Charging station energy storage capacity configuration optimization method considering photovoltaic output and load characteristics

Abstract

The invention relates to a charging station energy storage capacity configuration optimization method considering photovoltaic output and load characteristics, and the method comprises the following steps: carrying out the refined modeling calculation of the photovoltaic power generation output and charging load of a light storage and charging integrated charging station through the smooth characteristic and light storage complementary characteristic of the output power of a light storage system; the correlation between photovoltaic output and load characteristics is considered, and correlation modeling analysis is performed on the photovoltaic output and the load characteristics so as to support configuration optimization analysis of the photovoltaic storage and charging integrated charging station; on the basis of photovoltaic power generation characteristics and load characteristics, constructing a charging station energy storage capacity configuration optimization model considering charging load and photovoltaic power generation output characteristics; and performing optimization solution on the charging station energy storage capacity configuration optimization model in the step 2 by using an improved weed intrusion algorithm. 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 relates to a method for optimizing energy storage capacity configuration, and in particular to a method for optimizing energy storage capacity configuration of a charging station taking into account photovoltaic output and load characteristics. Background Art

[0002] The "photovoltaic storage and charging" mode can achieve the optimal comprehensive application of electric energy. The photovoltaic system converts solar energy into electric energy, and the energy storage system reduces the electricity cost of the charging station by formulating the optimal charging and discharging strategy. The advantages of the "photovoltaic storage and charging" integration are obvious. This form effectively alleviates the impact of charging peak on the power grid.

[0003] Many provinces and cities in my country implement peak-valley time-of-use electricity price policies according to the load conditions on the distribution network side. That is, 24 hours are divided into peak hours, valley hours and normal hours according to the distribution of electricity load within a day. Each electricity consumption period has its corresponding electricity price. If the photovoltaic power generation system directly connects the generated electricity to the power grid, its economic efficiency will be greatly reduced.

[0004] In addition, since the average daily mileage and initial charging time of electric vehicles are independent of each other and have great uncertainty, it is very difficult to build a high-precision charging load prediction model. Currently, there is still a lack of models that can accurately predict the charging load of electric vehicles.

[0005] Based on the randomness and intermittency of photovoltaic power generation output and charging load of the integrated photovoltaic storage and charging charging station, the two have typical probability distribution characteristics. Both photovoltaic power generation output and charging load change over time, and are not only affected by objective conditions such as light, seasonal climate environment, geography, time, etc., but also restricted by the charging load of the charging station. The correlation analysis modeling between the two is a multi-coupling and multi-influencing factor problem. The accuracy of the model will affect the optimization results of the charging station configuration; therefore, how to construct a mathematical model of photovoltaic output and load characteristics is a technical problem that this field has always been eager to solve. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides a method for optimizing the configuration of energy storage capacity of a charging station taking into account photovoltaic output and load characteristics, thereby solving the problem in the prior art that it is difficult to construct a mathematical model of photovoltaic output and load characteristics.

[0007] To achieve the above objectives, the present invention provides a method for optimizing the configuration of energy storage capacity of a charging station taking into account photovoltaic output and load characteristics, comprising the following steps:

[0008] Step 1: Using the smoothness of the output power of the photovoltaic storage system and the complementary characteristics of photovoltaic storage, the photovoltaic power generation output and charging load of the photovoltaic storage charging integrated charging station are refinedly modeled and calculated; considering the correlation between photovoltaic output and load characteristics, the correlation modeling analysis of the two is carried out to support the configuration optimization analysis of the photovoltaic storage charging integrated charging station;

[0009] Step 2: Based on the characteristics of photovoltaic power generation and load, a charging station energy storage capacity configuration optimization model is constructed that takes into account the characteristics of charging load and photovoltaic power generation output;

[0010] Step 3: Optimize and solve the charging station energy storage capacity configuration optimization model described in step 2 above using an improved weed invasion algorithm.

[0011] In step 1, according to the regulation mode of the energy storage system, the charging and discharging power and the amount of electricity present a dynamic change process over time, and the following relationship should be satisfied during the change process:

[0012] Energy storage battery charging status:

[0013] E(t)=E(t-1)(1-δ)+P ch (t)η ch ΔT (10)

[0014] Energy storage battery discharge status:

[0015]

[0016] Charge and discharge power constraints:

[0017] 0≤P ch (t)≤P ch,max (12)

[0018] 0≤P dis (t)≤P dis,max (13)

[0019] Where, E(t) is the total energy of the energy storage system in time period t; δ is the self-discharge rate of the energy storage system; P ch (t), P dis (t) are the charging and discharging power of the energy storage in period t; η ch , are the charging and discharging efficiency of the battery respectively;

[0020] To prevent the energy storage from being overcharged or over-discharged, its state of charge needs to be limited. SOC (Stage of Charge) is used to reflect the current state of the energy storage battery. Its value is expressed as: the ratio of the current battery power to the rated capacity of the battery; SOC = 1 means that the battery is fully charged, and SOC = 0 means that the battery is completely discharged. In order to prevent the energy storage battery from being over-discharged, it is stipulated here that the battery state of charge is reduced to the minimum allowable state of charge SOC min When the energy storage battery stops discharging, it is required to reach the maximum allowable state of charge SOC in order to prevent the energy storage battery from being overcharged. max When the battery is charged, it will stop charging.

[0021] Then the state of charge of the energy storage battery at any time can be expressed as:

[0022]

[0023] Among them, SOC0 is the initial state of the energy storage system; E ba is the rated capacity of the battery; P(t) is the charging and discharging power of the energy storage battery at time t;

[0024] In order to extend the life of the energy storage battery as much as possible, its discharge depth needs to be limited as follows:

[0025] SOC min ≤SOC≤SOC max (15)

[0026] Among them, SOC min , SOC max They are the minimum and maximum state of charge values ​​of the energy storage battery respectively.

[0027] In step 1, the Monte Carlo method is used to randomly extract the initial state of charge (SOC) of the electric vehicle, and the sampling interval of the initial charging time is compressed according to the condition of the charging time, the initial charging time is obtained and the charging load is calculated, and all charging loads are accumulated to obtain the total charging load; the specific steps of simulating the charging load of the electric vehicle are as follows:

[0028] (1) Initialize data, enter the parameters of the vehicle's initial state of charge (SOC), vehicle mileage, and parking time in the original state, and determine the simulation time;

[0029] (2) According to the probability density function, the vehicle's initial SOC and travel distance information are extracted to calculate the travel time and the vehicle battery SOC value data at the corresponding time; according to the travel distance probability density function, the vehicle's next travel distance is generated, and these two factors are used to determine the charging method and charging power requirement parameters selected by the current vehicle;

[0030] (3) Calculate the spatiotemporal distribution of the charging power demand of the next electric vehicle, and accumulate the load data of each vehicle one by one to form the current regional charging load demand;

[0031] (4) Determine whether the set time limit has been reached. If so, calculate the relevant charging load data and indicators to generate the final electric vehicle charging load curve and end the operation; otherwise, return to step (2) and continue to cycle the above operations until the last car is reached.

[0032] In step 2, a charging station energy storage capacity configuration optimization model taking into account the charging load and photovoltaic power generation output characteristics is constructed, including the following steps:

[0033] (1) Determine the objective function. On the premise of meeting the system performance indicators, the fixed investment cost of the energy storage system, the cost of purchasing electricity from the power grid for the charging station, and the reduced cost of abandoned photovoltaic power after increasing the proportion of photovoltaic electricity use are used as the optimization goals within the life cycle of the energy storage system;

[0034] 1) Initial annual operation cost of energy storage system,

[0035] The objective function C1 mainly includes the fixed investment purchase cost C of the charging station energy storage system in and operating cost C op ;

[0036]

[0037] In the formula is the investment cost per unit capacity of the energy storage system; W e is the rated capacity of the energy storage system; P is the charging and discharging cost per unit power of the energy storage system; e is the rated charging and discharging power of the energy storage system;

[0038] 2) The cost of purchasing electricity and the cost of abandoning solar power for energy storage systems,

[0039] The objective function C2 mainly includes the annual electricity purchase cost C of the charging station energy storage system EC And the cost of abandoned light after increasing the proportion of photovoltaic electricity consumption C AP ;

[0040]

[0041] In the formula, is the cost coefficient of abandoned light; P PV is the actual active power; P PV、pre is the predicted active power of photovoltaic power generation; π t , P g (t) are the interaction price and average power between the large power grid and the system in the tth period; Δt is the duration of the tth period;

[0042] In summary, under the objective function, the annual operating cost of the integrated photovoltaic storage and charging station is:

[0043] minC=min(C1+C2) (18)

[0044] (2) Determine the constraints,

[0045] 1) System power supply and demand balance,

[0046] P g (t)+P pv (t)+P ev (t)+P ba (t)=0 (19)

[0047] Where P ev (t) is the charging load power of electric vehicles; P pv (t) is the photovoltaic power generation power; P g (t) is the power of the distribution network; P ba (t) is the power of the energy storage battery;

[0048] 2) Constraints on power fluctuations,

[0049] α≤α max (20)

[0050] In the formula, α is the actual volatility of the system; α max is the maximum volatility allowed by the system;

[0051] 3) Confidence constraints,

[0052] The reliability and cost of charging stations constrain each other. It is necessary to achieve a balance between cost and performance at a certain reliability level.

[0053] η≥η min (twenty one)

[0054] Where η is the actual confidence of the system; η min is the minimum confidence allowed by the system.

[0055] In step 3, the improved weed invasion algorithm has the following specific calculation process:

[0056] 1) Using Logistic mapping expression, the chaotic inversion is initialized to generate chaotic variables;

[0057] δ i+1 =uδ i (1-δ i ) (twenty two)

[0058] In the formula, u is the attractor; when the system enters a chaotic state, the chaotic variables will generate chaotic weeds according to the following formula;

[0059] x ij =x min,j +δ ij (x max,j -x min,j ) (twenty three)

[0060] In the formula, x min,j is the minimum value of the search space, x max,j To find the maximum value of the search space, we combine it with the reverse learning technology to find a solution, get its inverse solution, compare it with the inverse solution, select the optimal solution, and finally get the initialized population;

[0061] 2) The fitness value of each weed is the numerical calculation of the objective function, and reproduction is based on the fitness value;

[0062]

[0063] Where f is the current adaptation value of weeds, f min is the minimum fitness value of weeds, f max is the maximum adaptation value of weeds; s min 、s max Producing seed minima and maxima for propagation with weeds;

[0064] 3) Differential evolution strategy, which processes the weed propagation seed mutation operation as follows:

[0065] U i =X r1 +F(X r2 -X r3 ) (25)

[0066] In the formula, X r1 , X r2 , X r3 are seeds propagated by three different weeds; F is the scaling factor;

[0067] 4) The law of competition for survival: when the population reaches its maximum size, the fittest will survive according to their fitness;

[0068] 5) Repeat steps 2)-4) until the optimal solution condition is reached or the maximum number of iterations is reached.

[0069] In step 3, according to the implementation process of the weed invasion algorithm, the objective function and constraints of the energy storage configuration optimization model are added to the entire operation process, and the differential evolution strategy is used to calculate the fitness value of each weed unit, that is, the objective function value. With 8760 as the number of cycles per year, the operation data of the integrated photovoltaic storage and charging station per hour in a year is calculated. The calculation process is as follows:

[0070] 1) Input light and charging load data;

[0071] 2) Input the upper and lower limit data of the decision variables;

[0072] 3) Calculate the photovoltaic power generation power P at this moment pv (t) and the electric vehicle charging load power P ev (t);

[0073] 4) Consider the energy storage battery power P ba (t) Actual situation, compare photovoltaic power generation power P pv (t) and charging load power P ev (t) size, according to the energy exchange strategy, calculate the energy storage charging and discharging power and the power P absorbed from the distribution network g (t);

[0074] 5) Calculate the charging and discharging power of the energy storage system, the power absorbed from the distribution network, and the charging power of electric vehicles;

[0075] 6) Add upper and lower limits for energy storage battery charging capacity;

[0076] 7) Determine whether the number of loops is greater than or equal to 8760. If so, end the process. If not, return to step 3).

[0077] The advantages and effects of the present invention are as follows:

[0078] The present invention studies the output characteristics of photovoltaic power generation, the charging and discharging model of the energy storage system, and the charging load requirements of electric vehicles, and constructs a charging station energy storage capacity configuration optimization model that takes into account the photovoltaic output and load characteristics; effectively solves the difficulties in the management and operation of the photovoltaic storage and charging energy system, expands and transforms the existing photovoltaic storage and charging energy system, improves the energy utilization efficiency of the photovoltaic storage and charging system, and reduces the supply pressure of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a flowchart of the present invention;

[0080] Figure 2 It is a schematic diagram of the applicable conditions of different charge states of the energy storage system of the present invention;

[0081] Figure 3 A flow chart of the Monte Carlo simulation of an electric vehicle charging load model of the present invention;

[0082] Figure 4 It is a schematic diagram of the distribution curve of the charging load simulation result of the present invention;

[0083] Figure 5 The figure is a schematic diagram of the operation flow of the improved differential evolution invasive weed algorithm of the present invention;

[0084] Figure 6 It is a schematic diagram of the process of solving the objective function of the photovoltaic storage model of the present invention. DETAILED DESCRIPTION

[0085] 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.

[0086] Example

[0087] The method for optimizing the configuration of energy storage capacity of a charging station taking into account photovoltaic output and load characteristics of the present invention comprises the following steps:

[0088] Step 1: Using the smoothness of the output power of the photovoltaic storage system and the complementary characteristics of photovoltaic storage, the photovoltaic power generation output and charging load of the photovoltaic storage charging integrated charging station are refinedly modeled and calculated; considering the correlation between photovoltaic output and load characteristics, the correlation modeling analysis of the two is carried out to support the configuration optimization analysis of the photovoltaic storage charging integrated charging station;

[0089] Step 2: Based on the characteristics of photovoltaic power generation and load, a charging station energy storage capacity configuration optimization model is constructed that takes into account the characteristics of charging load and photovoltaic power generation output;

[0090] Step 3: Optimize and solve the charging station energy storage capacity configuration optimization model described in step 2 above using an improved weed invasion algorithm.

[0091] In step 1, according to the regulation mode of the energy storage system, the charging and discharging power and the amount of electricity present a dynamic change process over time, and the following relationship should be satisfied during the change process:

[0092] Energy storage battery charging status:

[0093] E(t)=E(t-1)(1-δ)+P ch (t)η ch ΔT (10)

[0094] Energy storage battery discharge status:

[0095]

[0096] Charge and discharge power constraints:

[0097] 0≤P ch (t)≤P ch,max (12)

[0098] 0≤P dis (t)≤P dis,max (13)

[0099] Where, E(t) is the total energy of the energy storage system in time period t; δ is the self-discharge rate of the energy storage system; P ch (t), P dis (t) are the charging and discharging power of the energy storage in period t; η ch , are the charging and discharging efficiency of the battery respectively;

[0100] To prevent the energy storage from being overcharged or over-discharged, its state of charge needs to be limited. SOC (Stage of Charge) is used to reflect the current state of the energy storage battery. Its value is expressed as: the ratio of the current battery power to the rated capacity of the battery; SOC = 1 means that the battery is fully charged, and SOC = 0 means that the battery is completely discharged. In order to prevent the energy storage battery from being over-discharged, it is stipulated here that the battery state of charge is reduced to the minimum allowable state of charge SOC min When the energy storage battery stops discharging, it is required to reach the maximum allowable state of charge SOC in order to prevent the energy storage battery from being overcharged. max When the battery is charged, it will stop charging.

[0101] Then the state of charge of the energy storage battery at any time can be expressed as:

[0102]

[0103] Among them, SOC0 is the initial state of the energy storage system; E ba is the rated capacity of the battery; P(t) is the charging and discharging power of the energy storage battery at time t;

[0104] In order to extend the life of the energy storage battery as much as possible, its discharge depth needs to be limited as follows:

[0105] SOC min ≤SOC≤SOC max (15)

[0106] Among them, SOC min , SOC max They are the minimum and maximum state of charge values ​​of the energy storage battery respectively.

[0107] In step 1, the Monte Carlo method is used to randomly extract the initial state of charge (SOC) of the electric vehicle, and the sampling interval of the initial charging time is compressed according to the condition of the charging time, the initial charging time is obtained and the charging load is calculated, and all charging loads are accumulated to obtain the total charging load; the specific steps of simulating the charging load of the electric vehicle are as follows:

[0108] (1) Initialize the data and input the parameters of the initial state of charge (SOC), vehicle mileage, and parking time of the vehicle in its original state to determine the simulation time;

[0109] (2) According to the probability density function, the vehicle's initial SOC and travel distance information are extracted to calculate the travel time and the vehicle battery SOC value data at the corresponding time; according to the travel distance probability density function, the vehicle's next travel distance is generated, and these two factors are used to determine the charging method and charging power requirement parameters selected by the current vehicle;

[0110] (3) Calculate the spatiotemporal distribution of the charging power demand of the next electric vehicle, and accumulate the load data of each vehicle one by one to form the current regional charging load demand;

[0111] (4) Determine whether the set time limit has been reached. If so, calculate the relevant charging load data and indicators to generate the final electric vehicle charging load curve and end the operation; otherwise, return to step (2) and continue to cycle the above operations until the last car is reached.

[0112] In step 2, a charging station energy storage capacity configuration optimization model taking into account the charging load and photovoltaic power generation output characteristics is constructed, including the following steps:

[0113] (1) Determine the objective function. On the premise of meeting the system performance indicators, the fixed investment cost of the energy storage system, the cost of purchasing electricity from the power grid for the charging station, and the reduced cost of abandoned photovoltaic power after increasing the proportion of photovoltaic electricity use are used as the optimization goals within the life cycle of the energy storage system;

[0114] 1) Initial annual operation cost of energy storage system,

[0115] The objective function C1 mainly includes the fixed investment purchase cost C of the charging station energy storage system in and operating cost C op ;

[0116]

[0117] In the formula is the investment cost per unit capacity of the energy storage system; W e is the rated capacity of the energy storage system; P is the charging and discharging cost per unit power of the energy storage system; e is the rated charging and discharging power of the energy storage system;

[0118] 2) The cost of purchasing electricity and the cost of abandoning solar power for energy storage systems,

[0119] The objective function C2 mainly includes the annual electricity purchase cost C of the charging station energy storage system EC And the cost of abandoned light after increasing the proportion of photovoltaic electricity consumption C AP ;

[0120]

[0121] In the formula, is the cost coefficient of abandoned light; P PV is the actual active power; P PV、pre is the predicted active power of photovoltaic power generation; π t , P g (t) are the interaction price and average power between the large power grid and the system in the tth period; Δt is the duration of the tth period;

[0122] In summary, under the objective function, the annual operating cost of the integrated photovoltaic storage and charging station is:

[0123] minC=min(C1+C2) (18)

[0124] (2) Determine the constraints,

[0125] 1) System power supply and demand balance,

[0126] P g (t)+P pv (t)+P ev (t)+P ba (t)=0 (19)

[0127] Where P ev (t) is the charging load power of electric vehicles; P pv (t) is the photovoltaic power generation power; P g (t) is the power of the distribution network; P ba (t) is the power of the energy storage battery;

[0128] 2) Constraints on power fluctuations,

[0129] α≤α max (20)

[0130] In the formula, α is the actual volatility of the system; α max is the maximum volatility allowed by the system;

[0131] 3) Confidence constraints,

[0132] The reliability and cost of charging stations constrain each other. It is necessary to achieve a balance between cost and performance at a certain reliability level.

[0133] η≥η min (twenty one)

[0134] Where η is the actual confidence of the system; η min is the minimum confidence allowed by the system.

[0135] In step 3, the improved weed invasion algorithm has the following specific calculation process:

[0136] 1) Using the Logistic mapping expression, the chaotic inversion is initialized to generate chaotic variables;

[0137] δ i+1 =uδ i (1-δ i ) (twenty two)

[0138] In the formula, u is the attractor; when the system enters a chaotic state, the chaotic variables will generate chaotic weeds according to the following formula;

[0139] x ij =x min,j +δ ij (x max,j -x min,j ) (twenty three)

[0140] In the formula, x min,j is the minimum value of the search space, x max,j To find the maximum value of the search space, we combine it with the reverse learning technology to find a solution, get its inverse solution, compare it with the inverse solution, select the optimal solution, and finally get the initialized population;

[0141] 2) The fitness value of each weed is the numerical calculation of the objective function, and reproduction is based on the fitness value;

[0142]

[0143] Where f is the current adaptation value of weeds, f min is the minimum fitness value of weeds, f max is the maximum adaptation value of weeds; s min 、s max Producing seed minima and maxima for propagation with weeds;

[0144] 3) Differential evolution strategy, which processes the weed propagation seed mutation operation as follows:

[0145] U i =X r1 +F(X r2 -X r3 ) (25)

[0146] In the formula, X r1 , X r2 , X r3 are seeds propagated by three different weeds; F is the scaling factor;

[0147] 4) The law of competition for survival: when the population reaches its maximum size, the fittest will survive according to their fitness;

[0148] 5) Repeat steps 2)-4) until the optimal solution condition is reached or the maximum number of iterations is reached.

[0149] In step 3, according to the implementation process of the weed invasion algorithm, the objective function and constraints of the energy storage configuration optimization model are added to the entire operation process, and the differential evolution strategy is used to calculate the fitness value of each weed unit, that is, the objective function value. With 8760 as the number of cycles per year, the operation data of the integrated photovoltaic storage and charging station per hour in a year is calculated. The calculation process is as follows:

[0150] 1) Input light and charging load data;

[0151] 2) Input the upper and lower limit data of the decision variables;

[0152] 3) Calculate the photovoltaic power generation power P at this moment pv (t) and the electric vehicle charging load power P ev (t);

[0153] 4) Consider the energy storage battery power P ba (t) Actual situation, compare photovoltaic power generation power P pv (t) and charging load power P ev (t) size, according to the energy exchange strategy, calculate the energy storage charging and discharging power and the power P absorbed from the distribution network g (t);

[0154] 5) Calculate the charging and discharging power of the energy storage system, the power absorbed from the distribution network, and the charging power of electric vehicles;

[0155] 6) Add upper and lower limits for energy storage battery charging capacity;

[0156] 7) Determine whether the number of loops is greater than or equal to 8760. If so, end the process. If not, return to step 3).

[0157] In order to test the feasibility of the established charging load prediction model, 100 private electric vehicles are used as an example to simulate the charging load. The battery capacity of the electric vehicle is 35kWh, and it is charged once a day. The charging time follows the normal distribution N (17.6, 3.4). The conventional charging power is 7kW, the fast charging power is 30kW, the vehicle charging efficiency is 90%, and the vehicle power consumption per 100 kilometers is 16kW. According to the above method and Figure 4-5 The charging process is simulated, and the results are as follows Figure 4 shown.

[0158] From the load distribution curve, it can be seen that the charging load of private cars in this area starts to rise from 7:00 and gradually reaches the peak at around 18:30, with the maximum peak being about 130kW. At the same time, most private cars are in charging state during the period of 15:00-22:00. This is consistent with the charging habits of private cars in actual use, indicating that the trend of the prediction results is consistent with the actual situation.

[0159] 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.

[0160] 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 the energy storage capacity configuration of a charging station taking into account photovoltaic output and load characteristics, characterized in that The steps include: Step 1: Using the smoothness of the output power of the photovoltaic storage system and the complementary characteristics of photovoltaic storage, the photovoltaic power generation output and charging load of the photovoltaic storage charging integrated charging station are refinedly modeled and calculated; considering the correlation between photovoltaic output and load characteristics, the correlation modeling analysis of the two is carried out to support the configuration optimization analysis of the photovoltaic storage charging integrated charging station; Step 2: Based on the characteristics of photovoltaic power generation and load, a charging station energy storage capacity configuration optimization model is constructed that takes into account the characteristics of charging load and photovoltaic power generation output; Step 3: Optimize and solve the charging station energy storage capacity configuration optimization model described in step 2 above using an improved weed invasion algorithm.

2. 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 1, according to the regulation mode of the energy storage system, the charging and discharging power and the amount of electricity present a dynamic change process over time, and the following relationship should be satisfied during the change process: Energy storage battery charging status: E(t)=E(t-1)(1-δ)+P ch (t)h ch △T (10) Energy storage battery discharge status: Charge and discharge power constraints: 0≤P ch (t)≤P ch，max (12)0≤P dis (t)≤P dis，max (13) Where, E(t) is the total energy of the energy storage system in time period t; δ is the self-discharge rate of the energy storage system; P ch (t), P dis (t) are the charging and discharging power of the energy storage in period t respectively; η ch , They are the charging and discharging efficiency of the battery respectively: To prevent the energy storage from being overcharged or over-discharged, its state of charge needs to be limited. SOC (Stage of Charge) is used to reflect the current state of the energy storage battery. Its value is expressed as: the ratio of the current battery power to the rated capacity of the battery; SOC = 1 means that the battery is fully charged, and SOC = 0 means that the battery is completely discharged. In order to prevent the energy storage battery from being over-discharged, it is stipulated here that the battery state of charge is reduced to the minimum allowable state of charge SOC min When the energy storage battery stops discharging, it is required to reach the maximum allowable state of charge SOC in order to prevent the energy storage battery from being overcharged. max When the battery is charged, it will stop charging. Then the state of charge of the energy storage battery at any time can be expressed as: Among them, SOC0 is the initial state of the energy storage system; E ba is the rated capacity of the battery; P(t) is the charging and discharging power of the energy storage battery at time t; In order to extend the life of the energy storage battery as much as possible, its discharge depth needs to be limited as follows: SOC min ≤SOC≤SOC max (15) Among them, SOC min , SOC max They are the minimum and maximum state of charge values ​​of the energy storage battery respectively.

3. 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 1, the Monte Carlo method is used to randomly extract the initial state of charge (SOC) of the electric vehicle, and the sampling interval of the initial charging time is compressed according to the condition of the charging time, the initial charging time is obtained and the charging load is calculated, and all charging loads are accumulated to obtain the total charging load; the specific steps of simulating the charging load of the electric vehicle are as follows: (1) Initialize data, enter the parameters of the vehicle's initial state of charge (SOC), vehicle mileage, and parking time in the original state, and determine the simulation time; (2) According to the probability density function, the vehicle's initial SOC and travel distance information are extracted to calculate the travel time and the vehicle battery SOC value data at the corresponding time; according to the travel distance probability density function, the vehicle's next travel distance is generated, and these two factors are used to determine the charging method and charging power requirement parameters selected by the current vehicle; (3) Calculate the spatiotemporal distribution of the charging power demand of the next electric vehicle, and accumulate the load data of each vehicle one by one to form the current regional charging load demand; (4) Determine whether the set time limit has been reached. If so, calculate the relevant charging load data and indicators to generate the final electric vehicle charging load curve and end the operation; otherwise, return to step (2) and continue to cycle the above operations until the last car.

4. 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 2, a charging station energy storage capacity configuration optimization model taking into account the charging load and photovoltaic power generation output characteristics is constructed, including the following steps: (1) Determine the objective function. On the premise of meeting the system performance indicators, the fixed investment cost of the energy storage system, the cost of purchasing electricity from the power grid for the charging station, and the reduced cost of abandoned photovoltaic power after increasing the proportion of photovoltaic electricity use are used as the optimization goals within the life cycle of the energy storage system; 1) Initial annual operation cost of energy storage system, The objective function C1 mainly includes the fixed investment purchase cost C of the charging station energy storage system in and operating cost C op ; In the formula is the investment cost per unit capacity of the energy storage system; W e is the rated capacity of the energy storage system; P is the charging and discharging cost per unit power of the energy storage system; e is the rated charging and discharging power of the energy storage system; 2) The cost of purchasing electricity and the cost of abandoning solar power for energy storage systems, The objective function C2 mainly includes the annual electricity purchase cost C of the charging station energy storage system EC And the cost of abandoned photovoltaic power after increasing the proportion of photovoltaic power consumption c AP ; In the formula, is the cost coefficient of abandoned light; P PV is the actual active power; P PV、pre is the predicted active power of photovoltaic power generation; π t , P g (t) are the interaction price and average power between the large power grid and the system in the tth period; △t is the duration of the tth period; In summary, under the objective function, the annual operating cost of the integrated photovoltaic storage and charging station is: minC=min(C1+C2) (18) (2) Determine the constraints, 1) System power supply and demand balance, P g (t)+P pv (t)+P ev (t)+P ba (t)=0 (19) Where P ev (t) is the charging load power of electric vehicles; P pv (t) is the photovoltaic power generation power; P g (t) is the power of the distribution network; P ba (t) is the power of the energy storage battery; 2) Constraints on power fluctuations, α≤α max (20) In the formula, α is the actual volatility of the system; α max is the maximum volatility allowed by the system; 3) Confidence constraints, The reliability and cost of charging stations constrain each other. It is necessary to achieve a balance between cost and performance at a certain reliability level. η≥η min (21) Where η is the actual confidence of the system; η min is the minimum confidence allowed by the system.

5. 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, the improved weed invasion algorithm has the following specific calculation process: 1) Using the Logistic mapping expression, the chaotic inversion is initialized to generate chaotic variables; d i+1 =uδ i (1-d i ) (22) In the formula, u is the attractor; when the system enters a chaotic state, the chaotic variables will generate chaotic weeds according to the following formula; x ij =x min，j +δ ij (x max，j -x min，j ) (23) Where, X min,j is the minimum value of the search space, X max，j To find the maximum value of the search space, we combine it with the reverse learning technology to find a solution, get its inverse solution, compare it with the inverse solution, select the optimal solution, and finally get the initialized population; 2) The fitness value of each weed is the numerical calculation of the objective function, and reproduction is based on the fitness value; Where f is the current adaptation value of weeds, f min is the minimum fitness value of weeds, f max is the maximum adaptation value of weeds; s min 、s max Producing seed minima and maxima for propagation with weeds; 3) Differential evolution strategy, which processes the weed propagation seed mutation operation as follows: U i =X r1 +F(x r2 -X r3 ) (25) Where, X r1 , X r2 , X r3 are seeds propagated by three different weeds; F is the scaling factor; 4) The law of competition for survival: when the population reaches its maximum size, the fittest will survive according to their fitness; 5) Repeat steps 2)-4) until the optimal solution condition is reached or the maximum number of iterations is reached.

6. 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 5, characterized in that In step 3, according to the implementation process of the weed invasion algorithm, the objective function and constraints of the energy storage configuration optimization model are added to the entire operation process, and the differential evolution strategy is used to calculate the fitness value of each weed unit, that is, the objective function value. With 8760 as the number of cycles per year, the operation data of the integrated photovoltaic storage and charging station per hour in a year is calculated. The calculation process is as follows: 1) Input light and charging load data; 2) Input the upper and lower limit data of the decision variables; 3) Calculate the photovoltaic power generation power P at this moment pv (t) and electric vehicle charging load power P ev (t); 4) Consider the energy storage battery power P ba (t) Actual situation, compare photovoltaic power generation power P pv (t) and charging load power P ev (t) size, according to the energy exchange strategy, calculate the energy storage charging and discharging power and the power P absorbed from the distribution network g (t); 5) Calculate the charging and discharging power of the energy storage system, the power absorbed from the distribution network, and the charging power of electric vehicles; 6) Add upper and lower limits for energy storage battery charging capacity; 7) Determine whether the number of loops is greater than or equal to 8760. If so, end the process. If not, return to step 3).

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