Optical storage charging station energy storage configuration optimization method considering photovoltaic output

Abstract

The invention relates to an optical storage charging station energy storage configuration optimization method considering photovoltaic output. The method comprises the steps of performing mathematical modeling on photovoltaic power generation capacity and charging load requirements in an optical storage charging comprehensive charging station; analyzing the correlation between the photovoltaic power generation output and the charging load characteristics; based on the correlation between the photovoltaic power generation output characteristics and the charging load characteristics, constructing an optical storage charging station energy storage configuration optimization model comprehensively considering the photovoltaic output and the load demand; and carrying out optimization solution on the energy storage configuration optimization model of the optical storage charging station. The management and operation problems of the optical storage and charging energy system are effectively solved, the energy utilization efficiency of the optical storage and charging system is improved, and the supply pressure of a power grid is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage configuration of a photovoltaic charging station, and specifically relates to an energy storage configuration optimization method for a photovoltaic charging station taking photovoltaic output into consideration. Background Art

[0002] The photovoltaic storage and charging integrated mode can achieve efficient and comprehensive allocation of electric energy. Specifically, the photovoltaic system is responsible for converting solar energy into electrical energy, while the energy storage system operates through charging and discharging strategies, aiming to reduce the operating costs of charging stations, thus highlighting the significant advantages of the photovoltaic storage and charging integrated mode. This mode can effectively reduce the pressure on the power grid during the peak charging period of electric vehicles. Given that the photovoltaic power generation output and charging load in the photovoltaic storage and charging integrated charging station both show random and intermittent characteristics, they each present a typical probability distribution law. Both photovoltaic power generation output and charging load change dynamically over time. This change is not only affected by natural factors such as light intensity, seasonal climate, geographical location and time, but also by the distribution of charging load inside the charging station. The analysis of the relationship between photovoltaic power generation output and charging load is a complex problem involving multiple coupling factors and multi-dimensional influences. Therefore, seeking the optimization of energy storage configuration of photovoltaic storage charging stations is a technical problem that this field has always been eager to solve. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides a method for optimizing the energy storage configuration of a photovoltaic charging station taking into account photovoltaic output, which solves the problem in the prior art that the analysis of photovoltaic output and load characteristics is complex and it is difficult to obtain the optimal energy storage configuration of the photovoltaic charging station.

[0004] To achieve the above objectives, the present invention provides a method for optimizing energy storage configuration of a photovoltaic charging station taking photovoltaic output into consideration, comprising:

[0005] Step 1: mathematically model the photovoltaic power generation capacity and charging load demand in the photovoltaic storage and charging integrated charging station;

[0006] Step 2, analyzing the correlation between photovoltaic power generation output and charging load characteristics;

[0007] Step 3: Based on the correlation between photovoltaic power generation output characteristics and charging load characteristics, a photovoltaic charging station energy storage configuration optimization model is constructed that comprehensively considers photovoltaic output and load demand;

[0008] Step 4: Optimize and solve the energy storage configuration optimization model of the photovoltaic charging station.

[0009] In the step 1,

[0010] The photovoltaic power output power is described by the following mathematical expression:

[0011]

[0012] In the formula, G t (t) represents the irradiance of the sun’s rays irradiating vertically to the surface of the photovoltaic panel, G ref Its value is set to 1000W / m; P PVN Indicates the rated output power of the photovoltaic cell module under standard test conditions (STC); η PV Refers to the conversion efficiency of the entire photovoltaic power generation system; η PVC represents the transmission efficiency of photovoltaic energy; T N is the battery reference temperature under standard test conditions, β PV is the temperature coefficient, with typical values ​​ranging from 0.004 to 0.006; T C Indicates the actual operating temperature of the battery.

[0013] Furthermore, the T C It is described by the following mathematical equation:

[0014]

[0015] Where, T ENV Refers to the ambient temperature; T N is the temperature of the solar panel under normal operating conditions, α is the temperature coefficient, and Δθ is the change in ambient temperature caused by seasonal changes.

[0016] In step 1, the charging load is an electric vehicle charging load, and the electric vehicle charging load demand modeling includes the following steps:

[0017] According to the time when the electric vehicle starts charging and its initial battery charge state, the initial charging condition of the electric vehicle is obtained;

[0018] For each electric vehicle, the charging time is calculated based on its charging power, initial battery state of charge and target battery state of charge, and the charging end time is obtained based on the start time of charging;

[0019] Count the number of electric vehicles being charged at each moment throughout the day, accumulate the charging power of all vehicles at that moment, and obtain the total charging load demand f of electric vehicles at that moment. T (t),

[0020]

[0021] In the formula, K i is the weight coefficient of the i-th time period, t is the time variable, where t i With t i+1are the starting points of the i-th period and the i+1-th period respectively, α represents the temperature coefficient, and Δθ represents the change in ambient temperature caused by seasonal changes.

[0022] Furthermore, the initial battery charge state of the electric vehicle has a log-normal distribution statistical feature, that is, its probability density function f s (s0) is:

[0023]

[0024] Charging duration of electric vehicle t EV for:

[0025]

[0026] Where, its logarithmic mean is μ, and its logarithmic standard deviation is σ; the battery state of charge at the end of charging is represented by S1, the battery capacity of the car is Q, and the charging power of the electric car is P EV , and its charging efficiency is represented by η.

[0027] In the step 2,

[0028] Energy storage battery charging status:

[0029] E(t)=E(t-1)(1-δ)(1-β t )+P c (t)η c ΔT

[0030] Energy storage battery discharge status:

[0031]

[0032] Charge and discharge power constraints:

[0033] 0≤P c (t)≤P cmax

[0034] 0≤P D (t)≤P Dmax

[0035] Where E(t) is the total accumulated energy of the energy storage system in a specific time period; δ is the inherent self-discharge rate of the energy storage system; P c (t), P D (t) is the attenuation factor of the charging power and discharging power of the energy storage system in the period t, β is the attenuation factor, and its value is less than 1, indicating the capacity attenuation ratio per unit time; t is the number of charging times or the actual time, η c , is the charging efficiency and the discharging efficiency;

[0036] The battery state of charge (SOC) is used to characterize the current energy state of the energy storage battery. Its value is defined as the ratio of the battery's instantaneous power to its rated capacity. When SOC is equal to 1, it indicates that the battery is fully charged; and when SOC is equal to 0, it indicates that the battery is fully discharged. To prevent the energy storage battery from over-discharging, the discharge process is terminated when the battery state of charge drops to the preset minimum charge threshold. To prevent the battery from overcharging, the charging process is also stopped once the battery state of charge reaches the preset maximum charge threshold.

[0037] The state of charge of the energy storage battery at any given point in time is described by the following expression:

[0038]

[0039] Among them, SOC0 represents the initial state of charge of the energy storage system; η is the conversion efficiency, which indicates the efficiency of energy conversion during the battery charging and discharging process, and its value is between 0 and 1; E ESS is the rated capacity of the battery; P(t) represents the charging and discharging power of the energy storage battery at time t;

[0040] In order to maximize the service life of the energy storage battery, the following restrictions are implemented on the discharge depth of the battery:

[0041] SOC min ≤SOC≤SOC max

[0042] Among them, SOC min , SOC max They respectively represent the minimum state of charge and maximum state of charge limit values ​​allowed for the energy storage battery.

[0043] In step 3, a photovoltaic charging station energy storage configuration optimization model is constructed that comprehensively considers photovoltaic output and load demand, including objective functions and constraints.

[0044] 1) The annual comprehensive operating cost of the photovoltaic storage charging station is determined as the minimization objective function, namely:

[0045] minC=min(C1+C2);

[0046] C1 is the initial deployment cost of the energy storage system, which includes the fixed investment cost C of the charging station energy storage system. in and operation and maintenance costs due to seasonal changes C sea ;

[0047]

[0048] The specific parameters are defined as follows: Represents the investment cost per unit capacity of the energy storage system; W e is the rated capacity of the energy storage system; Refers to the charging and discharging cost per unit power of the energy storage system; P represents the rated charging and discharging power of the energy storage system;

[0049] The C2 is the power purchase and abandoned light loss cost of the energy storage system, which includes the annual power purchase cost C of the charging station energy storage system. ESS , and the cost of abandoned photovoltaic power reduced by increasing the proportion of photovoltaic power after considering the characteristics and utilization of regional photovoltaic resources. PV ;

[0050]

[0051] Specific parameters: γ represents the cost coefficient of abandoned light, which is used to quantify the cost caused by the underutilization of photovoltaic power; P PV Represents actual active power, that is, the actual power output of the system; P PV、pre The predicted active power of photovoltaic power generation, that is, the photovoltaic power output predicted based on historical data and models; π t , P g (t) represents the interactive electricity price and average power between the large power grid and the system in the tth period, reflecting the price changes in the power market and the power exchange between the system and the large power grid; Δt is the duration of the tth period, which is used to calculate the cost or total energy exchange in each period; the regional correction factor k region , to adjust and optimize the cost-benefit analysis of energy storage systems;

[0052] 2) Constraints include solar storage and charging power supply and demand balance constraints, confidence constraints, and power fluctuation constraints.

[0053] (1) Solar energy storage and charging power supply and demand balance constraints:

[0054] P g (t)+P pv (t)+(1-β t ) ESS (t)+P ev (t) = 0

[0055] Where P ev (t) represents the charging load power of the electric vehicle; P PV (t) represents the output power of the photovoltaic power generation system, that is, the power of the photovoltaic panel converting solar energy into electrical energy; P g (t) is the power of the distribution network; β is the attenuation factor, whose value is less than 1, indicating the capacity attenuation ratio per unit time; t is the actual charging time, P ESS(t) represents the charging and discharging power of the energy storage battery;

[0056] (2) Confidence constraints:

[0057] η≥η min

[0058] Where η is the actual confidence of the system; η min is the minimum confidence allowed by the system;

[0059] (3) Constraints on power fluctuations:

[0060] σ≤σ

[0061] max

[0062] In the formula, σ represents the actual volatility of the system, σ max Indicates the maximum volatility allowed by the system.

[0063] In step 4, a cycle of 8760 hours is set in one year to simulate the operation of the integrated photovoltaic storage and charging station. The specific steps are as follows:

[0064] Step 1), input historical data of light intensity and electric vehicle charging load;

[0065] Step 2), clarify the value range of the decision variables, that is, the upper and lower limits of each parameter;

[0066] Step 3), according to the current lighting conditions and charging requirements, respectively calculate the photovoltaic power generation power and the electric vehicle charging load power;

[0067] Step 4), compare the size of photovoltaic power generation and charging load power, and calculate the charging and discharging power of the energy storage battery and the power absorbed from the distribution network according to the preset energy exchange strategy;

[0068] Step 5), further calculating the charge and discharge power of the energy storage system, the power absorbed from the distribution network, and the charging power of the electric vehicle;

[0069] Step 6), during the calculation process, add upper and lower limit constraints of the energy storage battery charging capacity;

[0070] Step 7), determine whether the current number of cycles reaches or exceeds 8760 times. If so, the algorithm ends; otherwise, return to step 3) to continue the calculation of the next period.

[0071] A computer device comprising:

[0072] A processor; and a memory configured to store machine-readable instructions, which, when executed by the processor, execute the above-mentioned method for optimizing the energy storage configuration of a photovoltaic charging station taking into account photovoltaic output.

[0073] A storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned method for optimizing energy storage configuration of a photovoltaic charging station taking into account photovoltaic output.

[0074] The beneficial effects of the present invention are as follows:

[0075] This paper is dedicated to in-depth exploration of the output characteristics of photovoltaic power generation, the charging and discharging behavior model of the energy storage system, and the charging load requirements of electric vehicles, and based on this, a photovoltaic storage charging station energy storage configuration optimization model that comprehensively considers photovoltaic output factors is constructed. This model aims to overcome the complex problems in the management and operation of photovoltaic storage and charging energy systems, and provide an effective solution for the expansion and transformation of existing photovoltaic storage and charging energy systems, thereby improving the energy utilization efficiency of photovoltaic storage and charging systems and reducing the power supply burden of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A schematic diagram of the operation flow of an improved genetic algorithm of the present invention;

[0077] Figure 2 It is a schematic diagram of the process of solving the objective function of the photovoltaic storage model of the present invention;

[0078] Figure 3 This is a diagram of the operation mode of the photovoltaic charging station of the present invention. DETAILED DESCRIPTION

[0079] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in 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 creative work are within the scope of protection of the present invention.

[0080] Example 1

[0081] The present invention provides a method for optimizing energy storage configuration of a photovoltaic charging station taking photovoltaic output into consideration, comprising:

[0082] Step 1: mathematically model the photovoltaic power generation capacity and charging load demand in the photovoltaic storage and charging integrated charging station;

[0083] Step 2, analyzing the correlation between photovoltaic power generation output and charging load characteristics;

[0084] Step 3: Based on the correlation between photovoltaic power generation output characteristics and charging load characteristics, a photovoltaic charging station energy storage configuration optimization model is constructed that comprehensively considers photovoltaic output and load demand;

[0085] Step 4: Optimize and solve the energy storage configuration optimization model of the photovoltaic charging station.

[0086] In step 1, the regulation mechanism of the energy storage system causes the evolution of its charging and discharging power and electricity over time to present a dynamic change process. During this change process, a series of established relationship conditions must be met.

[0087] The power output level of a photovoltaic power generation system is determined by a variety of factors, including the operating temperature of the battery, the intensity of solar radiation, the physical area of ​​the solar panel, and its efficiency in absorbing light energy. The relationship between these factors and the power output of the photovoltaic power generation system is described by the following mathematical expression:

[0088]

[0089] In the formula, G t (t) represents the irradiance of the sun’s rays irradiating vertically to the surface of the photovoltaic panel, G ref Its value is set to 1000W / m; P PVN Indicates the rated output power of the photovoltaic cell module under standard test conditions (STC); η PV Refers to the conversion efficiency of the entire photovoltaic power generation system; η PVC represents the transmission efficiency of photovoltaic energy; T N is the battery reference temperature under standard test conditions, β PV is the temperature coefficient, with typical values ​​ranging from 0.004 to 0.006; T C Indicates the actual operating temperature of the battery.

[0090]

[0091] Where, T ENV Refers to the ambient temperature; T N is the temperature of the solar panel under normal operating conditions, α is the temperature coefficient, and Δθ is the change in ambient temperature caused by seasonal changes.

[0092] Furthermore, when constructing an electric vehicle charging load model, it is necessary to determine the electric vehicle charging demand at all times of the day. The calculation process includes the following steps:

[0093] Initialize the electric vehicle state: According to the time when the electric vehicle starts charging and its initial battery state of charge (SOC), a series of initial charging conditions of the electric vehicle are obtained.

[0094] Calculate charging time and end time: For each electric vehicle, calculate the residence time at the charging station (i.e. charging time) based on its charging power, initial SOC and target SOC, and combine it with the time when charging starts to calculate the corresponding charging end time.

[0095] Count charging load demand: traverse the whole day, count the number of electric vehicles being charged at each moment, and calculate the total charging load demand f of electric vehicles at that moment by accumulating the charging power of these vehicles. T (t).

[0096] Definition of charging time distribution function: The distribution of the time when electric vehicles start charging describes the probability distribution characteristics of the time when electric vehicles start charging within a day with seasonal changes, that is,

[0097]

[0098] The initial battery state of charge (SOC) of electric vehicles presents the statistical characteristics of log-normal distribution, that is, its probability density function conforms to the form of log-normal distribution.

[0099]

[0100] Charging duration of electric vehicle t EV :

[0101]

[0102] In the formula, K i is the weight coefficient of the i-th time period, t is the time variable, where t i With t i+1 They mark the starting points of the i-th period and the i+1-th period respectively, α represents the temperature coefficient, and Δθ represents the change in ambient temperature caused by seasonal changes. The probability density function of the time when the electric vehicle starts charging follows a log-normal distribution, with a logarithmic mean of μ and a logarithmic standard deviation of σ. The SOC value at the end of charging is denoted as S1, the battery capacity of the car is Q, and the charging power of the electric vehicle is recorded as P EV , and its charging efficiency is represented by η.

[0103] Example 2

[0104] In step 1, the Monte Carlo simulation method is used to randomly select the initial state of charge (SOC) of electric vehicles in the Northeast region, and the sampling range of the initial charging time is compressed according to the constraint of the charging time, so as to determine the initial charging time and calculate the charging load accordingly. Subsequently, the charging loads of all individuals are accumulated to obtain the overall charging load. The specific simulation process for the charging load of electric vehicles can be refined into the following steps:

[0105] (1) Perform data initialization operations and enter basic parameters such as the vehicle's initial state of charge (SOC), mileage, and stop time into the system; at the same time, considering the impact of the unique rainy and snowy weather and traffic conditions in the Northeast on electric vehicle charging behavior, these factors need to be taken into consideration as important variables.

[0106] (2) Based on the guidance of the probability density function, the initial SOC value and travel distance information of the vehicle are randomly extracted, and the driving time and the vehicle battery SOC value at the corresponding time are calculated. Subsequently, based on the probability density function of the driving distance, the predicted distance of the vehicle's next trip is generated, and the current SOC value and predicted distance are combined to jointly determine the vehicle's selected charging method and the required charging power.

[0107] (3) Based on the determined charging method and power demand, the temporal and spatial distribution of the charging power demand of the next electric vehicle is calculated, and the load data of the vehicle is accumulated one by one to form a charging load demand curve for the current area.

[0108] (4) Determine whether the current time has reached the set time limit. If it has, calculate and output the relevant charging load data and indicators, finally generate the charging load curve of the electric vehicle, and end the simulation process; if it has not, return to step (2) and continue to loop and execute the above operations until the charging demand of the last electric vehicle is processed.

[0109] Example 3

[0110] In step 2, the correlation between photovoltaic power generation output and charging load characteristics in Northeast China is deeply analyzed to provide a solid theoretical basis and support for the energy storage configuration optimization analysis of the photovoltaic storage and charging integrated charging station.

[0111] Furthermore, the energy storage battery charging status:

[0112] E(t)=E(t-1)(1-δ)(1-β t )+P c (t)η c ΔT

[0113] Energy storage battery discharge status:

[0114]

[0115] Charge and discharge power constraints:

[0116] 0≤P c (t)≤P cmax

[0117] 0≤P D (t)≤P Dmax

[0118] In the formula, E(t) defines the total amount of energy accumulated by the energy storage system in a specific time period; the inherent self-discharge rate δ of the energy storage system is also considered. In addition, P c (t), P D (t) distinguishes the charging power and discharging power attenuation factors of the energy storage system in the period t. β is the attenuation factor, and its value is less than 1, indicating the capacity attenuation ratio per unit time. t is the actual charging time. At the same time, two parameters η, charging efficiency and discharging efficiency, are introduced. c , To accurately describe the energy conversion efficiency of the battery during the charging and discharging process.

[0119] In order to ensure that the energy storage device avoids overcharging or over-discharging, it is necessary to implement limiting measures on its state of charge (SOC). SOC is a quantitative indicator used to characterize the current energy state of the energy storage battery, and its value is defined as the ratio of the battery's instantaneous power to the rated capacity. Specifically, when SOC is equal to 1, it indicates that the battery is fully charged; and when SOC is equal to 0, it indicates that the battery is fully discharged. In order to prevent the energy storage battery from over-discharging, this embodiment stipulates that when the battery's state of charge drops to the preset allowable minimum charge threshold, its discharge process will be terminated. Similarly, to prevent the battery from overcharging, once the battery's state of charge reaches the preset allowable maximum charge threshold, its charging process will also stop.

[0120] The state of charge of the energy storage battery at any given point in time can be described by the following expression:

[0121]

[0122] Among them, SOC0 represents the initial state of charge of the energy storage system; η is the conversion efficiency, which indicates the efficiency of energy conversion during the battery charging and discharging process, and its value is between 0 and 1. ESS is the rated capacity of the battery; P(t) represents the charging and discharging power of the energy storage battery at time t (positive value represents charging, negative value represents discharging).

[0123] In order to maximize the service life of energy storage batteries, the following restrictions need to be implemented on the depth of discharge of the batteries:

[0124] SOC min ≤SOC≤SOC max

[0125] Among them, SOC min , SOC max They respectively represent the minimum state of charge and maximum state of charge limit values ​​allowed for the energy storage battery.

[0126] The energy storage system's charge and discharge logic judgment mechanism and the method for determining the off-peak charging amount, the energy storage device's charging period covers: the low electricity price period, the midday photovoltaic surplus period; and its discharge period includes: the peak electricity price period when the photovoltaic output cannot supply the load and the peak electricity price period at night when there is no photovoltaic output.

[0127] During the power valley period, the preset charge of the energy storage system can be periodically adjusted to a specific SOC level, which is in the range of 20% to 90%. When entering the peak power period during the day, if the photovoltaic power generation cannot meet the load demand, the energy storage system will start the discharge mode to supplement the power supply; if the photovoltaic power generation is balanced with the user's power demand, the energy storage system will be in standby mode; and when there is a surplus of photovoltaic power generation and it can be connected to the grid, the energy storage system will switch to charging mode to store excess power. During the daytime power level period, if there is no surplus of photovoltaic power generation connected to the grid, or it just meets the user's load demand, the energy storage system will continue to remain in standby mode; if there is a surplus of photovoltaic power generation connected to the grid, the energy storage system will also charge. During the peak power period at night, since photovoltaic power generation cannot be carried out, the energy storage system will start the discharge mode again. The energy storage system implements a "two-charge and two-discharge strategy", storing energy when the electricity price is low and discharging when the electricity price is high to meet the power demand.

[0128] Example 4

[0129] In step 3, an optimal configuration model for energy storage capacity of charging stations based on charging load and photovoltaic power generation output characteristics in Northeast China is constructed, specifically covering the following aspects:

[0130] 1) Setting the optimization objective function aims to ensure that the system performance indicators meet the standards while taking into account the initial fixed asset investment cost of the energy storage system, the cost of purchasing electricity from the power grid for the charging station, and the cost of reducing the loss of abandoned light due to the increase in the proportion of photovoltaic power use, all of which are included in the optimization considerations of the entire life cycle of the energy storage system;

[0131] (1) Regarding the initial deployment cost of the energy storage system:

[0132] In this objective function C1, the core components include the fixed investment purchase cost C of the charging station energy storage system. in and operation and maintenance costs due to seasonal changes C sea ;

[0133]

[0134] The specific parameters are defined as follows: Represents the investment cost per unit capacity of the energy storage system; W e is the rated capacity of the energy storage system; It refers to the charging and discharging cost per unit power of the energy storage system; P represents the rated charging and discharging power of the energy storage system.

[0135] (2) Regarding the power procurement and abandoned solar power loss costs of energy storage systems:

[0136] In the construction of objective function C2, the key components include the annual electricity procurement cost C of the charging station energy storage system ESS , and the cost of abandoned photovoltaic power reduced by increasing the proportion of photovoltaic power after considering the characteristics and utilization of regional photovoltaic resources. PV .

[0137]

[0138] In the expression, the following parameters are introduced: γ represents the cost coefficient of abandoned light, which is used to quantify the cost caused by the underutilization of photovoltaic power; P PV represents the actual active power, that is, the actual power output of the system; PPV,pre is the predicted active power of photovoltaic power generation, that is, the photovoltaic power output predicted based on historical data and models; π t , P g (t) respectively represent the interactive electricity price and average power between the large power grid and the system in the tth period, reflecting the price changes in the power market and the power exchange between the system and the large power grid; Δt is the duration of the tth period, which is used to calculate the cost or total energy exchange in each period. In addition, in order to more accurately reflect regional differences, a regional correction factor k can be introduced region , to adjust and optimize the cost-benefit analysis of energy storage systems.

[0139] In summary, the annual comprehensive operating cost of the integrated photovoltaic storage and charging station is determined as the minimization objective function, minC=min(C1+C2).

[0140] 2) Constraints:

[0141] (1) Balance of solar energy storage and charging power supply and demand:

[0142] P g (t)+P pv (t)+(1-β t ) ESS (t)+P ev (t) = 0

[0143] In the formula, the parameters are defined as follows: ev (t) represents the charging load power of the electric vehicle, that is, the power absorbed by the electric vehicle from the power grid or energy storage system during the charging process; P PV (t) represents the output power of the photovoltaic power generation system, that is, the power of the photovoltaic panel converting solar energy into electrical energy; P g(t) is the power distribution network, which reflects the power exchange between the charging station and the power system; β is the attenuation factor, whose value is less than 1, indicating the capacity attenuation ratio per unit time. t is the actual charging time, P ESS (t) represents the charging and discharging power of the energy storage battery, reflecting the power change of the battery system during the charging and discharging process. In addition, considering the stability and efficiency of the system operation, the following power fluctuation constraints need to be introduced: Considering the energy loss during the battery energy storage process, the power change of the energy storage battery needs to be constrained to ensure the healthy operation of the battery system and minimize energy loss. Specifically, the fluctuation range of the energy storage battery power should be limited to avoid overcharging, over-discharging, and frequent charging and discharging switching, thereby extending the battery life and improving the overall efficiency of the system.

[0144] (2) Confidence constraints:

[0145] In the design and operation of integrated photovoltaic storage and charging stations, there is an inherent trade-off between reliability and economic cost. To achieve an optimal balance between the two, it is necessary to reasonably control cost expenditure while ensuring a certain level of reliability. Specifically, this involves an accurate assessment of the actual operating reliability of the system and setting an acceptable minimum reliability threshold accordingly.

[0146] η≥η min

[0147] Where η is the actual confidence of the system; η min is the minimum confidence allowed by the system.

[0148] (3) Constraints on power fluctuations:

[0149] σ≤σ

[0150] max

[0151] In the expression, the following two key parameters are introduced to characterize the stability constraint of the system: σ represents the actual volatility of the system, which is a comprehensive indicator that reflects the instability of the overall power or energy flow of the system caused by the state changes of electric vehicle charging load, photovoltaic power generation output, distribution network power fluctuations, and energy storage batteries (considering the energy loss during charging and discharging); σ max It represents the maximum fluctuation rate allowed by the system. It is a threshold set according to system design requirements, operation strategies and safety specifications. It is used to ensure that the system operates within an acceptable range of instability to avoid impacting the power grid or affecting user experience.

[0152] Example 5

[0153] In step 4, an improved genetic algorithm is used to perform specific calculations, and the detailed process is described as follows:

[0154] 1) Implementation of coding strategy: At the beginning of the algorithm, the parameters of the problem to be solved are converted into gene string form through specific coding rules, laying the foundation for subsequent genetic operations.

[0155] 2) Initial population construction: An initial population consisting of N gene strings is constructed by random generation. Each gene string represents an independent individual (i.e., chromosome), and these individuals together constitute the initial search space of the algorithm.

[0156] 3) Fitness evaluation: For each individual in the population, the fitness is calculated based on the information it carries (i.e. the parameter value after decoding the gene string) to quantitatively evaluate the quality of the individual (or solution).

[0157] 4) Application of selection mechanism: Based on the fitness evaluation results, a selection strategy is used to screen out individuals with higher fitness from the current population, giving them a higher probability of participating in subsequent genetic operations as parents, thereby reflecting the principle of survival of the fittest in natural selection.

[0158] 5) Crossover operation execution: As one of the core operations of genetic algorithms, crossover simulates the information exchange process in the biological world to transfer the genetic information of parent individuals to offspring, thereby generating new individuals carrying the characteristics of the parent.

[0159] 6) Introduction of mutation operation: In order to increase the diversity of the population and prevent premature convergence, some individuals are randomly selected with a lower probability to perform mutation operation, that is, changing the value of a certain gene segment in their gene string.

[0160] In the implementation of the genetic algorithm in step 4, the objective function and constraints of the energy storage configuration optimization model are further integrated. For each unit in the system, the optimization strategy is used to calculate its fitness value (i.e., the objective function value), and a cycle of 8760 hours per year is set to simulate the operation of the photovoltaic storage and charging integrated charging station. The specific calculation steps are as follows:

[0161] 1) Input data preparation: First, input the historical data of light intensity and electric vehicle charging load.

[0162] 2) Setting the boundaries of decision variables: Next, clarify the value range of the decision variables, that is, the upper and lower limits of each parameter.

[0163] 3) Power calculation: Calculate the photovoltaic power generation power and electric vehicle charging load power according to the current lighting conditions and charging requirements.

[0164] 4) Application of energy management strategy: Based on the power characteristics of the energy storage battery, the photovoltaic power generation power and the charging load power are compared, and according to the preset energy exchange strategy, the charging and discharging power of the energy storage battery and the power absorbed from the distribution network are calculated.

[0165] 5) Electricity statistics: further calculate the charging and discharging electricity of the energy storage system, the electricity absorbed from the distribution network, and the charging electricity of electric vehicles.

[0166] 6) Energy storage power constraint processing: During the calculation process, the upper and lower limit constraints of the energy storage battery charging power are added to ensure the safe and stable operation of the system.

[0167] 7) Loop termination judgment: Finally, judge whether the current number of loops reaches or exceeds 8760 times. If so, the algorithm ends; otherwise, return to step 3) to continue the calculation of the next period.

[0168] A computer device comprising:

[0169] A processor; and a memory configured to store machine-readable instructions, which, when executed by the processor, execute the above-mentioned method for optimizing the energy storage configuration of a photovoltaic charging station taking into account photovoltaic output.

[0170] A storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned method for optimizing energy storage configuration of a photovoltaic charging station taking into account photovoltaic output.

[0171] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0172] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing energy storage configuration of a photovoltaic charging station considering photovoltaic output, characterized in that: include: Step 1: mathematically model the photovoltaic power generation capacity and charging load demand in the photovoltaic storage and charging integrated charging station; Step 2, analyzing the correlation between photovoltaic power generation output and charging load characteristics; Step 3: Based on the correlation between photovoltaic power generation output characteristics and charging load characteristics, a photovoltaic charging station energy storage configuration optimization model is constructed that comprehensively considers photovoltaic output and load demand; Step 4: Optimize and solve the energy storage configuration optimization model of the photovoltaic charging station.

2. A method for optimizing energy storage configuration of a photovoltaic charging station considering photovoltaic output according to claim 1, characterized in that In the step 1, The photovoltaic power output power is described by the following mathematical expression: In the formula, G t (t) represents the irradiance of the sun’s rays irradiating vertically to the surface of the photovoltaic panel, G ref Its value is set to 1000W / m; P PVN Indicates the rated output power of the photovoltaic cell module under standard test conditions (STC); η PV Refers to the conversion efficiency of the entire photovoltaic power generation system; η PVC represents the transmission efficiency of photovoltaic energy; T N is the battery reference temperature under standard test conditions, β PV is the temperature coefficient, with typical values ​​ranging from 0.004 to 0.006; T C Indicates the actual operating temperature of the battery.

3. A method for optimizing energy storage configuration of a photovoltaic charging station considering photovoltaic output according to claim 2, characterized in that The T C It is described by the following mathematical equation: Where, T ENV Refers to the ambient temperature; T N is the temperature of the solar panel under normal operating conditions, α is the temperature coefficient, and Δθ is the change in ambient temperature caused by seasonal changes.

4. The method for optimizing energy storage configuration of a photovoltaic charging station considering photovoltaic output according to claim 1 is characterized in that In step 1, the charging load is an electric vehicle charging load, and the electric vehicle charging load demand modeling includes the following steps: According to the time when the electric vehicle starts charging and its initial battery charge state, the initial charging condition of the electric vehicle is obtained; For each electric vehicle, the charging time is calculated based on its charging power, initial battery state of charge and target battery state of charge, and the charging end time is obtained based on the start time of charging; Count the number of electric vehicles being charged at each moment throughout the day, accumulate the charging power of all vehicles at that moment, and obtain the total charging load demand f of electric vehicles at that moment. T (t), In the formula, K i is the weight coefficient of the i-th time period, t is the time variable, where t i With t i+1 are the starting points of the i-th period and the i+1-th period respectively, α represents the temperature coefficient, and Δθ represents the change in ambient temperature caused by seasonal changes.

5. A method for optimizing energy storage configuration of a photovoltaic charging station considering photovoltaic output according to claim 4, characterized in that The initial battery charge state of the electric vehicle is a log-normal distribution, that is, its probability density function f s (s0) is: Charging duration of electric vehicle t EV for: Where, its logarithmic mean is μ, and its logarithmic standard deviation is σ; the battery state of charge at the end of charging is represented by S1, the battery capacity of the car is Q, and the charging power of the electric car is P EV , and its charging efficiency is represented by η.

6. According to the method for optimizing energy storage configuration of a photovoltaic charging station taking into account photovoltaic output as described in claim 1, in said step 2, Energy storage battery charging status: E(t)=E(t-1)(1-δ)(1-β) t )+P c (t)h c ΔT Energy storage battery discharge status: Charge and discharge power constraints: 0≤P c (t)≤P cmax 0≤P D (t)≤P Dmax Where E(t) is the total accumulated energy of the energy storage system in a specific time period; δ is the inherent self-discharge rate of the energy storage system; P c (t), P D (t) is the attenuation factor of the charging power and discharging power of the energy storage system in the period t, β is the attenuation factor, and its value is less than 1, indicating the capacity attenuation ratio per unit time; t is the number of charging times or actual time, η c , is the charging efficiency and the discharging efficiency; The battery state of charge (SOC) is used to characterize the current energy state of the energy storage battery. Its value is defined as the ratio of the battery's instantaneous power to its rated capacity. When SOC is equal to 1, it indicates that the battery is fully charged; and when SOC is equal to 0, it indicates that the battery is fully discharged. To prevent the energy storage battery from over-discharging, the discharge process is terminated when the battery state of charge drops to the preset minimum charge threshold. To prevent the battery from overcharging, the charging process is also stopped once the battery state of charge reaches the preset maximum charge threshold. The state of charge of the energy storage battery at any given point in time is described by the following expression: Among them, SOC0 represents the initial state of charge of the energy storage system; η is the conversion efficiency, which indicates the efficiency of energy conversion during the battery charging and discharging process, and its value is between 0 and 1; E ESS is the rated capacity of the battery; P(t) represents the charging and discharging power of the energy storage battery at time t; In order to maximize the service life of the energy storage battery, the following restrictions are implemented on the discharge depth of the battery: SOC min ≤SOC≤SOC max Among them, SOC min , SOC max They respectively represent the minimum state of charge and maximum state of charge limit values ​​allowed for the energy storage battery.

7. The method for optimizing the configuration of energy storage capacity of a charging station taking into account photovoltaic output and load characteristics according to claim 1, characterized in that In step 3, a photovoltaic charging station energy storage configuration optimization model is constructed that comprehensively considers photovoltaic output and load demand, including objective functions and constraints. 1) The annual comprehensive operating cost of the photovoltaic storage charging station is determined as the minimization objective function, namely: minC=min(C1+C2); C1 is the initial deployment cost of the energy storage system, which includes the fixed investment cost C of the charging station energy storage system. in and operation and maintenance costs due to seasonal changes C sea ; The specific parameters are defined as follows: Represents the investment cost per unit capacity of the energy storage system; W e is the rated capacity of the energy storage system; Refers to the charging and discharging cost per unit power of the energy storage system; P represents the rated charging and discharging power of the energy storage system; The C2 is the power purchase and abandoned light loss cost of the energy storage system, which includes the annual power purchase cost C of the charging station energy storage system. ESS , and the cost of abandoned photovoltaic power reduced by increasing the proportion of photovoltaic power after considering the characteristics and utilization of regional photovoltaic resources. PV ; Specific parameters: γ represents the cost coefficient of abandoned light, which is used to quantify the cost caused by the underutilization of photovoltaic power; P PV Represents actual active power, that is, the actual power output of the system; P PV、pre The predicted active power of photovoltaic power generation, that is, the photovoltaic power output predicted based on historical data and models; π t , P g (t) represents the interactive electricity price and average power between the large power grid and the system in the tth period, reflecting the price changes in the power market and the power exchange between the system and the large power grid; Δt is the duration of the tth period, which is used to calculate the cost or total amount of energy exchange in each period; Regional correction factor k region , to adjust and optimize the cost-benefit analysis of energy storage systems; 2) Constraints include solar storage and charging power supply and demand balance constraints, confidence constraints, and power fluctuation constraints. (1) Solar energy storage and charging power supply and demand balance constraints: P g (t)+P pv (t)+(1-β t )P ESS (t)+P ev (t)=0 Where P ev (t) represents the charging load power of the electric vehicle; P PV (t) represents the output power of the photovoltaic power generation system, that is, the power of the photovoltaic panel converting solar energy into electrical energy; P g (t) is the power of the distribution network; β is the attenuation factor, whose value is less than 1, indicating the capacity attenuation ratio per unit time; t is the actual charging time, P ESS (t) represents the charging and discharging power of the energy storage battery; (2) Confidence constraints: η≥η min In the formula, η is the actual confidence of the system; η min is the minimum confidence allowed by the system; (3) Constraints on power fluctuations: σ≤σ max In the formula, σ represents the actual volatility of the system, σ max Indicates the maximum volatility allowed by the system.

8. The method for optimizing energy storage configuration of a photovoltaic charging station considering photovoltaic output according to claim 1 is characterized in that In step 4, a cycle of 8760 hours is set in one year to simulate the operation of the integrated photovoltaic storage and charging station, as follows: Step 1), input historical data of light intensity and electric vehicle charging load; Step 2), clarify the value range of the decision variables, that is, the upper and lower limits of each parameter; Step 3), according to the current lighting conditions and charging requirements, respectively calculate the photovoltaic power generation power and the electric vehicle charging load power; Step 4), compare the size of photovoltaic power generation and charging load power, and calculate the charging and discharging power of the energy storage battery and the power absorbed from the distribution network according to the preset energy exchange strategy; Step 5), further calculating the charge and discharge power of the energy storage system, the power absorbed from the distribution network, and the charging power of the electric vehicle; Step 6), during the calculation process, add upper and lower limit constraints of the energy storage battery charging capacity; Step 7), determine whether the current number of cycles reaches or exceeds 8760 times. If so, the algorithm ends; otherwise, return to step 3) to continue the calculation of the next period.

9. A computer device comprising: processor; as well as A memory configured to store machine-readable instructions, which, when executed by the processor, execute the method for optimizing energy storage configuration of a photovoltaic charging station taking into account photovoltaic output as described in any one of claims 1-8.

10. A storage medium storing a computer program, wherein the computer program is executed by a processor according to the method for optimizing energy storage configuration of a photovoltaic charging station taking into account photovoltaic output as described in any one of claims 1 to 8.

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