Double-layer coordination configuration optimization method for light storage and charging smart energy system

Abstract

The invention relates to a double-layer coordination configuration optimization method for a light storage and charging smart energy system, and the method comprises the following steps: constructing a smart light storage and charging configuration optimization mathematical model through an uncertain photovoltaic power generation output probability model, an uncertain charging load probability model, and a charging load probability model; and obtaining a double-layer coordination configuration optimal solution of the light storage and charging smart energy system through a double-layer optimization model solving algorithm. According to the method, a double-layer optimization mathematical model is established, and a double-layer optimization model objective function is established. The upper-layer optimization target is the rate of return on investment of the charging station, and the lower-layer optimization target is the charging station net income of the optical storage and charging system model. Then, the upper layer and the lower layer are solved through different algorithms, and collaborative optimization configuration is achieved for photovoltaic and energy storage with the charging load as the reference quantity.

Description

Technical Field

[0001] The present invention relates to an energy system configuration optimization method, specifically a double-layer coordinated configuration optimization method for a photovoltaic storage and charging smart energy system. 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 stations have problems such as low renewable energy utilization, large load peak-to-valley differences, and lack of active response to market mechanisms and electricity prices. Therefore, in the integrated photovoltaic, storage and charging stations, the photovoltaic, storage and load system composed of renewable photovoltaic energy, energy storage system, and electric vehicle charging piles is composed. It is an urgent problem to achieve coordinated configuration optimization. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a two-layer coordinated configuration optimization method for a photovoltaic storage and charging smart energy system, which solves the problem in the prior art that renewable photovoltaic energy, energy storage system, and electric vehicle charging piles cannot be coordinated in the photovoltaic storage and charging integrated charging station.

[0005] To achieve the above objectives, the dual-layer coordinated configuration optimization method of the photovoltaic storage and charging smart energy system of the present invention comprises the following steps:

[0006] Step 1: construct a mathematical model for optimizing the configuration of smart photovoltaic storage and charging through an uncertain photovoltaic power generation output probability model, an uncertain charging load probability model, and a charging load probability model;

[0007] Step 2: Obtain the optimal solution for the two-layer coordinated configuration of the photovoltaic storage and charging smart energy system through the two-layer optimization model solving algorithm.

[0008] In step 1, the photovoltaic power generation output probability model is established by using the improved nonparametric kernel density estimation method, assuming that 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:

[0009]

[0010] 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:

[0011]

[0012] set up The expansion of the quantile function G on the interval (-∞, 0) is estimated as:

[0013]

[0014] From the above formula, we can get the pseudo data X (-i) :

[0015]

[0016] The kernel density estimation based on the pseudo data method is:

[0017]

[0018] Where m is an integer greater than nh but smaller than n;

[0019] Assume that the photovoltaic power generation output sample is P pv1 ,P pv2 ,…,P pvn , that is, the measured data after normalization and standardization,

[0020] Its continuous probability density function is f(P pvi ), combines the improved kernel density estimation KDEP (Kernel Density Estimation with Pseudo-data) with the adaptive kernel density estimation AKDE (Adaptive Kernel Density Estimation), and uses the adaptive bandwidth h pv (P i ) replaces the fixed bandwidth h in the above formula, then the photovoltaic power generation output probability distribution model based on the pseudo-data method adaptive kernel density estimation AKDEP (Adaptive Kernel Density Estimation with Pseudo-data) is:

[0021]

[0022] Use MISE as the overall measure to analyze f n (P pv ) for f(P pv )The accuracy of the estimate;

[0023]

[0024] Ignoring the higher-order terms in the above equation, we get AMISE[f n (P pv )]for:

[0025]

[0026] From the above formula, we can see that VAR[f n (P pv )] and BIAS[f n (P pv )] and determines the estimation accuracy of the model.

[0027] In step 1, the uncertain charging load probability model includes:

[0028] (1) The probability of electric vehicle charging time,

[0029] By studying the distribution of charging time of electric vehicles from the beginning of the day, it is found that it obeys the normal distribution, and its probability density function is:

[0030]

[0031] In the formula, σ s =3 is the standard deviation, μ s =19 is the expected value of the starting charging time;

[0032] (2) The probability of daily mileage of electric vehicles,

[0033] A load calculation model for charging piles for electric vehicles is established based on the daily mileage x. The daily mileage x reflects the electric energy required for charging the load in the charging station. It is assumed that the daily mileage x of the vehicle obeys a log-normal distribution, and its distribution function is:

[0034]

[0035] In the formula, μ l =3.2 is the expected value of mileage, σ l =0.8 is the standard deviation;

[0036]

[0037]

[0038] (3) Electric vehicle charging time and charging power,

[0039] By collecting charging information of different models, the relationship curve between the charging power and the 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;

[0040] Daily driving mileage will affect the daily power consumption of electric vehicles. Under a certain charging power, the more daily power consumption, the longer the electric vehicle charging time. The calculated charging time is:

[0041]

[0042] Where W 100 is the power consumption per 100 kilometers, x is the daily mileage, P c is the charging power.

[0043] In the step 1, the charging load probability model includes:

[0044] First, the charging demand of the kth electric vehicle in a specific charging station in the T period is modeled. Then, according to the distribution of different types of electric vehicles, the charging demand of the charging station is calculated. The driving probability g(t) at time t is calculated by comparing the daily average mileage d α Multiply them together to get the expected mileage at each moment, as shown in the following formula:

[0045]

[0046] Daily mileage d α The charge shape of the kth electric car at time t is

[0047]

[0048] Assume that when the SOC of the electric vehicle reaches a certain value of the user's psychology At this time, the state of charge Less than Electric car users will try to charge it. Suppose the user tries to charge Start charging at any time When the battery SOC reaches the user's psychological target value At the end, the amount of electricity obtained by the kth electric car at time t is

[0049]

[0050] Where Δ is the charging time of the electric vehicle (h); L k Charging power for electric vehicles (kW);

[0051] The daily mileage probability F(d α ) can be expressed as follows:

[0052]

[0053] Taking into account all daily mileage d α The possible values ​​and the probability of their correlation, the expected charging demand of the kth electric vehicle at time t is as follows,

[0054]

[0055] Assuming that there are enough charging devices for the electric vehicles arriving at the station, the total expected charging demand of all n electric vehicles is as follows:

[0056]

[0057] In step 1, the mathematical model for optimizing the configuration of intelligent optical storage and charging includes two layers, each layer of optimization has corresponding objective functions and constraints. The upper layer objective optimization is affected by the constraints and key parameters of the layer as well as the optimization results of the lower layer. Similarly, the objective optimization problem of the lower layer is affected by the constraints and key parameters of the layer as well as the optimization results of the upper layer. The two-layer mathematical model is as follows:

[0058] min x F(x,y)stg(x,y)≤0 (45)

[0059] min y f(x,y)sth(x,y)≤0 (46)

[0060] In the formula, x, F are the decision variables, constraints and objective functions of the upper optimization layer; y, f are respectively the decision variables, constraints, and objective functions of the lower-level optimization. The upper-level decision variables of the charging station photovoltaic storage and load system collaborative configuration optimization model are the capacity of energy storage and the capacity of distributed photovoltaic power sources. The lower-level decision variables are the energy storage charging and discharging curves of the charging station. The upper-level optimization target is the return on investment of the charging station. The lower-level optimization target is the net income of the charging station of the photovoltaic storage and load system model. When determining the solution of the upper-level return on investment optimization target, it is necessary to configure the energy storage power and capacity based on the charge and discharge curve of the energy storage system according to the lower-level decision variable 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-level charging station, it is affected by the upper-level energy storage and photovoltaic power and capacity, and the optimized net income of the charging station is different under different power capacities.

[0061] (1) Upper-level optimization goals,

[0062] The upper layer of the optimization model takes the return on investment as the optimization target, and its return on investment is defined as the ratio of the average annual income to the investment cost, as an optimization evaluation of the coordinated configuration of the charging station of the solar-storage-load system.

[0063]

[0064] In the formula, NPV is the net profit of the photovoltaic load storage system of the charging station; 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 the energy storage, which is related to the scale of the energy storage system;

[0065] (2) Lower-level optimization goals,

[0066] The lower layer of the optimization model takes the net profit of the charging station as the optimization target, considers the health status of the energy storage battery, and establishes the net profit model of the charging station.

[0067]

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

[0069] In step 1, the constraint conditions of the mathematical model for optimizing the configuration of intelligent photovoltaic storage and charging are:

[0070] (1) Charging load demand constraints,

[0071] The basic function of a charging station is to meet the power demand of the charging load. The constraints are as follows:

[0072] Ppv (t)+P ess (t)+P pg (t) = P load (t) (49)

[0073] (2) Energy storage constraints,

[0074] Charging and discharging power P of the charging pile ess (t) not exceeding the energy storage discharge power limit P bess (t),

[0075] -P bess (t)≤P ess (t)≤P bess (t) (50)

[0076] 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.

[0077] SOC min ≤SOC≤SOC max (51)

[0078]

[0079] In the formula, SOH n-1 The health status of the energy storage battery.

[0080] 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 when the energy flows in the device. If the impact of energy conversion efficiency on the difference in charge and discharge power is considered, the power management analysis after considering the device loss efficiency is as follows:

[0081]

[0082] Where P bess-dis is the energy storage discharge power, P bess-ch Charging power for energy storage.

[0083] In step 2, the algorithm for solving the two-layer optimization model is:

[0084] The upper-level optimization is solved based on the traversal algorithm. On this basis, the configuration ratio boundary conditions of the charging station's photovoltaic storage-load system are set to perform a cyclic solution. When the cycle exceeds the set limit configuration ratio conditions, the cycle terminates. According to the characteristics of the annual decline in 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 both technical and economic aspects, the overall system planning is guided. The upper-level photovoltaic storage-load 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 based on the optimization results of the lower layer.

[0085] The lower-level optimization is solved based on the particle swarm algorithm. The key parameters of the photovoltaic storage-load system ratio of the charging station come from the upper-level optimization algorithm. The optimized target value is the net profit returned to the upper-level optimization for calculating the target value of 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 and the health status of the battery. According to the latest photovoltaic storage-load system configuration ratio, the next year's profit and battery health status are calculated until the termination conditions are met and exit.

[0086] The advantages and effects of the present invention are as follows:

[0087] 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 light storage and charging system model. Then, the upper and lower layers are solved by different algorithms respectively. The upper layer optimization is solved using the traversal algorithm, and the lower layer optimization is solved based on the particle swarm algorithm. The key parameters of the light storage and charging system ratio of the charging station are used for 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 optimization configuration of photovoltaics and energy storage.

[0088] The probability distribution function of photovoltaic power generation output established on the basis of pseudo-data method can better simulate the random characteristics of photovoltaic power generation output under different weather conditions, thereby avoiding boundary deviation, improving the local adaptability of photovoltaic power generation output, and accurately reflecting the overall trend of random distribution of photovoltaic power generation output. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 This is a flow chart of the algorithm for solving the double-layer optimization model of the present invention. DETAILED DESCRIPTION

[0090] 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.

[0091] Example

[0092] The dual-layer coordinated configuration optimization method of the photovoltaic storage and charging smart energy system of the present invention comprises the following steps:

[0093] Step 1: construct a mathematical model for optimizing the configuration of smart photovoltaic storage and charging through an uncertain photovoltaic power generation output probability model, an uncertain charging load probability model, and a charging load probability model;

[0094] Step 2: Obtain the optimal solution for the two-layer coordinated configuration of the photovoltaic storage and charging smart energy system through the two-layer optimization model solving algorithm.

[0095] In step 1, the photovoltaic power generation output probability model is established by using the improved nonparametric kernel density estimation method, assuming that 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:

[0096]

[0097] 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:

[0098]

[0099] set up The expansion of the quantile function G on the interval (-∞, 0) is estimated as:

[0100]

[0101] From the above formula, we can get the pseudo data X (-i) :

[0102]

[0103] The kernel density estimation based on the pseudo data method is:

[0104]

[0105] Where m is an integer greater than nh but smaller than n;

[0106] Under certain conditions, the improved kernel density estimation with pseudo-data (KDEP) can eliminate the boundary deviation of local areas, but KDEP may have adverse effects on random variables affected by multiple factors using fixed bandwidth. Therefore, the adaptive kernel density estimation (AKDE) is introduced to improve the local adaptability problem of KDEP. It is a density estimation method that uses bandwidth to adjust sample data. In fact, AKDE and NKDE are the same, except that different bandwidths are used instead of fixed bandwidths in NKDE.

[0107] Assume that the photovoltaic power generation output sample is P pv1 ,P pv2 ,…,P pvn , that is, the measured data after normalization and standardization,

[0108] Its continuous probability density function is f(P pvi ), combines the improved kernel density estimation KDEP (Kernel Density Estimation with Pseudo-data) with the adaptive kernel density estimation AKDE (Adaptive Kernel Density Estimation), and uses the adaptive bandwidth h pv (P i ) replaces the fixed bandwidth h in the above formula, then the photovoltaic power generation output probability distribution model based on the pseudo-data method adaptive kernel density estimation AKDEP (Adaptive Kernel Density Estimation with Pseudo-data) is:

[0109]

[0110] Use MISE as the overall measure to analyze f n (P pv ) for f(P pv )The accuracy of the estimate;

[0111]

[0112] Ignoring the higher-order terms in the above equation, we get AMISE[f n (Ppv )]for:

[0113]

[0114] From the above formula, we can see that VAR[f n (P pv )] and BIAS[f n (P pv )] and determines the estimation accuracy of the model.

[0115] In step 1, the uncertain charging load probability model includes:

[0116] (2) The probability of electric vehicle charging time,

[0117] By studying the distribution of charging time of electric vehicles from the beginning of the day, it is found that it obeys the normal distribution, and its probability density function is:

[0118]

[0119] In the formula, σ s =3 is the standard deviation, μ s =19 is the expected value of the starting charging time;

[0120] (2) The probability of daily mileage of electric vehicles,

[0121] A load calculation model for charging piles for electric vehicles is established based on the daily mileage x. The daily mileage x reflects the electric energy required for charging the load in the charging station. It is assumed that the daily mileage x of the vehicle obeys a log-normal distribution, and its distribution function is:

[0122]

[0123] In the formula, μ l =3.2 is the expected value of mileage, σ l =0.8 is the standard deviation;

[0124]

[0125]

[0126] (3) Electric vehicle charging time and charging power,

[0127] By collecting charging information of different models, the relationship curve between the charging power and the 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;

[0128] Daily driving mileage will affect the daily power consumption of electric vehicles. Under a certain charging power, the more daily power consumption, the longer the electric vehicle charging time. The calculated charging time is:

[0129]

[0130] Where W 100 is the power consumption per 100 kilometers, x is the daily mileage, P c is the charging power.

[0131] In the step 1, the charging load probability model includes:

[0132] First, the charging demand of the kth electric vehicle in a specific charging station in the T period is modeled. Then, according to the distribution of different types of electric vehicles, the charging demand of the charging station is calculated. The driving probability g(t) at time t is calculated by comparing the daily average mileage d α Multiply them together to get the expected mileage at each moment, as shown in the following formula:

[0133]

[0134] Daily mileage d α The charge shape of the kth electric car at time t is

[0135]

[0136] Assume that when the SOC of the electric vehicle reaches a certain value of the user's psychology At this time, the state of charge Less than Electric car users will try to charge it. Suppose the user tries to charge Start charging at any time When the battery SOC reaches the user's psychological target value At the end, the amount of electricity obtained by the kth electric car at time t is

[0137]

[0138] Where, Δ is the charging time of the electric vehicle (h); L k Charging power for electric vehicles (kW);

[0139] The daily mileage probability F(d α ) can be expressed as follows:

[0140]

[0141] Taking into account all daily mileage d α The possible values ​​and the probability of their correlation, the expected charging demand of the kth electric vehicle at time t is as follows,

[0142]

[0143] Assuming that there are enough charging devices for the electric vehicles arriving at the station, the total expected charging demand of all n electric vehicles is as follows:

[0144]

[0145] In step 1, the mathematical model for optimizing the configuration of intelligent optical storage and charging includes two layers, each layer of optimization has corresponding objective functions and constraints. The upper layer objective optimization is affected by the constraints and key parameters of the layer as well as the optimization results of the lower layer. Similarly, the objective optimization problem of the lower layer is affected by the constraints and key parameters of the layer as well as the optimization results of the upper layer. The two-layer mathematical model is as follows:

[0146] min x F(x,y) stg(x,y)≤0 (45)

[0147] min y f(x,y) stH(x,y)≤0 (46)

[0148] In the formula, x, F are the decision variables, constraints and objective functions of the upper optimization layer; y, f are respectively the decision variables, constraints, and objective functions of the lower-level optimization. The upper-level decision variables of the charging station photovoltaic storage and load system collaborative configuration optimization model are the capacity of energy storage and the capacity of distributed photovoltaic power sources. The lower-level decision variables are the energy storage charging and discharging curves of the charging station. The upper-level optimization target is the return on investment of the charging station. The lower-level optimization target is the net income of the charging station of the photovoltaic storage and load system model. When determining the solution of the upper-level return on investment optimization target, it is necessary to configure the energy storage power and capacity based on the charge and discharge curve of the energy storage system according to the lower-level decision variable 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-level charging station, it is affected by the upper-level energy storage and photovoltaic power and capacity, and the optimized net income of the charging station is different under different power capacities.

[0149] (1) Upper-level optimization goals,

[0150] The upper layer of the optimization model takes the return on investment as the optimization target, and its return on investment is defined as the ratio of the average annual income to the investment cost, as an optimization evaluation of the coordinated configuration of the charging station of the solar-storage-load system.

[0151]

[0152] In the formula, NPV is the net profit of the photovoltaic load storage system of the charging station; 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 the energy storage, which is related to the scale of the energy storage system;

[0153] (2) Lower-level optimization goals,

[0154] The lower layer of the optimization model takes the net profit of the charging station as the optimization target, considers the health status of the energy storage battery, and establishes the net profit model of the charging station.

[0155]

[0156] In the formula, I j (n) is the jth benefit in year n under the charging station solar-storage system model, including the operating benefits of charging and discharging of the charging station energy storage system, the benefits of delaying the expansion of the solar-storage system, and the benefits of reducing carbon emissions; C k (n) is the kth cost of the photovoltaic and energy storage system of the charging station in the nth year, including the cost of investing in photovoltaic and energy storage systems in the nth year.

[0157] 1) The operating benefits of charging and discharging the energy storage system of the charging station. From the perspective of reducing the electricity purchase cost of the photovoltaic storage system of the charging station, the power of the photovoltaic power source and the charging load is adjusted in time according to the power output status of the photovoltaic power generation system, the load power demand and the peak and valley time-of-use electricity price of the power grid.

[0158] 2) Energy storage delays the benefits of expanding the photovoltaic storage and load system. In the two-tier model of the photovoltaic storage and load system, the electricity market price and incentive mechanism can be considered, and energy storage participates in economic operation, reducing the capacity demand for distribution network equipment and lines.

[0159] 3) Benefits of reducing carbon emissions. Using clean renewable energy to reduce or replace the power transmitted by the distribution network is equivalent to reducing primary energy generation, which can reduce carbon emissions. The benefits of equivalent carbon emissions are as follows.

[0160] 4) Initial investment cost of integrated photovoltaic storage and charging station. The investment cost of photovoltaic storage system includes the cost of photovoltaic components and equipment, energy storage battery packs, etc.

[0161] 5) Operation and maintenance costs. The normal operation of the photovoltaic load storage system equipment requires daily maintenance.

[0162] 6) Energy storage battery replacement cost. During long-term operation, as the number of charge and discharge cycles increases, it will affect the self-discharge rate and charging response capability of the energy storage battery, making the characteristics between battery cells more discrete and accelerating the attenuation of the energy storage battery capacity.

[0163] In step 1, the constraint conditions of the mathematical model for optimizing the configuration of intelligent photovoltaic storage and charging are:

[0164] (1) Charging load demand constraints,

[0165] The basic function of a charging station is to meet the power demand of the charging load. The constraints are as follows:

[0166] P pv (t)+P ess (t)+P pg (t) = P load (t) (49)

[0167] (2) Energy storage constraints,

[0168] Charging and discharging power P of the charging pile ess (t) not exceeding the energy storage discharge power limit P bess (t),

[0169] -P bess (t)≤P ess (t)≤P bess (t) (50)

[0170] 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.

[0171] SOC min ≤SOC≤SOC max (51)

[0172]

[0173] In the formula, SOH n-1 The health status of the energy storage battery.

[0174] 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 when the energy flows in the device. If the impact of energy conversion efficiency on the difference in charge and discharge power is considered, the power management analysis after considering the device loss efficiency is as follows:

[0175]

[0176] Where P bess-dis is the energy storage discharge power, P bess-ch Charging power for energy storage.

[0177] In step 2, the algorithm for solving the two-layer optimization model is:

[0178] The upper-level optimization is solved based on the traversal algorithm. On this basis, the configuration ratio boundary conditions of the charging station's photovoltaic storage-load system are set to perform a cyclic solution. When the cycle exceeds the set limit configuration ratio conditions, the cycle terminates. According to the characteristics of the annual decline in 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 both technical and economic aspects, the overall system planning is guided. The upper-level photovoltaic storage-load 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 based on the optimization results of the lower layer.

[0179] The lower-level optimization is solved based on the particle swarm algorithm. The key parameters of the photovoltaic storage-load system ratio of the charging station come from the upper-level optimization algorithm. The optimized target value is the net profit returned to the upper-level optimization for calculating the target value of 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 and the health status of the battery. According to the latest photovoltaic storage-load system configuration ratio, the next year's profit and battery health status are calculated until the termination conditions are met and exit.

[0180] 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.

[0181] 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 two-layer coordinated configuration optimization method for a photovoltaic storage and charging smart energy system, characterized in that The steps include: Step 1: construct a mathematical model for optimizing the configuration of smart photovoltaic storage and charging through an uncertain photovoltaic power generation output probability model, an uncertain charging load probability model, and a charging load probability model; Step 2: Obtain the optimal solution for the two-layer coordinated configuration of the photovoltaic storage and charging smart energy system through the two-layer optimization model solving algorithm.

2. According to claim 1, the dual-layer coordinated configuration optimization method for the photovoltaic storage and charging smart energy system is characterized in that In step 1, the photovoltaic power generation output probability model is established by using the improved nonparametric kernel density estimation method, assuming that 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: 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: set up The expansion of the quantile function G on the interval (-∞, 0) is estimated as: From the above formula, we can get the pseudo data X (-i) : The kernel density estimation based on the pseudo data method is: Where m is an integer greater than nh but smaller than n; Assume that the photovoltaic power generation output sample is P pv1 ,P pv2 ,…,P pvn , that is, the measured data after normalization and standardization, Its continuous probability density function is f(P pvi ), combines the improved kernel density estimation KDEP (Kernel Density Estimation with Pseudo-data) with the adaptive kernel density estimation AKDE (Adaptive Kernel Density Estimation), and uses the adaptive bandwidth h pv (P i ) replaces the fixed bandwidth h in the above formula, then the photovoltaic power generation output probability distribution model based on the pseudo-data method adaptive kernel density estimation AKDEP (Adaptive Kernel Density Estimation with Pseudo-data) is: Use MISE as the overall measure to analyze f n (P pv ) for f(P pv )The accuracy of the estimate; Ignoring the higher-order terms in the above equation, we get AMISE[f n (P pv )]for: From the above formula, we can see that VAR[f n (P pv )] and BIAS[f n (P pv )] and determines the estimation accuracy of the model.

3. The dual-layer coordinated configuration optimization method of the photovoltaic storage and charging smart energy system according to claim 1 is characterized in that In step 1, the uncertain charging load probability model includes: (1) Probability of electric vehicle charging time, By studying the distribution of charging time of electric vehicles from the beginning of the day, it is found that it obeys the normal distribution, and its probability density function is: In the formula, σ s =3 is the standard deviation, μ s =19 is the expected value of the starting charging time; (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 x. The daily mileage x reflects the electric energy required for charging the load in the charging station. It is assumed that the daily mileage x of the vehicle obeys a log-normal distribution, and its distribution function is: In the formula, μ l =3.2 is the expected value of mileage, σ l =0.8 is the standard deviation; (3) Electric vehicle charging time and charging power, By collecting charging information of different models, the relationship curve between the charging power and the 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; Daily driving mileage will affect the daily power consumption of electric vehicles. Under a certain charging power, the more daily power consumption, the longer the electric vehicle charging time. The calculated charging time is: Where W 100 is the power consumption per 100 kilometers, x is the daily mileage, P c is the charging power.

4. The dual-layer coordinated configuration optimization method for the photovoltaic storage and charging smart energy system according to claim 1 is characterized in that In the step 1, the charging load probability model includes: First, the charging demand of the kth electric vehicle in a specific charging station in the T period is modeled. Then, according to the distribution of different types of electric vehicles, the charging demand of the charging station is calculated. The driving probability g(t) at time t is calculated by comparing the daily average mileage d α Multiply them together to get the expected mileage at each moment, as shown in the following formula: Daily mileage d α The charge shape of the kth electric car at time t is Assume that when the SOC of the electric vehicle reaches a certain value of the user's psychology At this time, the state of charge Less than Electric car users will try to charge their electric cars. Start charging at any time When the battery SOC reaches the user's psychological target value At the end, the amount of electricity obtained by the kth electric car at time t is Where Δ is the charging time of the electric vehicle (h); L k Charging power for electric vehicles (kW); The daily mileage probability F(d α ) can be expressed as follows: Taking into account all daily mileage d α The possible values ​​and the probability of their correlation, the expected charging demand of the kth electric vehicle at time t is as follows, Assuming that there are enough charging devices for the electric vehicles arriving at the station, the total expected charging demand of all n electric vehicles is as follows:

5. The dual-layer coordinated configuration optimization method of the photovoltaic storage and charging smart energy system according to claim 1 is characterized in that In step 1, the mathematical model for optimizing the configuration of intelligent optical storage and charging includes two layers, each layer of optimization has corresponding objective functions and constraints. The upper layer objective optimization is affected by the constraints and key parameters of the layer as well as the optimization results of the lower layer. Similarly, the objective optimization problem of the lower layer is affected by the constraints and key parameters of the layer as well as the optimization results of the upper layer. The two-layer mathematical model is as follows: min x F(x,y) s.t.g(x,y)≤0 (45) min u f(x,y) s.t.h(x,y)≤0 (46) In the formula, F are the decision variables, constraints and objective functions of the upper optimization layer; f are respectively the decision variables, constraints, and objective functions of the lower-level optimization. The upper-level decision variables of the charging station photovoltaic storage and load system collaborative configuration optimization model are the capacity of energy storage and the capacity of distributed photovoltaic power sources. The lower-level decision variables are the energy storage charging and discharging curves of the charging station. The upper-level optimization target is the return on investment of the charging station. The lower-level optimization target is the net income of the charging station of the photovoltaic storage and load system model. When determining the solution of the upper-level return on investment optimization target, it is necessary to configure the energy storage power and capacity based on the charge and discharge curve of the energy storage system according to the lower-level decision variable 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-level charging station, it is affected by the upper-level energy storage and photovoltaic power and capacity, and the optimized net income of the charging station is different under different power capacities. (1) Upper-level optimization goals, The upper layer of the optimization model takes the return on investment as the optimization target, and its return on investment is defined as the ratio of the average annual income to the investment cost, as an evaluation of the coordinated configuration optimization of the charging station of the solar-storage-load system. In the formula, NPV is the net profit of the photovoltaic load storage system of the charging station; 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 the energy storage, which is related to the scale of the energy storage system; (2) Lower-level optimization goals, The lower layer of the optimization model takes the net profit of the charging station as the optimization target, considers the health status of the energy storage battery, and establishes the net profit model of the charging station. In the formula, I j (n) is the jth benefit in year n under the charging station solar-storage system model, including the operating benefits of charging and discharging of the charging station energy storage system, the benefits of delaying the expansion of the solar-storage system, and the benefits of reducing carbon emissions; C k (n) is the kth cost of the photovoltaic and energy storage system of the charging station in the nth year, including the cost of investing in photovoltaic and energy storage systems in the nth year.

6. The dual-layer coordinated configuration optimization method of the photovoltaic storage and charging smart energy system according to claim 5 is characterized in that The constraints of the mathematical model for optimizing the configuration of intelligent photovoltaic storage and charging are: (1) Charging load demand constraints, The basic function of a charging station is to meet the power demand of the charging load. The constraints are as follows: P pv (t)+P ess (t)+P pg (t)=P load (t) (49) (2) Energy storage constraints, 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) (50) 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. SOC min ≤SOC≤SOC max (51) In the formula, SOH n-1 The health status of the energy storage battery. 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 when the energy flows in the device. If the impact of energy conversion efficiency on the difference in charge and discharge power is considered, the power management analysis after considering the device loss efficiency is as follows: Where P bess-dis is the energy storage discharge power, P bess-ch Charging power for energy storage.

7. The dual-layer coordinated configuration optimization method of the photovoltaic storage and charging smart energy system according to claim 1 is characterized in that In step 2, the algorithm for solving the two-layer optimization model is: The upper-level optimization is solved based on the traversal algorithm. On this basis, the configuration ratio boundary conditions of the charging station's photovoltaic storage-load system are set to perform a cyclic solution. When the cycle exceeds the set limit configuration ratio conditions, the cycle terminates. According to the characteristics of the annual decline in 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 both technical and economic aspects, the overall system planning is guided. The upper-level photovoltaic storage-load 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 based on the optimization results of the lower layer. The lower-level optimization is solved based on the particle swarm algorithm. The key parameters of the photovoltaic storage-load system ratio of the charging station come from the upper-level optimization algorithm. The optimized target value is the net profit returned to the upper-level optimization for calculating the target value of 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 and the health status of the battery. According to the latest photovoltaic storage-load system configuration ratio, the next year's profit and battery health status are calculated until the termination conditions are met and exit.

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