Intelligent charging infrastructure station optimization operation method and system considering station-network interaction

Abstract

The invention discloses an intelligent charging infrastructure station optimization operation method considering station network interaction, and the method comprises the following steps: simulating the charging load of an electric vehicle through Monte Carlo sampling, building a probability density function of the initial charging time, daily mileage and expected charging duration of a user, and calculating the shortest charging duration; modeling is carried out on photovoltaic, energy storage, electric vehicles and flexible load resources in the optical storage and charging station. And establishing an optical storage and charging station-electric vehicle user master-slave game optimization scheduling model by considering station network interaction requirements. According to the method, the bidirectional interaction characteristic of the power grid and the optical storage and charging station is considered, the optical storage and charging station can transmit electric energy to the power grid in time, the phenomenon that the peak is added on the peak of the power grid can be weakened, and regulation and control of the optical storage and charging station on the flexible charging load are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for optimizing operation of an intelligent charging infrastructure station taking into account station-network interaction. Background Art

[0002] New energy vehicles have been listed as one of my country's seven strategic emerging industries. In recent years, they have developed rapidly and have broad prospects. Various types of charging infrastructure supporting them are also booming. By the end of June 2024, the total number of charging piles in China will reach 10.244 million. Compared with independent electric vehicle charging piles, new intelligent charging infrastructure represented by "photovoltaic storage charging and discharge" integrated charging stations are being promoted on a large scale due to their green and economic advantages. However, at present, the degree of digitization, intelligence, greening and integration of charging infrastructure in various places is not high, and the operating efficiency of new intelligent charging infrastructure is still low. It is difficult to make full use of dispatchable resources such as photovoltaic storage and electric vehicles, which has brought economic losses to investors and increased the difficulty of managing the distribution network. Therefore, an effective optimization operation strategy for photovoltaic storage and charging stations is a necessary measure to promote the efficient and economic operation of stations and networks.

[0003] At present, the optimization operation of charging stations can be divided into two scales: time and space. On the spatial scale, the existing research is relatively detailed. Most of them guide the spatial distribution of electric vehicle charging load through the coupling of power grid and transportation network, taking into account road toll costs and charging costs, thereby improving the resilience of the power grid and promoting the economic operation of photovoltaic storage charging stations; on the time scale, the research focuses on discussing how to guide the distribution of electric vehicle charging load on the time scale through charging and discharging electricity prices. Among them, the two most widely used technical means are: user price response method based on psychophysics, and charging station-electric vehicle interaction method based on game theory. For the user price response method based on psychophysics, the paper "Electric Vehicle Charging and Discharging Optimal Scheduling Strategy Considering User Responsiveness" (Li Jun, Liang Jiacheng, Liu Ketian, etc., "Southern Power Grid Technology", Issue 8, 2023), comprehensively considers the charging and discharging prices and the state of charge of electric vehicles, and establishes an electric vehicle user responsiveness model based on the Weber-Fenaci law. The paper "Real-time Control Optimization Model of Electric Vehicles Based on Fuzzy Evaluation of Demand Response Potential" (Zhou Xingyue, Huang Xiangmin, Zhang Yongjun, etc., "Electric Power Automation Equipment", Issue 10, 2022, 2022-10-10) combines the objective response capability and subjective response willingness of electric vehicles to establish a user response potential evaluation model, and infers the impact of factors such as charging electricity price, electricity demand and residence time on user response willingness. The paper "Research on Electric Vehicles Participating in Grid Dispatching Considering Dynamic Time-of-Use Electricity Prices" (Tan Weiyu, Lei Yu, Li Jun, etc., "Renewable Energy", Issue 3, 2020) combines user economic satisfaction and electricity convenience satisfaction to construct user comprehensive satisfaction, and proposes a dynamic time-of-use charging electricity price formulation strategy for electric vehicles considering user satisfaction. For the game-based charging station-electric vehicle interaction method, the paper "Master-slave game optimization scheduling strategy considering the interaction between integrated charging and discharging stations and electric vehicles" (Zhu Yongsheng, Chang Wen, Wu Dongya, etc., "Power System Protection and Control", Issue 7, 2024, 2024-07-31) constructs a master-slave game two-layer optimization model of electric vehicles and photovoltaic storage charging and discharging stations and coordinates the benefits of multiple subjects.

[0004] Existing technologies lack consideration of station-grid interaction. If we only consider the economic operation of the PV-storage-charging station without considering coordination with the grid, the phenomenon of "peak on peak" in the grid may be aggravated. The lack of two-way interaction limits the possibility of PV-storage-charging stations to choose to send electricity back to the grid at the right time according to the grid-connected electricity price, and cannot give full play to the regulation potential of PV-storage-charging stations.

[0005] In the existing technology, the types of elements and modeling of photovoltaic storage and charging stations are not perfect. Specifically, the current photovoltaic storage and charging stations are usually equipped with flexible equipment such as smart air conditioners and street lights, which can further increase the scheduling potential of photovoltaic storage and charging stations; flexible loads also have a certain degree of controllability, and their uncertainty and controllability should be modeled according to actual conditions.

[0006] Therefore, there is an urgent need for an optimized operation method for intelligent charging infrastructure stations that takes into account station-network interaction and increases the control of flexible charging loads by photovoltaic storage and charging stations. Summary of the invention

[0007] In order to solve the shortcomings of the prior art such as the lack of consideration of station-network interaction resulting in poor practicality, and the small scope of application resulting from the incomplete types of elements and modeling of photovoltaic charging and discharging stations, the present invention provides an optimization operation method for intelligent charging infrastructure stations considering station-network interaction, starting from changing the distribution of the time scale of the charging load, and based on the perspective of station-network interaction, the optimization operation problem of photovoltaic charging and discharging stations is studied.

[0008] The present invention adopts the following technical solution.

[0009] The present invention discloses, on one hand, a method for optimizing operation of a smart charging infrastructure station taking into account station-network interaction, comprising:

[0010] Step 1: Use Monte Carlo sampling to simulate the electric vehicle charging load, analyze the probability density function of the electric vehicle's starting charging time, daily mileage, and expected charging time, and calculate the shortest charging time for the electric vehicle;

[0011] Step 2: Construct a multi-dimensional model of the photovoltaic storage and charging station;

[0012] Step 3: Based on the multi-dimensional model of the PV-storage-charging station and the interaction requirements between the PV-storage-charging station and the power grid, a master-slave game optimization scheduling model between the PV-storage-charging station and electric vehicle users is established;

[0013] Step 4: Solve the master-slave game optimization scheduling model between the solar-storage-charging station and electric vehicle users to optimize the operation of the solar-storage-charging station.

[0014] More preferably,

[0015] In step 1, the calculation formula for the shortest charging time of an electric vehicle is as follows:

[0016]

[0017] Where, T e is the shortest charging time, s is the daily mileage, W 100 represents the energy loss per 100 km traveled, P is the maximum charging power of the electric vehicle, and η is the charging efficiency of the electric vehicle.

[0018] More preferably,

[0019] In step 2, it is determined whether the shortest charging time of the electric vehicle is less than the expected charging time. If so, the vehicle has scheduling potential; if not, the vehicle does not have scheduling potential; each electric vehicle with scheduling potential is included in the multidimensional model of the solar-storage-charging station.

[0020] More preferably,

[0021] In step 2, the multi-dimensional model of the photovoltaic storage and charging station includes a photovoltaic model, an energy storage model, an electric vehicle model and a flexible load model.

[0022] More preferably,

[0023] Wherein, the energy storage model is:

[0024]

[0025] In the formula, is the charging state of the energy storage at time t; is the discharge state of the energy storage at time t; when the energy storage is charged When the energy storage is discharged When the energy storage is neither charged nor discharged is the charging power of energy storage at time t; is the discharge power of energy storage at time t; is the maximum discharge power of energy storage; is the maximum discharge power of energy storage; is the energy storage capacity at time t; is the energy storage capacity at time t+1; η ch is the charging efficiency of energy storage; η dch is the discharge efficiency of energy storage; soc start is the initial charge state of energy storage; The energy storage capacity at the first sampling moment; E is the energy storage capacity at the first sampling moment after an optimization cycle; ess Represents energy storage capacity; soc up 、soc low It is the upper and lower limits of the energy storage charge state.

[0026] More preferably,

[0027] In step 3, the master-slave game optimization scheduling model of the photovoltaic storage and charging station and electric vehicle users is a double-layer optimization model, in which the upper layer model is the photovoltaic storage and charging station optimization scheduling model, and the lower layer model is the electric vehicle user optimization scheduling model.

[0028] More preferably,

[0029] The optimization scheduling model of the photovoltaic storage and charging station includes the first photovoltaic storage and charging station objective function based on the load standard deviation, the second photovoltaic storage and charging station objective function based on the cost, and the photovoltaic storage and charging station constraints;

[0030] The constraints of the photovoltaic storage and charging station include photovoltaic output constraints of the photovoltaic storage and charging station, internal power balance constraints of the photovoltaic storage and charging station, and charging and discharging electricity price range constraints of the photovoltaic storage and charging station.

[0031] More preferably,

[0032] The objective function of the first photovoltaic charging and storage station is as follows:

[0033]

[0034] In the formula, f 1 is the standard deviation of the load within the station; f 1 [·] indicates that the standard deviation of the load in the station is obtained based on the variables in the brackets; is the amount of electricity exchanged between the solar-storage-charging station and the grid at time t, t represents the tth sampling time; min{·} represents the minimum value in brackets in the calculation.

[0035] More preferably,

[0036] The objective function of the second photovoltaic charging and storage station is as follows:

[0037] f 2 =min{f ess +f pvcut +f grid -f ev};

[0038] In the formula, f 2 is the daily operating cost of the solar-storage-charging station; min{·} represents the minimum value in brackets in the calculation; f ess is the energy storage charging and discharging loss cost; f pvcut is the total abandoned light cost; f grid is the cost of purchasing electricity from the distribution network for the photovoltaic storage and charging station; ev Profits from charging electric vehicles.

[0039] More preferably,

[0040] The internal power balance constraint of the solar energy storage and charging station is as follows:

[0041]

[0042] In the formula, is the interaction power between the solar energy storage and charging station and the power grid at time t, is the actual photovoltaic power output at time t, is the energy storage charging and discharging power at time t, is the total charging and discharging power of the electric vehicle at time t, is the total power of the flexible load at time t.

[0043] More preferably,

[0044] The electric vehicle user optimization scheduling model includes the electric vehicle scheduling objective function and the electric vehicle scheduling constraints;

[0045] The electric vehicle scheduling constraints include electric vehicle charging and discharging state constraints, electric vehicle charging and discharging power constraints and electric vehicle capacity constraints.

[0046] More preferably,

[0047] The electric vehicle scheduling objective function is as follows:

[0048]

[0049] Among them, f lower is the electric vehicle dispatch objective function; f lower [·] indicates that the electric vehicle scheduling objective function is obtained based on the variables in the brackets; i is an integer, and i∈[1, N], N is the total number of electric vehicles; T is the total number of sampling times in the optimization period; is the charging power of the i-th electric vehicle at time t; is the discharge power of the i-th electric vehicle at time t; min{·} represents the minimum value in brackets in the calculation; is the charging electricity price of electric vehicles at time t; is the electric vehicle discharge price at time t.

[0050] More preferably,

[0051] Among them, the charging and discharging state constraints of electric vehicles are shown as follows:

[0052]

[0053] Where i is an integer, and i∈[1, N], N is the total number of electric vehicles; t is an integer, and t∈[1, T], T is the total number of sampling moments in the optimization period; is the charging status of the i-th electric vehicle at time t; is the discharge state of the i-th electric vehicle at time t; when the i-th electric vehicle is charging at time t =0; when the i-th electric car discharges at time t When the i-th electric car is neither charged nor discharged at time t Charging power for electric vehicles; is the discharge power of the electric vehicle; Maximum power for charging electric vehicles; The maximum power discharged by the electric vehicle.

[0054] On the other hand, the present invention discloses an intelligent charging infrastructure station optimization operation system taking into account station-network interaction based on an intelligent charging infrastructure station optimization operation method, a shortest charging time calculation module, a photovoltaic storage charging station modeling module, an optimization scheduling model framework module and a photovoltaic storage charging station operation optimization module;

[0055] The shortest charging time calculation module uses Monte Carlo sampling to simulate the electric vehicle charging load, analyzes the probability density function of the electric vehicle's starting charging time, daily mileage, and expected charging time, and calculates the shortest charging time of the electric vehicle;

[0056] The photovoltaic storage and charging station modeling module constructs a multi-dimensional model of the photovoltaic storage and charging station;

[0057] The optimization scheduling model framework module establishes a master-slave game optimization scheduling model between the photovoltaic storage and charging station and the electric vehicle user based on the multi-dimensional model of the photovoltaic storage and charging station and the interaction requirements between the photovoltaic storage and charging station and the power grid;

[0058] The photovoltaic storage and charging station operation optimization module solves the photovoltaic storage and charging station-electric vehicle user master-slave game optimization scheduling model to optimize the operation of the photovoltaic storage and charging station.

[0059] On the other hand, the present application discloses an electronic device, including a processor and a storage medium; the characteristics are:

[0060] The storage medium is used to store instructions;

[0061] The processor is configured to operate according to the instructions to execute the aforementioned intelligent charging infrastructure station optimization operation method.

[0062] The present application also discloses a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method for optimizing operation of the smart charging infrastructure station.

[0063] The beneficial effect of the present invention is that, compared with the prior art, taking into account the bidirectional interactive characteristics of the power grid and the photovoltaic charging and storage station, the photovoltaic charging and storage station can timely feed electric energy back to the power grid, which can reduce the "peak-on-peak" phenomenon of the power grid and further enhance the economic efficiency of the operation of the photovoltaic charging and storage station. The elements within the station are fully modeled to promote the flexible coordination and complementarity of resources within the station and increase the dispatchable potential within the station. The master-slave game interaction between the photovoltaic charging and storage station and the electric vehicle users can reduce the charging costs of electric vehicle users while increasing the photovoltaic charging and storage station's regulation of flexible charging loads, thereby achieving a win-win situation for the photovoltaic charging and storage station and the electric vehicle users.

[0064] In addition, the present invention has outstanding advantages such as strong practicability and a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1It is a flow chart of a method for optimizing operation of a smart charging infrastructure station taking into account station-network interaction in an embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of the master-slave game optimization between the photovoltaic storage charging station and electric vehicles;

[0067] Figure 3 This is a schematic diagram of the genetic algorithm flow. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0069] This application discloses a novel intelligent charging infrastructure station optimization operation method considering station-network interaction, see attached Figure 1 ,include:

[0070] Step 1: Use Monte Carlo sampling to simulate the electric vehicle charging load, analyze the probability density function of the electric vehicle's starting charging time, daily mileage, and expected charging time, and calculate the shortest charging time for the electric vehicle;

[0071] The calculation formula for the shortest charging time of electric vehicles is as follows:

[0072]

[0073] Where, T e is the shortest charging time, s is the daily mileage, W 100 represents the energy loss per 100 km traveled, P is the maximum charging power of the electric vehicle, and η is the charging efficiency of the electric vehicle.

[0074] Step 2: Construct a multi-dimensional model of the photovoltaic storage and charging station;

[0075] Determine whether the shortest charging time of an electric vehicle is less than the expected charging time. If so, the vehicle has scheduling potential; if not, the vehicle does not have scheduling potential; incorporate each electric vehicle with scheduling potential into the multidimensional model of the solar-storage-charging station.

[0076] The multi-dimensional model of the photovoltaic storage and charging station includes a photovoltaic model, an energy storage model, an electric vehicle model and a flexible load model.

[0077] Wherein, the energy storage model is:

[0078]

[0079] In the formula, is the charging state of the energy storage at time t; is the discharge state of the energy storage at time t; when the energy storage is charged When the energy storage is discharged When the energy storage is neither charged nor discharged is the charging power of energy storage at time t; is the discharge power of energy storage at time t; is the maximum discharge power of energy storage; is the maximum discharge power of energy storage; is the energy storage capacity at time t; is the energy storage capacity at time t+1; η ch is the charging efficiency of energy storage; η dch is the discharge efficiency of energy storage; soc start is the initial charge state of energy storage; The energy storage capacity at the first sampling moment; E is the energy storage capacity at the first sampling moment after an optimization cycle; ess Represents energy storage capacity; soc up 、soc low Preferably, one optimization cycle is one day, including 24 sampling moments, i.e., sampling points.

[0080] Step 3: Based on the multi-dimensional model of the PV-storage-charging station and the interaction requirements between the PV-storage-charging station and the power grid, a master-slave game optimization scheduling model between the PV-storage-charging station and electric vehicle users is established;

[0081] The master-slave game optimization scheduling model of the photovoltaic storage and charging station and electric vehicle users is a two-layer optimization model, in which the upper layer model is the photovoltaic storage and charging station optimization scheduling model, and the lower layer model is the electric vehicle user optimization scheduling model.

[0082] Those skilled in the art should know that the photovoltaic storage and charging station optimization scheduling model is a model for optimizing the operation of photovoltaic storage and charging stations, and those skilled in the art can construct it according to actual conditions; in order to improve the calculation accuracy, the model construction method proposed in the embodiment of the present invention is only a preferred embodiment, and is not an inevitable limitation for implementing the method for optimizing the operation of intelligent charging infrastructure stations that consider station-network interaction of the present invention. With photovoltaic storage and charging stations, including photovoltaic, energy storage, electric vehicle groups, and flexible load elements as the main body, an optimization scheduling model for photovoltaic storage and charging stations is established; the specific model is as follows:

[0083] The optimization scheduling model of the photovoltaic storage and charging station includes the first photovoltaic storage and charging station objective function based on the load standard deviation, the second photovoltaic storage and charging station objective function based on the cost, and the photovoltaic storage and charging station constraints;

[0084] The objective function of the first photovoltaic charging and storage station is as follows:

[0085]

[0086] In the formula, f 1 is the standard deviation of the load within the station; f 1 [·] indicates that the standard deviation of the load in the station is obtained based on the variables in the brackets; is the amount of electricity exchanged between the solar-storage-charging station and the grid at time t, t represents the tth sampling time; min{·} represents the minimum value in brackets in the calculation.

[0087] The objective function of the second photovoltaic charging and storage station is as follows:

[0088] f 2 =min{f ess +f pvcut +f grid -f ev};

[0089] In the formula, f 2 is the daily operating cost of the solar-storage-charging station; min{·} represents the minimum value in brackets in the calculation; f ess is the energy storage charging and discharging loss cost; f pvcut is the total abandoned light cost; f grid is the cost of purchasing electricity from the distribution network for the photovoltaic storage and charging station; ev Profits from charging electric vehicles.

[0090] The constraints of the photovoltaic storage and charging station include photovoltaic output constraints of the photovoltaic storage and charging station, internal power balance constraints of the photovoltaic storage and charging station, and charging and discharging electricity price range constraints of the photovoltaic storage and charging station.

[0091] The internal power balance constraint of the solar energy storage and charging station is as follows:

[0092]

[0093] In the formula, is the interaction power between the solar energy storage and charging station and the power grid at time t, is the actual photovoltaic power output at time t, is the energy storage charging and discharging power at time t, is the total charging and discharging power of the electric vehicle at time t, is the total power of the flexible load at time t.

[0094] Those skilled in the art should know that the electric vehicle user is a model for optimizing the charging and discharging behavior of electric vehicle users, and those skilled in the art can construct it according to actual conditions; in order to improve the calculation accuracy, the model construction method proposed in the embodiment of the present invention is only a preferred embodiment, and is not an inevitable limitation for implementing the method for optimizing the operation of intelligent charging infrastructure stations that considers station-network interaction of the present invention. With electric vehicle users as the main body, an optimization scheduling model for electric vehicle users is established; the specific model is as follows:

[0095] The electric vehicle user optimization scheduling model includes the electric vehicle scheduling objective function and the electric vehicle scheduling constraints;

[0096] The electric vehicle scheduling objective function is as follows:

[0097]

[0098] Among them, f lower is the electric vehicle dispatch objective function; f lower [·] indicates that the electric vehicle scheduling objective function is obtained based on the variables in the brackets; i is an integer, and i∈[1, N], N is the total number of electric vehicles; T is the total number of sampling times in the optimization period; is the charging power of the i-th electric vehicle at time t; is the discharge power of the i-th electric vehicle at time t; min{·} represents the minimum value in brackets in the calculation; is the charging electricity price of electric vehicles at time t; is the electric vehicle discharge price at time t.

[0099] The electric vehicle scheduling constraints include electric vehicle charging and discharging state constraints, electric vehicle charging and discharging power constraints and electric vehicle capacity constraints.

[0100] Among them, the charging and discharging state constraints of electric vehicles are shown as follows:

[0101]

[0102] Where i is an integer, and i∈[1, N], N is the total number of electric vehicles; t is an integer, and t∈[1, T], T is the total number of sampling moments in the optimization period; is the charging status of the i-th electric vehicle at time t; is the discharge state of the i-th electric vehicle at time t; when the i-th electric vehicle is charging at time t When the i-th electric car discharges at time t When the i-th electric car is neither charged nor discharged at time t Charging power for electric vehicles; is the discharge power of the electric vehicle; Maximum power for charging electric vehicles; The maximum power discharged by the electric vehicle.

[0103] Step 4: Solve the master-slave game optimization scheduling model between the solar-storage-charging station and electric vehicle users to optimize the operation of the solar-storage-charging station.

[0104] Example 1

[0105] According to an embodiment of the present application, a method for optimizing operation of a smart charging infrastructure station considering station-network interaction is provided. Figure 1 , the method comprises the following steps:

[0106] Step 1: Use Monte Carlo sampling to simulate the electric vehicle charging load, analyze the probability density function of the electric vehicle's starting charging time, daily mileage, and expected charging time, and calculate the shortest charging time for the electric vehicle;

[0107] Monte Carlo sampling is used to simulate the charging load of electric vehicles, and the probability density function of the user's starting charging time, daily mileage, and expected charging time is described. The probability density function of each variable is used to sample N groups of electric vehicle charging behaviors and calculate the shortest charging time, that is, the data of N vehicles are sampled. Electric vehicles with the shortest charging time less than the expected charging time can participate in the electricity price response. Among them, the charging behavior of electric vehicles includes the starting charging time, daily mileage, and expected charging time. According to an embodiment of the present application, a method for optimizing the operation of an intelligent charging infrastructure station that considers station-network interaction, the probability density of the user's starting charging time, daily mileage, and expected charging time is specifically:

[0108] Initial charging time t s , Expected charging time t l It obeys normal distribution, and the probability density function is:

[0109]

[0110] In the formula, E a (t s ) is the probability density function of the starting charging time; E c (t l ) is the probability density function of the expected charging time; μ a , μ c are the means of the initial charging time and the expected charging time respectively; σ a , σ c are the standard deviations of the initial charging time and the expected charging time, respectively.

[0111] The daily mileage s conforms to the log-normal distribution, and the probability density function is:

[0112]

[0113] In the formula, E b (s) is the probability density function of daily mileage; μb and σb are the mean and standard deviation of the logarithm of daily mileage, respectively.

[0114] According to an intelligent charging infrastructure station optimization operation method considering station-network interaction according to an embodiment of the present application, the calculation of the shortest charging time is as follows:

[0115] Minimum charging time T e The physical meaning of is the charging time required for the vehicle to charge at maximum power to meet the daily driving mileage, which can be expressed as:

[0116]

[0117] Where s is the daily mileage, W is 100 represents the energy loss per 100 km traveled, in kwh / 100km, is the sampling data of the corresponding vehicle, P is the maximum charging power of the electric vehicle, is the sampling data of the corresponding vehicle, η is the charging efficiency of the electric vehicle, is the sampling data of the corresponding vehicle.

[0118] If the shortest charging time is T e Less than the expected charging time T l , then the vehicle has scheduling potential; if the short charging time is T e Greater than or equal to the expected charging time T l , then the vehicle has no scheduling potential and needs to be charged at maximum power all the time. Among them, the expected charging time is the sampling data of the corresponding vehicle.

[0119] Step 2: Construct a multi-dimensional model of the photovoltaic storage and charging station;

[0120] Model the photovoltaic, energy storage, electric vehicles, and flexible load resources in the photovoltaic storage and charging station. Electric vehicles and other flexible loads need to be modeled taking into account their actual controllable characteristics.

[0121] According to an optimized operation method of a smart charging infrastructure station considering station-network interaction according to an embodiment of the present application, the photovoltaic, energy storage, electric vehicle and flexible load modeling are specifically as follows:

[0122] The photovoltaic output model is as follows:

[0123]

[0124] In the formula, is the actual photovoltaic output of the photovoltaic storage and charging station at time t, is the maximum photovoltaic output at time t; It means that the formula is valid for any time t. Among them, the maximum photovoltaic output at time t is a fixed value, and the maximum value of the historical photovoltaic output data obtained is used as the maximum photovoltaic output at time t.

[0125] The energy storage charging and discharging model is as follows:

[0126]

[0127] In the formula, is the charging and discharging state of the energy storage at time t; when the energy storage is charging When the energy storage is discharged When the energy storage is neither charged nor discharged is the charging and discharging power of energy storage at time t, is the maximum charging and discharging power of energy storage, is the energy storage capacity at time t; is the energy storage capacity at time t+1, η ch , η dch is the charging and discharging efficiency of energy storage, soc start is the initial charge state of energy storage; is the energy storage capacity at the first sampling time of the day; T corresponds to the last sampling point, and in the present invention, T=24, that is, the total number of sampling times per day is 24; E is the energy storage capacity at the first sampling time of the next day; ess Represents energy storage capacity, unit is kW·h; soc up 、soc low Preferably, in order to extend the normal service life of the energy storage as much as possible, the upper limit of the present invention is selected as 0.8 and the lower limit is selected as 0.2.

[0128] The charging and discharging model of electric vehicles is as follows:

[0129]

[0130] In the formula, It means that this formula is valid for any electric car; are the charging and discharging states of the i-th electric vehicle at time t; when the i-th electric vehicle is charging at time t When the i-th electric car discharges at time t When the i-th electric car is neither charged nor discharged at time t are the starting and ending charging time of the i-th electric vehicle, are the charging and discharging power of the i-th electric vehicle, are the maximum power of electric vehicle charging and discharging, is the power of the i-th electric car at time t; is the power of the i-th electric car at time t+1, are the charging and discharging efficiency of electric vehicles respectively. For the i-th electric car The amount of electricity at the moment; For the i-th electric car The amount of electricity at the moment; is the total charging power of the i-th electric vehicle; is the initial charge state of the i-th electric vehicle; E ev is the battery capacity of electric vehicles;

[0131] For dispatchable vehicles, the total charging capacity of the i-th electric vehicle is calculated according to the following formula:

[0132]

[0133] Where, T ie The shortest charging time for the i-th electric car

[0134] For non-dispatchable vehicles, the total charging capacity of the i-th electric vehicle is calculated according to the following formula:

[0135]

[0136] Where, T il is the expected charging time of the i-th electric car.

[0137] Formula (11) describes the charging and discharging state of the electric vehicle, formula (12) describes the charging and discharging power of the electric vehicle, and formulas (13)-(15) describe the power limit of the electric vehicle. Among them, for the two main charging types, the maximum charging power of AC slow charging depends on the power of the on-board charger, and the maximum power of DC charging depends on the battery management system and the output power of the charging pile. In the present invention, the maximum charging and discharging power of the electric vehicle in the historical data can be used to calculate the maximum charging and discharging power of the electric vehicle. Set to a fixed value.

[0138] In one embodiment of the present invention, the modeling formula of flexible loads such as smart air conditioners and street lamps is as follows:

[0139]

[0140] In the formula, is the power of the jth flexible device, are the maximum and minimum values ​​to which the power of the j-th flexible device can be adjusted; for example, when the j-th flexible device is a street lamp, the power of the j-th flexible device It refers to the power consumed by the street lamp at time t; It means that any flexible device can be expressed by formula (18).

[0141] Step 3: Based on the multi-dimensional model of the PV-storage-charging station and the interaction requirements between the PV-storage-charging station and the power grid, a master-slave game optimization scheduling model between the PV-storage-charging station and electric vehicle users is established;

[0142] Taking into account the needs of station-network interaction, a master-slave game optimization scheduling model of photovoltaic storage and charging stations and electric vehicle users is established. The upper optimization subject of the model is the photovoltaic storage and charging station, which includes photovoltaic, energy storage, electric vehicle groups, and flexible load elements, and the lower optimization subject is the electric vehicle user.

[0143] According to an optimized operation method of a smart charging infrastructure station considering station-network interaction according to an embodiment of the present application, the master-slave game optimization scheduling model of a photovoltaic storage charging station and an electric vehicle user includes:

[0144] See attached Figure 2 , the photovoltaic charging station and the electric vehicle users constitute two interest entities, forming a master-slave game: the photovoltaic charging station as the leader announces the charging price to the public; the electric vehicle users as followers decide the charging time according to the charging price. This model is a two-layer optimization, and the upper layer optimization of the model is a multi-objective optimization problem. The first objective function minimizes the daily operating cost of the photovoltaic charging station, and the second objective function minimizes the standard deviation of the load in the station, that is:

[0145] The objective function of the first optical storage and charging station is:

[0146]

[0147] In the formula, f 1 is the objective function of the first photovoltaic storage and charging station, which is expressed as the standard deviation of the load within the station; f 1 [·] indicates that the standard deviation of the load in the station is obtained based on the variables in the brackets; is the amount of electricity exchanged between the solar-storage-charging station and the grid at time t, t represents the tth sampling time; min{·} represents the minimum value in brackets in the calculation.

[0148] The objective function of the second optical storage and charging station:

[0149]

[0150] In the formula, f 2 is the objective function of the second PV-storage-charging station, which is expressed as the daily operating cost of the PV-storage-charging station; f 2 [·] indicates that the daily operating cost of the solar-storage-charging station is calculated based on the variables in the brackets. Specifically,

[0151] The daily operating cost of the solar-storage-charging station is based on P tf and 9 variables are obtained. is the amount of electricity that the solar-storage-charging station interacts with the power grid at time t; is the charging power of energy storage at time t; is the discharge power of energy storage at time t; is the charging electricity price of electric vehicles at time t; is the electric vehicle discharge electricity price at time t;

[0152] is the charging state variable of the electric vehicle at time t; is the discharge state variable of the electric vehicle at time t; is the actual photovoltaic output at time t; P t f is the total power of the flexible load at time t, is the sum of the powers of all flexible devices at time t; min{·} represents the minimum value in brackets in the calculation; f ess is the energy storage charging and discharging loss cost; f pvcut is the total abandoned light cost; f grid is the cost of purchasing electricity from the distribution network for the photovoltaic storage and charging station; ev Profits from charging electric vehicles.

[0153] Among them, the energy storage charging and discharging loss cost is shown as follows:

[0154]

[0155] Among them, c es It is the energy storage charging and discharging loss cost, unit: Yuan / kWh.

[0156] The benefits of electric vehicle charging are as follows:

[0157]

[0158] Among them, P t evchs is the total charging power of electric vehicles at time t, that is, the sum of the charging powers of all electric vehicles at time t. t evdchs It is the total discharge power of electric vehicles at time t, that is, the sum of the discharge powers of all electric vehicles at time t.

[0159] The cost of purchasing electricity from the distribution network for a photovoltaic storage and charging station is as follows:

[0160]

[0161] in, is the electricity price purchased from the grid at time t.

[0162] The total cost of abandoned light is as follows:

[0163]

[0164] In the formula, c cut is the unit price of abandoned light cost; is the maximum photovoltaic output at time t. is the actual photovoltaic output of the photovoltaic storage and charging station at time t. The unit price of abandoned photovoltaic cost is set according to historical abandoned photovoltaic cost data, and its preferred value is 0.1.

[0165] Station-grid interaction, that is, the interaction between the solar-storage-charging station and the power grid is mainly through f in the objective function grid Power purchase cost and f 2 The standard deviation of the load in the station is realized. Specifically, f grid Describes the photovoltaic storage and charging microgrid that reasonably coordinates the resources within the station according to the time-of-use electricity price of the distribution network to minimize the cost of purchasing electricity and alleviate the "peak on peak" of the distribution network; 2 From the perspective of the distribution network, the load fluctuation within the station should be minimized to reduce the power supply pressure of the distribution network.

[0166] It should be noted that the multi-objective problem must first be transformed into a single-objective problem by min-max normalization to eliminate differences in magnitude and dimension.

[0167] The normalization formula is as follows:

[0168]

[0169] In the formula, f k ′ is the standardized objective function value of the kth optical storage and charging station, are the maximum and minimum values ​​of the objective function of the kth optical storage and charging station respectively.

[0170] The weighting formula is as follows:

[0171]

[0172] Where, F is the objective function after multi-objective processing, n is the number of multi-objectives, in this invention, n=2, w k is the weight coefficient of the objective function of the kth optical storage and charging station in the multi-objective, which needs to be set based on experience. Preferably, the present invention considers the economic operation process of the two in a balanced manner, and sets w1=0.5; w2=0.5.

[0173] The upper-level constraints of the master-slave game optimization scheduling model between the photovoltaic storage and charging station and electric vehicle users include:

[0174] Photovoltaic output constraints of photovoltaic storage and charging stations:

[0175]

[0176] In the formula, is the actual photovoltaic output of the photovoltaic storage and charging station at time t, is the maximum photovoltaic output at time t.

[0177] The energy storage charging and discharging state, power, and capacity constraint formulas are as follows:

[0178]

[0179] In the formula, is the charging and discharging state of the energy storage at time t; when the energy storage is charging When the energy storage is discharged When the energy storage is neither charged nor discharged is the charging and discharging power of energy storage at time t, is the maximum charging and discharging power of energy storage, is the energy storage capacity at time t; is the energy storage capacity at time t+1, η ch , η dch is the charging and discharging efficiency of energy storage, soc start is the initial charge state of energy storage; is the energy storage capacity at the first sampling time of the day; T corresponds to the last sampling point in the optimization cycle. In the present invention, T=24, that is, the total number of sampling times per day is 24; E is the energy storage capacity at the first sampling time of the next day; ess Represents energy storage capacity, unit is kW·h; soc up 、soc low are the upper and lower limits of the energy storage charge state; preferably, in order to extend the normal service life of the energy storage as much as possible, the present invention selects the upper limit as 0.8 and the lower limit as 0.2.

[0180] The internal power balance constraint formula of the solar storage charging station is as follows:

[0181]

[0182] In the formula, is the interaction power between the solar energy storage and charging station and the power grid at time t, is the actual photovoltaic power output at time t, is the energy storage charging and discharging power at time t, is the total charging and discharging power of the electric vehicle at time t, is the total power of the flexible load at time t.

[0183] Charging and discharging electricity price range constraints:

[0184]

[0185] In the formula, is the charging and discharging electricity price at time t; is the electricity price purchased from the power distribution network at time t, It is the maximum value of charging and discharging electricity price.

[0186] The lower optimization of the model aims to minimize the charging cost of electric vehicles lower Minimum, that is:

[0187]

[0188] In the formula, f lower is the electric vehicle dispatch objective function; f lower [·] indicates that the electric vehicle dispatch objective function is obtained based on the variables in the brackets. Specifically, represents f lower based on Find; i is an integer, and i∈[1, N], N is the total number of electric vehicles; T is the last sampling point in the optimization period, that is, the total number of sampling moments in the optimization period; is the charging and discharging power of the i-th electric vehicle at time t; are the charging and discharging electricity prices of electric vehicles at time t.

[0189] The constraints at the lower level of the model include:

[0190] Electric vehicle charging and discharging state constraints:

[0191]

[0192] Electric vehicle charging and discharging power constraints:

[0193]

[0194] Electric vehicle capacity constraints:

[0195]

[0196] In the formula, are the charging and discharging states of the electric vehicle, are the starting and ending charging time of the electric vehicle, are the charging and discharging power of electric vehicles, are the maximum power of electric vehicle charging and discharging, are the power of electric vehicles, are the charging and discharging efficiency of electric vehicles respectively.

[0197] Step 4: Solve the master-slave game optimization scheduling model between the solar-storage-charging station and electric vehicle users to optimize the operation of the solar-storage-charging station.

[0198] Those skilled in the art should know that the two-layer optimization model can be solved by convex optimization, gradient descent method, KKT condition and other methods. Those skilled in the art can choose according to the actual situation; in order to improve the calculation accuracy, the model solving method proposed in the embodiment of the present invention is only a preferred embodiment, and is not an inevitable limitation for implementing the intelligent charging infrastructure station optimization operation method considering the station network interaction of the present invention. Figure 3 The solution method of the master-slave game optimization scheduling model of the photovoltaic storage and charging station and electric vehicle users is as follows:

[0199] The upper-level optimization is based on the photovoltaic storage and charging station, and the specific optimization variables are: Each variable has 24 dimensions, a total of 216 variables, and a genetic algorithm is used for optimization and solution. For details of the genetic algorithm solution flow chart, see Figure 3 . Specifically, first, create an initial population, set the problem scale N = 216, the population size popsize = 216, the chromosome length chromlength = 20, the crossover probability pc = 0.8, and the mutation probability pm = 0.01; then, set the established objective function, and convert the constraints into penalty functions and add them to the original objective function to form a fitness function; again, substitute the initial population into the fitness function, calculate the fitness of the population and individuals, and enter the judgment: if the termination criteria are met, exit the loop and output the optimal individual value; if the termination criteria are not met, perform selection, crossover, and mutation operations in sequence, and return to the fitness calculation function for the next round of loops until the termination criteria are met and the loop is exited. In each round of fitness calculation, the main program inputs the fitness function Variable, output fitness calculation value, where the lower layer optimizes the input Variable, output The variables, the lower layer optimization is solved by yalmip and gurobi. In the embodiment of the present application, the termination criterion is the number of iterations n=100.

[0200] The present invention also provides an intelligent charging infrastructure station optimization operation system based on the intelligent charging infrastructure station optimization operation method and considering the station-network interaction, the system comprising a shortest charging time calculation module, a photovoltaic storage charging station modeling module, an optimization scheduling model framework module and a photovoltaic storage charging station operation optimization module;

[0201] The shortest charging time calculation module uses Monte Carlo sampling to simulate the electric vehicle charging load, analyzes the probability density function of the electric vehicle's starting charging time, daily mileage, and expected charging time, and calculates the shortest charging time of the electric vehicle;

[0202] The photovoltaic storage and charging station modeling module constructs a multi-dimensional model of the photovoltaic storage and charging station;

[0203] The optimization scheduling model framework module establishes a master-slave game optimization scheduling model between the photovoltaic storage and charging station and the electric vehicle user based on the multi-dimensional model of the photovoltaic storage and charging station and the interaction requirements between the photovoltaic storage and charging station and the power grid;

[0204] The photovoltaic storage and charging station operation optimization module solves the photovoltaic storage and charging station-electric vehicle user master-slave game optimization scheduling model to optimize the operation of the photovoltaic storage and charging station.

[0205] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0206] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0207] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0208] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0209] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the operation of a smart charging infrastructure station considering station-network interaction, characterized in that: The method comprises the following steps: Step 1: Use Monte Carlo sampling to simulate the electric vehicle charging load, analyze the probability density function of the electric vehicle's starting charging time, daily mileage, and expected charging time, and calculate the shortest charging time for the electric vehicle; Step 2: Construct a multi-dimensional model of the photovoltaic storage and charging station; Step 3: Based on the multi-dimensional model of the PV-storage-charging station and the interaction requirements between the PV-storage-charging station and the power grid, a master-slave game optimization scheduling model between the PV-storage-charging station and electric vehicle users is established; Step 4: Solve the master-slave game optimization scheduling model between the solar-storage-charging station and electric vehicle users to optimize the operation of the solar-storage-charging station.

2. The method for optimizing operation of a smart charging infrastructure station considering station-network interaction according to claim 1, characterized in that: In step 1, the calculation formula for the shortest charging time of an electric vehicle is as follows: Where, T e is the shortest charging time, s is the daily mileage, W 100 represents the energy loss per 100 km traveled, P is the maximum charging power of the electric vehicle, and η is the charging efficiency of the electric vehicle.

3. The method for optimizing operation of a smart charging infrastructure station considering station-network interaction according to claim 1, characterized in that: In step 2, it is determined whether the shortest charging time of the electric vehicle is less than the expected charging time. If so, the vehicle has scheduling potential; if not, the vehicle does not have scheduling potential; each electric vehicle with scheduling potential is included in the multidimensional model of the solar-storage-charging station.

4. The method for optimizing operation of a smart charging infrastructure station considering station-network interaction according to claim 1, characterized in that: In step 2, the multi-dimensional model of the photovoltaic storage and charging station includes a photovoltaic model, an energy storage model, an electric vehicle model and a flexible load model.

5. A method for optimizing operation of a smart charging infrastructure station considering station-network interaction according to claim 4, Features: Wherein, the energy storage model is: In the formula, is the charging state of the energy storage at time t; is the discharge state of the energy storage at time t; when the energy storage is charged When the energy storage is discharged When the energy storage is neither charged nor discharged is the charging power of energy storage at time t; is the discharge power of energy storage at time t; is the maximum discharge power of energy storage; is the maximum discharge power of energy storage; is the energy storage capacity at time t; is the energy storage capacity at time t+1; η ch is the charging efficiency of energy storage; η dch is the discharge efficiency of energy storage; soc start is the initial charge state of energy storage; The energy storage capacity at the first sampling moment; E is the energy storage capacity at the first sampling moment after an optimization cycle; ess Represents energy storage capacity; soc up 、soc low It is the upper and lower limits of the energy storage charge state.

6. The method for optimizing operation of a smart charging infrastructure station considering station-network interaction according to claim 1, characterized in that: In step 3, the master-slave game optimization scheduling model of the photovoltaic storage and charging station and electric vehicle users is a double-layer optimization model, in which the upper layer model is the photovoltaic storage and charging station optimization scheduling model, and the lower layer model is the electric vehicle user optimization scheduling model.

7. The method for optimizing operation of a smart charging infrastructure station considering station-network interaction according to claim 6, characterized in that: The optimization scheduling model of the photovoltaic storage and charging station includes the first photovoltaic storage and charging station objective function based on the load standard deviation, the second photovoltaic storage and charging station objective function based on the cost, and the photovoltaic storage and charging station constraints; The constraints of the photovoltaic storage and charging station include photovoltaic output constraints of the photovoltaic storage and charging station, internal power balance constraints of the photovoltaic storage and charging station, and charging and discharging electricity price range constraints of the photovoltaic storage and charging station.

8. The method for optimizing operation of a smart charging infrastructure station considering station-network interaction according to claim 7, characterized in that: The objective function of the first photovoltaic charging and storage station is as follows: Where, f1 is the standard deviation of the load within the station; f1[·] means the standard deviation of the load within the station is obtained based on the variables in the brackets; is the amount of electricity that the solar-storage-charging station interacts with the grid at time t, where t represents the tth sampling time; min{·} indicates the minimum value in the brackets in the calculation.

9. The method for optimizing operation of a smart charging infrastructure station considering station-network interaction according to claim 7, characterized in that: The objective function of the second photovoltaic charging and storage station is as follows: f2=min{f ess +f pvcut +f grid -f ev }; Where f2 is the daily operating cost of the solar-storage-charging station; min{·} represents the minimum value in brackets in the calculation; f ess is the energy storage charging and discharging loss cost; f pvcut is the total abandoned light cost; f grid is the cost of purchasing electricity from the distribution network for the photovoltaic storage and charging station; ev Profits from charging electric vehicles.

10. The method for optimizing operation of a smart charging infrastructure station considering station-network interaction according to claim 7, characterized in that: in, The internal power balance constraint of the solar energy storage and charging station is as follows: In the formula, is the interaction power between the solar energy storage and charging station and the power grid at time t, is the actual photovoltaic power output at time t, is the energy storage charging and discharging power at time t, is the total charging and discharging power of the electric vehicle at time t, is the total power of the flexible load at time t.

11. The method for optimizing operation of a smart charging infrastructure station considering station-network interaction according to claim 6, characterized in that: The electric vehicle user optimization scheduling model includes the electric vehicle scheduling objective function and the electric vehicle scheduling constraints; The electric vehicle scheduling constraints include electric vehicle charging and discharging state constraints, electric vehicle charging and discharging power constraints and electric vehicle capacity constraints.

12. The method for optimizing operation of a smart charging infrastructure station considering station-network interaction according to claim 11, characterized in that: The electric vehicle scheduling objective function is as follows: Among them, f lower is the electric vehicle dispatch objective function; f lower [·] indicates that the electric vehicle scheduling objective function is obtained based on the variables in the brackets; i is an integer, and i∈[1, N], N is the total number of electric vehicles; T is the total number of sampling times in the optimization period; is the charging power of the i-th electric vehicle at time t; is the discharge power of the i-th electric vehicle at time t; min{·} represents the minimum value in brackets in the calculation; is the charging electricity price of electric vehicles at time t; is the electric vehicle discharge price at time t.

13. The method for optimizing operation of a smart charging infrastructure station considering station-network interaction according to claim 11, characterized in that: in, The charging and discharging state constraints of electric vehicles are shown as follows: Where i is an integer, and i∈[1, N], N is the total number of electric vehicles; t is an integer, and t∈[1, T], T is the total number of sampling moments in the optimization period; is the charging status of the i-th electric vehicle at time t; is the discharge state of the i-th electric vehicle at time t; when the i-th electric vehicle is charging at time t When the i-th electric car discharges at time t When the i-th electric car is neither charged nor discharged at time t Charging power for electric vehicles; is the discharge power of the electric vehicle; Maximum power for charging electric vehicles; The maximum power discharged by the electric vehicle.

14. An intelligent charging infrastructure station optimization operation system considering station-network interaction using the method described in any one of claims 1 to 13, characterized in that: The system includes a shortest charging time calculation module, a photovoltaic storage charging station modeling module, an optimization scheduling model framework module and a photovoltaic storage charging station operation optimization module; The shortest charging time calculation module uses Monte Carlo sampling to simulate the electric vehicle charging load, analyzes the probability density function of the electric vehicle's starting charging time, daily mileage, and expected charging time, and calculates the shortest charging time of the electric vehicle; The photovoltaic storage and charging station modeling module constructs a multi-dimensional model of the photovoltaic storage and charging station; The optimization scheduling model framework module establishes a master-slave game optimization scheduling model between the photovoltaic storage and charging station and the electric vehicle user based on the multi-dimensional model of the photovoltaic storage and charging station and the interaction requirements between the photovoltaic storage and charging station and the power grid; The photovoltaic storage and charging station operation optimization module solves the photovoltaic storage and charging station-electric vehicle user master-slave game optimization scheduling model to optimize the operation of the photovoltaic storage and charging station.

15. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method for optimizing operation of a smart charging infrastructure station according to any one of claims 1-13.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for optimizing operation of a smart charging infrastructure station described in any one of claims 1 to 13 are implemented.

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