into a main power grid to be charged, the load of a power distribution network can be greatly increased.
In order to reduce the load of a power distribution network, a feasible solution is to establish an electric vehicle charging station fusing renewable energy sources (such as solar energy and wind energy), so that the renewable energy sources are developed and utilized, electricity getting from a power grid is reduced, and the requirement of an electric vehicle on a charging facility is met.
But renewable energy power generation is easily influenced by external environment, so that the power output of the renewable energy power generation has volatility and randomness, and the power output of the renewable energy is unstable, so that the reliability of the renewable energy power generation can be improved by arranging the energy storage battery system in the charging station.
At present, the research on the capacity configuration of the energy storage battery in the optical storage charging station is few, and most of the capacity configurations of the energy storage battery are simply determined according to design experience, so that the configuration rationality is not high.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method for configuring the capacity of an energy storage battery in an optical storage charging station, which more reasonably configures the capacity of the energy storage battery in the optical storage charging station and maximally improves the reliability of renewable energy power generation in the optical storage charging station.
The purpose of the invention is realized by adopting the following technical scheme:
the invention provides a method for configuring the capacity of an energy storage battery in an optical storage charging station, which is improved in that the method comprises the following steps:
acquiring an average load curve of the light storage charging station in a preset period;
determining the optimal configuration capacity of an energy storage battery in the optical storage charging station according to the average load curve of the optical storage charging station in a preset period;
and configuring the energy storage battery in the light storage charging station according to the optimal configuration capacity of the energy storage battery in the light storage charging station.
Preferably, the obtaining of the average load curve of the optical storage charging station in the preset period includes:
according to the battery pack capacity of each electric vehicle in the charging area corresponding to the optical storage charging station, simulating a battery pack capacity Gaussian distribution function met by the electric vehicle in the charging area corresponding to the optical storage charging station;
fitting the initial charging time Gaussian distribution function met by the electric vehicles in the charging area corresponding to the optical storage charging station according to the initial charging time of each electric vehicle in each preset period of the historical time period in the charging area corresponding to the optical storage charging station;
fitting SOC Gaussian distribution functions met by electric vehicles in the charging areas corresponding to the optical storage charging stations according to the SOC of each electric vehicle in the charging area corresponding to the optical storage charging stations at the starting moment of each preset period in the historical time period;
performing M groups of simulation on the charging behavior of the electric vehicle by adopting a Monte Carlo algorithm based on the set data and a battery pack capacity Gaussian distribution function, an initial charging time Gaussian distribution function and an SOC Gaussian distribution function which are met by the electric vehicle in a charging area corresponding to the optical storage charging station, and acquiring simulation load curves of the M optical storage charging stations in a preset period;
acquiring the average value of the loads of the M optical storage charging stations at the t-th moment in the simulation load curve of the preset period, and taking the average value as the load value of the optical storage charging stations at the t-th moment in the average load curve of the preset period;
wherein T belongs to (1-T), and T is the total number of moments contained in the preset period.
Further, the predetermined data includes:
the method comprises the following steps of determining the number of electric vehicles in a charging area corresponding to a light storage charging station, the number of charging piles in the light storage charging station, the charging power of the charging piles in the light storage charging station and the charging ending conditions of the electric vehicles in the charging area corresponding to the light storage charging station;
the charging end condition of the electric automobile in the charging area corresponding to the optical storage charging station is that the SOC of the electric automobile is greater than 80% of the maximum allowable SOC of the electric automobile.
Preferably, the determining the optimal configuration capacity of the energy storage battery in the optical storage charging station according to the average load curve of the optical storage charging station in the preset period includes:
substituting the average load curve of the optical storage charging station in a preset period into a pre-constructed energy storage battery capacity optimization calculation model, solving the energy storage battery capacity optimization calculation model, and obtaining the optimal configuration capacity of the energy storage battery in the optical storage charging station;
the energy storage battery capacity optimization calculation model is constructed by taking the lowest aging degree of the energy storage battery in the optical storage charging station, the lowest loss of photovoltaic power generation in the optical storage charging station and the lowest operation cost of the optical storage charging station as targets.
Further, an objective function of the pre-constructed energy storage battery capacity optimization calculation model is determined according to the following formula:
Figure BDA0002613432760000021
in the formula, f is an objective function value omega of a pre-constructed energy storage battery capacity optimization calculation model1For a weight corresponding to the degree of ageing of the energy storage cell, QL,tAging degree, omega, of the energy storage battery at the t-th moment of the preset period caused by the charging and discharging behaviors2Is the weight corresponding to the photovoltaic power generation loss rate, Peg,L,tThe loss amount P of photovoltaic power generation in the optical storage charging station at the t moment of the preset periodeg,tFor the t th photovoltaic power generation curve of the light storage charging station in the average preset periodElectric power generation amount at one time, CbThe purchase cost of the energy storage battery is reduced by the unit time, and the value of the purchase cost of the energy storage battery is equal to the total time, C, included in the service life of the energy storage batteryg,tCost, C, of purchasing electricity from the grid at the t-th moment of the preset periodmaxThe maximum limit value of the cost consumed at the tth moment of the preset period is T ∈ (1-T), and T is the total number of moments contained in the preset period;
determining the aging degree Q of the energy storage battery at the t-th moment of a preset period caused by the charging and discharging behaviors according to the following formulaL,t：
Figure BDA0002613432760000031
In the formula, Crate，tThe charging and discharging multiplying power of the energy storage battery in the optical storage charging station at the t moment of the preset period,
Figure BDA0002613432760000032
Pb,tthe charging and discharging power of the energy storage battery in the optical storage charging station at the T moment of the preset period, C is the capacity of the energy storage battery in the optical storage charging station, R is a gas constant, Tb,tThe temperature of the energy storage battery in the optical storage charging station at the t-th moment of the preset period, AhThe ampere-hour throughput of an energy storage battery in the optical storage charging station is shown, and z is a power exponential factor; determining the loss P of photovoltaic power generation in the optical storage charging station at the t moment of the preset period according to the following formulaeg,L,t：
Peg,L,t＝Peg,t-Ps,t-Pb,t
In the formula, Ps,tThe load of the light storage charging station at the t-th moment in the average load curve of the preset period is obtained;
determining the generated energy P of the light storage charging station in the t-th time period in the average photovoltaic power generation curve of the preset cycle according to the following formulaeg,t：
Figure BDA0002613432760000033
In the formula (I), the compound is shown in the specification,
Figure BDA0002613432760000034
the photovoltaic power generation capacity of the light storage charging station at the tth moment of the jth preset period in the historical time period is j epsilon (1-N)y)，NyThe total number of preset cycles in the history period.
Further, the constraint conditions of the objective function of the pre-constructed energy storage battery capacity optimization calculation model include: the method comprises the following steps of (1) power balance constraint conditions, energy storage battery charging and discharging power constraint conditions, energy storage battery charging energy constraint conditions, energy storage battery discharging energy constraint conditions and energy storage battery capacity constraint conditions;
wherein the power balance constraint is determined as follows:
Pg,t＝Ps,t-Peg,t-Pb,t
in the formula, Pg,tThe electric power is provided for the power grid at the t moment of the preset period;
determining the charge and discharge power constraint condition of the energy storage battery according to the following formula:
Pb,min≤Pb,t≤Pb,max
in the formula, Pb,minThe minimum limit value of the charging and discharging power of the energy storage battery in the optical storage charging station at the t moment of the preset period is Pb,maxThe maximum limit value of the charging and discharging power of the energy storage battery in the optical storage charging station at the t moment of the preset period is set;
determining the energy storage battery charging energy constraint condition according to the following formula:
Et+1＝Et+Δt·pb,t·η
determining the discharge energy constraint condition of the energy storage battery according to the following formula:
Figure BDA0002613432760000041
wherein η is the charging and discharging efficiency of the energy storage battery in the optical storage charging station, and Δ t is two adjacent charging and discharging efficiencies of the preset periodTime of day, EtFor the energy of the energy storage battery in the light storage charging station at the t-th moment of the preset period, Et+1The energy of an energy storage battery in the optical storage charging station is at the t +1 th moment of a preset period;
determining the energy storage battery capacity constraint condition according to the following formula:
0≤C≤Cmax
in the formula, CmaxThe maximum energy storage battery capacity allowed to be configured in the optical storage charging station.
The invention provides a system for configuring the capacity of an energy storage battery in an optical storage charging station, which is improved in that the system comprises:
the acquisition module is used for acquiring an average load curve of the optical storage charging station in a preset period;
the determining module is used for determining the optimal configuration capacity of an energy storage battery in the optical storage charging station according to the average load curve of the optical storage charging station in a preset period;
and the configuration module is used for configuring the energy storage battery in the optical storage charging station according to the optimal configuration capacity of the energy storage battery in the optical storage charging station.
Preferably, the obtaining module includes:
the first fitting unit is used for fitting a battery pack capacity Gaussian distribution function met by the electric vehicles in the charging area corresponding to the optical storage charging station according to the battery pack capacity of each electric vehicle in the charging area corresponding to the optical storage charging station;
the second fitting unit is used for fitting the Gaussian distribution function of the initial charging time met by the electric vehicles in the charging area corresponding to the optical storage charging station according to the initial charging time of each electric vehicle in each preset period of the historical time period in the charging area corresponding to the optical storage charging station;
the third fitting unit is used for fitting SOC Gaussian distribution functions which are met by the electric automobiles in the charging areas corresponding to the optical storage charging stations according to the SOC of each electric automobile in the charging area corresponding to the optical storage charging station at the starting moment of each preset period in the historical time period;
the simulation unit is used for performing M-group simulation on the charging behavior of the electric vehicle by adopting a Monte Carlo algorithm based on the set data and a battery pack capacity Gaussian distribution function, an initial charging time Gaussian distribution function and an SOC Gaussian distribution function which are met by the electric vehicle in a charging area corresponding to the optical storage charging station, and acquiring simulation load curves of the M optical storage charging stations in a preset period;
the device comprises a unit, a load calculation unit and a load calculation unit, wherein the unit is used for acquiring the average value of loads of M optical storage charging stations at the t-th moment in a simulation load curve of a preset period, and taking the average value as the load value of the optical storage charging stations at the t-th moment in the average load curve of the preset period;
wherein T belongs to (1-T), and T is the total number of moments contained in the preset period.
Further, the predetermined data includes:
the method comprises the following steps of determining the number of electric vehicles in a charging area corresponding to a light storage charging station, the number of charging piles in the light storage charging station, the charging power of the charging piles in the light storage charging station and the charging ending conditions of the electric vehicles in the charging area corresponding to the light storage charging station;
the charging end condition of the electric automobile in the charging area corresponding to the optical storage charging station is that the SOC of the electric automobile is greater than 80% of the maximum allowable SOC of the electric automobile.
Preferably, the determining module is configured to:
substituting the average load curve of the optical storage charging station in a preset period into a pre-constructed energy storage battery capacity optimization calculation model, solving the energy storage battery capacity optimization calculation model, and obtaining the optimal configuration capacity of the energy storage battery in the optical storage charging station;
the energy storage battery capacity optimization calculation model is constructed by taking the lowest aging degree of the energy storage battery in the optical storage charging station, the lowest loss of photovoltaic power generation in the optical storage charging station and the lowest operation cost of the optical storage charging station as targets.
Compared with the closest prior art, the invention has the following beneficial effects:
according to the technical scheme provided by the invention, an average load curve of the optical storage charging station in a preset period is obtained; determining the optimal configuration capacity of an energy storage battery in the optical storage charging station according to the average load curve of the optical storage charging station in a preset period; and configuring the energy storage battery in the light storage charging station according to the optimal configuration capacity of the energy storage battery in the light storage charging station. The capacity of the energy storage battery in the light storage charging station is configured more reasonably, and the reliability of renewable energy power generation in the light storage charging station is improved to the maximum extent.
According to the technical scheme provided by the invention, the battery pack capacity Gaussian distribution function, the initial charging time Gaussian distribution function and the SOC Gaussian distribution function which are met by the electric automobile in the charging area corresponding to the optical storage charging station are fitted by utilizing the electric automobile random charging behavior data in the charging area, and the average load curve of the optical storage charging station in the preset period is obtained by utilizing the Gaussian distribution function simulation, so that the load condition of the optical storage charging station in the preset period is more accurately predicted.
According to the technical scheme provided by the invention, the optimal configuration of the comprehensive benefits of the energy storage batteries in the charging station is realized by aiming at the lowest operation cost of the optical storage charging station, the lowest loss rate of photovoltaic power generation and the lowest aging degree of the energy storage batteries in the optical storage charging station.
Drawings
Fig. 1 is a flow chart of a method for configuring the capacity of an energy storage cell in an optical storage charging station;
FIG. 2 is a graph of the daily average load of the optical storage charging station according to an embodiment of the present invention;
FIG. 3 is a graph of the daily average photovoltaic power generation of the optical storage charging station according to an embodiment of the present invention;
FIG. 4 is a graph of output power versus time for the energy storage battery and the power grid in the optical storage charging station according to an embodiment of the present invention;
FIG. 5 is a graph illustrating the energy state change of the energy storage battery in the optical storage charging station according to an embodiment of the present invention;
fig. 6 is a structural diagram of a system for configuring the capacity of an energy storage battery in an optical storage charging station.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a method for configuring the capacity of an energy storage battery in an optical storage charging station, as shown in fig. 1, the method comprises the following steps:
step 101, obtaining an average load curve of the optical storage charging station in a preset period;
102, determining the optimal configuration capacity of an energy storage battery in the optical storage charging station according to an average load curve of the optical storage charging station in a preset period;
and 103, configuring the energy storage batteries in the optical storage charging station according to the optimal configuration capacity of the energy storage batteries in the optical storage charging station.
The invention relates to a light storage charging station which generally comprises a photovoltaic system, an energy storage battery system and a charging pile; the photovoltaic power generation board can be conveniently arranged at the top of a parking shed of the charging pile, and the charging station is connected with a power distribution network;
according to the working principle of the optical storage charging station (when the photovoltaic system can meet the charging load of the electric automobile, the photovoltaic system charges the energy storage battery and simultaneously provides the charging electric energy for the electric automobile to the charging pile; when the photovoltaic system can not meet the charging load of the electric automobile, but the photovoltaic system and the energy storage battery can meet the charging load of the electric automobile, the photovoltaic system and the energy storage battery jointly provide the charging electric energy for the electric automobile to the charging pile; otherwise, the charging pile can obtain the electric energy from the power distribution network, and simultaneously the charging pile can charge the energy storage battery by using the low valley electricity price at the charging station at night), the configuration of the energy storage battery in the optical storage charging station can be one of the most important factors influencing the safe and reliable;
when the capacity of an energy storage battery in the optical storage charging station is configured, firstly, an average load curve of the optical storage charging station in a preset period needs to be considered; the average load condition of the optical storage charging station in a preset period is regular, and is related to the charging behavior (the charging starting time of the electric vehicle, the SOC of the charging starting time of the electric vehicle and the battery pack capacity of the electric vehicle) rule of the optical storage charging station in the preset period; therefore, the charging behavior law of the optical storage charging station in the preset period can be analyzed (fitted) by utilizing probabilistic statistics, and the average load curve of the optical storage charging station in the preset period can be obtained through simulation based on the law, specifically:
the step 101 includes:
step 101-1, fitting a battery pack capacity Gaussian distribution function met by electric vehicles in a charging area corresponding to an optical storage charging station according to the battery pack capacity of each electric vehicle in the charging area corresponding to the optical storage charging station;
in the preferred embodiment of the present invention, all the battery capacities of the electric vehicles of the general charging stations approximately follow the gaussian distribution function, and the battery capacities of the electric vehicles are concentrated in the range Cmin,Cmax]Therefore, we can set the mean value of the Gaussian distribution function to be
Figure BDA0002613432760000071
Variance of
Figure BDA0002613432760000072
Instead of a complex fitting operation.
Step 101-2, fitting an initial charging time Gaussian distribution function which is satisfied by each electric vehicle in the charging area corresponding to the optical storage charging station according to the initial charging time of each electric vehicle in each preset period of the historical time period in the charging area corresponding to the optical storage charging station;
in the preferred embodiment of the present invention, the charging start times of all electric vehicles of the charging station generally approximately follow the gaussian distribution function, and the charging start times of the electric vehicles are concentrated within a certain time period range [ t ] of the preset periodmin,tmax]Therefore, we can set the mean value of the Gaussian distribution function directly according to experience
Figure BDA0002613432760000073
Variance of
Figure BDA0002613432760000074
Instead of a complex fitting operation.
Step 101-3, fitting SOC Gaussian distribution functions met by electric vehicles in the charging areas corresponding to the optical storage charging stations according to the SOC of each electric vehicle in the charging areas corresponding to the optical storage charging stations at the starting moment of each preset period in the historical time period;
in the preferred embodiment of the present invention, the SOC of the charging start time of all the electric vehicles of the charging station generally follows the Gaussian distribution function approximately, and the SOC of the charging start time of the electric vehicles is concentrated in a certain electric quantity range [ SOC ]min,SOCmax]Therefore, we can set the mean value of the Gaussian distribution function to be
Figure BDA0002613432760000075
Variance of
Figure BDA0002613432760000076
Instead of a complex fitting operation.
Step 101-4, performing M groups of simulation on the charging behavior of the electric vehicle by adopting a Monte Carlo algorithm based on set data and a battery pack capacity Gaussian distribution function, an initial charging time Gaussian distribution function and an SOC Gaussian distribution function which are met by the electric vehicle in a charging area corresponding to the optical storage charging station, and acquiring simulation load curves of the M optical storage charging stations in a preset period;
in the specific embodiment of the invention, because the battery pack of the electric automobile has large capacity and long charging time, the time granularity of each group of simulation is set to be in the order of minutes, thereby effectively simulating the charging process of the electric automobile.
Step 101-5, obtaining an average value of loads of the M optical storage charging stations at the t-th moment in a simulation load curve of a preset period, and taking the average value as a load value of the optical storage charging stations at the t-th moment in the average load curve of the preset period;
wherein T belongs to (1-T), and T is the total number of moments contained in the preset period.
In the preferred embodiment of the present invention, the preset period is set to 1 day, the total number of the moments included in the preset period is 24, and the historical time period is 1 year, and the daily average load curve of the optical storage charging station shown in fig. 2 is obtained by using the above operation steps.
Further, the predetermined data includes:
the calculation formula of the charging station load power of the optical storage charging station at t moments according to the simulation load curve of the optical storage charging station in a preset period is obtained;
the charging end condition of the electric automobile in the charging area corresponding to the optical storage charging station is that the SOC of the electric automobile is greater than 80% of the maximum allowable SOC of the electric automobile;
calculating the charging station load power P of the light storage charging station at the t-th moment of the simulated load curve of the preset period according to the following formulask,t：
Figure BDA0002613432760000081
Wherein alpha isj Represents 0 or 1, alpha is when the jth electric vehicle in the charging area corresponding to the optical storage charging station is chargedjIs 1, otherwise 0, PchgAnd j belongs to (1-N) as the charging power of the charging piles in the optical storage charging station, wherein N is the total number of the electric vehicles in the charging area corresponding to the optical storage charging station.
Specifically, the step 102 includes:
substituting the average load curve of the optical storage charging station in a preset period into a pre-constructed energy storage battery capacity optimization calculation model, solving the energy storage battery capacity optimization calculation model, and obtaining the optimal configuration capacity of the energy storage battery in the optical storage charging station;
the energy storage battery capacity optimization calculation model is constructed by taking the lowest aging degree of the energy storage battery in the optical storage charging station, the lowest loss of photovoltaic power generation in the optical storage charging station and the lowest operation cost of the optical storage charging station as targets.
Further, an objective function of the pre-constructed energy storage battery capacity optimization calculation model is determined according to the following formula:
Figure BDA0002613432760000082
in the formula, f is an objective function value omega of a pre-constructed energy storage battery capacity optimization calculation model1For a weight corresponding to the degree of ageing of the energy storage cell, QL,tAging degree, omega, of the energy storage battery at the t-th moment of the preset period caused by the charging and discharging behaviors2Is the weight corresponding to the photovoltaic power generation loss rate, Peg,L,tThe loss amount P of photovoltaic power generation in the optical storage charging station at the t moment of the preset periodeg,tGenerating capacity C of the light storage charging station at the t-th moment in the average photovoltaic power generation curve of the preset periodbThe purchase cost of the energy storage battery is reduced by the unit time, and the value of the purchase cost of the energy storage battery is equal to the total time, C, included in the service life of the energy storage batteryg,tCost, C, of purchasing electricity from the grid at the t-th moment of the preset periodmaxThe maximum limit value of the cost consumed at the tth moment of the preset period is T ∈ (1-T), and T is the total number of moments contained in the preset period;
in the best embodiment of the invention, the capacity of the energy storage battery can affect the distribution of the power supply power of the charging station between the power grid and the energy storage battery and the aging degree of the energy storage battery during operation, and considering that the energy storage battery equipment is still expensive at present, the charging station needs to meet the charging requirement of the electric vehicle, reduce the dependence on the power grid, reduce the aging of the energy storage battery and ensure the comprehensive benefit of the capacity configuration of the energy storage battery in the charging station.
In the best embodiment of the invention, renewable energy power generation and an energy storage battery system are preferentially utilized, so that the on-site consumption of the renewable energy power generation is realized, and the load of a power grid is reduced. In addition, when the rest of the photovoltaic power generation energy is higher than the bearing range of the energy storage battery, the photovoltaic power generation energy becomes the utilization loss part of the renewable energy power generation.
Determining the aging degree Q of the energy storage battery at the t-th moment of a preset period caused by the charging and discharging behaviors according to the following formulaL,t：
Figure BDA0002613432760000091
In the formula, Crate，tThe charging and discharging multiplying power of the energy storage battery in the optical storage charging station at the t moment of the preset period,
Figure BDA0002613432760000092
Pb,tthe charging and discharging power of the energy storage battery in the optical storage charging station at the T moment of the preset period, C is the capacity of the energy storage battery in the optical storage charging station, R is a gas constant, Tb,tThe temperature of the energy storage battery in the optical storage charging station at the t-th moment of the preset period, AhFor the ampere-hour throughput of an energy storage battery in an optical storage charging station, z is a power exponent factor, and is generally 0.55;
determining the loss P of photovoltaic power generation in the optical storage charging station at the t moment of the preset period according to the following formulaeg,L,t：
Peg,L,t＝Peg,t-Ps,t-Pb,t
In the formula, Ps,tThe load of the light storage charging station at the t-th moment in the average load curve of the preset period is obtained;
in an embodiment of the present invention, the photovoltaic power generation panels are arranged on the top of the parking shed, the laying area is constant, the photovoltaic system parameter is peak power 75kWp, and a daily average photovoltaic power generation power curve of the optical storage charging station as shown in fig. 3 is calculated according to the statistical data of the illumination of one year, wherein the power generation amount P of the optical storage charging station in the t-th time period in the average photovoltaic power generation curve of the preset cycle is determined according to the following formulaeg,t：
Figure BDA0002613432760000101
In the formula (I), the compound is shown in the specification,
Figure BDA0002613432760000102
the photovoltaic power generation capacity of the light storage charging station at the tth moment of the jth preset period in the historical time period is j epsilon (1-N)y)，NyThe total number of preset cycles in the history period.
Still further, the constraint conditions of the objective function of the pre-constructed energy storage battery capacity optimization calculation model include: the method comprises the following steps of (1) power balance constraint conditions, energy storage battery charging and discharging power constraint conditions, energy storage battery charging energy constraint conditions, energy storage battery discharging energy constraint conditions and energy storage battery capacity constraint conditions;
wherein the power balance constraint is determined as follows:
Pg,t＝Ps,t-Peg,t-Pb,t
in the formula, Pg,tThe electric power is provided for the power grid at the t moment of the preset period;
determining the charge and discharge power constraint condition of the energy storage battery according to the following formula:
Pb,min≤Pb,t≤Pb,max
in the formula, Pb,minThe minimum limit value of the charging and discharging power of the energy storage battery in the optical storage charging station at the t moment of the preset period is Pb,maxThe maximum limit value of the charging and discharging power of the energy storage battery in the optical storage charging station at the t moment of the preset period is set;
determining the energy storage battery charging energy constraint condition according to the following formula:
Et+1＝Et+Δt·pb,t·η
determining the discharge energy constraint condition of the energy storage battery according to the following formula:
Figure BDA0002613432760000103
wherein eta is the charging and discharging efficiency of the energy storage battery in the optical storage charging station, delta t is the time of two adjacent moments of the preset period, EtFor the energy of the energy storage battery in the light storage charging station at the t-th moment of the preset period, Et+1The energy of an energy storage battery in the optical storage charging station is at the t +1 th moment of a preset period;
determining the energy storage battery capacity constraint condition according to the following formula:
0≤C≤Cmax
in the formula, CmaxThe maximum energy storage battery capacity allowed to be configured in the optical storage charging station is related to physical constraints such as actual conditions, occupied space and the like, and the optimal configuration of the energy storage battery can be obtained by solving the objective function and the constraint limit through a genetic algorithm.
In the best embodiment of the invention, the price of the large industrial electricity ladder in a certain area is shown in table 1, the capacity range of the energy storage battery is between 0 and 500kWh, the allowable energy variation range of the energy storage battery is between 0.1 and 0.9 times of rated capacity, the price of the energy storage battery is 3000RMB/kWh, a preset period is set to be 1 day, the total number of moments contained in the preset period is 24, the historical time period is 1 year, the charging behavior data of the electric vehicle in the area is counted, and the daily average load curve and the solar photovoltaic power generation curve of the area are obtained through simulation;
TABLE 1
Figure BDA0002613432760000111
By utilizing the technical scheme of the invention, the optimal energy storage battery configuration capacity obtained by solving through a genetic algorithm is 272 kWh; a graph of output power and time of the energy storage battery and the power grid in the optical storage charging station is shown in fig. 4, and a graph of energy state change of the energy storage battery in the optical storage charging station is shown in fig. 5;
in addition, the objective function without considering the aging problem of the energy storage battery and the objective function with considering the aging problem of the energy storage battery are respectively solved, the obtained optimal configuration capacity of the energy storage battery in the optical storage charging station is shown in table 2, and the table can be compared to obtain that the scheme without considering the aging problem of the energy storage battery needs a battery with larger capacity than the scheme without considering the aging problem, but the scheme has the advantages that the aging of the energy storage battery is reduced, the operation life of the energy storage battery is longer, and the cost for purchasing electricity from a power grid is lower.
TABLE 2
Figure BDA0002613432760000112
The present invention provides a system for configuring the capacity of an energy storage battery in an optical storage charging station, as shown in fig. 6, the system includes:
the acquisition module is used for acquiring an average load curve of the optical storage charging station in a preset period;
the determining module is used for determining the optimal configuration capacity of an energy storage battery in the optical storage charging station according to the average load curve of the optical storage charging station in a preset period;
and the configuration module is used for configuring the energy storage battery in the optical storage charging station according to the optimal configuration capacity of the energy storage battery in the optical storage charging station.
Specifically, the obtaining module includes:
the first fitting unit is used for fitting a battery pack capacity Gaussian distribution function met by the electric vehicles in the charging area corresponding to the optical storage charging station according to the battery pack capacity of each electric vehicle in the charging area corresponding to the optical storage charging station;
the second fitting unit is used for fitting the Gaussian distribution function of the initial charging time met by the electric vehicles in the charging area corresponding to the optical storage charging station according to the initial charging time of each electric vehicle in each preset period of the historical time period in the charging area corresponding to the optical storage charging station;
the third fitting unit is used for fitting SOC Gaussian distribution functions which are met by the electric automobiles in the charging areas corresponding to the optical storage charging stations according to the SOC of each electric automobile in the charging area corresponding to the optical storage charging station at the starting moment of each preset period in the historical time period;
the simulation unit is used for performing M-group simulation on the charging behavior of the electric vehicle by adopting a Monte Carlo algorithm based on the set data and a battery pack capacity Gaussian distribution function, an initial charging time Gaussian distribution function and an SOC Gaussian distribution function which are met by the electric vehicle in a charging area corresponding to the optical storage charging station, and acquiring simulation load curves of the M optical storage charging stations in a preset period;
the device comprises a unit, a load calculation unit and a load calculation unit, wherein the unit is used for acquiring the average value of loads of M optical storage charging stations at the t-th moment in a simulation load curve of a preset period, and taking the average value as the load value of the optical storage charging stations at the t-th moment in the average load curve of the preset period;
wherein T belongs to (1-T), and T is the total number of moments contained in the preset period.
Further, the predetermined data includes:
the method comprises the following steps of determining the number of electric vehicles in a charging area corresponding to a light storage charging station, the number of charging piles in the light storage charging station, the charging power of the charging piles in the light storage charging station and the charging ending conditions of the electric vehicles in the charging area corresponding to the light storage charging station;
the charging end condition of the electric automobile in the charging area corresponding to the optical storage charging station is that the SOC of the electric automobile is greater than 80% of the maximum allowable SOC of the electric automobile.
Specifically, the determining module is configured to:
substituting the average load curve of the optical storage charging station in a preset period into a pre-constructed energy storage battery capacity optimization calculation model, solving the energy storage battery capacity optimization calculation model, and obtaining the optimal configuration capacity of the energy storage battery in the optical storage charging station;
the energy storage battery capacity optimization calculation model is constructed by taking the lowest aging degree of the energy storage battery in the optical storage charging station, the lowest loss of photovoltaic power generation in the optical storage charging station and the lowest operation cost of the optical storage charging station as targets.
Further, an objective function of the pre-constructed energy storage battery capacity optimization calculation model is determined according to the following formula:
Figure BDA0002613432760000131
in the formula, f is an objective function value omega of a pre-constructed energy storage battery capacity optimization calculation model1For a weight corresponding to the degree of ageing of the energy storage cell, QL,tAging degree, omega, of the energy storage battery at the t-th moment of the preset period caused by the charging and discharging behaviors2Is the weight corresponding to the photovoltaic power generation loss rate, Peg,L,tThe loss amount P of photovoltaic power generation in the optical storage charging station at the t moment of the preset periodeg,tGenerating capacity C of the light storage charging station at the t-th moment in the average photovoltaic power generation curve of the preset periodbThe purchase cost of the energy storage battery is reduced by the unit time, and the value of the purchase cost of the energy storage battery is equal to the total time, C, included in the service life of the energy storage batteryg,tCost, C, of purchasing electricity from the grid at the t-th moment of the preset periodmaxThe maximum limit value of the cost consumed at the tth moment of the preset period is T ∈ (1-T), and T is the total number of moments contained in the preset period;
determining the aging degree Q of the energy storage battery at the t-th moment of a preset period caused by the charging and discharging behaviors according to the following formulaL,t：
Figure BDA0002613432760000132
In the formula, Crate，tThe charging and discharging multiplying power of the energy storage battery in the optical storage charging station at the t moment of the preset period,
Figure BDA0002613432760000133
Pb,tthe charging and discharging power of the energy storage battery in the optical storage charging station at the T moment of the preset period, C is the capacity of the energy storage battery in the optical storage charging station, R is a gas constant, Tb,tThe temperature of the energy storage battery in the optical storage charging station at the t-th moment of the preset period, AhThe ampere-hour throughput of an energy storage battery in the optical storage charging station is shown, and z is a power exponential factor; determining the loss P of photovoltaic power generation in the optical storage charging station at the t moment of the preset period according to the following formulaeg,L,t：
Peg,L,t＝Peg,t-Ps,t-Pb,t
In the formula, Ps,tThe load of the light storage charging station at the t-th moment in the average load curve of the preset period is obtained;
determining the generated energy P of the light storage charging station in the t-th time period in the average photovoltaic power generation curve of the preset cycle according to the following formulaeg,t：
Figure BDA0002613432760000134
In the formula (I), the compound is shown in the specification,
Figure BDA0002613432760000135
the photovoltaic power generation capacity of the light storage charging station at the tth moment of the jth preset period in the historical time period is j epsilon (1-N)y)，NyThe total number of preset cycles in the history period.
Still further, the constraint conditions of the objective function of the pre-constructed energy storage battery capacity optimization calculation model include: the method comprises the following steps of (1) power balance constraint conditions, energy storage battery charging and discharging power constraint conditions, energy storage battery charging energy constraint conditions, energy storage battery discharging energy constraint conditions and energy storage battery capacity constraint conditions;
wherein the power balance constraint is determined as follows:
Pg,t＝Ps,t-Peg,t-Pb,t
in the formula, Pg,tThe electric power is provided for the power grid at the t moment of the preset period;
determining the charge and discharge power constraint condition of the energy storage battery according to the following formula:
Pb,min≤Pb,t≤Pb,max
in the formula, Pb,minThe minimum limit value of the charging and discharging power of the energy storage battery in the optical storage charging station at the t moment of the preset period is Pb,maxThe maximum limit value of the charging and discharging power of the energy storage battery in the optical storage charging station at the t moment of the preset period is set;
determining the energy storage battery charging energy constraint condition according to the following formula:
Et+1＝Et+Δt·pb,t·η
determining the discharge energy constraint condition of the energy storage battery according to the following formula:
Figure BDA0002613432760000141
wherein eta is the charging and discharging efficiency of the energy storage battery in the optical storage charging station, delta t is the time of two adjacent moments of the preset period, EtFor the energy of the energy storage battery in the light storage charging station at the t-th moment of the preset period, Et+1The energy of an energy storage battery in the optical storage charging station is at the t +1 th moment of a preset period;
determining the energy storage battery capacity constraint condition according to the following formula:
0≤C≤Cmax
in the formula, CmaxThe maximum energy storage battery capacity allowed to be configured in the optical storage charging station.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (10)

1. A method for configuring the capacity of an energy storage battery in an optical storage charging station, the method comprising:
acquiring an average load curve of the light storage charging station in a preset period;
determining the optimal configuration capacity of an energy storage battery in the optical storage charging station according to the average load curve of the optical storage charging station in a preset period;
and configuring the energy storage battery in the light storage charging station according to the optimal configuration capacity of the energy storage battery in the light storage charging station.
2. The method of claim 1, wherein the obtaining the average load curve of the optical storage charging station over a preset period comprises:
according to the battery pack capacity of each electric vehicle in the charging area corresponding to the optical storage charging station, simulating a battery pack capacity Gaussian distribution function met by the electric vehicle in the charging area corresponding to the optical storage charging station;
fitting the initial charging time Gaussian distribution function met by the electric vehicles in the charging area corresponding to the optical storage charging station according to the initial charging time of each electric vehicle in each preset period of the historical time period in the charging area corresponding to the optical storage charging station;
fitting SOC Gaussian distribution functions met by electric vehicles in the charging areas corresponding to the optical storage charging stations according to the SOC of each electric vehicle in the charging area corresponding to the optical storage charging stations at the starting moment of each preset period in the historical time period;
performing M groups of simulation on the charging behavior of the electric vehicle by adopting a Monte Carlo algorithm based on the set data and a battery pack capacity Gaussian distribution function, an initial charging time Gaussian distribution function and an SOC Gaussian distribution function which are met by the electric vehicle in a charging area corresponding to the optical storage charging station, and acquiring simulation load curves of the M optical storage charging stations in a preset period;
acquiring the average value of the loads of the M optical storage charging stations at the t-th moment in the simulation load curve of the preset period, and taking the average value as the load value of the optical storage charging stations at the t-th moment in the average load curve of the preset period;
wherein T belongs to (1-T), and T is the total number of moments contained in the preset period.
3. The method of claim 2, wherein the predetermined data comprises:
the method comprises the following steps of determining the number of electric vehicles in a charging area corresponding to a light storage charging station, the number of charging piles in the light storage charging station, the charging power of the charging piles in the light storage charging station and the charging ending conditions of the electric vehicles in the charging area corresponding to the light storage charging station;
the charging end condition of the electric automobile in the charging area corresponding to the optical storage charging station is that the SOC of the electric automobile is greater than 80% of the maximum allowable SOC of the electric automobile.
4. The method of claim 1, wherein determining the optimal configuration capacity of the energy storage battery in the optical storage charging station according to the average load curve of the optical storage charging station in the preset period comprises:
substituting the average load curve of the optical storage charging station in a preset period into a pre-constructed energy storage battery capacity optimization calculation model, solving the energy storage battery capacity optimization calculation model, and obtaining the optimal configuration capacity of the energy storage battery in the optical storage charging station;
the energy storage battery capacity optimization calculation model is constructed by taking the lowest aging degree of the energy storage battery in the optical storage charging station, the lowest loss of photovoltaic power generation in the optical storage charging station and the lowest operation cost of the optical storage charging station as targets.
5. The method of claim 4, wherein the objective function of the pre-constructed energy storage battery capacity optimization calculation model is determined as follows:
Figure FDA0002613432750000021
in the formula, f is an objective function value omega of a pre-constructed energy storage battery capacity optimization calculation model1For a weight corresponding to the degree of ageing of the energy storage cell, QL,tAging degree, omega, of the energy storage battery at the t-th moment of the preset period caused by the charging and discharging behaviors2Is the weight corresponding to the photovoltaic power generation loss rate, Peg,L,tThe loss amount P of photovoltaic power generation in the optical storage charging station at the t moment of the preset periodeg,tGenerating capacity C of the light storage charging station at the t-th moment in the average photovoltaic power generation curve of the preset periodbThe purchase cost of the energy storage battery is reduced by the unit time, and the value of the purchase cost of the energy storage battery is equal to the total time, C, included in the service life of the energy storage batteryg,tFor a predetermined periodCost, C, of electricity purchase from the grid at the t-th momentmaxThe maximum limit value of the cost consumed at the tth moment of the preset period is T ∈ (1-T), and T is the total number of moments contained in the preset period;
determining the aging degree Q of the energy storage battery at the t-th moment of a preset period caused by the charging and discharging behaviors according to the following formulaL,t：
Figure FDA0002613432750000022
In the formula, Crate，tThe charging and discharging multiplying power of the energy storage battery in the optical storage charging station at the t moment of the preset period,
Figure FDA0002613432750000023
Pb,tthe charging and discharging power of the energy storage battery in the optical storage charging station at the T moment of the preset period, C is the capacity of the energy storage battery in the optical storage charging station, R is a gas constant, Tb,tThe temperature of the energy storage battery in the optical storage charging station at the t-th moment of the preset period, AhThe ampere-hour throughput of an energy storage battery in the optical storage charging station is shown, and z is a power exponential factor;
determining the loss P of photovoltaic power generation in the optical storage charging station at the t moment of the preset period according to the following formulaeg,L,t：
Peg,L,t＝Peg,t-Ps,t-Pb,t
In the formula, Ps,tThe load of the light storage charging station at the t-th moment in the average load curve of the preset period is obtained;
determining the generated energy P of the light storage charging station in the t-th time period in the average photovoltaic power generation curve of the preset cycle according to the following formulaeg,t：
Figure FDA0002613432750000024
In the formula (I), the compound is shown in the specification,
Figure FDA0002613432750000025
the photovoltaic power generation capacity of the light storage charging station at the tth moment of the jth preset period in the historical time period is j epsilon (1-N)y)，NyThe total number of preset cycles in the history period.
6. The method of claim 5, wherein the constraints of the objective function of the pre-constructed energy storage battery capacity optimization calculation model comprise: the method comprises the following steps of (1) power balance constraint conditions, energy storage battery charging and discharging power constraint conditions, energy storage battery charging energy constraint conditions, energy storage battery discharging energy constraint conditions and energy storage battery capacity constraint conditions;
wherein the power balance constraint is determined as follows:
Pg,t＝Ps,t-Peg,t-Pb,t
in the formula, Pg,tThe electric power is provided for the power grid at the t moment of the preset period;
determining the charge and discharge power constraint condition of the energy storage battery according to the following formula:
Pb,min≤Pb,t≤Pb,max
in the formula, Pb,minThe minimum limit value of the charging and discharging power of the energy storage battery in the optical storage charging station at the t moment of the preset period is Pb,maxThe maximum limit value of the charging and discharging power of the energy storage battery in the optical storage charging station at the t moment of the preset period is set;
determining the energy storage battery charging energy constraint condition according to the following formula:
Et+1＝Et+Δt·pb,t·η
determining the discharge energy constraint condition of the energy storage battery according to the following formula:
Figure FDA0002613432750000031
wherein eta is the charging and discharging efficiency of the energy storage battery in the optical storage charging station, delta t is the time of two adjacent moments of the preset period, EtFor the energy of the energy storage battery in the light storage charging station at the t-th moment of the preset period, Et+1The energy of an energy storage battery in the optical storage charging station is at the t +1 th moment of a preset period;
determining the energy storage battery capacity constraint condition according to the following formula:
0≤C≤Cmax
in the formula, CmaxThe maximum energy storage battery capacity allowed to be configured in the optical storage charging station.
7. A system for configuring the capacity of an energy storage cell in an optical storage and charging station, the system comprising:
the acquisition module is used for acquiring an average load curve of the optical storage charging station in a preset period;
the determining module is used for determining the optimal configuration capacity of an energy storage battery in the optical storage charging station according to the average load curve of the optical storage charging station in a preset period;
and the configuration module is used for configuring the energy storage battery in the optical storage charging station according to the optimal configuration capacity of the energy storage battery in the optical storage charging station.
8. The system of claim 7, wherein the acquisition module comprises:
the first fitting unit is used for fitting a battery pack capacity Gaussian distribution function met by the electric vehicles in the charging area corresponding to the optical storage charging station according to the battery pack capacity of each electric vehicle in the charging area corresponding to the optical storage charging station;
the second fitting unit is used for fitting the Gaussian distribution function of the initial charging time met by the electric vehicles in the charging area corresponding to the optical storage charging station according to the initial charging time of each electric vehicle in each preset period of the historical time period in the charging area corresponding to the optical storage charging station;
the third fitting unit is used for fitting SOC Gaussian distribution functions which are met by the electric automobiles in the charging areas corresponding to the optical storage charging stations according to the SOC of each electric automobile in the charging area corresponding to the optical storage charging station at the starting moment of each preset period in the historical time period;
the simulation unit is used for performing M-group simulation on the charging behavior of the electric vehicle by adopting a Monte Carlo algorithm based on the set data and a battery pack capacity Gaussian distribution function, an initial charging time Gaussian distribution function and an SOC Gaussian distribution function which are met by the electric vehicle in a charging area corresponding to the optical storage charging station, and acquiring simulation load curves of the M optical storage charging stations in a preset period;
the device comprises a unit, a load calculation unit and a load calculation unit, wherein the unit is used for acquiring the average value of loads of M optical storage charging stations at the t-th moment in a simulation load curve of a preset period, and taking the average value as the load value of the optical storage charging stations at the t-th moment in the average load curve of the preset period;
wherein T belongs to (1-T), and T is the total number of moments contained in the preset period.
9. The system of claim 8, wherein the predetermined data comprises:
the method comprises the following steps of determining the number of electric vehicles in a charging area corresponding to a light storage charging station, the number of charging piles in the light storage charging station, the charging power of the charging piles in the light storage charging station and the charging ending conditions of the electric vehicles in the charging area corresponding to the light storage charging station;
the charging end condition of the electric automobile in the charging area corresponding to the optical storage charging station is that the SOC of the electric automobile is greater than 80% of the maximum allowable SOC of the electric automobile.
10. The system of claim 7, wherein the determination module is to:
substituting the average load curve of the optical storage charging station in a preset period into a pre-constructed energy storage battery capacity optimization calculation model, solving the energy storage battery capacity optimization calculation model, and obtaining the optimal configuration capacity of the energy storage battery in the optical storage charging station;
the energy storage battery capacity optimization calculation model is constructed by taking the lowest aging degree of the energy storage battery in the optical storage charging station, the lowest loss of photovoltaic power generation in the optical storage charging station and the lowest operation cost of the optical storage charging station as targets.
CN202010762384.6A 2020-07-31 2020-07-31 Method and system for configuring capacity of energy storage battery in optical storage charging station Pending CN112003381A (en)

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CN112600205A (en) * 2020-12-22 2021-04-02 国网北京市电力公司 Configuration method for energy storage of charging station
CN113224758A (en) * 2021-05-25 2021-08-06 上海玫克生储能科技有限公司 Energy storage charging and discharging control method, system, equipment and medium of optical storage charging station
CN113733963A (en) * 2021-08-31 2021-12-03 国网北京市电力公司 Day-ahead scheduling method, system and device for light storage and charging integrated station and storage medium
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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112600205A (en) * 2020-12-22 2021-04-02 国网北京市电力公司 Configuration method for energy storage of charging station
CN112600205B (en) * 2020-12-22 2022-09-02 国网北京市电力公司 Configuration method for energy storage of charging station
CN113224758A (en) * 2021-05-25 2021-08-06 上海玫克生储能科技有限公司 Energy storage charging and discharging control method, system, equipment and medium of optical storage charging station
CN113733963A (en) * 2021-08-31 2021-12-03 国网北京市电力公司 Day-ahead scheduling method, system and device for light storage and charging integrated station and storage medium
CN114312426A (en) * 2021-12-30 2022-04-12 广东电网有限责任公司 Method and device for optimizing configuration of net zero energy consumption optical storage charging station and storage medium
CN116579475A (en) * 2023-05-08 2023-08-11 浙江大学 Electric vehicle charging scheduling and charging station configuration joint optimization method considering charging randomness
CN116579475B (en) * 2023-05-08 2024-02-13 浙江大学 Electric vehicle charging scheduling and charging station configuration joint optimization method considering charging randomness
CN117318165A (en) * 2023-08-03 2023-12-29 广州高新区能源技术研究院有限公司 Energy storage device capacity optimal configuration method of optical storage and charging integrated system
CN117318165B (en) * 2023-08-03 2024-05-07 广州高新区能源技术研究院有限公司 Energy storage device capacity optimal configuration method of optical storage and charging integrated system

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