Multi-objective optimization scheduling light storage and charging integrated coordination control method and system

Abstract

The invention discloses a multi-objective optimization scheduling optical storage and charging integrated coordination control method and system, and the method comprises the steps: analyzing the working characteristics of an optical storage and charging integrated system, and obtaining the operation characteristics of all components; carrying out resource modeling on the light storage and charging integrated system, and establishing a photovoltaic power generation model, an energy storage battery model and an electric vehicle load characteristic model; establishing a multi-target optimization scheduling model taking the lowest electricity purchase cost and the lowest cycle electricity quantity as targets; and solving through a multi-target genetic algorithm to obtain an optimal solution of the scheduling scheme which comprehensively considers the cost and the cyclic electric quantity. According to the multi-objective optimization solution, the importance degrees of the two objective functions can be artificially accepted and rejected, and optimization control strategies under the condition that the objective function results are different are obtained; the algorithm is high in adjustability, and the effectiveness of the optimized scheduling strategy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging and replacement, and in particular to a photovoltaic storage and charging integrated coordinated control method and system for multi-objective optimization scheduling. Background Art

[0002] Due to the intermittent nature of renewable energy generation, if there is no energy storage system with appropriate capacity, the disorderly charging of electric vehicles will affect the stability and economic performance of microgrid power supply.

[0003] At present, there are obvious deficiencies in the combination of photovoltaic charging stations and the mobile energy storage characteristics of electric vehicles. Two-way energy interaction cannot be achieved between "car-pile-grid", and users are not willing to participate in car-grid interaction. Therefore, coordinating the energy optimization management between various micro-sources, energy storage systems, conventional loads and electric vehicle energy consumption has become a problem that the current power industry needs to solve. It is urgent to carry out research on the relevant characteristics of orderly charging and discharging, combine factors such as electricity prices at different time periods, vehicle parking habits, and vehicle power status, and fully tap the potential for coordinated interaction between the car and the grid to achieve the optimal charging and discharging control strategy. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a multi-objective optimization scheduling photovoltaic storage and charging integrated coordinated control method to solve the problem of coordinating the energy optimization management between various micro-sources, energy storage systems, conventional loads and electric vehicle energy consumption.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for coordinated control of a photovoltaic storage and charging integration system with multi-objective optimization scheduling, comprising: analyzing the working characteristics of the photovoltaic storage and charging integration system to obtain the operating characteristics of each component;

[0008] Based on the above operating characteristics, the resources of the photovoltaic storage and charging integrated system are modeled, and models of photovoltaic power generation, energy storage batteries and electric vehicle load characteristics are established;

[0009] Based on the resource model of the integrated photovoltaic storage and charging system, a multi-objective optimization scheduling model with the lowest electricity purchase cost and the lowest circulating electricity quantity as the goals is established;

[0010] The multi-objective optimization scheduling model is solved by a multi-objective genetic algorithm to obtain an optimal solution for the scheduling scheme that comprehensively considers cost and circulating power.

[0011] As a preferred solution of the photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling described in the present invention, the operation characteristics of each component are obtained including:

[0012] The operating characteristics of each component include the operating characteristics of photovoltaic power generation, the operating characteristics of battery energy storage, and the charging behavior characteristics of electric vehicles;

[0013] According to the energy conversion of photovoltaic solar cells, the photovoltaic power generation operation characteristics are obtained, and the conversion efficiency of the photovoltaic cells is calculated. The amount of electrical energy converted from the light energy irradiated on the solar cells is expressed by the ratio of output energy to incident energy;

[0014] Analyze the effect on battery capacity decay according to battery cycle times or shelf time, temperature conditions, charge and discharge current rates, discharge depth conditions, cut-off voltage and abuse conditions to obtain the battery energy storage operation characteristics;

[0015] The electric vehicle charging behavior characteristics include bus charging behavior characteristics, utility vehicle charging behavior characteristics, official vehicle charging behavior characteristics, and private vehicle charging behavior characteristics, and analyze the spatiotemporal distribution characteristics of electric vehicle charging loads.

[0016] As a preferred solution of the photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling described in the present invention, the charging behavior characteristics of the electric vehicle are obtained including:

[0017] The bus charging behavior characteristics, the same bus two charging starting SOC is expressed as:

[0018] SOC 1 +SOC 2 =2-d oneday / d full

[0019] Among them, SOC 1 Indicates the initial state of charge of the battery, SOC 2 Indicates the charge state of the same vehicle at the beginning of nighttime charging, d oneday Indicates the total mileage of the vehicle in one day, d full Indicates the maximum mileage of the vehicle when the battery is fully charged;

[0020] The taxi charging behavior characteristics are analyzed based on the actual driving demand of one charging hour and the randomness of the taxi driving route to obtain the normal distribution N of the taxi initial charging SOC. 1 ;

[0021] The charging behavior characteristics of official vehicles are analyzed to obtain the normal distribution N of the initial charging SOC of official vehicles by building charging stations according to the scale of centralized parking of official vehicles or directly using charging piles to charge official vehicles during the night shutdown period. 2 ;

[0022] The charging behavior characteristics of private cars are analyzed based on the mileage and probability density of private car trips to obtain the probability distribution of the starting SOC when the private car is charging:

[0023]

[0024] Where d represents the vehicle's mileage, μ and σ represent the mean and standard deviation of the probability function, and SOC O Indicates the initial state of charge of the battery, SOC i Indicates the charge state of the same vehicle at the beginning of nighttime charging, d full Indicates the maximum mileage of the vehicle when the battery is fully charged.

[0025] As a preferred solution of the photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling described in the present invention, the photovoltaic storage and charging integrated system resource modeling includes:

[0026] Photovoltaic power generation modeling, according to the probability density function of light intensity and the relationship between the active output of the photovoltaic array and the light intensity, the probability density function of the active output of the photovoltaic array f is obtained S (p s ), expressed as:

[0027]

[0028] Among them, γ max represents the maximum light intensity in the area, Γ represents the gamma function, α and β are the shape parameters of the Beta distribution, and p s is the active output of the photovoltaic array, r is the light intensity at the corresponding moment, A is the area of ​​the photovoltaic array, and η is the conversion efficiency coefficient of the photovoltaic array;

[0029] The energy storage battery pack modeling includes two parts: charging and discharging. The battery charging process and the discharging process are respectively expressed as:

[0030] E(t)-(1-α)E(t-1)=P c (t)f c T c

[0031] E(t)+P f (t)f f T f =(1-α)E(t-1)

[0032] Among them, E(t) represents the battery power at time t, E(t-1) represents the battery power at the previous moment, α represents the self-discharge rate, and P c (t) represents the charging power, f c Indicates charging efficiency, T c Indicates charging time, P f (t) represents the discharge power, f f Indicates charging efficiency, T f Indicates the discharge time;

[0033] Modeling of electric vehicle load characteristics. When the power battery is charged by fast charging, the maximum acceptable charging power of the power battery at different charge states is expressed as:

[0034]

[0035] T fast,charg =χ(SOC exp )-χ(SOC arr )

[0036] Among them, P max Indicates the maximum acceptable charging power of the power battery, SOC 0 Indicates the state of charge when the power battery first reaches the maximum acceptable power, SOC 1 Indicates the state of charge when the maximum acceptable power of the power battery begins to decrease, SOC max Indicates the maximum state of charge of the power battery, β 0 is a coefficient, T fast,charg represents the fast charging time of the power battery at different initial states of charge and expected states of charge, χ(SOC exp ) is the charging time function of the power battery, which indicates the charging time taken for the power battery to charge from the lower limit of the power safety value to the desired state of charge, χ(SOC arr ) represents the actual charging time taken for the power battery to charge from the lower limit of the power safety value to the desired state of charge.

[0037] As a preferred solution of the photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling described in the present invention, the establishment of the multi-objective optimization scheduling model includes:

[0038] The electricity purchase cost C is the lowest, expressed as:

[0039]

[0040] Where p represents the constant charging power of the battery, r i Indicates the number of cycles, P gi The battery charging power at different time intervals is expressed as Δti Indicates a time interval.

[0041] As a preferred solution of the photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling described in the present invention, the establishment of the multi-objective optimization scheduling model also includes:

[0042] The cycle charge Ec is the lowest, expressed as:

[0043]

[0044] Among them, P bi Indicates the charging and discharging power of the battery pack at different time periods.

[0045] As a preferred solution of the photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling described in the present invention, solving the multi-objective optimization scheduling model includes:

[0046] The NSGA-Ⅱ multi-objective genetic algorithm is used to solve the problem. According to the multi-objective optimization scheduling model, the problem to be solved is expressed as:

[0047] (minC,minE c )=f(t sk ,P bi )

[0048] Among them, t sk Indicates the charging start time of the electric vehicle.

[0049] In a second aspect, the present invention provides a multi-objective optimization scheduling photovoltaic storage and charging integrated coordinated control system, comprising:

[0050] The analysis module is used to analyze the working characteristics of the integrated photovoltaic storage and charging system and obtain the operating characteristics of each component;

[0051] The first building module is used to model the resources of the photovoltaic storage and charging integrated system based on the operating characteristics, and establish a model of photovoltaic power generation, energy storage batteries and electric vehicle load characteristics;

[0052] The second building block is used to establish a multi-objective optimization scheduling model with the lowest electricity purchase cost and the lowest circulating electricity quantity as the goals based on the photovoltaic storage and charging integrated system resource model;

[0053] The solution module is used to solve the multi-objective optimization scheduling model through a multi-objective genetic algorithm to obtain an optimal solution for the scheduling scheme that comprehensively considers cost and circulating power.

[0054] In a third aspect, the present invention provides an electronic device, comprising:

[0055] Memory and processor;

[0056] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the integrated photovoltaic storage and charging coordinated control method for multi-objective optimization scheduling are implemented.

[0057] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the integrated photovoltaic storage and charging coordinated control method for multi-objective optimization scheduling.

[0058] Compared with the prior art, the present invention has the following beneficial effects: the present invention comprehensively considers the economic benefits of the charging and discharging station and the service life of the equipment, and the obtained multi-objective optimization solution can artificially choose the importance of the two objective functions, and obtain the optimization control strategy under different objective function results; the algorithm is highly adjustable, and through different battery pack initial SOCs, the photovoltaic output curve can obtain different power change curves and different Pareto solution sets, which can be selectively chosen in the actual application process, and the effectiveness of the optimization scheduling strategy can be improved by setting a reasonable initial SOC. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0060] Figure 1 It is a schematic diagram of the overall process of the photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling according to an embodiment of the present invention;

[0061] Figure 2 A schematic diagram of a charging power curve of a single 100A·h lithium iron phosphate battery of a multi-objective optimized scheduling photovoltaic storage and charging integrated coordinated control method according to an embodiment of the present invention;

[0062] Figure 3 A schematic diagram of a maximum acceptable charging power curve of a power battery under different charge states in a photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling according to an embodiment of the present invention;

[0063] Figure 4 This is a schematic diagram of the maximum acceptable charging power curve of the power battery under different charging times in the integrated photovoltaic storage and charging coordinated control method for multi-objective optimal scheduling described in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0067] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0068] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0069] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0070] Example 1

[0071] Reference Figure 1-Figure 4, which is an embodiment of the present invention, provides a multi-objective optimization scheduling photovoltaic storage and charging integrated coordinated control method, such as Figure 1 ,include:

[0072] S101, analyzing the working characteristics of the integrated photovoltaic storage and charging system to obtain the operating characteristics of each component.

[0073] Preferably, the operating characteristics of each component include photovoltaic power generation operating characteristics, battery energy storage operating characteristics, and electric vehicle charging behavior characteristics;

[0074] According to the energy conversion of photovoltaic solar cells, the photovoltaic power generation operation characteristics are obtained, and the conversion efficiency of photovoltaic cells is calculated. The amount of electrical energy converted from the light energy irradiated on the solar cell is expressed by the ratio of output energy to incident energy;

[0075] According to the battery cycle number or shelf time, temperature conditions, charge and discharge current rate, discharge depth conditions, cut-off voltage and abuse conditions, the effect on battery capacity decay is analyzed to obtain the battery energy storage operation characteristics;

[0076] The charging behavior characteristics of electric vehicles include the charging behavior characteristics of buses, utility vehicles, official vehicles, and private cars, and the temporal and spatial distribution characteristics of electric vehicle charging loads are analyzed.

[0077] Specifically, the battery performance parameter indicators are set, including the battery rated capacity, nominal charging rate, state of charge, power warning value, power safety value and expected state of charge, etc.

[0078] Step 1: Calculate the rated capacity of the battery. The maximum amount of electricity that a fully charged battery can discharge under specific circumstances is called the rated capacity of the battery, which is described in watt-hours (W·h) or kilowatt-hours (kW·h);

[0079] Step 2: Calculate the nominal charging rate, which is the battery capacity rate achieved by charging the power battery in 1 hour under nominal conditions (20°C);

[0080] Step 3: Calculate the state of charge of the power battery. The remaining power of the power battery is called the state of charge of the battery, which is mainly affected by the load side current and temperature. It is usually expressed by the physical quantity SOC, and its calculation formula is expressed as follows:

[0081]

[0082] Wherein, Qr represents the remaining capacity of the power battery in kW·h, and Q represents the rated capacity of the power battery in kW·h;

[0083] Step 4: Set the power battery power warning value, which means that the remaining power of the power battery is not allowed to be lower than the specified battery power. It is expressed by the physical quantity SOCw, and 25% is taken as a sign that the electric vehicle must be charged;

[0084] Step 5: Set the power battery power safety value. When the power battery is discharged, its remaining power is not allowed to be lower than the specified minimum battery remaining power. The specified minimum and maximum battery power are called battery power safety values, which can be expressed by physical quantities SOCmin and SOCmax, which are 5% and 100% respectively.

[0085] Step 6: Set the expected state of charge of the power battery, expressed by the physical quantity SOCexp. When the electric vehicle is traveling at high speed, the expected state of charge of the power battery is generally set to 85%. When the electric vehicle stays in a unit parking lot or residential area for a long time, the expected state of charge of the power battery is set to 100%.

[0086] Specifically, the photovoltaic power generation operation characteristics, the conversion efficiency of photovoltaic cells refers to the amount of light energy irradiated on the solar cell converted into electrical energy, which is expressed as the ratio of output energy to incident energy:

[0087] η = (output energy of solar cell / incident solar energy) × 100%

[0088] Among them, the output of solar cells varies with the intensity of solar radiation (light intensity). Generally, the short-circuit current is proportional to the radiation intensity, the open-circuit voltage increases slowly with the increase of radiation intensity, and the maximum output power increases almost proportionally with the radiation intensity. At the same time, the output of solar cells varies with the temperature of the battery surface. As the temperature rises, the battery voltage and conversion efficiency decrease significantly, which leads to the conclusion that the energy conversion losses of photovoltaic solar cells are mainly electrical losses and optical losses.

[0089] Battery energy storage operation characteristics, clarify the role of factors such as cycle number or shelf time, temperature conditions, charge and discharge current rate, discharge depth conditions, cut-off voltage and abuse conditions on battery capacity degradation. Among them, the effect of cycle number or shelf time on battery degradation is as follows, which expresses the functional relationship between storage time and cycle number, specifically:

[0090] R = a 1 t 1 / 2 +a 2,t t+a 2,N N

[0091] t=(A / 2B)x 2 +(e 0 / B)x

[0092] Among them, a 1 and a 2represent temperature and shelf voltage respectively, N represents the number of battery cycles, and t represents the mathematical model of lithium ion loss and time;

[0093] Furthermore, the effect of temperature conditions on battery degradation is expressed as the following Arrhenius equation, and the relationship between temperature and capacity degradation or internal resistance increase is obtained. The specific formula is:

[0094] R(t,T,SOC)=a(SOC){exp[d(SOC / T)t 1 / 2 ]}+c(SOC){exp[d(SOC / T]}

[0095] Among them, T represents, a represents, d represents, and c represents;

[0096] It should be noted that the effect of charge and discharge current rate on battery degradation obeys the inverse power rate model, indicating that the contribution of low current rate range and high current rate range to capacity degradation rate is different. Due to the limitation of diffusion coefficient and ionic conductivity of electrolyte and electrode materials, higher current rate will cause higher polarization voltage and significant increase of battery temperature. The effect of discharge depth condition on battery degradation, different material systems and the same material system have different electrochemical properties in different SOC working ranges, so the effect of discharge depth on capacity degradation is also different; the effect of cut-off voltage and abuse conditions on battery degradation, analysis shows that since electrochemical systems have a certain voltage working range, the stability of electrolyte decreases significantly after exceeding it, reducing the charge cut-off voltage and increasing the discharge cut-off voltage can effectively alleviate the battery degradation rate and extend the battery life.

[0097] Preferably, obtaining the electric vehicle charging behavior characteristics includes:

[0098] The charging behavior characteristics of buses are that the SOC at the start of charging at night is related to the driving distance before charging during the day. Therefore, the SOC at the start of two charges for the same vehicle is expressed as:

[0099] SOC 1 +SOC 2 =2-d oneday / d full

[0100] Among them, SOC 1 Indicates the initial state of charge of the battery, corresponding to the SOC below T , SOC 2 Indicates the state of charge at the start of nighttime charging for the same vehicle, corresponding to the SOC below i , d oneday Indicates the total mileage of the vehicle in one day, d full Indicates the maximum mileage of the vehicle when the battery is fully charged;

[0101] Specifically, the actual cruising range of electric buses is difficult to meet the full-day operation needs, so they need to be charged at least once during the daytime operation. During the peak operation period, the bus cannot be charged. After stopping at night and returning to the depot, it is charged for the second time to ensure that the electric bus is fully charged when it departs the next day. Assuming that the state of charge SOC of the electric bus battery is used as the standard for mandatory charging, the mileage of the electric bus before charging during the day is expressed as:

[0102] d=(SOC O -SOC T )×d full

[0103] Among them, SOC O Indicates the SOC of the battery after the last energy replenishment of the electric vehicle, which is generally 1. T Indicates the SOC threshold for charging settings, that is, the starting SOC for charging the battery, d full It represents the maximum mileage of the vehicle when the battery is fully charged, and d represents the mileage of the vehicle between two energy replenishments;

[0104] According to the above results, the time of energy replenishment during the day is calculated. After continued operation, the charge state when the operation stops at night meets the following conditions, which is expressed as:

[0105] SOC i =(SOC O -d / d full )×100%

[0106] The characteristics of taxi charging behavior are analyzed based on the actual driving demand of a single charge and the randomness of the taxi driving route, and the normal distribution N of the taxi initial charging SOC is obtained. 1 ;

[0107] Specifically, taxis generally operate in two shifts, with shifts every 12 hours. The shift handover time is mainly concentrated between 4:00-6:00 and 16:00-18:00. The actual range of pure electric taxis is 200-250km. A single charge can meet its actual driving needs. Combined with the randomness of taxi driving routes, the analysis shows that the initial charging SOC of taxis follows a normal distribution N. 1 (0.3, 0.052).

[0108] The charging behavior characteristics of official vehicles are analyzed. According to the scale of centralized parking of official vehicles, charging stations are built or charging piles are directly used to charge official vehicles during the evening shutdown period. The normal distribution N of the initial charging SOC of official vehicles is obtained. 2 ;

[0109] Specifically, official vehicles are mainly used for official business on weekdays and travel during the day. The travel time and travel route are highly random and difficult to determine. At the same time, most official vehicles currently park at designated parking locations at night, that is, they have relatively fixed and concentrated parking spaces. Therefore, charging stations are built according to the scale of centralized parking of official vehicles, or charging piles are directly used to charge official vehicles during the night stop period. Analysis shows that the initial charging SOC of official vehicles follows the normal distribution N 2 (0.4, 0.12).

[0110] The charging behavior characteristics of private cars are analyzed based on the mileage and probability density of private car trips to obtain the probability distribution of the starting SOC when private cars are charging:

[0111]

[0112] Where d represents the vehicle's mileage, μ and σ represent the mean and standard deviation of the probability function, and SOC O Indicates the initial state of charge of the battery, SOC i Indicates the charge state of the same vehicle at the beginning of nighttime charging, d full Indicates the maximum driving range of the vehicle when the battery is fully charged.

[0113] Specifically, the characteristics of private car charging behavior. In addition to being mainly used for car owners to go to and from get off work, some private cars are also used for shopping and leisure. The corresponding charging locations include residential parking lots, office parking lots, supermarkets and entertainment venues. The peak travel time in the morning is 6:30-8:00, and the travel volume reaches 70.92%. The peak travel time in the afternoon is 16:30-18:00, and the travel volume is 65%. The mileage of private car travel is log-normally distributed, and its probability density function is expressed as:

[0114]

[0115] Where d represents the vehicle's mileage, μ and σ represent the mean and standard deviation of the probability function.

[0116] S102, based on the operation characteristics, model the resources of the photovoltaic storage and charging integrated system, and establish models of photovoltaic power generation, energy storage batteries and electric vehicle load characteristics;

[0117] Preferably, resource modeling of the integrated photovoltaic storage and charging system includes:

[0118] Photovoltaic power generation modeling, according to the probability density function of light intensity and the relationship between the active output of the photovoltaic array and the light intensity, the probability density function of the active output of the photovoltaic array f is obtained S (p s ), expressed as:

[0119]

[0120] Among them, γ max represents the maximum light intensity in the area, Γ represents the gamma function, α and β are the shape parameters of the Beta distribution, and p s is the active output of the photovoltaic array, r is the light intensity at the corresponding moment, A is the area of ​​the photovoltaic array, and η is the conversion efficiency coefficient of the photovoltaic array;

[0121] Specifically, for photovoltaic power generation modeling, a photovoltaic cell unit refers to a cell with an area of ​​4 cm 2 Up to 200cm 2 The output voltage of a photovoltaic cell unit is about 0.5V. The output current generated by the photovoltaic cell unit through the internal photoelectric effect is related to the radiation spectrum of sunlight, the temperature of the environment, the intensity of sunlight and other factors. The volatility of light intensity changes in most areas can be described by Beta distribution. The probability density function of light intensity f γ (γ) can be expressed as:

[0122]

[0123] Among them, γ max represents the maximum light intensity in the area, Γ represents the gamma function, α and β are the shape parameters of the Beta distribution, which can be obtained by the light intensity fluctuation mean μ and the light intensity fluctuation standard deviation σ of the sample observation value. The conversion relationship is as follows:

[0124]

[0125] The active output of the photovoltaic array is related to factors such as the light intensity at the corresponding moment and the area of ​​the photovoltaic array. The mathematical expression of the active output of the photovoltaic array is:

[0126] p s = rAη

[0127] Among them, p s is the active output of the photovoltaic array, r is the light intensity at the corresponding moment, A is the area of ​​the photovoltaic array, η is the conversion efficiency coefficient of the photovoltaic array, the randomness and volatility of the light intensity change, the active output of the photovoltaic array will also have a high degree of uncertainty, according to the probability density function of the light intensity f γ (γ) and the relationship between the active output of the photovoltaic array and the light intensity, and then the probability density function f of the active output of the photovoltaic array is obtained S (p s ).

[0128] Energy storage battery bank modeling, such as Figure 2, including charging and discharging. The battery charging process and discharging process are respectively expressed as:

[0129] E(t)-(1-α)E(t-1)=P c (t)f c T c

[0130] E(t)+P f (t)f f T f =(1-α)E(t-1)

[0131] Among them, E(t) represents the battery power at time t, E(t-1) represents the battery power at the previous moment, α represents the self-discharge rate, and P c (t) represents the charging power, f c Indicates charging efficiency, T c Indicates charging time, P f (t) represents the discharge power, f f Indicates charging efficiency, T f Indicates the discharge time;

[0132] It should be noted that in a photovoltaic electric vehicle charging station, when the photovoltaic output meets the charging needs of the electric vehicle, if there is any surplus, the surplus will be stored in the energy storage battery; when the photovoltaic output is not enough to meet the charging needs of the electric vehicle, the energy storage battery will supply the remaining electricity to the electric vehicle for charging. Therefore, the mathematical model of the energy storage battery is divided into two parts: charging and discharging.

[0133] Modeling of electric vehicle load characteristics, such as Figure 3-4 When the power battery is charged by fast charging, the maximum acceptable charging power of the power battery under different charge states is expressed as:

[0134]

[0135] Among them, P max Indicates the maximum acceptable charging power of the power battery, in kW, SOC 0 Indicates the state of charge when the power battery first reaches the maximum acceptable power, SOC 1 Indicates the state of charge when the maximum acceptable power of the power battery begins to decrease, SOC max Indicates the maximum state of charge of the power battery, β 0 is a coefficient;

[0136] The load characteristics of electric vehicles are modeled, and a two-stage charging method is used to establish a power battery charging power model. Two-stage charging means that constant current charging is used in the early stage of charging, and when the battery voltage rises to the maximum allowable charging voltage of the battery, it is replaced by constant voltage charging, so as to avoid the problem of excessive charging current in the early stage of charging and overcharging in the later stage of constant current charging;

[0137] The charging power of the power battery based on the two-stage charging method can be expressed as:

[0138]

[0139] Among them, I con and Ucon represent the constant current and constant voltage of battery charging, in units of A and V respectively; T con,charg and T tot,charg They are the charging time of the battery in the constant current stage and the total charging time in the two stages, both in h;

[0140] Calculate the charging time of the power battery in slow charging mode. The calculation formula is as follows:

[0141]

[0142] Among them, SOC exp Indicates the expected state of charge of the power battery, SOC arr Indicates the initial charge state of the power battery;

[0143] Since the fast charging time of the power battery is generally related to the initial state of charge, the expected state of charge and the maximum acceptable charging power curve, the calculation formula for the fast charging time (minimum charging time) of the power battery at different initial states of charge and expected states of charge is:

[0144] T fast,charg =χ(SOC exp )-χ(SOC arr )

[0145] Among them, T fast,charg represents the fast charging time of the power battery at different initial states of charge and expected states of charge, χ(SOC exp ) is the charging time function of the power battery, which indicates the charging time taken for the power battery to charge from the lower limit of the power safety value to the desired state of charge, χ(SOC arr ) represents the actual charging time taken for the power battery to charge from the lower limit of the power safety value to the desired state of charge;

[0146] Among them, the power battery charging time function is expressed as:

[0147]

[0148] Among them, t 1 ,t 2 and t 3 is the inherent charging time characteristic coefficient of the battery. The power curve that changes with time during fast charging can be described by an exponential function. The parameter β 0 and α 0 Satisfies the following expression:

[0149]

[0150] The above formula can be solved by numerical calculation method to obtain the parameter α 0 .

[0151] S103, based on the photovoltaic storage and charging integrated system resource model, establish a multi-objective optimization scheduling model with the lowest electricity purchase cost and the lowest cycle power as the goals;

[0152] Preferably, step 1: establishing an objective function includes:

[0153] The first goal is to minimize the electricity purchase cost C, which can be expressed as:

[0154]

[0155] Where p represents the constant charging power of the battery, r i Indicates the number of cycles, P gi The battery charging power at different time intervals is expressed as Δt i Indicates a time interval;

[0156] The battery pack cycle capacity is defined as the sum of the discharge capacity in each period. The second goal is to minimize the cycle capacity Ec, which is expressed as:

[0157]

[0158] Among them, P bi Indicates the charging and discharging power of the battery pack at different time periods.

[0159] It should be noted that the first goal of optimized scheduling is to minimize the electricity purchase cost C. During operation, no additional electricity purchase cost is required for photovoltaic power generation. The charging cost mainly comes from the cost of the charging station purchasing electricity from the power grid. All the electricity of the charging station comes from the distribution network and the photovoltaic system. Therefore, reducing the purchase of electricity from the power grid can also improve the utilization rate of photovoltaic power generation.

[0160] Step 2: Create constraints

[0161] Constraint 1: Establish the charging time constraint of electric vehicles. The charging time is subject to the parking time constraint. The charging start and end time must be within the parking period of the electric vehicle. Set the following constraints:

[0162] t pk<t sk <t sk +E evk / P 0 <t 1k

[0163] Among them, t pk and t lk are the time when the kth electric car arrives and leaves the parking space, E evk Indicates the charging power of electric vehicles, P 0 Represents the total charging energy, t 1k represents the charging time constraint;

[0164] Constraint 2: Battery pack charge and discharge power and SOC range constraints. The battery pack charge and discharge power is constrained by the rated power of the bidirectional DC-DC converter, which can be expressed as:

[0165] |P bi |≤P bn η dd

[0166] Among them, P bn Indicates the rated power of the bidirectional DC-DC converter. If the depth of discharge is too large, it will affect the battery life. dd Represents the constraint factor, and the SOC range constraints on the battery pack are as follows:

[0167] 1-D≤SOC i ≤1

[0168] Where D is the maximum discharge depth of the battery pack, SOC i is the state of charge of the battery pack in the i-th period. According to the charge and discharge power, the state relationship of the battery pack SOC at different times can be expressed as:

[0169]

[0170] Among them, E bn is the rated capacity of the battery pack;

[0171] Constraint 3: Power supply constraint of distribution network. The power supply of distribution network to the system is subject to the rated capacity constraints of distribution transformer and AC-DC module, which can be expressed as:

[0172] P gi ≤min(P T ,P AD )

[0173] Among them, P T and P AD Respectively represent the rated capacity of the distribution transformer and AC-DC converter;

[0174] Constraint 4: System power balance constraint. The photovoltaic charging station system must satisfy the power balance relationship during operation. When the battery is in the charging state, the constraint is expressed as:

[0175]

[0176] Among them, P evi Represents the total charging power of the electric vehicle, P pvi Indicates the discharge power of the electric vehicle;

[0177] When the battery is in a discharged state, the constraint is expressed as:

[0178]

[0179] S104, solving the multi-objective optimization scheduling model through a multi-objective genetic algorithm to obtain an optimal solution for the scheduling scheme that comprehensively considers the cost and the circulating power.

[0180] Preferably, the problem is solved by using the NSGA-Ⅱ multi-objective genetic algorithm. According to the multi-objective optimization scheduling model, the problem to be solved is expressed as:

[0181] (minC,minE c )=f(t sk ,P bi )

[0182] Among them, t sk Indicates the charging start time of the electric vehicle.

[0183] Specifically, the solution steps are as follows:

[0184] Calculate the photovoltaic power generation, and obtain the output power of the photovoltaic system at each moment in the next 24 hours and the output power of the photovoltaic system based on the historical data of photovoltaic power generation and weather forecast;

[0185] Calculate the total charging power of electric vehicles, count the behavior patterns of private cars, and find that the parking start and end times and the remaining battery power all satisfy the normal distribution. According to the statistical law, predict the parking start and end time t of electric vehicles pk ,t lk and the remaining power, calculate the power demand E rk ; Randomly generate the charging start time t for each electric vehicle under the constraint sk ; Find the charging power P of each vehicle at each time ki , find the total charging power P of the electric vehicles in the station at the i-th moment evi ;

[0186] Get the battery pack charging and discharging power P bi, randomly generate the charging and discharging power of the battery pack at each moment, calculate the battery state of charge at the i-th moment, check whether the battery state of charge constraint is met, and for P that is not satisfied bi , use penalty function for processing;

[0187] Calculate the power that the solar-storage charging station purchases from the distribution network, and according to the system power balance relationship, find out the power that the system purchases from the distribution network at each moment;

[0188] Calculate the first objective function - electricity purchase cost, and obtain the total electricity purchase cost during the optimization period based on the time-of-use electricity price.

[0189] Calculate the second objective function - battery cycle power. According to the positive or negative value of Pbi, determine whether the battery is in a charging or discharging state, and obtain the battery cycle power in the optimized period.

[0190] It should be noted that the present invention comprehensively considers the economic benefits of the charging and discharging station and the service life of the equipment. The multi-objective optimization solution obtained can artificially choose the importance of the two objective functions, and obtain the optimization control strategy under different objective function results; the algorithm is highly adjustable, and through different battery pack initial SOCs, the photovoltaic output curve can obtain different power change curves and different Pareto solution sets. In the actual application process, selective choices can be made, and the effectiveness of the optimization scheduling strategy can be improved by setting a reasonable initial SOC.

[0191] The above is a schematic scheme of a multi-objective optimized scheduling photovoltaic storage and charging integrated coordinated control method of this embodiment. It should be noted that the technical scheme of the photovoltaic storage and charging integrated coordinated control system of this multi-objective optimized scheduling and the technical scheme of the photovoltaic storage and charging integrated coordinated control method of the multi-objective optimized scheduling belong to the same concept. For the details not described in detail in the technical scheme of the photovoltaic storage and charging integrated coordinated control system of the multi-objective optimized scheduling in this embodiment, please refer to the description of the technical scheme of the photovoltaic storage and charging integrated coordinated control method of the multi-objective optimized scheduling.

[0192] The integrated photovoltaic storage and charging coordinated control system of multi-objective optimization scheduling in this embodiment includes:

[0193] The analysis module is used to analyze the working characteristics of the integrated photovoltaic storage and charging system and obtain the operating characteristics of each component;

[0194] The first building module is used to model the resources of the photovoltaic storage and charging integrated system based on the operating characteristics, and to establish models of photovoltaic power generation, energy storage batteries and electric vehicle load characteristics;

[0195] The second building block is used to establish a multi-objective optimization scheduling model with the lowest electricity purchase cost and the lowest circulating electricity quantity as the goals based on the photovoltaic storage and charging integrated system resource model;

[0196] The solution module is used to solve the multi-objective optimization scheduling model through a multi-objective genetic algorithm to obtain the optimal solution of the scheduling plan that comprehensively considers the cost and the circulating power.

[0197] This embodiment also provides an electronic device, which is applicable to the case of integrated photovoltaic storage and charging coordinated control of multi-objective optimization scheduling, including:

[0198] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the integrated coordinated control method of photovoltaic storage and charging for realizing multi-objective optimization scheduling as proposed in the above embodiment.

[0199] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the photovoltaic storage and charging integrated coordinated control method for realizing multi-objective optimization scheduling as proposed in the above embodiment is implemented.

[0200] The storage medium proposed in this embodiment and the integrated coordinated control method of photovoltaic storage and charging to achieve multi-objective optimization scheduling proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0201] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0202] Example 2

[0203] Referring to Table 1, an embodiment of the present invention provides a multi-objective optimization scheduling photovoltaic storage and charging integrated coordinated control method. In order to verify its beneficial effects, comparison results of two schemes are provided.

[0204] By implementing this method to conduct simulation experiments, the time period of the simulation environment is set, the area and efficiency of the photovoltaic panels are defined, and the battery performance parameter indicators are set, including the battery rated capacity, nominal charging rate, state of charge, power warning value, power safety value and expected state of charge, etc. The statistical characteristics of the electric vehicle charging demand are determined, data collection and model construction are carried out, and the multi-objective optimization scheduling model is solved by the multi-objective genetic algorithm to obtain the comparative test results shown in Table 1.

[0205] Table 1: Comparative test index data

[0206]

[0207] As can be seen from the table, the present invention surpasses traditional methods in many key indicators, especially in terms of energy utilization efficiency, system stability, economic benefits, etc., showing significant advantages as an integrated photovoltaic storage and charging coordinated control method. This shows that the use of new methods for optimized scheduling can not only reduce costs and improve energy efficiency, but also enhance system stability and environmental sustainability.

[0208] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-objective optimization scheduling integrated photovoltaic storage and charging coordinated control method, characterized in that: include: Analyze the working characteristics of the integrated photovoltaic storage and charging system to obtain the operating characteristics of each component; Based on the above operating characteristics, the resources of the photovoltaic storage and charging integrated system are modeled, and models of photovoltaic power generation, energy storage batteries and electric vehicle load characteristics are established; Based on the resource model of the integrated photovoltaic storage and charging system, a multi-objective optimization scheduling model with the lowest electricity purchase cost and the lowest circulating electricity quantity as the goals is established; The multi-objective optimization scheduling model is solved by a multi-objective genetic algorithm to obtain an optimal solution for the scheduling scheme that comprehensively considers cost and circulating power.

2. The photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling according to claim 1 is characterized in that: The operating characteristics of each component include: The operating characteristics of each component include the operating characteristics of photovoltaic power generation, the operating characteristics of battery energy storage, and the charging behavior characteristics of electric vehicles; According to the energy conversion of photovoltaic solar cells, the photovoltaic power generation operation characteristics are obtained, and the conversion efficiency of the photovoltaic cells is calculated. The amount of electrical energy converted from the light energy irradiated on the solar cells is expressed by the ratio of output energy to incident energy; Analyze the effect on battery capacity decay according to battery cycle times or shelf time, temperature conditions, charge and discharge current rates, discharge depth conditions, cut-off voltage and abuse conditions to obtain the battery energy storage operation characteristics; The electric vehicle charging behavior characteristics include bus charging behavior characteristics, utility vehicle charging behavior characteristics, official vehicle charging behavior characteristics, and private vehicle charging behavior characteristics, and analyze the spatiotemporal distribution characteristics of electric vehicle charging loads.

3. The photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling according to claim 2 is characterized in that: Obtaining the electric vehicle charging behavior characteristics includes: The bus charging behavior characteristics, the same bus two charging starting SOC is expressed as: SOC1+SOC2=2-d oneday / d full Among them, SOC1 represents the initial state of charge of the battery, SOC2 represents the initial state of charge of the same vehicle at night, and d oneday Indicates the total mileage of the vehicle in one day, d full Indicates the maximum mileage of the vehicle when the battery is fully charged; The taxi charging behavior characteristics are analyzed based on the actual driving demand that can be met by charging once, combined with the randomness of the taxi driving route, to obtain the normal distribution N1 of the taxi's initial charging SOC; The charging behavior characteristics of official vehicles are analyzed to obtain the normal distribution N2 of the initial charging SOC of official vehicles by building charging stations according to the scale of centralized parking of official vehicles or directly using charging piles to charge official vehicles during the evening downtime period; The charging behavior characteristics of private cars are analyzed based on the mileage and probability density of private car trips to obtain the probability distribution of the starting SOC when the private car is charging: Where d represents the vehicle's mileage, μ and σ represent the mean and standard deviation of the probability function, and SOC O Indicates the initial state of charge of the battery, SOC i Indicates the charge state of the same vehicle at the beginning of nighttime charging, d full Indicates the maximum mileage of the vehicle when the battery is fully charged.

4. The photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling according to claim 1 or 3 is characterized in that: The resource modeling of the integrated photovoltaic storage and charging system includes: Photovoltaic power generation modeling, according to the probability density function of light intensity and the relationship between the active output of the photovoltaic array and the light intensity, the probability density function of the active output of the photovoltaic array f is obtained S (p s ), expressed as: Among them, γ max represents the maximum light intensity in the area, Γ represents the gamma function, α and β are the shape parameters of the Beta distribution, and p s is the active output of the photovoltaic array, r is the light intensity at the corresponding moment, A is the area of ​​the photovoltaic array, and η is the conversion efficiency coefficient of the photovoltaic array; The energy storage battery pack modeling includes two parts: charging and discharging. The battery charging process and the discharging process are respectively expressed as: E(t)-(1-α)E(t-1)=P c (t)f c T c E(t)+P f (t)f f T f =(1-α)E(t-1) Among them, E(t) represents the battery power at time t, E(t-1) represents the battery power at the previous moment, α represents the self-discharge rate, and P c (t) represents the charging power, f c Indicates charging efficiency, T c Indicates charging time, P f (t) represents the discharge power, f f Indicates charging efficiency, T f Indicates the discharge time; Modeling of electric vehicle load characteristics. When the power battery is charged by fast charging, the maximum acceptable charging power of the power battery at different charge states is expressed as: T fast,charg =χ(SOC exp )-χ(SOC arr ) Among them, P max Indicates the maximum acceptable charging power of the power battery. SOC0 indicates the state of charge when the power battery first reaches the maximum acceptable power. SOC1 indicates the state of charge when the maximum acceptable power of the power battery begins to decrease. SOC max Indicates the maximum state of charge of the power battery, β0 is a coefficient, T fast,charg represents the fast charging time of the power battery at different initial states of charge and expected states of charge, χ(SOC exp ) is the charging time function of the power battery, which indicates the charging time taken for the power battery to charge from the lower limit of the power safety value to the desired state of charge, χ(SOC arr ) represents the actual charging time taken for the power battery to charge from the lower limit of the power safety value to the desired state of charge.

5. The photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling as claimed in claim 4 is characterized in that: Establishing the multi-objective optimization scheduling model includes: The electricity purchase cost C is the lowest, expressed as: Where p represents the constant charging power of the battery, r i Indicates the number of cycles, P gi The battery charging power at different time intervals is expressed as Δt i Indicates a time interval.

6. The photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling as claimed in claim 5 is characterized in that: Establishing the multi-objective optimization scheduling model also includes: The cycle charge Ec is the lowest, expressed as: Among them, P bi Indicates the charging and discharging power of the battery pack at different time periods.

7. The photovoltaic storage and charging integrated coordinated control method for multi-objective optimization scheduling according to claim 6 is characterized in that: Solving the multi-objective optimization scheduling model includes: The NSGA-Ⅱ multi-objective genetic algorithm is used to solve the problem. According to the multi-objective optimization scheduling model, the problem to be solved is expressed as: (minC,minE c )=f(t sk ,P bi ) Among them, t sk Indicates the charging start time of the electric vehicle.

8. A multi-objective optimization scheduling photovoltaic storage and charging integrated coordinated control system, characterized in that: include, The analysis module is used to analyze the working characteristics of the integrated photovoltaic storage and charging system and obtain the operating characteristics of each component; The first building module is used to model the resources of the photovoltaic storage and charging integrated system based on the operating characteristics, and establish a model of photovoltaic power generation, energy storage batteries and electric vehicle load characteristics; The second building block is used to establish a multi-objective optimization scheduling model with the lowest electricity purchase cost and the lowest circulating electricity quantity as the goals based on the photovoltaic storage and charging integrated system resource model; The solution module is used to solve the multi-objective optimization scheduling model through a multi-objective genetic algorithm to obtain an optimal solution for the scheduling scheme that comprehensively considers cost and circulating power.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the integrated coordinated control method for photovoltaic storage and charging with multi-objective optimization scheduling as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, implement the steps of the integrated coordinated control method for photovoltaic storage and charging with multi-objective optimization scheduling as described in any one of claims 1 to 7.

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