A method and system for configuring energy storage battery capacity in a photovoltaic charging station

Abstract

The present invention relates to a method and system for configuring the capacity of energy storage batteries within a photovoltaic charging station. The method comprises the following steps: obtaining an average load curve for the photovoltaic charging station over a preset period; determining the optimal configuration capacity of the energy storage batteries within the photovoltaic charging station based on the average load curve for the photovoltaic charging station over the preset period; and configuring the energy storage batteries within the photovoltaic charging station according to the optimal configuration capacity. The present invention more rationally configures the capacity of the energy storage batteries within the photovoltaic charging station, maximizing the reliability of renewable energy generation within the photovoltaic charging station.

Description

Technical Field

[0001] The present invention relates to the field of intelligent electricity consumption and energy storage technology, and in particular to a method and system for configuring the capacity of energy storage batteries in a photovoltaic charging station. Background Art

[0002] As energy crisis and environmental pollution issues have attracted more and more attention, electric vehicles with energy-saving and environmentally friendly characteristics have become the development direction of the global automotive industry.

[0003] To promote electric vehicles into people's daily lives, it is necessary to solve the problem of charging electric vehicles. If electric vehicles are directly connected to the main power grid for charging, it will greatly increase the load on the distribution network.

[0004] In order to reduce the load on the distribution network, a feasible solution is to build electric vehicle charging stations that integrate renewable energy (such as solar and wind energy). This not only realizes the development and utilization of renewable energy, but also reduces the power consumption from the grid and meets the demand of electric vehicles for charging facilities.

[0005] However, renewable energy generation is easily affected by the external environment, which causes its power output to be volatile and random. The power output of renewable energy is unstable. Therefore, equipping charging stations with energy storage battery systems can improve the reliability of renewable energy generation.

[0006] Currently, there is little research on the capacity configuration of energy storage batteries in photovoltaic charging stations. Most energy storage battery capacity configurations are simply determined based on design experience, and the configuration rationality is not high. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a method for configuring the capacity of energy storage batteries in a photovoltaic charging station. This method more reasonably configures the capacity of the energy storage batteries in the photovoltaic charging station and maximizes the reliability of renewable energy power generation in the photovoltaic charging station.

[0008] The purpose of the present invention is achieved by adopting the following technical solutions:

[0009] The present invention provides a method for configuring the capacity of energy storage batteries in a photovoltaic charging station, wherein the method comprises:

[0010] Obtain the average load curve of the solar-storage charging station during a preset period;

[0011] Determine the optimal configuration capacity of the energy storage battery in the solar-storage charging station based on the average load curve of the solar-storage charging station in a preset period;

[0012] The energy storage batteries in the photovoltaic charging station are configured according to the optimal configuration capacity of the energy storage batteries in the photovoltaic charging station.

[0013] Preferably, obtaining the average load curve of the solar-storage charging station in a preset period includes:

[0014] According to the battery capacity of each electric vehicle in the charging area corresponding to the solar-storage charging station, a Gaussian distribution function of the battery capacity of the electric vehicles in the charging area corresponding to the solar-storage charging station is fitted;

[0015] Fitting the Gaussian distribution function of the starting charging time satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the starting charging time of each preset cycle in the historical period of each electric vehicle in the charging area corresponding to the solar-storage charging station;

[0016] Fitting the SOC Gaussian distribution function satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the SOC of each electric vehicle in the charging area corresponding to the solar-storage charging station at the starting time of each preset cycle in the historical period;

[0017] Based on the established data and the Gaussian distribution function of the battery pack capacity, the Gaussian distribution function of the starting charging time, and the SOC Gaussian distribution function of the electric vehicles in the charging area corresponding to the solar-storage charging station, a Monte Carlo algorithm is used to perform M groups of simulations on the charging behavior of the electric vehicles to obtain M simulated load curves of the solar-storage charging station in a preset period.

[0018] Obtain an average value of the loads of the M solar-energy storage charging stations at the t-th moment in the simulated load curve of the preset period, and use the average value as the load value of the solar-energy storage charging station at the t-th moment in the average load curve of the preset period;

[0019] Wherein, t∈(1~T), T is the total number of moments included in the preset period.

[0020] Furthermore, the predetermined data includes:

[0021] The number of electric vehicles in the charging area corresponding to the solar-storage charging station, the number of charging piles in the solar-storage charging station, the charging power of the charging piles in the solar-storage charging station, and the conditions for the end of charging of electric vehicles in the charging area corresponding to the solar-storage charging station;

[0022] Among them, the condition for ending charging of electric vehicles in the charging area corresponding to the solar-storage charging station is that the SOC of the electric vehicle is greater than 80% of the maximum allowable SOC of the electric vehicle.

[0023] Preferably, the determining the optimal configuration capacity of the energy storage battery in the solar-energy storage charging station according to the average load curve of the solar-energy storage charging station in a preset period includes:

[0024] Substituting the average load curve of the solar-storage charging station in a preset period into a pre-built 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 solar-storage charging station;

[0025] The energy storage battery capacity optimization calculation model is constructed with the goals of minimizing the aging degree of the energy storage batteries in the photovoltaic storage charging station, minimizing the loss of photovoltaic power generation in the photovoltaic storage charging station, and minimizing the operating cost of the photovoltaic storage charging station.

[0026] Furthermore, the objective function of the pre-built energy storage battery capacity optimization calculation model is determined as follows:

[0027]

[0028] Where f is the objective function value of the pre-built energy storage battery capacity optimization calculation model, ω1 is the weight corresponding to the aging degree of the energy storage battery, and Q L,t is the aging degree of the energy storage battery at the tth moment of the preset cycle caused by the charging and discharging behavior, ω2 is the weight corresponding to the photovoltaic power generation loss rate, P eg,L,t is the loss of photovoltaic power generation in the photovoltaic charging station at the tth moment of the preset cycle, P eg,t is the power generation of the photovoltaic charging station at the tth moment in the average photovoltaic power generation curve of the preset period, C b The purchase cost of the energy storage battery per unit time is equal to the purchase cost of the energy storage battery divided by the total time included in the service life of the energy storage battery. g,t is the cost of purchasing electricity from the grid at the tth moment in the preset cycle, C max is the maximum limit of the cost consumed at the t-th moment of the preset period, t∈(1~T), T is the total number of moments included in the preset period;

[0029] The aging degree Q of the energy storage battery at the tth moment of the preset cycle caused by the charging and discharging behavior is determined by the following formula: L,t :

[0030]

[0031] Where C rate，t is the charge and discharge rate of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, P b,t is the charge and discharge power of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, C is the capacity of the energy storage battery in the solar storage charging station, R is the gas constant, T b,t is the temperature of the energy storage battery in the solar energy storage charging station at the tth moment of the preset cycle, A h is the ampere-hour throughput of the energy storage battery in the photovoltaic charging station, z is the power exponential factor; the photovoltaic power loss P in the photovoltaic charging station at the tth moment of the preset cycle is determined by the following formula eg,L,t :

[0032] P eg,L,t =Peg,t -P s,t -P b,t

[0033] Where, P s,t is the load of the solar-storage charging station at the tth moment in the average load curve of the preset period;

[0034] The power generation P of the photovoltaic charging station in the tth period of the average photovoltaic power generation curve of the preset period is determined by the following formula: eg,t :

[0035]

[0036] Where, is the photovoltaic power generation of the photovoltaic storage charging station at the tth moment of the jth preset cycle in the historical period, j∈(1~N y ), N y The total number of preset periods in the historical period.

[0037] Furthermore, the constraints of the objective function of the pre-built energy storage battery capacity optimization calculation model include: power balance constraints, energy storage battery charging and discharging power constraints, energy storage battery charging energy constraints, energy storage battery discharging energy constraints and energy storage battery capacity constraints;

[0038] The power balance constraint condition is determined as follows:

[0039] P g,t =P s,t -P eg,t -P b,t

[0040] Where, P g,t The electric power provided by the grid at the tth moment of the preset period;

[0041] The energy storage battery charging and discharging power constraint condition is determined as follows:

[0042] P b,min ≤P b,t ≤P b,max

[0043] Where, P b,min is the minimum charge and discharge power limit of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, P b,max The maximum charge and discharge power limit of the energy storage battery in the solar-storage charging station at the tth moment of the preset cycle;

[0044] The energy storage battery charging energy constraint condition is determined as follows:

[0045] E t+1 =E t+Δt·p b,t ·η

[0046] The energy storage battery discharge energy constraint condition is determined as follows:

[0047]

[0048] Where η is the charge and discharge efficiency of the energy storage battery in the photovoltaic charging station, Δt is the time between two adjacent moments in the preset cycle, and E t is the energy of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, E t+1 The energy of the energy storage battery in the solar-storage charging station at the t+1th moment of the preset cycle;

[0049] The energy storage battery capacity constraint condition is determined as follows:

[0050] 0≤C≤C max

[0051] Where C max The maximum energy storage battery capacity that can be configured in a solar-storage charging station.

[0052] The present invention provides a system for configuring the capacity of energy storage batteries in a photovoltaic charging station. The improvement thereof is that the system comprises:

[0053] An acquisition module is used to obtain an average load curve of the solar-storage charging station in a preset period;

[0054] A determination module is used to determine the optimal configuration capacity of the energy storage battery in the photovoltaic charging station based on the average load curve of the photovoltaic charging station in a preset period;

[0055] The configuration module is used to configure the energy storage batteries in the photovoltaic charging station according to the optimal configuration capacity of the energy storage batteries in the photovoltaic charging station.

[0056] Preferably, the acquisition module includes:

[0057] The first fitting unit is used to fit the battery pack capacity Gaussian distribution function satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the battery pack capacity of each electric vehicle in the charging area corresponding to the solar-storage charging station;

[0058] The second fitting unit is used to fit the Gaussian distribution function of the starting charging time satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the starting charging time of each electric vehicle in each preset cycle in the historical period;

[0059] The third fitting unit is used to fit the SOC Gaussian distribution function satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the SOC of each electric vehicle in the charging area corresponding to the solar-storage charging station at the starting time of each preset cycle in the historical period;

[0060] A simulation unit is configured to perform M sets of simulations on the charging behavior of electric vehicles using a Monte Carlo algorithm based on established data and a Gaussian distribution function of battery pack capacity, a Gaussian distribution function of a starting charging time, and a Gaussian distribution function of a state of charge (SOC) satisfied by electric vehicles in a charging area corresponding to the photovoltaic charging station, and obtain M simulated load curves of the photovoltaic charging station in a preset period;

[0061] As a unit, used to obtain the average value of the load of M solar-storage charging stations at the t-th moment in the simulated load curve of the preset period, and use the average value as the load value of the solar-storage charging station at the t-th moment in the average load curve of the preset period;

[0062] Wherein, t∈(1~T), T is the total number of moments included in the preset period.

[0063] Furthermore, the predetermined data includes:

[0064] The number of electric vehicles in the charging area corresponding to the solar-storage charging station, the number of charging piles in the solar-storage charging station, the charging power of the charging piles in the solar-storage charging station, and the conditions for the end of charging of electric vehicles in the charging area corresponding to the solar-storage charging station;

[0065] Among them, the condition for ending charging of electric vehicles in the charging area corresponding to the solar-storage charging station is that the SOC of the electric vehicle is greater than 80% of the maximum allowable SOC of the electric vehicle.

[0066] Preferably, the determining module is used to:

[0067] Substituting the average load curve of the solar-storage charging station in a preset period into a pre-built 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 solar-storage charging station;

[0068] The energy storage battery capacity optimization calculation model is constructed with the goals of minimizing the aging degree of the energy storage batteries in the photovoltaic storage charging station, minimizing the loss of photovoltaic power generation in the photovoltaic storage charging station, and minimizing the operating cost of the photovoltaic storage charging station.

[0069] Compared with the closest prior art, the present invention has the following beneficial effects:

[0070] The technical solution provided by this invention obtains the average load curve of a solar-powered charging station over a preset period; determines the optimal configuration capacity of the energy storage batteries within the solar-powered charging station based on the average load curve of the solar-powered charging station over the preset period; and configures the energy storage batteries within the solar-powered charging station according to the optimal configuration capacity. This invention more rationally configures the capacity of the energy storage batteries within the solar-powered charging station, maximizing the reliability of renewable energy generation in the solar-powered charging station.

[0071] The technical solution provided by the present invention uses the random charging behavior data of electric vehicles in the charging area corresponding to the photovoltaic charging station to fit the Gaussian distribution function of the battery pack capacity, the Gaussian distribution function of the starting charging time and the SOC Gaussian distribution function satisfied by the electric vehicles in the area, and uses the above-mentioned Gaussian distribution function to simulate the average load curve of the photovoltaic charging station in a preset period, so as to more accurately predict the load situation of the photovoltaic charging station in the preset period.

[0072] The technical solution provided by the present invention aims to minimize the operating cost of the photovoltaic storage charging station, minimize the loss rate of photovoltaic power generation, and minimize the aging degree of the energy storage batteries in the photovoltaic storage charging station, thereby achieving the optimal configuration of the comprehensive benefits of the energy storage batteries in the charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a flow chart of a method for configuring the capacity of energy storage batteries in a photovoltaic charging station;

[0074] Figure 2 is a daily average load curve diagram of a photovoltaic charging station in an embodiment of the present invention;

[0075] Figure 3 is a daily average photovoltaic power generation curve of the photovoltaic storage charging station in an embodiment of the present invention;

[0076] Figure 4 This is a time curve diagram of the output power of the energy storage battery and the power grid in the photovoltaic charging station according to an embodiment of the present invention;

[0077] Figure 5 is the energy state change curve of the energy storage battery in the photovoltaic charging station in an embodiment of the present invention;

[0078] Figure 6 It is a structural diagram of the energy storage battery capacity configuration system in a photovoltaic charging station. DETAILED DESCRIPTION

[0079] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0081] The present invention provides a method for configuring the capacity of energy storage batteries in a photovoltaic charging station. Figure 1 As shown, the method includes:

[0082] Step 101 is used to obtain an average load curve of a solar-powered charging station in a preset period;

[0083] Step 102 is used to determine the optimal configuration capacity of the energy storage battery in the photovoltaic charging station based on the average load curve of the photovoltaic charging station in a preset period;

[0084] Step 103 is used to configure the energy storage batteries in the solar-storage charging station according to the optimal configuration capacity of the energy storage batteries in the solar-storage charging station.

[0085] The photovoltaic charging station described in the present invention generally includes a photovoltaic system, an energy storage battery system, and a charging pile. The photovoltaic panels can be conveniently arranged on the roof of the parking shed of the charging pile, and the charging station is connected to the power distribution network.

[0086] According to the working principle of the photovoltaic charging station (when the photovoltaic system can meet the charging load of the electric vehicle, the photovoltaic system charges the energy storage battery and provides the charging pile with electric energy for the electric vehicle; when the photovoltaic system cannot meet the charging load of the electric vehicle, but the photovoltaic system and the energy storage battery can meet the charging load of the electric vehicle, the photovoltaic system and the energy storage battery jointly provide the charging pile with electric energy for the electric vehicle; otherwise, the charging pile can obtain electricity from the distribution network, and the charging station can use the low-peak electricity price to charge the energy storage battery at night), it can be concluded that the configuration of the energy storage battery in the photovoltaic charging station is one of the most important factors affecting the safe and reliable operation of the charging station;

[0087] When configuring the capacity of the energy storage battery in the photovoltaic charging station, the average load curve of the photovoltaic charging station in the preset period must be considered first. The average load of the photovoltaic charging station in the preset period is regular, which is related to the charging behavior of the photovoltaic charging station in the preset period (the charging start time of the electric vehicle, the SOC of the electric vehicle at the charging start time, and the battery pack capacity of the electric vehicle). Therefore, the charging behavior of the photovoltaic charging station in the preset period can be analyzed (fitted) using probabilistic statistics. Based on the said law, the average load curve of the photovoltaic charging station in the preset period can be simulated, which is specifically:

[0088] The step 101 includes:

[0089] Step 101-1 is used to fit the battery pack capacity Gaussian distribution function of the electric vehicles in the charging area corresponding to the solar-storage charging station according to the battery pack capacity of each electric vehicle in the charging area corresponding to the solar-storage charging station;

[0090] In the best embodiment of the present invention, the battery pack capacities of all electric vehicles in the charging station generally approximately obey the Gaussian distribution function, and the battery pack capacities of the electric vehicles are concentrated in the range [C min ,C max ], so we can directly set the mean of the Gaussian distribution function based on experience to be The variance is This replaces complex fitting operations.

[0091] Step 101-2 is used to fit the Gaussian distribution function of the starting charging time of the electric vehicles in the charging area corresponding to the solar-storage charging station according to the starting charging time of each electric vehicle in each preset cycle in the historical period;

[0092] In the best embodiment of the present invention, the charging start time of all electric vehicles at the charging station generally follows a Gaussian distribution function, and the charging start time of the electric vehicles is concentrated within a certain time period of the preset period [t min ,t max ], so we can directly set the mean of the Gaussian distribution function based on experience to be The variance is This replaces complex fitting operations.

[0093] Step 101-3 is used to fit the SOC Gaussian distribution function satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station based on the SOC of each electric vehicle in the charging area corresponding to the solar-storage charging station at the start time of each preset cycle in the historical period;

[0094] In the best embodiment of the present invention, the SOC of all electric vehicles at the charging station at the start of charging generally obeys a Gaussian distribution function, and the SOC of the electric vehicles at the start of charging are concentrated in a certain power range [SOC min ,SOC max ], so we can directly set the mean of the Gaussian distribution function based on experience to be The variance is This replaces complex fitting operations.

[0095] Step 101-4 is for performing M simulations of the electric vehicle charging behavior using a Monte Carlo algorithm based on the established data and the Gaussian distribution function of the battery pack capacity, the Gaussian distribution function of the starting charging time, and the SOC Gaussian distribution function of the electric vehicles in the charging area corresponding to the solar-energy storage charging station, to obtain M simulated load curves of the solar-energy storage charging station in a preset period;

[0096] In a specific embodiment of the present invention, since some electric vehicles have large battery packs and long charging times, the time granularity of each simulation group is set to minutes, thereby effectively simulating the charging process of the electric vehicle.

[0097] Step 101-5 is used to obtain the average value of the loads of the M solar-powered charging stations at the t-th moment in the simulated load curve of the preset period, and use the average value as the load value of the solar-powered charging station at the t-th moment in the average load curve of the preset period;

[0098] Wherein, t∈(1~T), T is the total number of moments included in the preset period.

[0099] In the preferred embodiment of the present invention, the preset period is set to 1 day, the total number of moments included in the preset period is 24, and the historical period is 1 year. The above steps are used to obtain Figure 2 The daily average load curve of the photovoltaic storage charging station is shown.

[0100] Furthermore, the predetermined data includes:

[0101] The number of electric vehicles within the charging area corresponding to the solar-storage charging station, the number of charging piles within the solar-storage charging station, the charging power of the charging piles within the solar-storage charging station, the conditions for the end of charging for electric vehicles within the charging area corresponding to the solar-storage charging station, and the calculation formula for the charging station load power at time t of the simulated load curve of the solar-storage charging station in a preset period;

[0102] Among them, the charging termination condition for electric vehicles in the charging area corresponding to the solar-storage charging station is that the SOC of the electric vehicle is greater than 80% of the maximum allowable SOC of the electric vehicle;

[0103] The charging station load power P of the solar storage charging station at the tth moment of the simulated load curve of the preset period is calculated as follows: sk,t :

[0104]

[0105] Among them, α j Represents 0 or 1. When the jth electric vehicle is charging in the charging area corresponding to the solar storage charging station, α j is 1, otherwise it is 0, P chgis the charging power of the charging pile in the solar-storage charging station, j∈(1~N), and N is the total number of electric vehicles in the charging area corresponding to the solar-storage charging station.

[0106] Specifically, step 102 includes:

[0107] Substituting the average load curve of the solar-storage charging station in a preset period into a pre-built 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 solar-storage charging station;

[0108] The energy storage battery capacity optimization calculation model is constructed with the goals of minimizing the aging degree of the energy storage batteries in the photovoltaic storage charging station, minimizing the loss of photovoltaic power generation in the photovoltaic storage charging station, and minimizing the operating cost of the photovoltaic storage charging station.

[0109] Furthermore, the objective function of the pre-built energy storage battery capacity optimization calculation model is determined as follows:

[0110]

[0111] Where f is the objective function value of the pre-built energy storage battery capacity optimization calculation model, ω1 is the weight corresponding to the aging degree of the energy storage battery, and Q L,t is the aging degree of the energy storage battery at the tth moment of the preset cycle caused by the charging and discharging behavior, ω2 is the weight corresponding to the photovoltaic power generation loss rate, P eg,L,t is the loss of photovoltaic power generation in the photovoltaic charging station at the tth moment of the preset cycle, P eg,t is the power generation of the photovoltaic charging station at the tth moment in the average photovoltaic power generation curve of the preset period, C b The purchase cost of the energy storage battery per unit time is equal to the purchase cost of the energy storage battery divided by the total time included in the service life of the energy storage battery. g,t is the cost of purchasing electricity from the grid at the tth moment in the preset cycle, C max is the maximum limit of the cost consumed at the t-th moment of the preset period, t∈(1~T), T is the total number of moments included in the preset period;

[0112] In the preferred embodiment of the present invention, the capacity of the energy storage battery affects the distribution of the charging station's power supply between the grid and the energy storage battery, as well as the degree of aging caused by the operation of the energy storage battery. Considering that energy storage battery equipment is still relatively expensive, it is necessary to enable the charging station to meet the charging needs of electric vehicles and reduce dependence on the grid while reducing the aging of the energy storage battery and ensuring the comprehensive benefits of the energy storage battery capacity configuration in the charging station.

[0113] In the preferred embodiment of the present invention, by prioritizing renewable energy generation and energy storage battery systems, renewable energy generation can be consumed locally, reducing the load on the power grid. Furthermore, when excess photovoltaic energy exceeds the capacity of the energy storage batteries, this excess photovoltaic energy becomes a loss in renewable energy generation.

[0114] The aging degree Q of the energy storage battery at the tth moment of the preset cycle caused by the charging and discharging behavior is determined by the following formula: L,t :

[0115]

[0116] Where C rate，t is the charge and discharge rate of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, P b,t is the charge and discharge power of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, C is the capacity of the energy storage battery in the solar storage charging station, R is the gas constant, T b,t is the temperature of the energy storage battery in the solar energy storage charging station at the tth moment of the preset cycle, A h is the ampere-hour throughput of the energy storage battery in the solar-storage charging station, z is the power exponential factor, which is generally 0.55;

[0117] The loss of photovoltaic power generation P in the photovoltaic charging station at the tth moment of the preset cycle is determined by the following formula: eg,L,t :

[0118] P eg,L,t =P eg,t -P s,t -P b,t

[0119] Where, P s,t is the load of the solar-storage charging station at the tth moment in the average load curve of the preset period;

[0120] In a specific embodiment of the present invention, photovoltaic panels are arranged on the top of the parking shed, with a certain paving area, and the photovoltaic system parameters are a peak power of 75kWp. Based on the statistical data of sunlight for one year, the following calculation is made: Figure 3 The daily average photovoltaic power generation curve of the photovoltaic charging station is shown in the figure, where the power generation P of the photovoltaic charging station in the tth period of the average photovoltaic power generation curve of the preset period is determined by the following formula eg,t :

[0121]

[0122] Where, is the photovoltaic power generation of the photovoltaic storage charging station at the tth moment of the jth preset cycle in the historical period, j∈(1~N y ), Ny The total number of preset periods in the historical period.

[0123] Furthermore, the constraints of the objective function of the pre-constructed energy storage battery capacity optimization calculation model include: power balance constraints, energy storage battery charging and discharging power constraints, energy storage battery charging energy constraints, energy storage battery discharging energy constraints and energy storage battery capacity constraints;

[0124] The power balance constraint condition is determined as follows:

[0125] P g,t =P s,t -P eg,t -P b,t

[0126] Where, P g,t The electric power provided by the grid at the tth moment of the preset period;

[0127] The energy storage battery charging and discharging power constraint condition is determined as follows:

[0128] P b,min ≤P b,t ≤P b,max

[0129] Where, P b,min is the minimum charge and discharge power limit of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, P b,max The maximum charge and discharge power limit of the energy storage battery in the solar-storage charging station at the tth moment of the preset cycle;

[0130] The energy storage battery charging energy constraint condition is determined as follows:

[0131] E t+1 =E t +Δt·p b,t ·η

[0132] The energy storage battery discharge energy constraint condition is determined as follows:

[0133]

[0134] Where η is the charge and discharge efficiency of the energy storage battery in the photovoltaic charging station, Δt is the time between two adjacent moments in the preset cycle, and E t is the energy of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, E t+1 The energy of the energy storage battery in the solar-storage charging station at the t+1th moment of the preset cycle;

[0135] The energy storage battery capacity constraint condition is determined as follows:

[0136] 0≤C≤C max

[0137] Where C max The maximum energy storage battery capacity that can be configured in the photovoltaic charging station is related to the actual situation and physical constraints such as floor space. The genetic algorithm can be used to solve the above objective function and constraints to obtain the optimal configuration of the energy storage battery.

[0138] In a preferred embodiment of the present invention, the tiered electricity price for large industrial users in a certain region is shown in Table 1. The energy storage battery capacity range is between 0 and 500 kWh, the permissible energy variation range of the energy storage battery is between 0.1 and 0.9 times the rated capacity, the price of the energy storage battery is 3000 RMB / kWh, the preset period is set to 1 day, the total number of moments included in the preset period is 24, and the historical period is 1 year. The charging behavior data of electric vehicles in the region is collected, and the daily average load curve and daily photovoltaic power generation curve of the region are simulated.

[0139] Table 1

[0140]

[0141] Using the technical solution of the present invention, the optimal energy storage battery configuration capacity obtained by genetic algorithm is 272kWh; the output power time curve of the energy storage battery and the power grid in the photovoltaic charging station is as follows: Figure 4 As shown in the figure, the energy state change curve of the energy storage battery in the photovoltaic charging station is as follows: Figure 5 As shown;

[0142] In addition, the objective function without considering the aging problem of the energy storage battery and the objective function considering the aging problem of the energy storage battery are solved respectively, and the optimal configuration capacity of the energy storage battery in the photovoltaic charging station is shown in Table 2. It can be concluded from the table that the solution considering the aging of the energy storage battery requires a larger capacity battery than the solution not considering the aging, but the benefit is that the aging of the energy storage battery can be reduced, the operating life of the energy storage battery is longer, and the cost of purchasing electricity from the power grid is lower.

[0143] Table 2

[0144]

[0145] The present invention provides a system for configuring the capacity of energy storage batteries in a photovoltaic charging station. Figure 6 As shown, the system includes:

[0146] An acquisition module is used to obtain an average load curve of the solar-storage charging station in a preset period;

[0147] A determination module is used to determine the optimal configuration capacity of the energy storage battery in the photovoltaic charging station based on the average load curve of the photovoltaic charging station in a preset period;

[0148] The configuration module is used to configure the energy storage batteries in the photovoltaic charging station according to the optimal configuration capacity of the energy storage batteries in the photovoltaic charging station.

[0149] Specifically, the acquisition module includes:

[0150] The first fitting unit is used to fit the battery pack capacity Gaussian distribution function satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the battery pack capacity of each electric vehicle in the charging area corresponding to the solar-storage charging station;

[0151] The second fitting unit is used to fit the Gaussian distribution function of the starting charging time satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the starting charging time of each electric vehicle in each preset cycle in the historical period;

[0152] The third fitting unit is used to fit the SOC Gaussian distribution function satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the SOC of each electric vehicle in the charging area corresponding to the solar-storage charging station at the starting time of each preset cycle in the historical period;

[0153] A simulation unit is configured to perform M sets of simulations on the charging behavior of electric vehicles using a Monte Carlo algorithm based on established data and a Gaussian distribution function of battery pack capacity, a Gaussian distribution function of a starting charging time, and a Gaussian distribution function of a state of charge (SOC) satisfied by electric vehicles in a charging area corresponding to the photovoltaic charging station, and obtain M simulated load curves of the photovoltaic charging station in a preset period;

[0154] As a unit, used to obtain the average value of the load of M solar-storage charging stations at the t-th moment in the simulated load curve of the preset period, and use the average value as the load value of the solar-storage charging station at the t-th moment in the average load curve of the preset period;

[0155] Wherein, t∈(1~T), T is the total number of moments included in the preset period.

[0156] Furthermore, the predetermined data includes:

[0157] The number of electric vehicles in the charging area corresponding to the solar-storage charging station, the number of charging piles in the solar-storage charging station, the charging power of the charging piles in the solar-storage charging station, and the conditions for the end of charging of electric vehicles in the charging area corresponding to the solar-storage charging station;

[0158] Among them, the condition for ending charging of electric vehicles in the charging area corresponding to the solar-storage charging station is that the SOC of the electric vehicle is greater than 80% of the maximum allowable SOC of the electric vehicle.

[0159] Specifically, the determining module is used to:

[0160] Substituting the average load curve of the solar-storage charging station in a preset period into a pre-built 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 solar-storage charging station;

[0161] The energy storage battery capacity optimization calculation model is constructed with the goals of minimizing the aging degree of the energy storage batteries in the photovoltaic storage charging station, minimizing the loss of photovoltaic power generation in the photovoltaic storage charging station, and minimizing the operating cost of the photovoltaic storage charging station.

[0162] Furthermore, the objective function of the pre-built energy storage battery capacity optimization calculation model is determined as follows:

[0163]

[0164] Where f is the objective function value of the pre-built energy storage battery capacity optimization calculation model, ω1 is the weight corresponding to the aging degree of the energy storage battery, and Q L,t is the aging degree of the energy storage battery at the tth moment of the preset cycle caused by the charging and discharging behavior, ω2 is the weight corresponding to the photovoltaic power generation loss rate, P eg,L,t is the loss of photovoltaic power generation in the photovoltaic charging station at the tth moment of the preset cycle, P eg,t is the power generation of the photovoltaic charging station at the tth moment in the average photovoltaic power generation curve of the preset period, C b The purchase cost of the energy storage battery per unit time is equal to the purchase cost of the energy storage battery divided by the total time included in the service life of the energy storage battery. g,t is the cost of purchasing electricity from the grid at the tth moment in the preset cycle, C max is the maximum limit of the cost consumed at the t-th moment of the preset period, t∈(1~T), T is the total number of moments included in the preset period;

[0165] The aging degree Q of the energy storage battery at the tth moment of the preset cycle caused by the charging and discharging behavior is determined by the following formula: L,t :

[0166]

[0167] Where C rate，t is the charge and discharge rate of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, P b,t is the charge and discharge power of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, C is the capacity of the energy storage battery in the solar storage charging station, R is the gas constant, T b,t is the temperature of the energy storage battery in the solar energy storage charging station at the tth moment of the preset cycle, A his the ampere-hour throughput of the energy storage battery in the photovoltaic charging station, z is the power exponential factor; the photovoltaic power loss P in the photovoltaic charging station at the tth moment of the preset cycle is determined by the following formula eg,L,t :

[0168] P eg,L,t =P eg,t -P s,t -P b,t

[0169] Where, P s,t is the load of the solar-storage charging station at the tth moment in the average load curve of the preset period;

[0170] The power generation P of the photovoltaic charging station in the tth period of the average photovoltaic power generation curve of the preset period is determined by the following formula: eg,t :

[0171]

[0172] Where, is the photovoltaic power generation of the photovoltaic storage charging station at the tth moment of the jth preset cycle in the historical period, j∈(1~N y ), N y The total number of preset periods in the historical period.

[0173] Furthermore, the constraints of the objective function of the pre-constructed energy storage battery capacity optimization calculation model include: power balance constraints, energy storage battery charging and discharging power constraints, energy storage battery charging energy constraints, energy storage battery discharging energy constraints and energy storage battery capacity constraints;

[0174] The power balance constraint condition is determined as follows:

[0175] P g,t =P s,t -P eg,t -P b,t

[0176] Where, P g,t The electric power provided by the grid at the tth moment of the preset period;

[0177] The energy storage battery charging and discharging power constraint condition is determined as follows:

[0178] P b,min ≤P b,t ≤P b,max

[0179] Where, P b,min is the minimum charge and discharge power limit of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, P b,maxThe maximum charge and discharge power limit of the energy storage battery in the solar-storage charging station at the tth moment of the preset cycle;

[0180] The energy storage battery charging energy constraint condition is determined as follows:

[0181] E t+1 =E t +Δt·p b,t ·η

[0182] The energy storage battery discharge energy constraint condition is determined as follows:

[0183]

[0184] Where η is the charge and discharge efficiency of the energy storage battery in the photovoltaic charging station, Δt is the time between two adjacent moments in the preset cycle, and E t is the energy of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, E t+1 The energy of the energy storage battery in the solar-storage charging station at the t+1th moment of the preset cycle;

[0185] The energy storage battery capacity constraint condition is determined as follows:

[0186] 0≤C≤C max

[0187] Where C max The maximum energy storage battery capacity that can be configured in a solar-storage charging station.

[0188] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0189] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0190] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0191] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0192] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for configuring the capacity of energy storage batteries in a photovoltaic charging station, characterized in that: The method comprises: Obtain the average load curve of the solar-storage charging station during a preset period; Determine the optimal configuration capacity of the energy storage battery in the solar-storage charging station based on the average load curve of the solar-storage charging station in a preset period; Configure the energy storage batteries in the solar-storage charging station according to the optimal configuration capacity of the energy storage batteries in the solar-storage charging station; The method of determining the optimal configuration capacity of the energy storage battery in the photovoltaic charging station according to the average load curve of the photovoltaic charging station in a preset period includes: Substituting the average load curve of the solar-storage charging station in a preset period into a pre-built 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 solar-storage charging station; The energy storage battery capacity optimization calculation model is constructed with the goal of minimizing the aging of the energy storage batteries in the photovoltaic charging station, minimizing the loss of photovoltaic power generation in the photovoltaic charging station, and minimizing the operating cost of the photovoltaic charging station; The objective function of the pre-built energy storage battery capacity optimization calculation model is determined as follows: Where f is the objective function value of the pre-built energy storage battery capacity optimization calculation model, ω1 is the weight corresponding to the aging degree of the energy storage battery, and Q L,t is the aging degree of the energy storage battery at the tth moment of the preset cycle caused by the charging and discharging behavior, ω2 is the weight corresponding to the photovoltaic power generation loss rate, P eg,L,t is the loss of photovoltaic power generation in the photovoltaic charging station at the tth moment of the preset cycle, P eg,t is the power generation of the photovoltaic charging station at the tth moment in the average photovoltaic power generation curve of the preset period, C b The purchase cost of the energy storage battery per unit time is equal to the purchase cost of the energy storage battery divided by the total time included in the service life of the energy storage battery. g,t is the cost of purchasing electricity from the grid at the tth moment in the preset cycle, C max is the maximum limit of the cost consumed at the t-th moment of the preset period, t∈(1~T), T is the total number of moments included in the preset period; The aging degree Q of the energy storage battery at the tth moment of the preset cycle caused by the charging and discharging behavior is determined by the following formula: L,t : Where C rate，t is the charge and discharge rate of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, P b,t is the charge and discharge power of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, C is the capacity of the energy storage battery in the solar storage charging station, R is the gas constant, T b,t is the temperature of the energy storage battery in the solar energy storage charging station at the tth moment of the preset cycle, A h is the ampere-hour throughput of the energy storage battery in the solar-storage charging station, and z is the power exponential factor; The loss of photovoltaic power generation P in the photovoltaic charging station at the tth moment of the preset cycle is determined by the following formula: eg,L,t : P eg,L,t =P eg,t -P s,t -P b,t Where, P s,t is the load of the solar-storage charging station at the tth moment in the average load curve of the preset period; The power generation P of the photovoltaic charging station in the tth period of the average photovoltaic power generation curve of the preset period is determined by the following formula: eg,t : Where, is the photovoltaic power generation of the photovoltaic storage charging station at the tth moment of the jth preset cycle in the historical period, j∈(1~N y ), N y The total number of preset periods in the historical period.

2. The method according to claim 1, wherein The step of obtaining an average load curve of the solar-storage charging station during a preset period includes: According to the battery capacity of each electric vehicle in the charging area corresponding to the solar-storage charging station, a Gaussian distribution function of the battery capacity of the electric vehicles in the charging area corresponding to the solar-storage charging station is fitted; Fitting the Gaussian distribution function of the starting charging time satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the starting charging time of each preset cycle in the historical period of each electric vehicle in the charging area corresponding to the solar-storage charging station; Fitting the SOC Gaussian distribution function satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the SOC of each electric vehicle in the charging area corresponding to the solar-storage charging station at the starting time of each preset cycle in the historical period; Based on the established data and the Gaussian distribution function of the battery pack capacity, the Gaussian distribution function of the starting charging time, and the SOC Gaussian distribution function of the electric vehicles in the charging area corresponding to the solar-storage charging station, a Monte Carlo algorithm is used to perform M groups of simulations on the charging behavior of the electric vehicles to obtain M simulated load curves of the solar-storage charging station in a preset period. Obtain an average value of the loads of the M solar-energy storage charging stations at the t-th moment in the simulated load curve of the preset period, and use the average value as the load value of the solar-energy storage charging station at the t-th moment in the average load curve of the preset period; Wherein, t∈(1~T), T is the total number of moments included in the preset period.

3. The method according to claim 2, wherein The established data includes: The number of electric vehicles in the charging area corresponding to the solar-storage charging station, the number of charging piles in the solar-storage charging station, the charging power of the charging piles in the solar-storage charging station, and the conditions for the end of charging of electric vehicles in the charging area corresponding to the solar-storage charging station; Among them, the condition for ending charging of electric vehicles in the charging area corresponding to the solar-storage charging station is that the SOC of the electric vehicle is greater than 80% of the maximum allowable SOC of the electric vehicle.

4. The method according to claim 1, wherein The constraints of the objective function of the pre-built energy storage battery capacity optimization calculation model include: power balance constraints, energy storage battery charging and discharging power constraints, energy storage battery charging energy constraints, energy storage battery discharging energy constraints and energy storage battery capacity constraints; The power balance constraint condition is determined as follows: P g,t =P s,t -P eg,t -P b,t Where, P g,t The electric power provided by the grid at the tth moment of the preset period; The energy storage battery charging and discharging power constraint condition is determined as follows: P b,min ≤P b,t ≤P b,max Where, P b,min is the minimum charge and discharge power limit of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, P b,max The maximum charge and discharge power limit of the energy storage battery in the solar-storage charging station at the tth moment of the preset cycle; The energy storage battery charging energy constraint condition is determined as follows: AND t+1 =And t +Δt·p b,t ·η The energy storage battery discharge energy constraint condition is determined as follows: Where η is the charge and discharge efficiency of the energy storage battery in the photovoltaic charging station, Δt is the time between two adjacent moments in the preset cycle, and E t is the energy of the energy storage battery in the solar storage charging station at the tth moment of the preset cycle, E t+1 The energy of the energy storage battery in the solar-storage charging station at the t+1th moment of the preset cycle; The energy storage battery capacity constraint condition is determined as follows: 0≤C≤C max Where C max The maximum energy storage battery capacity that can be configured in a solar-storage charging station.

5. A system for configuring the capacity of energy storage batteries in a photovoltaic charging station, for implementing the method for configuring the capacity of energy storage batteries in a photovoltaic charging station as claimed in claim 1, characterized in that: The system comprises: An acquisition module is used to obtain an average load curve of the solar-storage charging station in a preset period; A determination module is used to determine the optimal configuration capacity of the energy storage battery in the photovoltaic charging station based on the average load curve of the photovoltaic charging station in a preset period; A configuration module is used to configure the energy storage batteries in the solar energy storage charging station according to the optimal configuration capacity of the energy storage batteries in the solar energy storage charging station; The determining module is configured to: Substituting the average load curve of the solar-storage charging station in a preset period into a pre-built 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 solar-storage charging station; The energy storage battery capacity optimization calculation model is constructed with the goals of minimizing the aging degree of the energy storage batteries in the photovoltaic storage charging station, minimizing the loss of photovoltaic power generation in the photovoltaic storage charging station, and minimizing the operating cost of the photovoltaic storage charging station.

6. The system according to claim 5, wherein: The acquisition module includes: The first fitting unit is used to fit the battery pack capacity Gaussian distribution function satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the battery pack capacity of each electric vehicle in the charging area corresponding to the solar-storage charging station; The second fitting unit is used to fit the Gaussian distribution function of the starting charging time satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the starting charging time of each electric vehicle in each preset cycle in the historical period; The third fitting unit is used to fit the SOC Gaussian distribution function satisfied by the electric vehicles in the charging area corresponding to the solar-storage charging station according to the SOC of each electric vehicle in the charging area corresponding to the solar-storage charging station at the starting time of each preset cycle in the historical period; A simulation unit is configured to perform M sets of simulations on the charging behavior of electric vehicles using a Monte Carlo algorithm based on established data and a Gaussian distribution function of battery pack capacity, a Gaussian distribution function of a starting charging time, and a Gaussian distribution function of a state of charge (SOC) satisfied by electric vehicles in a charging area corresponding to the photovoltaic charging station, and obtain M simulated load curves of the photovoltaic charging station in a preset period; As a unit, used to obtain the average value of the load of M solar-storage charging stations at the t-th moment in the simulated load curve of the preset period, and use the average value as the load value of the solar-storage charging station at the t-th moment in the average load curve of the preset period; Wherein, t∈(1~T), T is the total number of moments included in the preset period.

7. The system according to claim 6, wherein: The established data includes: The number of electric vehicles in the charging area corresponding to the solar-storage charging station, the number of charging piles in the solar-storage charging station, the charging power of the charging piles in the solar-storage charging station, and the conditions for the end of charging of electric vehicles in the charging area corresponding to the solar-storage charging station; Among them, the condition for ending charging of electric vehicles in the charging area corresponding to the solar-storage charging station is that the SOC of the electric vehicle is greater than 80% of the maximum allowable SOC of the electric vehicle.

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