Power optimization control method for electric vehicle optical storage and charging integrated station

Abstract

The invention discloses a power optimization control method for an electric vehicle optical storage and charging integrated station, and relates to the technical field of energy optimization control. The method comprises the following steps: acquiring charging data of the electric vehicle; the charging data comprises a charging quantity and a charging load; constructing a power optimization control model; the power optimization control model comprises a photovoltaic power generation model and an energy storage system model; the power optimization control model is constructed according to a target function and constraint conditions; and inputting the charging data into the power optimization control model for distribution to obtain an optimal output result of the charging station, and performing optimization scheduling according to the optimal output result. The operation efficiency of the charging station can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization control, and in particular to a power optimization control method for an electric vehicle photovoltaic storage and charging integrated station. Background Art

[0002] As the world pays more attention to environmental protection and sustainable development, electric vehicles, as representatives of new energy vehicles, are rapidly gaining market penetration. However, the widespread use of electric vehicles also places higher demands on charging infrastructure, especially in terms of charging power management, energy efficiency, and grid stability. Traditional charging stations mostly rely on power supply from the grid, which has problems such as insufficient power supply stability and high load pressure during peak hours.

[0003] In order to solve these problems, the concept of an integrated photovoltaic, energy storage and charging station for electric vehicles came into being. This integrated station combines photovoltaic power generation, energy storage systems and electric vehicle charging stations to form a comprehensive system that integrates power generation, energy storage and charging. The photovoltaic power generation system can convert solar energy into electrical energy, providing green and renewable energy for electric vehicles; the energy storage system can serve as a backup power source to ensure the normal charging of electric vehicles when photovoltaic power generation is insufficient or the power supply of the power grid is unstable; and the electric vehicle charging station is an important part of this system, responsible for transmitting electrical energy to electric vehicles.

[0004] However, in the actual operation of electric vehicle photovoltaic storage and charging integrated stations, how to effectively manage and optimize the power distribution of each part to achieve the overall optimal operation of the system is an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present invention is to provide a power optimization control method for an electric vehicle photovoltaic storage and charging integrated station, which can improve the operating efficiency of the charging station.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A power optimization control method for an electric vehicle photovoltaic storage and charging integrated station, comprising:

[0008] Acquiring charging data of the electric vehicle, wherein the charging data includes charging quantity and charging load;

[0009] Construct a power optimization control model, which includes a photovoltaic power generation model and an energy storage system model. The objective function of the power optimization control model is constructed with the goal of minimizing the power exchange between the power grid and the photovoltaic storage and charging integrated station. The constraints of the power optimization control model include the maximum photovoltaic power generation output, power balance constraints, equipment quantity constraints, and energy storage SOC upper and lower limits;

[0010] The charging data is input into the power optimization control model for distribution, an optimal output result of the charging station is obtained, and optimization scheduling is performed according to the optimal output result.

[0011] Optionally, the optimization scheduling scheme includes: when the photovoltaic power generation is greater than the maximum energy storage capacity, using photovoltaic power generation to charge the charging station battery; when the photovoltaic power generation is less than the maximum energy storage capacity, using photovoltaic power generation and the distribution network to charge the charging station battery.

[0012] Optionally, the photovoltaic power generation model is:

[0013]

[0014] Among them, P PV.th (t) is the theoretical power generated by a single solar panel; P STC is the rated power of a single solar panel; G STC is the standard light intensity; T STC is the standard ambient temperature; G(t) is the light intensity at time t; T C (t) is the surface temperature of the solar panel at time t; T EN (t) is the ambient temperature at time t; k is the power temperature coefficient.

[0015] Optionally, when photovoltaic power generation and the power distribution network are used together to charge the battery of the charging station, the power supply distribution of the photovoltaic power generation and the power distribution network is determined according to the peak-to-valley difference rate of the power distribution network.

[0016] Optionally, the calculation formula of the peak-to-valley difference rate of the distribution network is:

[0017]

[0018] Among them, f1 is the peak-to-valley difference rate of the distribution network, P max With P min It is the maximum and minimum value of distribution network load.

[0019] Optionally, the energy storage system model includes:

[0020] During the battery charging process: SOC(t) = SOC(t-1) + P B.C η C Δt / E C ;

[0021] During battery discharge: SOC(t) = SOC(t-1)-P B.D Δt / (E C η D );

[0022] Where SOC(t) and SOC(t-1) are the battery state of charge at time t and time t-1 respectively; P B.C , P B.D are the battery charging and discharging power respectively; η C , η D E is the battery charging and discharging efficiency; C is the rated capacity of the battery, and Δt is the time difference.

[0023] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0024] The present invention discloses a power optimization control method for an electric vehicle photovoltaic storage and charging integrated station, the method comprising obtaining charging data of the electric vehicle; the charging data comprising charging quantity and charging load; constructing a power optimization control model; the power optimization control model comprising a photovoltaic power generation model and an energy storage system model; the power optimization control model is constructed according to an objective function and constraint conditions; the charging data is input into the power optimization control model for distribution, the optimal output result of the charging station is obtained, and optimization scheduling is performed according to the optimal output result. The present invention can improve the operating efficiency of the charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative labor.

[0026] Figure 1 The figure is a flow chart of the power optimization control method of the integrated photovoltaic storage and charging station for electric vehicles of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The purpose of the present invention is to provide a power optimization control method for an electric vehicle photovoltaic storage and charging integrated station, which can improve the operating efficiency of the charging station.

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] like Figure 1 As shown, the present invention provides a power optimization control method for an electric vehicle photovoltaic storage and charging integrated station, comprising:

[0031] Step 100: Acquire charging data of the electric vehicle; the charging data includes charging quantity and charging load.

[0032] Step 200: construct a power optimization control model; the power optimization control model includes a photovoltaic power generation model and an energy storage system model; the power optimization control model is constructed according to an objective function and constraint conditions.

[0033] Step 300: Input the charging data into the power optimization control model for distribution, obtain the optimal output result of the charging station, and perform optimization scheduling according to the optimal output result.

[0034] In this embodiment, the objective function is constructed with the goal of minimizing the power exchange between the power grid and the photovoltaic storage and charging integrated station. The constraints include the maximum photovoltaic power generation output, power balance constraints, equipment quantity constraints, and energy storage SOC upper and lower limits.

[0035] The optimization scheduling scheme includes: when the photovoltaic power generation is greater than the maximum energy storage capacity, using photovoltaic power generation to charge the charging station battery; when the photovoltaic power generation is less than the maximum energy storage capacity, using photovoltaic power generation and the distribution network to charge the charging station battery.

[0036] When photovoltaic power generation and the distribution network are used together to charge the battery of the charging station, the power supply distribution of the photovoltaic power generation and the distribution network is determined according to the peak-to-valley difference rate of the distribution network.

[0037] As a specific implementation, the calculation formula of the peak-to-valley difference rate of the distribution network is:

[0038]

[0039] Among them, f1 is the peak-to-valley difference rate of the distribution network, P max With P min It is the maximum and minimum value of distribution network load.

[0040] As a specific implementation method, the photovoltaic power generation model and the energy storage system model are specifically expressed as follows.

[0041] The photovoltaic power generation model is:

[0042]

[0043] Among them, P PV .th(t) is the theoretical power output of a single solar panel; P STC is the rated power of a single solar panel; G STCis the standard light intensity; T STC is the standard ambient temperature; G(t) is the light intensity at time t; T C (t) is the surface temperature of the solar panel at time t; T EN (t) is the ambient temperature at time t; k is the power temperature coefficient.

[0044] The energy storage system model includes:

[0045] During the battery charging process: SOC(t) = SOC(t-1) + P B.C η C Δt / E C ;

[0046] During battery discharge: SOC(t) = SOC(t-1)-P B.D Δt / (E C η D );

[0047] Where SOC(t) and SOC(t-1) are the battery state of charge at time t and time t-1 respectively; P B.C , P B.D are the battery charging and discharging power respectively; η C , η D E is the battery charging and discharging efficiency; C is the rated capacity of the battery, and Δt is the time difference.

[0048] As another specific implementation, the difference from the previous implementation is that the photovoltaic power generation model mainly describes the power generation capacity of the photovoltaic system under different light and temperature conditions. The model can be constructed based on the physical properties of the photovoltaic cell or a simplified empirical formula.

[0049] Photovoltaic power generation P PV (t) can be calculated based on the light intensity I(t) and other environmental factors (such as temperature, humidity, etc., but some factors are often ignored for simplicity). A simplified formula is:

[0050] P PV (t) = η·I(t)·A·(1+k·(T c (t)-T ref ))

[0051] Where η is the efficiency of the photovoltaic panel, I(t) is the light intensity at time t (unit: W / m 2 ), A is the total area of ​​the photovoltaic panels (unit: m 2 ), k is the temperature coefficient (negative value, because the efficiency decreases with increasing temperature), T c (t) is the actual temperature of the photovoltaic panel, T refis the reference temperature (usually 25°C).

[0052] The SOC change of the energy storage battery can be expressed as:

[0053]

[0054] Among them, P ES (t) is the charge and discharge power of the energy storage battery at time t (positive value indicates charging, negative value indicates discharging), η ES is the charging and discharging efficiency of the energy storage battery, E cap is the rated capacity of the energy storage battery, and Δt is the time interval.

[0055] Taking the photovoltaic power generation model and energy storage system model in the second implementation as an example, the construction process of the objective function and constraint conditions is provided:

[0056] The objective function aims to minimize the power exchange between the grid and the photovoltaic storage and charging integrated station to reduce the load pressure of the grid and improve the self-sufficiency of the system. It is specifically expressed as:

[0057]

[0058] Among them, P grid (t) is the power exchange amount between the grid and the integrated photovoltaic storage and charging station at time t (a positive value indicates that the grid supplies power to the integrated station, and a negative value indicates that the integrated station supplies power to the grid), and T is the total time length.

[0059] Among the constraints, the maximum photovoltaic power generation output constraint is: The power balance constraint is: P PV (t)+P ES (t)+P grid (t) = P load (t). Where P load (t) is the charging power required by the electric vehicle at time t.

[0060] The equipment quantity constraint usually involves the capacity of the energy storage battery, the number and power of the charging piles, etc. In this embodiment, the SOC of the energy storage battery cannot exceed its upper and lower limits: SOC min ≤SOC(t)≤SOC max .

[0061] At the same time, the charging and discharging power of the energy storage battery is also limited by its maximum charging and discharging power:

[0062]

[0063] Based on the above technical solution, the following embodiments are provided.

[0064] Take the following setting as an example: an electric vehicle photovoltaic storage and charging integrated station is equipped with 100 solar panels, each with a rated power of 300W and a standard light intensity of 1000W / m 2 , the standard ambient temperature is 25℃, and the power temperature coefficient is -0.0045 / ℃. At the same time, the station has a battery pack with a total capacity of 1MWh, with a rated charge and discharge power of 500kW and 400kW, and a charge and discharge efficiency of 90% and 85%, respectively. In addition, the station serves electric vehicles in the surrounding communities, and the daily charging demand fluctuates greatly, which requires optimized scheduling based on real-time data.

[0065] Step 100: Get the charging data of the electric vehicle

[0066] At different times of the day, the charging station management system obtains the charging data of electric vehicles. For example, between 9:00 and 10:00 in the morning, 20 electric vehicles are connected for charging, with a total charging load of 120kW; between 15:00 and 16:00 in the afternoon, the number of charging vehicles increases to 40, with a total charging load of 280kW.

[0067] Step 200: Constructing a power optimization control model

[0068] According to the objective function (minimum power exchange between the grid and the photovoltaic storage and charging integrated station) and the constraints (maximum photovoltaic power generation output, power balance, number of equipment, upper and lower limits of energy storage SOC), a power optimization control model is constructed. Specifically, the photovoltaic power generation model and energy storage system model are as follows:

[0069] (1) Photovoltaic power generation model:

[0070] Assume that the light intensity G(t) at time t is 800W / m 2 , the ambient temperature TEN(t) is 28℃, the theoretical power output of a single solar panel PPV.th(t) is calculated according to the formula, and then multiplied by the number of solar panels (100 pieces) to get the total photovoltaic power generation.

[0071] (2) Energy storage system model:

[0072] In each time step Δt (e.g., 15 minutes), the SOC value is updated according to the charge and discharge status of the battery. For example, between 9:00 and 9:15 in the morning, if the photovoltaic power generation is greater than the charging load and less than the maximum storage capacity, the excess photovoltaic power generation is used to charge the battery; if the photovoltaic power generation is not enough to meet the charging load, power is obtained from the distribution network at the same time.

[0073] Step 300: Perform allocation and optimization scheduling

[0074] 9:00am to 10:00am:

[0075] At this time, the photovoltaic power generation is sufficient, but not enough to meet the charging needs of all electric vehicles alone. According to the photovoltaic power generation model and the energy storage system model, the optimal charging distribution plan is calculated. Since the photovoltaic power generation is greater than the maximum storage capacity (assuming it is 20% of the total capacity, i.e. 200kWh), photovoltaic power generation is used to charge the charging station battery to the upper limit of SOC (such as 80%), and the remaining photovoltaic power generation is directly supplied to the electric vehicles for charging.

[0076] Afternoon 15:00 to 16:00:

[0077] At this time, the demand for charging surges, and the photovoltaic power generation is not enough to meet all the needs. According to the calculation results of the peak-to-valley difference rate (f1) of the distribution network, the power supply distribution ratio of photovoltaic and distribution network is determined. Assuming that this is the peak of power consumption in the power grid, the peak-to-valley difference rate is large. In order to reduce the pressure on the power grid, photovoltaic power generation and battery discharge are used first to meet the charging demand, and the shortfall is supplemented by the distribution network.

[0078] Optimized scheduling:

[0079] During the entire process, the system monitors data such as photovoltaic power generation, battery SOC, and charging load in real time, and dynamically adjusts the power supply strategy to ensure that the power exchange between the power grid and the integrated photovoltaic storage and charging station is minimized while meeting the charging needs of electric vehicles.

[0080] Through the above embodiments, it can be seen that the power optimization control method of the electric vehicle photovoltaic storage and charging integrated station provided by the present invention can effectively optimize power distribution according to real-time data and realize green and efficient energy utilization. Therefore, it has the following beneficial effects:

[0081] This embodiment first obtains the charging data of electric vehicles to understand the current charging demand; then, a power optimization control model is constructed based on the photovoltaic power generation model and the energy storage system model. This model will comprehensively consider factors such as the output of photovoltaic power generation, the charging and discharging capacity of the energy storage system, the charging demand of electric vehicles, and the power supply stability of the power grid, and solve the optimal output result of the system by setting the objective function and constraints. Finally, optimization scheduling is performed based on the optimal output result to achieve efficient and stable operation of the electric vehicle photovoltaic storage and charging integrated station.

[0082] In summary, the power optimization control method of the electric vehicle photovoltaic storage and charging integrated station can not only effectively improve the energy utilization efficiency of the system, but also reduce the power supply pressure of the power grid, providing strong support for the widespread application of electric vehicles.

[0083] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0084] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A power optimization control method for an electric vehicle photovoltaic storage and charging integrated station, characterized in that: include: Acquiring charging data of the electric vehicle, wherein the charging data includes charging quantity and charging load; Construct a power optimization control model, which includes a photovoltaic power generation model and an energy storage system model. The objective function of the power optimization control model is constructed with the goal of minimizing the power exchange between the power grid and the photovoltaic storage and charging integrated station. The constraints of the power optimization control model include the maximum photovoltaic power generation output, power balance constraints, equipment quantity constraints, and energy storage SOC upper and lower limits; The charging data is input into the power optimization control model for distribution, an optimal output result of the charging station is obtained, and optimization scheduling is performed according to the optimal output result.

2. The power optimization control method of the electric vehicle photovoltaic storage and charging integrated station according to claim 1 is characterized in that: The optimization scheduling scheme includes: when the photovoltaic power generation is greater than the maximum energy storage capacity, using photovoltaic power generation to charge the charging station battery; when the photovoltaic power generation is less than the maximum energy storage capacity, using photovoltaic power generation and the distribution network to charge the charging station battery.

3. The power optimization control method of the electric vehicle photovoltaic storage and charging integrated station according to claim 1 is characterized in that: The photovoltaic power generation model is: Among them, P PV.th (t) is the theoretical power generated by a single solar panel; P STC is the rated power of a single solar panel; G STC is the standard light intensity; T STC is the standard ambient temperature; G(t) is the light intensity at time t; T C (t) is the surface temperature of the solar panel at time t; T EN (t) is the ambient temperature at time t; k is the power temperature coefficient.

4. The power optimization control method of the electric vehicle photovoltaic storage and charging integrated station according to claim 2 is characterized in that: When the photovoltaic power generation and the distribution network are used together to charge the battery of the charging station, the power supply distribution of the photovoltaic power generation and the distribution network is determined according to the peak-to-valley difference rate of the distribution network.

5. The power optimization control method of the electric vehicle photovoltaic storage and charging integrated station according to claim 4 is characterized in that: The calculation formula of the peak-to-valley difference rate of the distribution network is: Among them, f1 is the peak-to-valley difference rate of the distribution network, P max With P min It is the maximum and minimum value of distribution network load.

6. The power optimization control method of the electric vehicle photovoltaic storage and charging integrated station according to claim 1 is characterized in that: The energy storage system model includes: During the battery charging process: SOC(t) = SOC(t-1) + P B.C η C Δt / E C ; During battery discharge: SOC(t) = SOC(t-1) - P B.D Δt / (E C η D ); Where SOC(t) and SOC(t-1) are the battery state of charge at time t and time t-1 respectively; P B.C , P B.D are the battery charging and discharging power respectively; η C , η D E is the battery charging and discharging efficiency; C is the rated capacity of the battery, and Δt is the time difference.

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