Optical storage charging station battery energy storage configuration method, configuration terminal and storage medium

Abstract

The application belongs to the technical field of power grids, and provides a battery energy storage configuration method for a light-storage charging station, a configuration terminal and a storage medium.The method comprises the following steps: obtaining charging load data and photovoltaic power generation data of the 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.The application establishes an energy storage optimization configuration model according to the charging load data and the photovoltaic power generation data, performs optimization solving to obtain an optimization configuration scheme, comprehensively considers multiple factors, and makes the configuration scheme more reasonable, so that the advantages of the light-storage charging station can be effectively exerted, and the light-storage charging station can better participate in power grid regulation and control.

Description

Technical Field

[0001] This invention belongs to the field of power grid technology, and in particular relates to a battery energy storage configuration method, configuration terminal and storage medium for a photovoltaic-storage charging station. Background Technology

[0002] Electric vehicles, as a new type of electrified transportation, account for a significant portion of the overall regional load on charging. Electric buses, in particular, are at the forefront and are the main force in the development of new energy vehicles. Electric bus charging stations experience significant fluctuations in charging load power, with high peak power, placing high demands on the capacity of distribution transformers and power lines. Therefore, configuring photovoltaic and energy storage systems within electric bus charging stations to form photovoltaic-storage charging stations not only helps reduce the impact of peak charging loads on the power distribution network but also allows for the absorption of photovoltaic power generated during the day.

[0003] Existing technologies lack optimization methods for the battery energy storage configuration of photovoltaic-storage charging stations, which fails to effectively leverage the advantages of these stations and enable them to better participate in grid regulation. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method for configuring battery energy storage in a photovoltaic-storage charging station, a configuration terminal, and a storage medium, to solve the problem in the prior art that there is a lack of planning for the configuration of battery energy storage in photovoltaic-storage charging stations, and that photovoltaic-storage charging stations cannot effectively participate in grid regulation.

[0005] A first aspect of this invention provides a battery energy storage configuration method for a photovoltaic-energy storage charging station, comprising:

[0006] Acquire charging load data and photovoltaic power generation data of photovoltaic-storage charging stations;

[0007] Based on charging load data and photovoltaic power generation data, the objective function and constraints are determined, and an energy storage optimization configuration model is established based on the objective function and constraints.

[0008] Solve the energy storage optimization configuration model to obtain the target energy storage configuration scheme.

[0009] A second aspect of the present invention provides a battery energy storage configuration device for a photovoltaic-energy storage charging station, comprising:

[0010] The parameter acquisition module is used to acquire charging load data and photovoltaic power generation data of the photovoltaic-storage charging station;

[0011] The model building module is used to determine the objective function and constraints based on charging load data and photovoltaic power generation data, and to build an energy storage optimization configuration model based on the objective function and constraints.

[0012] The model solving module is used to solve the energy storage optimization configuration model to obtain the target energy storage configuration scheme.

[0013] A third aspect 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. When the processor executes the computer program, it implements the steps of the photovoltaic-storage charging station battery energy storage configuration method provided in the first aspect of the present invention.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the battery energy storage configuration method for a photovoltaic-storage charging station provided in the first aspect of the present invention.

[0015] This invention provides a method for configuring battery energy storage in a photovoltaic-energy storage charging station, a configuration terminal, and a storage medium. The method includes: acquiring charging load data and photovoltaic power generation data of the photovoltaic-energy storage charging station; determining an objective function and constraints based on the charging load data and photovoltaic power generation data, and establishing an energy storage optimization configuration model based on the objective function and constraints; solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme. This invention establishes an energy storage optimization configuration model based on charging load data and photovoltaic power generation data, and obtains an optimized configuration scheme by optimizing the solution. By comprehensively considering multiple factors, the configuration scheme is more reasonable, effectively leveraging the advantages of the photovoltaic-energy storage charging station and enabling it to better participate in grid regulation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating the implementation process of a battery energy storage configuration method for a photovoltaic-energy storage charging station provided in an embodiment of the present invention;

[0018] Figure 2 This is the charging load curve of the photovoltaic energy storage charging station provided in the embodiment of the present invention;

[0019] Figure 3 This is the fitness iteration trend in the model solving process provided by the chicken flock algorithm in the embodiments of the present invention;

[0020] Figure 4 This is the objective function scan curve of the model provided in the embodiments of the present invention;

[0021] Figure 5This is a fitness iteration trend diagram of the model solving process using the chicken flock algorithm after reducing the electricity price difference, provided in an embodiment of the present invention.

[0022] Figure 6 This is the objective function scanning curve of the model after reducing the electricity price difference provided in the embodiments of the present invention;

[0023] Figure 7 This is a schematic diagram of the battery energy storage configuration device for the photovoltaic-energy storage charging station provided in an embodiment of the present invention;

[0024] Figure 8 This is a schematic diagram of the configuration terminal provided in an embodiment of the present invention. Detailed Implementation

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0026] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0027] refer to Figure 1 This invention provides a method for configuring battery energy storage in a photovoltaic-energy storage charging station, comprising:

[0028] S101: Obtain charging load data and photovoltaic power generation data of the photovoltaic-storage charging station;

[0029] S102: Based on the charging load data and photovoltaic power generation data, determine the objective function and constraints, and establish an energy storage optimization configuration model based on the objective function and constraints;

[0030] S103: Solve the energy storage optimization configuration model to obtain the target energy storage configuration scheme.

[0031] In this embodiment of the invention, an energy storage optimization configuration model is established based on charging data and photovoltaic power generation data. The optimization solution is obtained by optimizing the solution. Taking into account multiple factors, the configuration solution is more reasonable and can effectively give full play to the advantages of photovoltaic-energy storage charging stations, enabling them to better participate in grid regulation.

[0032] Before acquiring the charging load data, the process includes: acquiring the Yuanshu charging load data, removing data with zero electricity consumption and charging status display malfunctions, and then filtering the valid data to obtain pre-processed charging load data (valid data includes charging pile number, transaction electricity, charging start time, charging end time, and photovoltaic power generation). Based on the pre-processed charging load data, the load charging load value is calculated and processed into daily load values ​​at 15-minute intervals to obtain the daily load curve. Simultaneously, the photovoltaic power generation is processed into power values ​​at 15-minute intervals to obtain the charging load data.

[0033] In some embodiments, the objective function can be calculated using the following formula:

[0034]

[0035] Where F is the total cost, F PSC The total daily cost of a photovoltaic-storage charging station, M L ω1 and ω2 are the penalty factors, and F is the weighting coefficient. BAT For the purchase cost of a battery energy storage system over its service life, calculated based on one charge-discharge cycle per day, F OM F calculates the daily cost of operating and maintaining a battery energy storage system. DEP For the electricity price difference revenue, F PV The goal is to leverage the increased revenue generated by photovoltaic (PV) electricity through battery energy storage systems. The battery energy storage system, located within a PV-storage charging station, is used to store electrical energy and consists of multiple storage batteries and other components.

[0036] In some embodiments, the purchase cost F of the photovoltaic-storage charging station over its service life is calculated based on one charge-discharge cycle per day. BAT The calculation formula can be:

[0037] F BAT =(F ES +c BAT S BAT ) / T life

[0038] Electricity price difference revenue F DEP The calculation formula can be:

[0039]

[0040] The revenue F from battery energy storage systems absorbing photovoltaic power generation and increasing the price of photovoltaic electricity. PV The calculation formula can be:

[0041] F PV =E PV (c e -cPV )

[0042] Punishment factor M L The calculation formula can be:

[0043]

[0044] Among them, F ES For the cost of battery energy storage system equipment, c BAT For the unit capacity cost of energy storage batteries, S BAT T represents the total capacity of the battery energy storage system. life c is the expected cycle life of the energy storage battery; e,t For real-time electricity pricing, P BC,t For real-time charging power of energy storage batteries, P BD,t E represents the real-time discharge power of the energy storage battery, where T is the billing time; PV For the electricity generated by photovoltaics, c e For the average electricity price, c PV For photovoltaic feed-in tariff; P Lmax P represents the maximum load power of the power supply line. max This represents the maximum actual total load of the charging station after the addition of battery energy storage.

[0045] In some embodiments, the daily cost F of the operation and maintenance of the photovoltaic-storage charging station is calculated. OM The calculation formula can be:

[0046] F OM =rc BAT S BAT / T life

[0047] or,

[0048]

[0049] Where r is the daily maintenance cost, and c BAT For the unit capacity cost of energy storage batteries, S BAT T represents the total capacity of the battery energy storage system. life c is the expected cycle life of the energy storage battery; WS,t T represents the out-of-warranty operation and maintenance cost per unit capacity of photovoltaic and energy storage systems. OW This refers to the warranty period after the warranty expires.

[0050] Generally, it can be adopted Determine the daily cost of operation and maintenance of the photovoltaic-storage charging station. To simplify the calculation, the above formula can also be simplified to F. OM =rc BAT S BAT / T life Both of the above formulas can be used to determine F.OM .

[0051] In this embodiment of the invention, factors such as the uncertainty of load growth, fluctuations in grid electricity prices, capacity decay of energy storage batteries, and decline in energy storage battery prices are comprehensively considered. Based on the fixed investment and maintenance costs of energy storage batteries, battery life depreciation costs, charging costs and discharging revenues, and using the margin of the reduced peak load power relative to the power supply line capacity as a penalty function, the objective function of the single objective problem is obtained, which is more comprehensive.

[0052] In some embodiments, the constraints may include: power supply line power capacity constraints, energy storage battery charge / discharge capacity constraints, energy storage battery charge / discharge depth constraints, and energy storage battery charge / discharge rate constraints.

[0053] Photovoltaic-storage charging stations need to cope with drastic load fluctuations and have a sufficiently large power throughput capacity to compensate for short-term peak power of charging piles or absorb excess power generated by photovoltaic modules. The maximum allowable power of the power supply line can be referenced to the rated capacity of the distribution transformer.

[0054] In some embodiments, the power capacity constraint of the power supply line can be:

[0055]

[0056] The role of battery energy storage systems is to smooth out power fluctuations; the minimum capacity S of the energy storage battery... BAT_min The calculated minimum cannot be lower than the maximum cumulative energy value during the over-supply capacity or reverse power transmission in the daily average load fluctuation curve. In other words, the charge / discharge capacity constraint of the energy storage battery can be:

[0057]

[0058] The depth of charge / discharge constraints for energy storage batteries can be:

[0059] SOC min ≤SOC t ≤SOC max

[0060] P b,t >0, SOC t =SOC t-1 -P b,t ΔT / (η d S BAT )

[0061] P b,t <0, SOC t =SOC t-1 -P b,t ΔTη c / S BAT

[0062] The charge / discharge rate refers to the ratio of the charge / discharge current to the Ah capacity of the energy storage battery. Different types of energy storage batteries have different permissible charge / discharge rates; for lithium iron phosphate batteries, excessively high charge / discharge rates are generally not recommended. Furthermore, the permissible charge and discharge rates for energy storage batteries vary depending on their State of Charge (SOC). However, a fixed rate can be used as a constraint when configuring the energy storage battery capacity. Here, the energy storage battery capacity is expressed in kWh, therefore the charge / discharge rate is also expressed as a multiple of the battery capacity. In other words, the permissible charge / discharge rate constraint for the energy storage battery can be:

[0063] -β bc S BAT <P b,t <β bd S BAT

[0064] Among them, P bat_min For the lowest power of a battery energy storage system, λ bat This is the margin coefficient. The highest peak power, For the lowest peak power, P in_max For the maximum power supplied in the forward direction, P out_max This represents the maximum power output for reverse power transmission; S BAT_min For the minimum capacity of energy storage batteries, P ol,t The load power exceeds the maximum forward power P. in_max The portion that accumulates within a day is the excess energy consumption; P t - The power during reverse feeding is the amount of photovoltaic energy (kWh) that the energy storage battery needs to receive over a day; T is the billing cycle; SOC t Let SOC be the actual state of charge of the energy storage battery at time t. min The minimum state of charge (SOC) limit for the battery. max The maximum state of charge (SOC) limit for the battery. t-1 For, P b,t Let η be the output power of the battery energy storage system at time t, ΔT be the charging / discharging sampling time, and η be the output power of the battery energy storage system at time t. d For the discharge efficiency of the energy storage battery, η c For the charging efficiency of energy storage batteries, S BAT β represents the total capacity of the battery energy storage system. bc β is the charging rate. bd This represents the discharge rate.

[0065] In some embodiments, SOC min It can be 20%, SOC max It can be 80%.

[0066] The actual allowable charge and discharge space for energy storage batteries is approximately 60% of their rated capacity.

[0067] Considering the uncertainty of charging station load growth, population growth and the continuous development of electric vehicles will increase the electricity demand of charging stations. Therefore, when optimizing the battery energy storage configuration of photovoltaic-storage charging stations, a load growth factor is considered. The annual load curve is obtained by multiplying the initial load curve by the load growth coefficient.

[0068] In some embodiments, S103 may include:

[0069] S1031: Used to solve the energy storage optimization configuration model using the chicken flock algorithm to obtain the target energy storage configuration scheme.

[0070] The objective function and constraints in this embodiment of the invention are quite complex. Therefore, it is difficult to solve the energy storage optimization configuration model using conventional optimization methods. Thus, this embodiment of the invention uses an improved competitive particle swarm optimization algorithm (chicken swarm algorithm) to solve the energy storage optimization configuration model. The correctness and accuracy are both high, which can meet the needs of practical applications.

[0071] The specific steps of the chicken flock algorithm are as follows:

[0072] 1. Let the foraging space be D-dimensional, and the total number of individuals in the cluster be C. num The number of roosters is C. rnum The number of hens is C hnum The number of offspring is C pnum .

[0073] The foraging behavior of chickens is as follows:

[0074] 1) The foraging behavior of roosters; the position of the i-th rooster in the j-th dimension after the m-th foraging is... The location after the (m+1)th foraging session is:

[0075]

[0076]

[0077] k = [1, C rnum ],k≠i

[0078] Where rand(0,σ) 2 The distribution is Gaussian, with a mean of 0 and a standard deviation of σ. 2 f i f k Let ε be the fitness of the i-th and k-th roosters, and let ε be a constant.

[0079] 2) Foraging behavior of hens; the position of the i-th hen in the j-th dimension after the m-th foraging is... The location after the (m+1)th foraging session is:

[0080]

[0081] Where rand is a random number uniformly distributed between [0,1], r is the mate of the i-th hen, s is any rooster except the r-th rooster, and f i Let f be the fitness of the i-th hen. s Let r represent the fitness of the r-th and s-th roosters.

[0082] 3) Foraging behavior of the cubs; the position of the i-th cub in the j-th dimension after the m-th foraging is... The location after the (m+1)th foraging session is:

[0083]

[0084] Where t is the hen that the i-th chick follows, and conf is the following coefficient of the chick following the hen.

[0085] 2. Initialize parameters. Initially configure algorithm parameters, mainly the size of the flock, the number of iterations, the update frequency of population relationships, the dimension of individual positions, and the ratio of roosters, hens, and chicks in the flock.

[0086] 3. Initialize the flock. The flock is sorted and graded according to fitness values, with roosters as the first few individuals, chicks as the last few individuals, and the rest being hens. The flock is divided into groups based on the number of roosters, and hens are randomly assigned to groups, establishing rooster-hen partnerships. A number of hens are randomly selected to lead the chicks, establishing mother-child relationships.

[0087] 4. When the algorithm iteration begins, it first determines whether it is necessary to update the group and the relationships within the flock. If so, the flock group and the relationships within the flock are updated; otherwise, the positions of the rooster, hen, and chicks are updated one by one according to their respective position update strategies, and the fitness value of the updated position is calculated at the same time.

[0088] 5. Individual position update. The fitness value of the new position is compared with the fitness value of the original position. If the fitness value of the new position is smaller, the individual position is updated; otherwise, the original position is kept unchanged.

[0089] 6. Stop iterating after reaching the maximum number of iterations and output the optimal solution; otherwise, return to step 3 and iterate in a loop to search.

[0090] The above method will be described in detail below with reference to specific embodiments.

[0091] Figure 2 The charging load curve of the photovoltaic-storage charging station is shown. Figure 2This chart shows the average load curve, maximum load envelope, minimum load envelope, and standard deviation curve for a photovoltaic-storage charging station in December of a given year. Assume the 500kVA distribution transformer has a power supply capacity of 400kW. Both the average and maximum loads exceed the upper limit of the power supply capacity, while the minimum load would lead to reverse feed-in from the photovoltaic power generation.

[0092] From the formula of the objective function, we can know

[0093]

[0094] Battery energy storage system cost F BAT It mainly consists of two parts: converter control equipment and battery and its management system, with an operation and maintenance cost of F. OM For ease of calculation, this is also linked to system cost. The main cost components of a battery energy storage system are shown in Table 1.

[0095] Table 1. Main Cost Components of Battery Energy Storage Systems

[0096] Battery energy storage system cost Price range Discounted Price Converter control equipment 300-600 yuan / kW Approximately 0.123 / kW Lithium iron phosphate batteries and BMS 700-2000 yuan / kWh Approximately 0.60 yuan / kWh Operation and maintenance costs Battery cost 10% Approximately 0.06 yuan / kWh

[0097] *The cycle life of lithium iron phosphate batteries is calculated based on 2000 cycles, and the life of converter equipment is calculated based on 10 years.

[0098] As shown in Table 1, the cost of a battery energy storage system mainly depends on the cost of the battery, and the depreciated cost of the battery depends on its cycle life. The profitability of a battery energy storage system is highly sensitive to the peak-valley electricity price difference.

[0099] Time-of-use (TOU) electricity prices vary depending on the region, electricity capacity, time of day, season, and regional characteristics. Table 2, as an example of TOU pricing, is used here as the basis for calculating price differences.

[0100] Table 2 Example of Time-of-Use Electricity Pricing

[0101] Time period name Time range Electricity price Peak segment 8:00-11:00 18:00-23:00 1.015 flat section 7:00-8:00 11:00-18:00 0.6262 Valley section 23:00-7:00 0.3271

[0102] The photovoltaic (PV) electricity price policy is also constantly being adjusted. In 2020, the guidance prices for newly added centralized PV power plants in resource areas I-III, which were already included in the national fiscal subsidy scope, were reduced to RMB 0.35, RMB 0.4, and RMB 0.49 per kilowatt-hour (including tax, the same below), respectively. The subsidy standard for the total power generation of industrial and commercial distributed PV power generation projects adopting the "self-consumption with surplus power fed into the grid" model was reduced to RMB 0.05 per kilowatt-hour. The PV power generation project involved in this example is positioned as an industrial and commercial distributed PV power generation project under the "self-consumption with surplus power fed into the grid" model, and the grid-connected electricity price is RMB 0.3644 / kWh, which is the benchmark price of desulfurized coal electricity.

[0103] An improved competitive particle swarm optimization (PSO) algorithm was developed using Python. By introducing scaling factors and other improvements to the traditional PSO algorithm, the convergence speed and accuracy of the PSO algorithm were enhanced. Testing showed that the improved PSO algorithm exhibited good convergence performance. For example, referencing... Figure 3 .

[0104] To test the correctness and accuracy of the improved competitive particle swarm optimization algorithm provided in this embodiment of the invention in searching for the objective function solution, a test was conducted using an input quantity with linearly increasing battery capacity, and the results were as follows: Figure 4 The scanning curve is shown. A high-precision scan was performed near its poles, yielding the comparison results shown in Table 3.

[0105] Table 3 Comparison of Solution Algorithms

[0106] Battery energy storage configuration capacity Optimization value of objective function Chicken flock algorithm results 577.1380019481085 -1570.2070590960825 Scan results 577.138 -1570.207059096082

[0107] As shown in Table 3, the algorithm used in this invention is almost identical to the results of high-precision scanning, thus proving the correctness and high precision of the improved competitive particle swarm optimization algorithm (chicken flock algorithm).

[0108] Based on the above calculations and considering an appropriate margin, the rechargeable / dischargeable capacity of the energy storage battery is selected as 600kWh. According to the battery charge / discharge depth constraints, the nominal capacity of the energy storage battery should be:

[0109] S BAT =S / 0.6 = 1000kWh

[0110] To test the sensitivity of the capacity optimization method to the peak-valley electricity price difference, the peak-valley electricity price difference was reduced by 0.1 yuan, and the optimization calculation was performed again. The results are shown in Table 4.

[0111] Table 4 Comparison of Solution Methods After Electricity Price Difference Reduction

[0112] Optimized energy storage configuration capacity Optimization value of objective function Particle Swarm Optimization Results 355.137994265637 -922.8714512016086 Scan results 355.13799 -922.871451201608

[0113] Based on Table 4 Figure 5 and Figure 6 It can be seen that the solution results of the particle swarm optimization algorithm for the model after the price reduction are basically consistent with the optimal results obtained by scanning. However, the minimum value of the objective function is -922.871 (the negative sign indicates profitability), which is much lower than the value of -1570.207 before the price reduction, indicating that the profitability of the energy storage system has decreased significantly.

[0114] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0115] refer to Figure 7 The present invention also provides a battery energy storage configuration device for a photovoltaic-energy storage charging station, comprising:

[0116] The parameter acquisition module 21 is used to acquire the charging load data and photovoltaic power generation data of the photovoltaic energy storage charging station;

[0117] The model building module 22 is used to determine the objective function and constraints based on the charging load data and photovoltaic power generation data, and to build an energy storage optimization configuration model based on the objective function and constraints.

[0118] The model solving module 23 is used to solve the energy storage optimization configuration model to obtain the target energy storage configuration scheme.

[0119] In some embodiments, the objective function can be calculated using the following formula:

[0120]

[0121] Where F is the total cost, F PSC The total daily cost of a photovoltaic-storage charging station, M L ω1 and ω2 are the penalty factors, and F is the weighting coefficient. BAT For the purchase cost of a battery energy storage system over its service life, calculated based on one charge-discharge cycle per day, F OM F calculates the daily cost of operating and maintaining a battery energy storage system. DEP For the electricity price difference revenue, F PV The benefits of battery energy storage systems absorbing photovoltaic power generation to increase the price of photovoltaic power.

[0122] In some embodiments, the purchase cost F of the photovoltaic-storage charging station over its service life is calculated based on one charge-discharge cycle per day. BAT The calculation formula can be:

[0123] F BAT =(F ES +c BAT S BAT ) / T life

[0124] Electricity price difference revenue F DEP The calculation formula can be:

[0125]

[0126] The revenue F from battery energy storage systems absorbing photovoltaic power generation and increasing the price of photovoltaic electricity. PV The calculation formula can be:

[0127] F PV =E PV (c e -cPV )

[0128] Punishment factor M L The calculation formula can be:

[0129]

[0130] Among them, F ES For the cost of battery energy storage system equipment, c BAT For the unit capacity cost of energy storage batteries, S BAT T represents the total capacity of the battery energy storage system. life c is the expected cycle life of the energy storage battery; e,t For real-time electricity pricing, P BC,t For real-time charging power of energy storage batteries, P BD,t E represents the real-time discharge power of the energy storage battery, where T is the billing time; PV For the electricity generated by photovoltaics, c e For the average electricity price, c PV For photovoltaic feed-in tariff; P Lmax P represents the maximum load power of the power supply line. max This represents the maximum actual total load of the charging station after the addition of battery energy storage.

[0131] In some embodiments, the daily cost F of the operation and maintenance of the photovoltaic-storage charging station is calculated. OM The calculation formula can be:

[0132] F OM =rc BAT S BAT / T life

[0133] or,

[0134]

[0135] Where r is the daily maintenance cost, and c BAT For the unit capacity cost of energy storage batteries, S BAT T represents the total capacity of the battery energy storage system. life c is the expected cycle life of the energy storage battery; WS,t T represents the out-of-warranty operation and maintenance cost per unit capacity of photovoltaic and energy storage systems. OW This refers to the warranty period after the warranty expires.

[0136] In some embodiments, the constraints may include: power supply line power capacity constraints, energy storage battery charge / discharge capacity constraints, energy storage battery charge / discharge depth constraints, and energy storage battery charge / discharge rate constraints.

[0137] In some embodiments, the power capacity constraint of the power supply line can be:

[0138]

[0139] The charge / discharge capacity constraint of energy storage batteries can be:

[0140]

[0141] The depth of charge / discharge constraints for energy storage batteries can be:

[0142] SOC min ≤SOC t ≤SOC max

[0143] P b,t >0, SOC t =SOC t-1 -P b,t ΔT / (η d S BAT )

[0144] P b,t <0, SOC t =SOC t-1 -P b,t ΔTη c / S BAT

[0145] The charge / discharge rate constraint for energy storage batteries can be:

[0146] -β bc S BAT <P b,t <β bd S BAT

[0147] Among them, P bat_min For the lowest power of a battery energy storage system, λ bat This is the margin coefficient. The highest peak power, For the lowest peak power, P in_max For the maximum power supplied in the forward direction, P out_max This represents the maximum power output for reverse power transmission.

[0148] S BAT_min For the minimum capacity of energy storage batteries, P ol,t The load power exceeds the maximum forward power P. in_max Part P t - The power during reverse feeding, T is the billing cycle; SOC t Let SOC be the actual state of charge of the energy storage battery at time t. min The minimum state of charge (SOC) limit for the battery. maxThe maximum state of charge (SOC) limit for the battery. t-1 For, P b,t Let η be the output power of the battery energy storage system at time t, ΔT be the charging / discharging sampling time, and η be the output power of the battery energy storage system at time t. d For the discharge efficiency of the energy storage battery, η c For the charging efficiency of energy storage batteries, S BAT β represents the total capacity of the battery energy storage system. bc β is the charging rate. bd This represents the discharge rate.

[0149] In some embodiments, the model solving module 23 may include:

[0150] The optimization unit 231 is used to solve the energy storage optimization configuration model using the chicken flock algorithm to obtain the target energy storage configuration scheme.

[0151] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the configuration terminal can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0152] Figure 8 This is a schematic block diagram of a configuration terminal provided in an embodiment of the present invention. Figure 8 As shown, the configuration terminal 4 in 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 processor 40. When the processor 40 executes the computer program 42, it implements the steps in the various embodiments of the photovoltaic-energy storage charging station battery energy storage configuration method described above, for example... Figure 1 Steps S101 to S103 are shown. Alternatively, when processor 40 executes computer program 42, it implements the functions of each module / unit in the above-described embodiment of the photovoltaic-charging station battery energy storage configuration device, for example... Figure 7 The functions of modules 21 to 23 are shown.

[0153] Exemplarily, computer program 42 can be divided into one or more modules / units, one or more of which are stored in memory 41 and executed by processor 40 to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of computer program 42 in configuration terminal 4. For example, computer program 42 can be divided into parameter acquisition module 21, model building module 22, and model solving module 23.

[0154] The parameter acquisition module 21 is used to acquire the charging load data and photovoltaic power generation data of the photovoltaic energy storage charging station;

[0155] The model building module 22 is used to determine the objective function and constraints based on the charging load data and photovoltaic power generation data, and to build an energy storage optimization configuration model based on the objective function and constraints.

[0156] The model solving module 23 is used to solve the energy storage optimization configuration model to obtain the target energy storage configuration scheme.

[0157] Other modules or units will not be described in detail here.

[0158] The configuration terminal 4 includes, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will understand that... Figure 8 This is merely an example of a configuration terminal and does not constitute a limitation on configuration terminal 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, configuration terminal 4 may also include input devices, output devices, network access devices, buses, etc.

[0159] The processor 40 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0160] The memory 41 can be an internal storage unit of the configuration terminal, such as a hard disk or memory. The memory 41 can also be an external storage device of the configuration terminal, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 41 can include both internal and external storage units. The memory 41 is used to store the computer program 42 and other programs and data required by the configuration terminal. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0161] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0162] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0163] In the embodiments provided in this application, it should be understood that the disclosed configuration terminal and method can be implemented in other ways. For example, the configuration terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0164] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0166] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0167] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for battery energy storage configuration of a light storage charging station, characterized in that, The method comprises the following steps: acquiring charging load data and photovoltaic power generation data of the 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; solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme; a calculation formula of the target function is: wherein, Ctotai is the total cost, Cdaily is the total cost of the light storage charging station converted to daily, P is the penalty factor, and W is the weight coefficient; Cbattery is the purchase cost of the battery energy storage system converted to daily 1-time charge-discharge cycle within the service life, Coperation is the operation cost of the battery energy storage system converted to daily, Cprice is the price difference income, Cpv is the income brought by the battery energy storage system absorbing photovoltaic power generation capacity to increase the price of photovoltaic power. The cost of the light storage charging station per day of charge-discharge cycle in the service life The calculation formula is: The electricity price difference income The calculation formula is: The battery energy storage system absorbs photovoltaic power generation electricity to improve the income of photovoltaic power price The calculation formula is: The penalty factor The calculation formula is: wherein, is the cost of the battery energy storage system equipment, is the cost of the energy storage battery per unit of capacity, is the total capacity of the battery energy storage system, is the expected cycle life of the energy storage battery; is the real-time electricity price, is the real-time charging power of the energy storage battery, is the real-time discharging power of the energy storage battery, is the billing time; is the photovoltaic generated electricity, is the average electricity price, is the photovoltaic on-grid electricity price; is the maximum load power of the power supply line, is the actual total load maximum value of the charging station after joining the battery energy storage.

2. The optical storage charging station battery storage configuration method of claim 1, wherein, The operation and maintenance cost of the light storage charging station is converted to daily cost The calculation formula is: or, wherein, is the daily operation and maintenance cost, is the unit capacity cost of energy storage battery, is the total capacity of the battery energy storage system, is the expected cycle life of the energy storage battery; is the unit capacity operation and maintenance cost of the PV and energy storage system, is the maintenance period.

3. The optical storage charging station battery storage configuration method of claim 1, wherein, the constraint condition comprises a power supply line power capacity constraint, an energy storage battery chargeable and dischargeable capacity constraint, an energy storage battery chargeable and dischargeable depth constraint, and an energy storage battery chargeable and dischargeable rate constraint.

4. The light-storage charging station battery energy storage configuration method according to claim 3, wherein the power supply line power capacity constraint is: the energy storage battery chargeable and dischargeable capacity constraint is: the energy storage battery chargeable and dischargeable depth constraint is: ， ， the energy storage battery chargeable and dischargeable rate constraint is: in, This is the minimum power for a battery energy storage system. This is the margin coefficient. The highest peak power, For the lowest peak power, This is the maximum power output for forward power supply. This represents the maximum power output for reverse power transmission. For the minimum capacity of energy storage batteries, The load power exceeds the maximum forward power supply. Part of This refers to the power during reverse feeding. For billing cycles; for The actual state of charge of the energy storage battery at all times. This is the minimum state of charge limit for the battery. This is the maximum state of charge limit for the battery. for, For battery energy storage systems Output power at any moment For charging and discharging sampling time, For the discharge efficiency of energy storage batteries, For the charging efficiency of energy storage batteries, This refers to the total capacity of the battery energy storage system. This refers to the charging rate. This represents the discharge rate.

5. The optical storage charging station battery storage configuration method of claim 1, wherein, the solving of the energy storage optimization configuration model to obtain the target energy storage configuration scheme comprises: the solving of the energy storage optimization configuration model to obtain the target energy storage configuration scheme comprises:

6. A battery energy storage configuration device for a light storage charging station, comprising: acquiring charging load data and photovoltaic power generation data of the 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; solving the energy storage optimization configuration model to obtain a target energy storage configuration scheme; a calculation formula of the target function is: the processor executes the computer program to realize the steps of the light-storage charging station battery energy storage configuration method according to any one of claims 1 to 5. wherein, Ctotai is the total cost, Cdaily is the total cost of the light storage charging station converted to daily, P is the penalty factor, and W is the weight coefficient; Cbattery is the purchase cost of the battery energy storage system converted to daily 1 charge-discharge cycle in the service life, Coperation is the operation cost of the battery energy storage system converted to daily, Cprice difference is the price difference income, Cpv is the income brought by the battery energy storage system absorbing photovoltaic power generation capacity to increase the price of photovoltaic power. The cost of ownership of the light storage charging station, converted to daily 1 charge-discharge cycle in service life The calculation formula is: The electricity price difference income The calculation formula is: The battery energy storage system absorbs photovoltaic power generation electricity to improve the income of photovoltaic power price The calculation formula is: The penalty factor The formula for calculating the penalty factor is: wherein, is the battery energy storage system device cost, is the energy storage battery unit capacity cost, is the total capacity of the battery energy storage system, is the expected cycle life of the energy storage battery; is the real-time electricity price, is the real-time charging power of the energy storage battery, is the real-time discharging power of the energy storage battery, is the billing time; is the photovoltaic generated electricity, is the average electricity price, is the photovoltaic on-grid electricity price; is the maximum load power of the power supply line, is the actual total load maximum value of the charging station after joining the battery energy storage.

7. A configuration terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, the computer program is executed by the processor to realize the steps of the light-storage charging station battery energy storage configuration method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising: ​

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