Light storage charging station collaborative optimization method considering charging characteristics of electric vehicle

Abstract

The invention relates to an optical storage charging station collaborative optimization method considering charging characteristics of an electric vehicle. The method comprises the following steps: constructing a photovoltaic power generation output probability model; electric vehicle charging characteristics and regional differences under time-of-use electricity price are considered, and an electric vehicle charging load probability model is constructed; based on the photovoltaic power generation output probability model and the electric vehicle charging load probability model, constructing a light storage charging station double-layer collaborative optimization mathematical model; adding constraint conditions to the double-layer collaborative optimization mathematical model; and solving the double-layer collaborative optimization mathematical model added with the constraint condition to obtain a double-layer collaborative optimization optimal solution of the optical storage charging station. According to the method, boundary deviation is avoided, the local adaptability of photovoltaic power generation output is improved, the overall trend of random distribution of the photovoltaic power generation output can be accurately reflected, and collaborative operation optimization of photovoltaic and energy storage is realized.

Description

Technical Field

[0001] The present invention relates to a method for optimizing a photovoltaic storage charging station system, and in particular to a method for collaboratively optimizing photovoltaic storage charging stations taking into account the charging characteristics of electric vehicles. 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] Electric vehicles are an important part of the future energy Internet and one of the most market-oriented entities on the electricity sales side. The existing integrated photovoltaic, storage and charging charging stations have problems such as low utilization of renewable energy, large peak-to-valley differences in load, and lack of active response to market mechanisms and time-of-use electricity prices. Therefore, in the integrated photovoltaic, storage and charging charging stations, the photovoltaic, storage and charging system is composed of renewable photovoltaic energy, energy storage systems, and electric vehicle charging piles. It is an urgent problem to achieve coordinated operation optimization. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for collaborative optimization of photovoltaic and energy storage charging stations taking into account the charging characteristics of electric vehicles, thereby solving the problem in the prior art that renewable photovoltaic energy, energy storage systems, and electric vehicle charging piles cannot be coordinated and efficiently operated in integrated photovoltaic and energy storage charging stations.

[0005] To achieve the above objectives, the present invention provides a method for collaboratively optimizing a photovoltaic and energy storage charging station taking into account the charging characteristics of electric vehicles, including the following steps:

[0006] Construct a photovoltaic power generation output probability model;

[0007] Considering the characteristics of electric vehicle charging and regional differences under time-of-use electricity prices, a probability model of electric vehicle charging load is constructed;

[0008] Based on the probability model of photovoltaic power generation output and the probability model of electric vehicle charging load, a two-layer collaborative optimization mathematical model of photovoltaic storage charging station is constructed;

[0009] Add constraints to the two-layer collaborative optimization mathematical model;

[0010] The mathematical model of double-layer collaborative optimization with constraints is solved to obtain the optimal solution of double-layer collaborative optimization of photovoltaic and energy storage charging stations.

[0011] The construction of the photovoltaic power generation output probability model includes:

[0012] Use the pseudo data method to correct the boundary deviation of the kernel density estimation. Suppose the samples of n random variables X are X1, X2, …, X n, in the interval (0,1) its continuous probability density function f(x) has an r-order kernel function, then its kernel density estimate f n (x) satisfies the following formula:

[0013]

[0014] In the formula, K(u) is the kernel function, u is the independent variable of the kernel function, and c is the correction coefficient;

[0015] The continuous statistics of the random variable X value from small samples to large samples give the order statistics X of the random variable X. (1) , X (2) , ..., X (n) , when sample X i The distribution function is G = F -1 , F is the cumulative distribution function of the random variable X, then the pseudo data generated on the interval (-∞, 0) is:

[0016]

[0017] Among them, o(n δ- 1 ) is an infinitesimal quantity;

[0018] set up The expansion of the quantile function G on the interval (-∞, 0) is estimated as:

[0019]

[0020] 0≤k≤r+1

[0021] Where k represents the k-th derivative of function G;

[0022] The kernel density estimation formula based on pseudo data method is as follows:

[0023]

[0024] Where h is the bandwidth, n is the sample, a and b are the deviation coefficients, and m is an integer greater than nh but smaller than n;

[0025] The comprehensive photovoltaic power generation output model is established as follows:

[0026]

[0027] Where P pv Represents photovoltaic power generation, P STC is the rated output power of photovoltaic under standard rated conditions, G C is the light intensity at the working point, G STC is the light intensity under standard conditions, k is the power temperature coefficient, T Cis the battery temperature at the operating point, T STC is the standard ambient temperature, A represents the number of PV modules, and ε represents the system power generation efficiency;

[0028] Combined with the above photovoltaic power generation output model, the improved kernel function density estimation is combined with the adaptive kernel density estimation, and the adaptive bandwidth h is used. pv (P i ) replaces the fixed bandwidth h in the above formula, then the photovoltaic power generation output probability model based on pseudo data method adaptive kernel density estimation is:

[0029]

[0030] P pv is the photovoltaic power generation, a and b are the deviation coefficients, and h pv (P i ) is the adaptive bandwidth.

[0031] The construction of the electric vehicle charging load probability model includes:

[0032] The time distribution of electric vehicle charging from the beginning of the day can be expressed by normal distribution, that is, the probability density function of electric vehicle charging time is:

[0033]

[0034] In the formula, σ s is the standard deviation of the start time of charging, κ is the center of the distribution of charging time, x is the start time of charging, μ s is the expected value of the time to start charging;

[0035] A load calculation model for charging piles for electric vehicles is established based on the daily mileage v. The daily mileage v reflects the electric energy required for charging the load in the charging station. The probability density function of the daily mileage of electric vehicles is:

[0036]

[0037] In the formula, v is the daily mileage, α is the regional mileage difference coefficient, μ l is the expectation of ln v, σ l is the standard deviation of ln v;

[0038] The energy consumption per kilometer of the i-th electric vehicle is E i The distribution relationship with temperature T is:

[0039]

[0040] In the formula, λ0, λ1, λ2, and λ3 are influencing factors, and t is the seasonal coefficient, which is used to distinguish the energy consumption differences in different seasons;

[0041] Air conditioning load P of each electric vehicle air for:

[0042]

[0043] Where, e is the driving speed, P′ air is the air conditioning power of the electric vehicle during driving, w is the temperature coefficient, and S is the driving distance;

[0044] The charging time required for an electric vehicle with a daily mileage v is T e for:

[0045]

[0046] Where W 100 is the power consumption per 100 kilometers, v is the daily mileage, P c is the charging power, P air is the air conditioning load, η c For charging efficiency;

[0047] The charging load of each electric vehicle is obtained from the charging start time and charging duration. The charging load of each period is accumulated to obtain the daily load of the photovoltaic storage charging station:

[0048]

[0049] In the formula, is the SOC of the ith tram i Charging power under l，i is a 1-0 variable used to determine whether the i-th tram is charging at time t, d l , i=1 means the user is charging, d l , i=0 means the user does not charge; N is the number of trams.

[0050] Furthermore, the daily load of the photovoltaic charging station is obtained by accumulating the charging loads of all electric vehicles in a day, and the Monte Carlo simulation method is used to solve it. The specific steps are as follows:

[0051] 1) For each type of electric vehicle, set the number of cycles M, the number of electric vehicles, the power consumption per kilometer of the electric vehicle, the battery capacity of the electric vehicle, and the charging power;

[0052] 2) Randomly select charging time and electric vehicle mileage;

[0053] 3) Calculate charging load;

[0054] 4) Repeat steps 2) and 3) to obtain the daily load of each electric vehicle;

[0055] 5) Add up the loads of all electric vehicles to obtain the daily load of the solar-storage charging station;

[0056] 6) Repeat the above steps M times to obtain M daily load curves of photovoltaic charging stations;

[0057] 7) Take the average value of the M groups of data to obtain the final daily load curve of the solar-storage charging station.

[0058] In the aforementioned construction of a two-layer collaborative optimization mathematical model for a photovoltaic charging station, the photovoltaic charging station operation optimization mathematical model includes two layers, the upper and lower layers, and each layer of optimization has corresponding objective functions and constraints; the upper layer objective optimization is not only affected by the constraints and parameters of the layer, but also by the optimization results of the lower layer, and the lower layer objective optimization is not only affected by the constraints and parameters of the layer, but also by the optimization results of the upper layer. The two-layer collaborative optimization mathematical model is as follows:

[0059] min x F(x,y)stg(x,y)≤0

[0060] min y f(x,y)sth(x,y)≤0

[0061] In the formula, F are the decision variables, constraints and objective functions of the upper optimization layer; f are the decision variables, constraints and objective functions of the lower layer optimization respectively.

[0062] Furthermore, the upper decision variables of the two-layer collaborative optimization mathematical model are the capacity of energy storage and the power of photovoltaic power generation, and the lower decision variables are the energy storage charging and discharging curves of the charging station;

[0063] The upper optimization target is the return on investment of the charging station, which is defined as the ratio of the average annual income to the investment cost. It is used as an optimization evaluation of the coordinated operation of the charging station of the solar energy storage system.

[0064]

[0065] In the formula, NPV is the net profit of the photovoltaic storage charging station system; i0 is the expected rate of return; N is the service life of the energy storage battery obtained through the lower-level optimization; C1 is the initial commissioning cost of energy storage, and C2 is the initial investment cost of photovoltaics;

[0066] The lower optimization target is the net profit of the charging station. The net profit model of the charging station is:

[0067]

[0068] In the formula, I j(n) is the jth benefit in year n under the solar-storage-charging station system model, including the operating benefits of charging and discharging the energy storage system of the charging station, the benefits of delaying the expansion of the solar-storage-charging system, and the benefits of reducing carbon emissions. is the health status parameter of the energy storage battery, C k (n) is the kth cost of the photovoltaic and energy storage charging station system in year n, including the cost of investing in photovoltaic and energy storage systems in n years.

[0069] The constraints of the dual-layer collaborative optimization mathematical model of photovoltaic and photovoltaic charging stations include energy storage and photovoltaic equipment capacity constraints, energy storage power constraints and power balance constraints.

[0070] The energy storage and photovoltaic equipment capacity constraints are:

[0071] 0≤V b ≤V b，max

[0072] 0≤V p ≤V p，max

[0073] Where V p，max is the upper limit of energy storage device capacity, V b，max The upper limit of the capacity of photovoltaic equipment;

[0074] The energy storage power constraint is:

[0075] Charging and discharging power P of the charging pile ess (t) not exceeding the energy storage discharge power limit P bess (t),

[0076] -P bess (t)≤P ess (t)≤P bess (t);

[0077] The power balance constraint is:

[0078] P pv (t)+P ess (t)+P bess (t)+P pg (t)-P load (t) = 0

[0079] Where P pv (t) is the photovoltaic power output power, P pg (t) is the power purchased by the photovoltaic charging station from the grid, P load Charging load for electric vehicles at photovoltaic and energy storage charging stations.

[0080] Furthermore, the constraint condition also includes constraining the state of charge SOC of the energy storage battery.

[0081] SOC min ≤SOC≤SOC max

[0082]

[0083] In the formula, SOH n-1 is the health status of the energy storage battery, E(t) is the charging demand of the electric vehicle, and δ is the charging loss coefficient:

[0084]

[0085] Where P bess-dis is the energy storage discharge power, P bess-ch is the energy storage charging power, h discharge(SOH) is the battery self-discharge rate, and η is the energy conversion efficiency.

[0086] The two-layer collaborative optimization mathematical model is solved as follows:

[0087] The upper-level optimization is solved based on the traversal algorithm. On this basis, the configuration ratio boundary conditions of the photovoltaic storage charging station system are set to perform cyclical solutions. When the cycle exceeds the set limit configuration ratio conditions, the cycle terminates. According to the characteristics of the annual decline of the health status (SOH) of the energy storage battery, the energy storage battery exit conditions are set as the evaluation operation cycle to analyze the configuration optimization ratio of energy storage. The upper-level photovoltaic storage charging system configuration ratio needs to be passed to the lower-level energy storage optimization algorithm first, and the health status and cycle of the energy storage battery are evaluated according to the optimization results of the lower layer.

[0088] The lower-level optimization is solved using the rat swarm optimization algorithm. Based on the constraints, a dynamic balance is achieved between local search and global search to obtain the optimized net profit value. The parameters of the photovoltaic storage charging station system ratio value come from the upper-level optimization algorithm. The optimized target value is the net profit returned to the upper-level optimization to calculate the target value of the return on investment. The decision variables of the lower-level optimization, the energy storage charging and discharging power and the SOC curve, are used to calculate the evaluation time required for the lower-level optimization, as well as the health status of the battery. According to the configuration ratio of the photovoltaic storage charging system, the next year's profit and battery health status are calculated until the termination conditions are met and exit.

[0089] Compared with the prior art, the present invention has the following beneficial effects:

[0090] The present invention establishes a double-layer optimization mathematical model and an objective function of the double-layer optimization model. The optimization target of the upper layer is the return on investment of the charging station, and the optimization target of the lower layer is the net income of the charging station in the photovoltaic storage and charging system model. Then, the upper and lower layers are solved by different algorithms respectively. The upper layer optimization is solved by the traversal algorithm, and the lower layer optimization is solved by the mouse swarm optimization algorithm. The key parameters of the photovoltaic storage and charging station system ratio value come from the upper layer optimization algorithm. The decision variables after the lower layer optimization, the energy storage charging and discharging power, and the SOC curve are used to calculate the evaluation time required for the lower layer optimization, as well as the health status of the battery. The charging load is used as the benchmark to achieve coordinated operation optimization of photovoltaic and energy storage.

[0091] The photovoltaic power generation model established by the present invention takes into account factors such as light intensity, ambient temperature conditions, photovoltaic power generation efficiency, etc., and the photovoltaic power generation output model established in combination with the kernel density estimation method can grasp the random distribution characteristics of specific regions or specific photovoltaic outputs. At the same time, the photovoltaic power generation output probability distribution function modified on the basis of the pseudo-data method can better simulate the random characteristics of photovoltaic power generation output under different weather conditions, thereby avoiding boundary deviations, improving the local adaptability of photovoltaic power generation output, and accurately reflecting the overall trend of the random distribution of photovoltaic power generation output. According to the optimization results, the present invention can improve the overall economy of the photovoltaic storage charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 This is a flowchart of the mouse colony optimization algorithm of the present invention;

[0093] Figure 2 This is a flow chart for solving the double-layer optimization model of the photovoltaic charging station of the present invention;

[0094] Figure 3 This is an operation curve diagram of the photovoltaic charging station of the present invention. DETAILED DESCRIPTION

[0095] 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.

[0096] Example

[0097] The present invention provides a method for collaboratively optimizing a photovoltaic and energy storage charging station taking into account the charging characteristics of electric vehicles, comprising the following steps:

[0098] Construct a photovoltaic power generation output probability model;

[0099] Considering the characteristics of electric vehicle charging and regional differences under time-of-use electricity prices, a probability model of electric vehicle charging load is constructed;

[0100] Based on the probability model of photovoltaic power generation output and the probability model of electric vehicle charging load, a two-layer collaborative optimization mathematical model of photovoltaic and storage charging stations is constructed;

[0101] Add constraints to the two-layer collaborative optimization mathematical model;

[0102] The mathematical model of double-layer collaborative optimization with constraints is solved to obtain the optimal solution of double-layer collaborative optimization of photovoltaic and energy storage charging stations.

[0103] In the construction of the photovoltaic power generation output probability model, in order to solve the problems of poor model accuracy and low analysis accuracy, the photovoltaic power generation output probability model is established by using the improved non-parametric kernel density estimation method; in order to improve the model accuracy, the pseudo data method is used to correct the kernel density estimation boundary deviation, and the samples of n random variables X are set as X1, X2, ..., X n , in the interval (0, 1) its continuous probability density function f(x) has an r-order kernel function, then its kernel density estimate fn(x) satisfies the following formula:

[0104]

[0105] Where K(u) is the kernel function, u is the independent variable of the kernel function, and c is the correction coefficient.

[0106] The continuous statistics of the random variable X value from small samples to large samples give the order statistics X of the random variable X. (1) , X (2) , ..., X (n) , when sample X i The quantile function is G = F -1 , F is the cumulative distribution function of the random variable X, then the pseudo data generated on the interval (-∞, 0) is:

[0107]

[0108] Among them, o(n δ-1 ) is an infinitesimal quantity.

[0109] set up The expansion of the quantile function G on the interval (-∞, 0) is estimated as:

[0110]

[0111] 0≤k≤r+1

[0112] Where k represents the k-th order derivative of the function G.

[0113] The kernel density estimation formula based on pseudo data method is as follows:

[0114]

[0115] Where h is the bandwidth (smoothing coefficient), n is the sample, a and b are the deviation coefficients, and m is an integer larger than nh but smaller than n.

[0116] For the photovoltaic power generation output model, the performance of the photovoltaic power generation system is affected by a variety of external conditions in different working environments, among which the light intensity has a direct effect on the photocurrent inside the photovoltaic module, while the ambient temperature has a significant effect on the current of the diode. Therefore, the power output of the solar panel is closely related to the light and temperature of its environment. The main factors affecting the output power of the photovoltaic array include the intensity of sunlight and the ambient temperature conditions. At the same time, the daily power generation of the photovoltaic system is usually proportional to the effective sunshine time during the day, especially in cloudy, shaded or rainy weather conditions, the effective sunshine time will be reduced, which in turn affects the power generation. In addition, the power generation efficiency of the photovoltaic module is also restricted by the efficiency of each component inside the system and its material properties, resulting in differences in the rated output power of a single photovoltaic panel under standard test conditions. Therefore, the output power of a single photovoltaic panel is under standard rated conditions (the standard solar irradiance is 1000W / m 2 , the relative atmospheric optical quality is AM1.5, and the photovoltaic cell temperature standard scenario is 25℃) can be obtained from the relationship between the rated output power, light intensity and ambient temperature, and then the comprehensive photovoltaic power generation output model can be established as follows:

[0117]

[0118] Where P pv Represents photovoltaic power generation, P STC is the rated output power of photovoltaic under standard rated conditions, G C is the light intensity at the working point, G STC is the light intensity under standard conditions, k is the power temperature coefficient, and the k value of different photovoltaic cells is different, generally k = 0.45. C is the battery temperature at the operating point, T STC is the standard ambient temperature, A represents the number of photovoltaic modules, and ε represents the power generation efficiency of the system, which is often determined by the solar panel material, photovoltaic array efficiency, inverter conversion efficiency, etc.

[0119] Combined with the above photovoltaic output model, the improved kernel function density estimation is combined with the adaptive kernel density estimation, and the adaptive bandwidth h is used. pv (P i ) replaces the fixed bandwidth h in the above formula, then the photovoltaic power generation output probability model based on pseudo data method adaptive kernel density estimation is:

[0120]

[0121] P pvis the photovoltaic power generation output, a and b are the deviation coefficients, and h pv (P i ) is the adaptive bandwidth.

[0122] The electric vehicle charging load probability modeling includes:

[0123] (1) The probability of the charging time of electric vehicles. By studying the distribution of the time when electric vehicles start charging in a day, it is found that it can be represented by a normal distribution, and its probability density function is:

[0124]

[0125] In the formula, σ s is the standard deviation of the start time of charging, κ is the center of the distribution of charging time, x is the start time of charging, μ s is the expected value of the charging start time. The present invention mainly considers the charging time of electric vehicles in the Northeast region. Therefore, in this embodiment, σ s The value of μ is 3, s The value of is 19;

[0126] (2) The probability of daily mileage of electric vehicles. A load calculation model for charging piles for electric vehicles is established based on the daily mileage v. The daily mileage v reflects the electric energy required for charging the load in the charging station. Assuming that the daily mileage v of electric vehicles satisfies the log-normal distribution, its probability density function is:

[0127]

[0128] In the formula, v is the daily mileage, α is the regional mileage difference coefficient, μ l is the expectation of ln v, σ l is the standard deviation of ln v, μ l and σ l The value of varies with different models. This embodiment is mainly for the Northeast region, so let μ l =3.2,σ l =0.8;

[0129] (3) The length of time and power of electric vehicle charging. The length of time of electric vehicle charging is affected by the time-of-use electricity price. Different charging distribution characteristics are found in different time periods. Therefore, the response degree of electric vehicle users to charging needs to be considered. The time-of-use electricity price mechanism is designed based on the time value of electric energy. According to the load changes of the power grid, the 24 hours of each day are divided into three periods: peak, flat and valley. By giving full play to the role of time-of-use electricity price signals, users can be better guided to adjust their electricity load, reduce peaks and fill valleys, ensure the safe, stable and economic operation of the power system, reduce the electricity cost of the whole society, serve the construction of a new power system with new energy as the main body, and promote the green and low-carbon development of energy. Taking Liaoning as an example, the peak and valley periods are divided according to the characteristics of the system electricity load and the consumption of new energy. The daily electricity consumption is divided into three periods: peak, normal and valley, each of which is 8 hours. Specifically, the peak period is 07:30-10:30 and 16:00-21:00, the valley period is 11:30-12:30 and 22:00-05:00, and the rest is the normal period. Among them, the peak period is from 17:00 to 19:00 every day in summer (July and August) and winter (January and December), and the peak electricity price is implemented. During the peak electricity price period, electric vehicles are charged less and charged during the low electricity price period, which reduces user costs and is conducive to peak load regulation of the power grid and reduces the peak-to-valley difference of the power grid.

[0130] The difference between peak and valley electricity prices for different users is different. The peak electricity price is x higher than the benchmark charging electricity price, and the valley electricity price is y lower than the benchmark electricity price. Definition It is the ratio of the difference between peak and valley electricity prices to the base electricity price, that is:

[0131]

[0132] Where P p is the peak electricity price, P v is the off-peak electricity price, P0 is the base electricity price, k is the user model coefficient, 0<x<50%, 0 <y<50%。

[0133] After calculation, when When the user does not respond; When , all users respond; when , 90% of users respond. The function of the degree of response can be expressed as:

[0134]

[0135] By collecting charging information of different models, the relationship curve between the charging power and battery SOC of the electric vehicle battery is obtained, and the corresponding charging power SOC0 is recorded as the charging power at the start of charging. Then, based on the charging power at the previous moment, the battery charging state at the next moment is approximately estimated, and so on, until the battery state reaches the state of charge at the end of charging (SOC1), that is, the expected battery state of charge at the end of charging, and the charging time and the charging power during this period are recorded.

[0136] Considering that the travel temperature in different regions has a certain impact on the energy consumption of electric vehicles, the energy consumption per kilometer of the i-th electric vehicle is E i The distribution relationship with temperature T is:

[0137]

[0138] Where λ0, λ1, λ2, and λ3 are influencing factors, and t is the seasonal coefficient, which is used to distinguish the energy consumption differences in different seasons.

[0139] Considering the changes in the air conditioning load demand of electric vehicles caused by changes in weather temperature, the air conditioning load P of each electric vehicle is air for:

[0140]

[0141] Where, e is the driving speed, P′ air is the air conditioning power of the electric vehicle during driving. The air conditioning load is closely related to temperature, so w is introduced as the temperature coefficient. P′ in spring and autumn air The power consumption ratio of heating in winter and cooling in summer is 2:1.5.

[0142] Daily driving mileage is the main factor affecting the daily power consumption of electric vehicles. Under a certain charging power, the more daily power consumption, the longer the charging time of electric vehicles. Considering the climate environment in Northeast China, there are losses during the charging process of electric vehicles. Combined with the air conditioning load demand of electric vehicles, the charging time T required for electric vehicles with a daily driving mileage v is e for:

[0143]

[0144] Where W 100 is the power consumption per 100 kilometers, v is the daily mileage, P c is the charging power, P air is the air conditioning load, η c For charging efficiency.

[0145] The charging load of each electric vehicle is obtained from the charging start time and charging duration. The charging load of each period is accumulated to obtain the daily load of the photovoltaic storage charging station:

[0146]

[0147] In the formula, is the SOC of the ith tram i Charging power under l，i is a 1-0 variable used to determine whether the i-th tram is charging at time t, d l，i =1 means the user is charging, d l，i =0 means the user does not charge; N is the number of electric vehicles.

[0148] The charging power and charging amount of each electric vehicle can be calculated from the above. The daily load of the photovoltaic storage charging station can be obtained by adding up the charging loads of all electric vehicles in a day, and the Monte Carlo simulation method is used to solve it.

[0149] The specific steps are:

[0150] 1) For each type of electric vehicle, set the number of cycles M, the number of electric vehicles, the power consumption per kilometer of the electric vehicle, the battery capacity of the electric vehicle, and the charging power.

[0151] 2) Randomly select charging time and mileage of electric vehicles.

[0152] 3) Calculate the charging load.

[0153] 4) Repeat steps 2) and 3) to obtain the daily load of each electric vehicle.

[0154] 5) Add up the loads of all electric vehicles to obtain the daily load of the solar-storage charging station.

[0155] 6) Repeat the above steps M times to obtain M daily load curves of photovoltaic charging stations.

[0156] 7) Take the average value of the M groups of data to obtain the final daily load curve of the solar-storage charging station.

[0157] In the described double-layer collaborative optimization mathematical model for constructing a photovoltaic charging station, the double-layer collaborative optimization mathematical model includes two layers, the upper and lower layers, and each layer of optimization has corresponding objective functions and constraints; the upper layer objective optimization is not only affected by the constraints and parameters of the layer, but also by the optimization results of the lower layer. Similarly, the lower layer objective optimization is not only affected by the constraints and key parameters of the layer, but also by the optimization results of the upper layer. The double-layer collaborative optimization mathematical model is as follows. Figure 2 :

[0158] min x F(x,y)stg(x,y)≤0

[0159] min yf(x,y)sth(x,y)≤0

[0160] In the formula, F are the decision variables, constraints and objective functions of the upper optimization layer; f are the decision variables, constraints and objective functions of the lower layer optimization respectively.

[0161] The upper decision variables of the two-layer collaborative optimization mathematical model are the capacity of energy storage and the power of photovoltaic power generation, the lower decision variables are the energy storage charging and discharging curves of the charging station, the upper optimization target is the return on investment of the charging station, and the lower optimization target is the net income of the charging station. When determining the solution of the upper investment return optimization target, it is necessary to configure the energy storage power and capacity based on the lower decision variable energy storage system charging and discharging curve and consider the health status of the energy storage battery; conversely, when determining the solution of the optimization target of the net income of the lower charging station, it is affected by the upper energy storage and photovoltaic power and capacity, and the optimized net income of the charging station is different under different power capacities;

[0162] (1) The upper optimization target is the return on investment of the charging station, which is defined as the ratio of the average annual income to the investment cost. It is used as an optimization evaluation of the coordinated operation of the charging station of the solar-storage-charging system.

[0163]

[0164] In the formula, NPV is the net profit of the photovoltaic storage charging station system; i0 is the expected rate of return; N is the service life of the energy storage battery obtained through the lower-level optimization; C1 is the initial commissioning cost of energy storage, which is related to the scale of the energy storage system; C2 is the initial investment cost of photovoltaics;

[0165] (2) The lower optimization target is the net profit of the charging station. Considering the health status of the energy storage battery, a net profit model of the charging station is established:

[0166]

[0167] In the formula, I j (n) is the jth benefit in year n under the solar-storage-charging station system model, including the operating benefits of charging and discharging the energy storage system of the charging station, the benefits of delaying the expansion of the solar-storage-charging system, and the benefits of reducing carbon emissions. is the health status parameter of the energy storage battery, C k (n) is the kth cost of the photovoltaic and energy storage charging station system in year n, including the cost of investing in photovoltaic and energy storage systems in n years.

[0168] The constraints of the dual-layer collaborative optimization mathematical model of the photovoltaic storage charging station are:

[0169] (1) Energy storage and photovoltaic equipment capacity constraints:

[0170] 0≤V b ≤V b，max

[0171] 0≤V p ≤V p，max

[0172] Where V p，max is the upper limit of energy storage device capacity, V b，max The upper limit of photovoltaic equipment capacity.

[0173] (2) Energy storage power constraints:

[0174] Charging and discharging power P of the charging pile ess (t) not exceeding the energy storage discharge power limit P bess (t),

[0175] -P bess (t)≤P ess (t)≤P bess (t)

[0176] (3) Power balance constraints:

[0177] The basic function of a charging station is to meet the power demand of the charging load. The constraints are as follows:

[0178] P pv (t)+P ess (t)+P bess (t)+P pg (t)-P load (t) = 0

[0179] Where P pg (t) is the power purchased by the photovoltaic charging station from the grid, P load Charging load for electric vehicles at photovoltaic and energy storage charging stations.

[0180] In order to extend the service life of energy storage, the charge and discharge depth of the energy storage battery must be kept within an appropriate limit range, that is, the state of charge SOC of the energy storage battery must be constrained.

[0181] SOC min ≤SOC≤SOC max

[0182]

[0183] In the formula, SOH n-1 is the health status of the energy storage battery, E(t) is the charging demand of the electric vehicle, and δ is the charging loss coefficient.

[0184] During the charge and discharge cycle of the energy storage system, the energy storage battery itself has a certain amount of self-discharge and energy loss due to the flow of electric energy in the device. If the impact of energy conversion efficiency η on the difference in charge and discharge power is considered, the power management analysis after considering the device loss efficiency is as follows:

[0185]

[0186] Where P bess-dis is the energy storage discharge power, P bess-ch is the energy storage charging power, h discharge(SOH ) is the battery self-discharge rate, which is usually related to the battery health status. As the battery health status decreases, the self-discharge rate will gradually increase. η is the energy conversion efficiency.

[0187] The two-layer collaborative optimization mathematical model solving algorithm is specifically:

[0188] The upper-level optimization is solved based on the traversal algorithm. On this basis, the configuration ratio boundary conditions of the photovoltaic storage charging station system are set to perform cyclical solutions. When the cycle exceeds the set limit configuration ratio conditions, the cycle terminates. According to the characteristics of the annual decline of the health status (SOH) of the energy storage battery, the energy storage battery exit conditions are set as the evaluation operation cycle to analyze the configuration optimization ratio of the energy storage. From the technical and economic aspects, the overall planning of the system is guided. The configuration ratio of the upper-level photovoltaic storage charging system needs to be passed to the lower-level energy storage optimization algorithm first, and the health status and cycle of the energy storage battery are evaluated according to the optimization results of the lower layer.

[0189] The lower-level optimization is solved using the rat swarm optimization algorithm, which takes into account multiple constraints and achieves a dynamic balance between local search and global search to obtain the optimized net profit value. The key parameters of the photovoltaic storage charging station system ratio value come from the upper-level optimization algorithm. The optimized target value is the net profit returned to the upper-level optimization to calculate the target value of the return on investment. The decision variables of the energy storage charging and discharging power and SOC curve after the lower-level optimization are used to calculate the evaluation time required for the lower-level optimization, as well as the health status of the battery. According to the latest photovoltaic storage charging system configuration ratio, the next year's profit and battery health status are calculated until the termination conditions are met and exit.

[0190] A computer device comprising:

[0191] A processor; and a memory configured to store machine-readable instructions, which, when executed by the processor, execute the above-mentioned photovoltaic and energy storage charging station collaborative optimization method considering the charging characteristics of electric vehicles.

[0192] A storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned photovoltaic energy storage charging station collaborative optimization method considering the charging characteristics of electric vehicles.

[0193] 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.

[0194] 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 collaborative optimization method for photovoltaic and energy storage charging stations considering the charging characteristics of electric vehicles, characterized by The steps include: Construct a photovoltaic power generation output probability model; Considering the characteristics of electric vehicle charging and regional differences under time-of-use electricity prices, a probability model of electric vehicle charging load is constructed; Based on the probability model of photovoltaic power generation output and the probability model of electric vehicle charging load, a two-layer collaborative optimization mathematical model of photovoltaic and storage charging stations is constructed; Add constraints to the two-layer collaborative optimization mathematical model; The mathematical model of double-layer collaborative optimization with constraints is solved to obtain the optimal solution of double-layer collaborative optimization of photovoltaic and energy storage charging stations.

2. The method for collaborative optimization of photovoltaic and energy storage charging stations considering the charging characteristics of electric vehicles according to claim 1 is characterized in that The construction of the photovoltaic power generation output probability model includes: Use the pseudo data method to correct the boundary deviation of the kernel density estimation. Suppose the samples of n random variables X are X1, X2, …, X n , in the interval (0,1) its continuous probability density function f(x) has an r-order kernel function, then its kernel density estimate f n (x) satisfies the following formula: In the formula, K(u) is the kernel function, u is the independent variable of the kernel function, and c is the correction coefficient; The continuous statistics of the random variable X value from small samples to large samples give the order statistics X of the random variable X. (1) ,X (2) ,…,X (n) , when sample X i The distribution function is G = F -1 , F is the cumulative distribution function of the random variable X, then the pseudo data generated on the interval (-∞, 0) is: Among them, o(n δ-1 ) is an infinitesimal quantity; set up The expansion of the quantile function G on the interval (-∞, 0) is estimated as: 0≤k≤r+1 Where k represents the k-th derivative of function G; The kernel density estimation formula based on pseudo data method is as follows: Where h is the bandwidth, n is the sample, a and b are the deviation coefficients, and m is an integer greater than nh but smaller than n; The comprehensive photovoltaic power generation output model is established as follows: Where P pv Represents photovoltaic power generation, P STC is the rated output power of photovoltaic under standard rated conditions, G C is the light intensity at the working point, G STC is the light intensity under standard conditions, k is the power temperature coefficient, T C is the battery temperature at the operating point, T STC is the standard ambient temperature, A represents the number of PV modules, and ε represents the system power generation efficiency; Combined with the above photovoltaic power generation output model, the improved kernel function density estimation is combined with the adaptive kernel density estimation, and the adaptive bandwidth h is used. pv (P i ) replaces the fixed bandwidth h in the above formula, then the photovoltaic power generation output probability model based on pseudo data method adaptive kernel density estimation is: P pv is the photovoltaic power generation, a and b are the deviation coefficients, and h pv (P i ) is the adaptive bandwidth.

3. The method for collaborative optimization of photovoltaic and energy storage charging stations considering the charging characteristics of electric vehicles according to claim 1 is characterized in that The construction of the electric vehicle charging load probability model includes: The time distribution of electric vehicle charging from the beginning of the day can be expressed by normal distribution, that is, the probability density function of electric vehicle charging time is: In the formula, σ s is the standard deviation of the start time of charging, κ is the center of the distribution of charging time, x is the start time of charging, μ s is the expected value of the time to start charging; A load calculation model for charging piles for electric vehicles is established based on the daily mileage v. The daily mileage v reflects the electric energy required for charging the load in the charging station. The probability density function of the daily mileage of electric vehicles is: In the formula, v is the daily mileage, α is the regional mileage difference coefficient, μ l is the expectation of lnv, σ l is the standard deviation of lnv; The energy consumption per kilometer of the i-th electric vehicle is E i The distribution relationship with temperature T is: In the formula, λ0, λ1, λ2, and λ3 are influencing factors, and t is the seasonal coefficient, which is used to distinguish the energy consumption differences in different seasons; Air conditioning load P of each electric vehicle air for: Where, e is the driving speed, P a ' ir is the air conditioning power of the electric vehicle during driving, w is the temperature coefficient, and S is the driving distance; The charging time required for an electric vehicle with a daily mileage v is T e for: Where W 100 is the power consumption per 100 kilometers, v is the daily mileage, P c is the charging power, P air is the air conditioning load, η c For charging efficiency; The charging load of each electric vehicle is obtained from the charging start time and charging duration. The charging load of each period is accumulated to obtain the daily load of the photovoltaic storage charging station: In the formula, is the SOC of the ith tram i Charging power under l,i is a 1-0 variable used to determine whether the i-th tram is charging at time t, d l,i =1 means the user is charging, d l,i =0 means the user does not charge; N is the number of electric vehicles.

4. The method for collaborative optimization of photovoltaic and energy storage charging stations considering the charging characteristics of electric vehicles according to claim 3 is characterized in that The daily load of the photovoltaic charging station is obtained by accumulating the charging load of all electric vehicles in a day, and the Monte Carlo simulation method is used to solve it. The specific steps are: 1) For each type of electric vehicle, set the number of cycles M, the number of electric vehicles, the power consumption per kilometer of the electric vehicle, the battery capacity of the electric vehicle, and the charging power; 2) Randomly select charging time and electric vehicle mileage; 3) Calculate charging load; 4) Repeat steps 2) and 3) to obtain the daily load of each electric vehicle; 5) Add up the loads of all electric vehicles to obtain the daily load of the solar-storage charging station; 6) Repeat the above steps M times to obtain M daily load curves of photovoltaic charging stations; 7) Take the average value of the M groups of data to obtain the final daily load curve of the solar-storage charging station.

5. The method for collaborative optimization of photovoltaic and energy storage charging stations considering the charging characteristics of electric vehicles according to claim 1 is characterized in that In the aforementioned construction of a two-layer collaborative optimization mathematical model for a photovoltaic charging station, the photovoltaic charging station operation optimization mathematical model includes two layers, the upper and lower layers, and each layer of optimization has corresponding objective functions and constraints; the upper layer objective optimization is not only affected by the constraints and parameters of the layer, but also by the optimization results of the lower layer, and the lower layer objective optimization is not only affected by the constraints and parameters of the layer, but also by the optimization results of the upper layer. The two-layer collaborative optimization mathematical model is as follows: min x F(x,y)s.t.g(x,y)≤0 min y f(x,y)s.t.h(x,y)≤0 In the formula, F are the decision variables, constraints and objective functions of the upper optimization layer; f are the decision variables, constraints and objective functions of the lower layer optimization respectively.

6. The method for collaborative optimization of photovoltaic and energy storage charging stations considering the charging characteristics of electric vehicles according to claim 5 is characterized in that: The upper decision variables of the two-layer collaborative optimization mathematical model are the capacity of energy storage and the power of photovoltaic power generation, and the lower decision variables are the energy storage charging and discharging curves of the charging station; The upper optimization target is the return on investment of the charging station, which is defined as the ratio of the average annual income to the investment cost. It is used as an optimization evaluation of the coordinated operation of the charging station of the solar energy storage system. In the formula, NPV is the net profit of the photovoltaic storage charging station system; i0 is the expected rate of return; N is the service life of the energy storage battery obtained through the lower-level optimization; C1 is the initial commissioning cost of energy storage, and C2 is the initial investment cost of photovoltaics; The lower optimization target is the net profit of the charging station. The net profit model of the charging station is: In the formula, I j (n) is the jth benefit in year n under the solar-storage-charging station system model, including the operating benefits of charging and discharging the charging station energy storage system, the benefits of delaying the expansion of the solar-storage-charging system, and the benefits of reducing carbon emissions. is the health status parameter of the energy storage battery, C k (n) is the kth cost of the photovoltaic and energy storage charging station system in year n, including the cost of investing in photovoltaic and energy storage systems in n years.

7. The method for collaborative optimization of photovoltaic and energy storage charging stations considering electric vehicle charging characteristics according to claim 1 is characterized in that: The constraints of the dual-layer collaborative optimization mathematical model of photovoltaic and photovoltaic charging stations include energy storage and photovoltaic equipment capacity constraints, energy storage power constraints and power balance constraints. The energy storage and photovoltaic equipment capacity constraints are: 0≤V b ≤V b,max 0≤V p ≤V p,max Where V p,max is the upper limit of energy storage device capacity, V b,max The upper limit of the capacity of photovoltaic equipment; The energy storage power constraint is: Charging and discharging power P of the charging pile ess (t) not exceeding the energy storage discharge power limit P bess (t), -P bess (t)≤P ess (t)≤P bess (t); The power balance constraint is: P pv (t)+P ess (t)+P bess (t)+P pg (t)-P load (t)=0 Where P pv (t) is the photovoltaic power output power, P pg (t) is the power purchased by the photovoltaic charging station from the grid, P load Charging load for electric vehicles at photovoltaic and energy storage charging stations.

8. The method for collaborative optimization of photovoltaic and energy storage charging stations considering the charging characteristics of electric vehicles according to claim 7 is characterized in that: The constraints also include constraining the state of charge SOC of the energy storage battery. SOC min ≤SOC≤SOC max In the formula, SOH n-1 is the health status of the energy storage battery, E(t) is the charging demand of the electric vehicle, and δ is the charging loss coefficient; Where P bess-dis is the energy storage discharge power, P bess-ch is the energy storage charging power, h discharge(SOH) is the battery self-discharge rate, and η is the energy conversion efficiency.

9. The method for collaborative optimization of photovoltaic and energy storage charging stations considering electric vehicle charging characteristics according to claim 1 is characterized in that The two-layer collaborative optimization mathematical model is solved as follows: The upper-level optimization is solved based on the traversal algorithm. On this basis, the configuration ratio boundary conditions of the photovoltaic storage charging station system are set to perform cyclical solutions. When the cycle exceeds the set limit configuration ratio conditions, the cycle terminates. According to the characteristics of the annual decline of the health status (SOH) of the energy storage battery, the energy storage battery exit conditions are set as the evaluation operation cycle to analyze the configuration optimization ratio of energy storage. The upper-level photovoltaic storage charging system configuration ratio needs to be passed to the lower-level energy storage optimization algorithm first, and the health status and cycle of the energy storage battery are evaluated according to the optimization results of the lower layer. The lower-level optimization is solved using the rat swarm optimization algorithm. Based on the constraints, a dynamic balance is achieved between local search and global search to obtain the optimized net profit value. The parameters of the photovoltaic storage charging station system ratio value come from the upper-level optimization algorithm. The optimized target value is the net profit returned to the upper-level optimization to calculate the target value of the return on investment. The decision variables of the lower-level optimization, the energy storage charging and discharging power and the SOC curve, are used to calculate the evaluation time required for the lower-level optimization, as well as the health status of the battery. According to the configuration ratio of the photovoltaic storage charging system, the next year's profit and battery health status are calculated until the termination conditions are met and exit.

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