Electric vehicle charging optimization method and device and electronic equipment

Abstract

The invention provides an electric vehicle charging optimization method and device and electronic equipment, and belongs to the technical field of new energy consumption. The method comprises the following steps: acquiring a charging power curve, a discharging power curve and a power distribution network load curve of the electric vehicle in a set time period; constructing a first objective function based on the charging power curve and the discharging power curve; the first target function represents the minimum charging cost corresponding to the charging power curve consumed by the distributed power supply in a set time period; constructing a second objective function based on the power distribution network load curve and the historical load average value curve of the power distribution network; the second objective function represents a deviation between a minimum load of the power distribution network and a historical load average value of the power distribution network in a set time period; and solving the first objective function and the second objective function, and performing charging optimization control based on a solving result of the first objective function and a solving result of the second objective function. According to the invention, the problems of low reliability and poor effect of an existing charging method for an electric power system are solved.

Description

Technical Field

[0001] The present invention relates to the field of new energy consumption technology, and specifically to an electric vehicle charging optimization method, an electric vehicle charging optimization device, an electronic device, a machine-readable storage medium, and a computer program product. Background Art

[0002] With the rapid development of electric vehicle technology, the number of electric vehicles in use has also increased rapidly. Consequently, a large number of electric vehicles have begun to be connected to the grid for charging. The randomness of electric vehicle charging during this process can significantly impact the load on the power system, posing a significant challenge to the stable and reliable operation of the power system.

[0003] Currently, the main method for optimizing electric vehicle charging periods is to use a peak-valley electricity price strategy. However, this existing solution still has the disadvantages of low reliability and poor effectiveness for the power system. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide an electric vehicle charging optimization method, device and electronic device to solve the problem that the existing electric vehicle charging period optimization means still have low reliability and poor effect for the power system.

[0005] To achieve the above objectives, an embodiment of the present invention provides an electric vehicle charging optimization method, comprising: Obtaining the charging power curve, discharging power curve and distribution network load curve of the electric vehicle within a set time period; A first objective function is constructed based on the charging power curve and the discharging power curve; the first objective function represents the minimization of charging costs corresponding to the dissipation of the charging power curve by the distributed power source within the set time period; Constructing a second objective function based on the distribution network load curve and the distribution network historical load average curve; the second objective function represents minimizing the deviation between the distribution network load and the distribution network historical load average within the set time period; The first objective function and the second objective function are solved, and charging optimization control is performed based on the solution results of the first objective function and the solution results of the second objective function.

[0006] Optionally, the distributed power source includes wind power generation, photovoltaic power generation, and gas turbine power generation; and constructing the first objective function based on the charging power curve and the discharging power curve includes: determining the wind power generation power, photovoltaic power generation power, and gas turbine power generation power at each moment in a set time period based on the charging power at each moment in the charging power curve; wherein the charging power at each moment in the charging power curve is the same as the total power generation power of the distributed power source; and the total power generation power of the distributed power source is equal to the sum of the wind power generation power, the photovoltaic power generation power, and the gas turbine power generation power; Calculating the charging cost of the electric vehicle based on the charging power curve, the discharging power curve, the charging electricity price curve within the set time period, and the discharging electricity price curve within the set time period; Calculating the wind power generation cost based on the wind power generation power, the wind turbine unit operation and maintenance coefficient, and the wind turbine unit management cost coefficient; Calculating the photovoltaic power generation cost based on the photovoltaic power generation power, the photovoltaic generator unit operation and maintenance coefficient, and the photovoltaic generator unit management cost coefficient; Calculating the power generation cost of the gas turbine based on the power generation capacity of the gas turbine, the power generation cost coefficient of the gas turbine, and the unit operation and maintenance coefficient of the gas turbine; A first objective function is constructed based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost.

[0007] Optionally, constructing a first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost includes: constructing a first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, the gas turbine power generation cost, a first weight coefficient, a second weight coefficient, and a third weight coefficient; Among them, the first weight coefficient represents the weight coefficient of the wind power generation cost; the second weight coefficient represents the weight coefficient of the photovoltaic power generation cost; and the third weight coefficient represents the weight coefficient of the gas turbine power generation cost.

[0008] Optionally, constructing a second objective function based on the distribution network load curve and the distribution network historical load average value curve includes: Calculating the distribution network load standard deviation within the set time period based on the distribution network load curve and the distribution network historical load average curve; Minimizing the standard deviation of the distribution network load to construct the second objective function; The distribution network historical load average value curve includes the distribution network historical load average value at each moment within the set time period.

[0009] On the other hand, an embodiment of the present invention further provides an electric vehicle charging optimization device, comprising: An acquisition module is used to obtain the charging power curve, discharging power curve and distribution network load curve of the electric vehicle within a set time period; A first construction module is configured to construct a first objective function based on the charging power curve and the discharging power curve; the first objective function represents the minimum charging cost corresponding to the charging power curve being absorbed by the distributed power source within the set time period; A second construction module is configured to construct a second objective function based on the distribution network load curve and the distribution network historical load average curve; the second objective function represents minimizing the deviation between the load of the distribution network and the historical load average of the distribution network within the set time period; A control module is configured to solve the first objective function and the second objective function, and perform charging optimization control based on the solution results of the first objective function and the solution results of the second objective function.

[0010] Optionally, the distributed power source includes wind power generation, photovoltaic power generation, and gas turbine power generation; and constructing the first objective function based on the charging power curve and the discharging power curve includes: determining the wind power generation power, photovoltaic power generation power, and gas turbine power generation power at each moment in a set time period based on the charging power at each moment in the charging power curve; wherein the charging power at each moment in the charging power curve is the same as the total power generation power of the distributed power source; and the total power generation power of the distributed power source is equal to the sum of the wind power generation power, the photovoltaic power generation power, and the gas turbine power generation power; Calculating the charging cost of the electric vehicle based on the charging power curve, the discharging power curve, the charging electricity price curve within the set time period, and the discharging electricity price curve within the set time period; Calculating the wind power generation cost based on the wind power generation power, the wind turbine unit operation and maintenance coefficient, and the wind turbine unit management cost coefficient; Calculating the photovoltaic power generation cost based on the photovoltaic power generation power, the photovoltaic generator unit operation and maintenance coefficient, and the photovoltaic generator unit management cost coefficient; Calculating the power generation cost of the gas turbine based on the power generation capacity of the gas turbine, the power generation cost coefficient of the gas turbine, and the unit operation and maintenance coefficient of the gas turbine; A first objective function is constructed based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost.

[0011] Optionally, constructing a first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost includes: constructing a first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, the gas turbine power generation cost, a first weight coefficient, a second weight coefficient, and a third weight coefficient; Among them, the first weight coefficient represents the weight coefficient of the wind power generation cost; the second weight coefficient represents the weight coefficient of the photovoltaic power generation cost; and the third weight coefficient represents the weight coefficient of the gas turbine power generation cost.

[0012] Optionally, constructing a second objective function based on the distribution network load curve and the distribution network historical load average value curve includes: Calculating the distribution network load standard deviation within the set time period based on the distribution network load curve and the distribution network historical load average curve; Minimizing the standard deviation of the distribution network load to construct the second objective function; The distribution network historical load average value curve includes the distribution network historical load average value at each moment within the set time period.

[0013] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned electric vehicle charging optimization method when executing the program.

[0014] On the other hand, the present invention also provides a machine-readable storage medium having a computer program stored thereon, which implements the above-mentioned electric vehicle charging optimization method when executed by a processor.

[0015] On the other hand, the present invention further provides a computer program product, comprising a computer program, which implements the above-mentioned electric vehicle charging optimization method when executed by a processor.

[0016] Through the above technical solution, the embodiment of the present invention takes into account the consumption of distributed power sources in the calculation of charging costs by constructing a first objective function; and minimizes the deviation between the load of the distribution network and the historical load average of the distribution network by constructing a second objective function, thereby improving the stability of the power system. Therefore, the embodiment of the present invention improves the reliability and charging optimization effect of electric vehicle charging optimization.

[0017] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings: Figure 1 It is a flow chart of the electric vehicle charging optimization method provided by the present invention; Figure 2 Schematic diagram of an application scenario of the electric vehicle charging optimization method provided by the present invention; Figure 3 It is a structural schematic diagram of the electric vehicle charging optimization device provided by the present invention; Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention and is not used to limit the embodiment of the present invention.

[0020] Method Example Please refer to Figure 1 , an embodiment of the present invention provides an electric vehicle charging optimization method, comprising: Step 100: Obtain the charging power curve, discharging power curve, and distribution network load curve of the electric vehicle within a set time period.

[0021] Please refer to Figure 2 The application scenario of the embodiment of the present invention is similar to the public slow charging stations near industrial parks and residential areas, and on this basis, orderly charging optimization of electric vehicles is carried out. The user drives an electric car into the charging station and sets the expected departure time. The power of the charging pile is fixed. As the control subject of this scenario, the electronic device responds to the time-of-use electricity price, integrates load forecast information with information such as the electric vehicle power, and regulates the charging period of the charging pile. On the basis of meeting the user's travel needs, the electronic device must minimize the user's charging expenses and the load fluctuations of the distribution network to the greatest extent, and play a role in peak shaving and valley filling. Electric vehicles can be various vehicles that use electricity as energy. For example, electric vehicles can be pure electric vehicles, extended-range hybrid vehicles, and plug-in hybrid vehicles.

[0022] The electronic device obtains the electric vehicle's charging power curve, discharging power curve, and distribution network load curve within a set time period. The set time period can be any charging period, such as 8:00 AM to 10:00 AM. It should be noted that the embodiments of the present invention also consider the electric vehicle's discharge power. The electric vehicle's energy storage battery is connected to the distribution network through a charging station. The charging pile in a V2G charging station generally consists of two components: an AC-DC circuit and a DC-DC circuit. When the grid load is high, the electric vehicle transmits the remaining energy in the energy storage battery to the distribution network via V2G. The electric vehicle's energy storage battery, which has both charging and discharging capabilities, offers active regulation and flexible coordination capabilities, reducing peak and valley load variations while significantly improving power quality. Energy storage battery technology effectively reduces energy variability, enabling distributed energy resources to operate with high penetration within an active distribution network. It also coordinates the dynamic balance of system power, maintaining a balance between energy supply and demand on the generation and demand sides. Electric vehicle energy storage battery technology capable of operating independently in emergency situations effectively improves the reliability of the distribution network. For industrial parks, the benefits of discharging electric vehicles represent revenue, while the costs of charging electric vehicles represent costs, in addition to battery wear and maintenance costs.

[0023] Step 200: construct a first objective function based on the charging power curve and the discharging power curve; the first objective function represents the minimization of charging costs corresponding to absorbing the charging power curve through the distributed power supply within the set time period.

[0024] The embodiment of the present invention needs to establish a multi-objective optimization function in the following form. ; .in, is the Mth objective function to be optimized; x is the variable to be optimized, is the inequality constraint of x, is the equality constraint for x.

[0025] The distributed power supply of the embodiment of the present invention may include at least one of wind power generation, photovoltaic power generation, and gas turbine power generation. That is, the distributed power supply of the embodiment of the present invention may include any one of wind power generation, photovoltaic power generation, and gas turbine power generation; or the distributed power supply of the embodiment of the present invention may include two or three of wind power generation, photovoltaic power generation, and gas turbine power generation.

[0026] In one embodiment, in order to consider the consumption of various new energy sources and improve the stability of the distribution network, the distributed power source in the embodiment of the present invention includes wind power generation, photovoltaic power generation and gas turbine power generation. The constructing of the first objective function based on the charging power curve and the discharging power curve includes: determining the wind power generation power, photovoltaic power generation power, and gas turbine power generation power at each moment in a set time period based on the charging power at each moment in the charging power curve; wherein the charging power at each moment in the charging power curve is the same as the total power generation power of the distributed power source; and the total power generation power of the distributed power source is equal to the sum of the wind power generation power, the photovoltaic power generation power, and the gas turbine power generation power; calculating the electric vehicle charging cost based on the charging power curve, the discharging power curve, the charging electricity price curve within the set time period, and the discharging electricity price curve within the set time period; calculating the wind power generation cost based on the wind power generation power, the wind turbine unit operation and maintenance coefficient, and the wind turbine unit management expense coefficient; calculating the photovoltaic power generation cost based on the photovoltaic power generation power, the photovoltaic generator unit operation and maintenance coefficient, and the photovoltaic generator unit management expense coefficient; calculating the gas turbine power generation cost based on the gas turbine power generation power, the gas turbine power generation cost coefficient, and the gas turbine unit operation and maintenance coefficient; and constructing the first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost.

[0027] The embodiment of the present invention takes into account the consumption of multiple new energy sources in the charging fee, that is, the charging power of electric vehicles is supplied by multiple power generation powers of wind power generation, photovoltaic power generation and gas turbine power generation. Therefore, the charging power at each moment of the charging power curve in the embodiment of the present invention is equal to the sum of the wind power generation power, the photovoltaic power generation power and the gas turbine power generation power. .in, represents the charging power at time t in the charging power curve, represents the wind power generation at time t, , .

[0028] In some embodiments, the charging cost of an electric vehicle can be calculated based on the charging power curve, the discharging power curve, the charging electricity price curve within the set time period, the discharging electricity price curve within the set time period, the battery unit loss cost, and the battery unit maintenance cost. The charging electricity price curve within the set time period includes the charging electricity price at each moment within the set time period. The discharging electricity price curve within the set time period includes the discharging electricity price at each moment within the set time period. The battery unit loss cost represents the battery loss cost per unit power. The battery unit maintenance cost represents the battery maintenance cost per unit power. Specifically, the calculation formula for the electric vehicle charging cost is as follows: ; in, is the charging electricity price of the electric vehicle at time t in the charging electricity price curve; is the discharge electricity price of the electric vehicle at time t in the discharge electricity price curve; is the charging power at time t in the charging power curve of the electric vehicle; is the discharge power at time t in the charging power curve of the electric vehicle; is the unit loss cost of the battery; is the unit maintenance cost of the battery, and the time period from 1 to T represents the set period; represents the charging power at time t in the charging power curve; C EV The cost of charging an electric vehicle.

[0029] In some embodiments, the cost of wind power generation is calculated based on the sum of maintenance costs and management costs. The maintenance costs of wind power generation are calculated based on the wind power generation power at each moment in a set period and the unit operation and maintenance coefficient of the wind turbine. The management costs of wind power generation are calculated based on the wind power generation power at each moment in a set period and the unit management cost coefficient of the wind turbine. The unit operation and maintenance coefficient of the wind turbine represents the operation and maintenance coefficient per unit power of the wind turbine. The unit management cost coefficient of the wind turbine represents the management cost coefficient per unit power of the wind turbine. Specifically, the cost of wind power generation is calculated using the following formula: ; ; .in Maintenance costs for wind power generation costs; is the unit operation and maintenance coefficient of the wind turbine; Administrative expenses for wind power generation costs; is the unit management cost coefficient of the wind turbine; is the wind power generation power at time t; the time period from 1 to T represents the set period; C WT The cost of wind power generation.

[0030] In some embodiments, the photovoltaic power generation cost is calculated based on the sum of maintenance costs and management costs. The maintenance cost of the photovoltaic power generation cost is calculated based on the photovoltaic power generation power and the unit operation and maintenance coefficient of the photovoltaic generator at each moment in a set period. The management cost of the photovoltaic power generation cost is calculated based on the wind power generation power and the unit management cost coefficient of the photovoltaic generator at each moment in a set period. The unit operation and maintenance coefficient of the photovoltaic generator represents the operation and maintenance coefficient per unit power of the photovoltaic generator. The unit management cost coefficient of the photovoltaic generator represents the management cost coefficient per unit power of the photovoltaic generator. Specifically, the photovoltaic power generation cost is calculated using the following formula: ; ; .in, Maintenance costs for photovoltaic power generation costs; is the unit operation and maintenance coefficient of the photovoltaic generator; The administrative expenses for the cost of photovoltaic power generation; is the unit management cost coefficient of the photovoltaic generator; is the photovoltaic power generation at time t; the time period from 1 to T represents the set period; C PV The cost of photovoltaic power generation.

[0031] In some embodiments, the gas turbine power generation cost is calculated based on the sum of fuel costs and management costs. The fuel costs of the gas turbine power generation cost are calculated based on the gas turbine power generation output and the gas turbine power generation cost coefficient. The management costs of the gas turbine power generation cost are calculated based on the gas turbine power generation output and the gas turbine unit operation and maintenance coefficient. Specifically, the gas turbine power generation cost is calculated using the following formula: ; ; .in, is the power generation capacity of the gas turbine at time t; e, f, and g are the power generation cost coefficients of the gas turbine. The unit operation and maintenance factor of the gas turbine is the unit operation and maintenance factor of the gas turbine. C MT1 Fuel cost represents the cost of generating electricity using a gas turbine. C MT2 Represents the administrative overhead of the gas turbine power generation cost. C MT The time period from 1 to T represents the set period.

[0032] The electronic device constructs a first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost. In one embodiment, to allocate the cost of consuming new energy, constructing the first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost includes: constructing the first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, the gas turbine power generation cost, a first weight coefficient, a second weight coefficient, and a third weight coefficient; wherein the first weight coefficient represents the weight coefficient of the wind power generation cost; the second weight coefficient represents the weight coefficient of the photovoltaic power generation cost; and the third weight coefficient represents the weight coefficient of the gas turbine power generation cost.

[0033] Specifically, the first objective function is expressed by the following formula: ;in, C EV the cost of charging electric vehicles; C WT The cost of wind power generation; C PV is the cost of photovoltaic power generation; C MT represents the power generation cost of the gas turbine; the time period from 1 to T represents the set period; a1 is the first weight coefficient; a2 is the second weight coefficient; a3 is the third weight coefficient; f1 is the first objective function.

[0034] Therefore, the embodiment of the present invention takes into account the consumption of multiple new energy sources in the calculation of charging costs, reduces the impact of electric vehicle charging behavior on the distribution network, and thus improves the stability of the distribution network (power system).

[0035] In addition, the first objective function also has corresponding constraints. In one embodiment, the charging station must meet the user's travel needs, that is, the state of charge of the electric vehicle battery should be fully charged when the user leaves the charging station. The embodiment of the present invention constrains the charging time, charging power and battery state of charge to avoid undercharging and overcharging during the charging process. Therefore, the constraints may include charging time constraints, charging power constraints, and electric vehicle battery state of charge constraints. Specifically, the charging time constraint is: ; and are the arrival time and expected departure time of the i-th electric vehicle respectively. The charging power constraint is: ; is the charging power of the i-th electric vehicle at time t, is the maximum charging power of the electric vehicle. The SOC constraint of the electric vehicle battery is: ; ; .in, and are the state of charge of the i-th electric vehicle at time t and t+1 respectively; Charging efficiency for electric vehicles; and Respectively i The initial state of charge of an electric vehicle when it arrives at the charging station and the maximum state of charge of the electric vehicle; Indicates that electric vehicles must be fully charged when leaving the charging station.

[0036] Step 300: construct a second objective function based on the distribution network load curve and the distribution network historical load average curve; the second objective function represents minimizing the deviation between the distribution network load and the distribution network historical load average within the set time period.

[0037] The electronic device can calculate the distribution network load standard deviation or distribution network load difference based on the distribution network load curve and the distribution network historical load average curve, so as to characterize the deviation between the load of the distribution network minimized and the distribution network historical load average value within the set time period. In one embodiment, the second objective function is constructed based on the distribution network load curve and the distribution network historical load average curve, including: calculating the distribution network load standard deviation within the set time period based on the distribution network load curve and the distribution network historical load average curve; minimizing the distribution network load standard deviation to construct the second objective function; wherein the distribution network historical load average curve includes the distribution network historical load average value at each moment within the set time period. Specifically, the second objective function is expressed by the following formula: ;in f 2 represents the second objective function, where P t is the distribution network historical load average value curve at time t The load of the distribution network; P av is the distribution network historical load average value curve at time t The average historical load of the distribution network; P av It can be the load average curve of the distribution network during the same period in history. The time period from 1 to T represents the set period.

[0038] The embodiments of the present invention minimize the standard deviation of the load of the distribution network during electric vehicle charging, thereby ensuring that the load fluctuation of the distribution network during electric vehicle charging is minimized, thereby further improving the stability of the distribution network (power system).

[0039] Step 400: Solve the first objective function and the second objective function, and perform charging optimization control based on the solution results of the first objective function and the solution results of the second objective function.

[0040] By solving the first objective function and the second objective function, the wind power generation power, the photovoltaic power generation power, the gas turbine power generation power, and the distribution network load at each moment within a set time period are obtained to minimize the charging cost and minimize the standard deviation of the distribution network load, thereby performing charging optimization control based on the above parameters. By constructing the first objective function, the embodiment of the present invention takes into account the absorption of distributed power sources in the charging cost calculation; and by constructing the second objective function, minimizes the deviation of the distribution network load from the historical average value of the distribution network load, thereby improving the stability of the power system. Therefore, the embodiment of the present invention improves the reliability and charging optimization effect of electric vehicle charging optimization.

[0041] The present invention, taking into account the consumption of new energy, provides a highly reliable, effective, and objectively scientific method for optimizing the orderly charging of electric vehicles in power systems. By establishing charging scenarios, it minimizes user charging costs and the standard deviation of the total load on the distribution network, thereby improving the reliability and effectiveness of electric vehicle charging optimization.

[0042] Device embodiment Please refer to Figure 3 On the other hand, an embodiment of the present invention further provides an electric vehicle charging optimization device, comprising: An acquisition module 301 is used to acquire a charging power curve, a discharging power curve, and a distribution network load curve of an electric vehicle within a set time period; A first construction module 302 is configured to construct a first objective function based on the charging power curve and the discharging power curve; the first objective function represents the minimum charging cost corresponding to the charging power curve being absorbed by the distributed power source within the set time period; A second construction module 303 is configured to construct a second objective function based on the distribution network load curve and the distribution network historical load average curve; the second objective function represents minimizing the deviation between the distribution network load and the distribution network historical load average within the set time period; The control module 304 is configured to solve the first objective function and the second objective function, and perform charging optimization control based on the solution results of the first objective function and the solution results of the second objective function.

[0043] The embodiment of the present invention takes into account the consumption of distributed power sources in the calculation of charging costs by constructing a first objective function; and minimizes the deviation between the load of the distribution network and the historical average load of the distribution network by constructing a second objective function, thereby improving the stability of the power system. Therefore, the embodiment of the present invention improves the reliability and charging optimization effect of electric vehicle charging optimization.

[0044] Optionally, the distributed power source includes wind power generation, photovoltaic power generation, and gas turbine power generation; and constructing the first objective function based on the charging power curve and the discharging power curve includes: determining the wind power generation power, photovoltaic power generation power, and gas turbine power generation power at each moment in a set time period based on the charging power at each moment in the charging power curve; wherein the charging power at each moment in the charging power curve is the same as the total power generation power of the distributed power source; and the total power generation power of the distributed power source is equal to the sum of the wind power generation power, the photovoltaic power generation power, and the gas turbine power generation power; Calculating the charging cost of the electric vehicle based on the charging power curve, the discharging power curve, the charging electricity price curve within the set time period, and the discharging electricity price curve within the set time period; Calculating the wind power generation cost based on the wind power generation power, the wind turbine unit operation and maintenance coefficient, and the wind turbine unit management cost coefficient; Calculating the photovoltaic power generation cost based on the photovoltaic power generation power, the photovoltaic generator unit operation and maintenance coefficient, and the photovoltaic generator unit management cost coefficient; Calculating the power generation cost of the gas turbine based on the power generation capacity of the gas turbine, the power generation cost coefficient of the gas turbine, and the unit operation and maintenance coefficient of the gas turbine; A first objective function is constructed based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost.

[0045] Optionally, constructing a first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost includes: constructing a first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, the gas turbine power generation cost, a first weight coefficient, a second weight coefficient, and a third weight coefficient; Among them, the first weight coefficient represents the weight coefficient of the wind power generation cost; the second weight coefficient represents the weight coefficient of the photovoltaic power generation cost; and the third weight coefficient represents the weight coefficient of the gas turbine power generation cost.

[0046] Optionally, constructing a second objective function based on the distribution network load curve and the distribution network historical load average value curve includes: Calculating the distribution network load standard deviation within the set time period based on the distribution network load curve and the distribution network historical load average curve; Minimizing the standard deviation of the distribution network load to construct the second objective function; The distribution network historical load average value curve includes the distribution network historical load average value at each moment within the set time period.

[0047] The electric vehicle charging optimization device includes a processor and a memory. The acquisition module 301, the first construction module 302, the second construction module 303 and the control module 304 are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.

[0048] The processor includes a kernel, which calls the corresponding program unit from the memory. There can be one or more kernels.

[0049] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0050] Figure 4 An example of a physical structure diagram of an electronic device is shown below. Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the electric vehicle charging optimization method, which includes: obtaining the charging power curve, the discharging power curve, and the distribution network load curve of the electric vehicle within a set time period; constructing a first objective function based on the charging power curve and the discharging power curve; the first objective function represents the minimization of the charging cost corresponding to the charging power curve absorbed by the distributed power source within the set time period; constructing a second objective function based on the distribution network load curve and the distribution network historical load average curve; the second objective function represents the minimization of the deviation between the distribution network load and the distribution network historical load average within the set time period; solving the first objective function and the second objective function, and performing charging optimization control based on the solution results of the first objective function and the second objective function.

[0051] Furthermore, the logic instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0052] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a machine-readable storage medium. When the computer program is executed by a processor, the computer can execute an electric vehicle charging optimization method, which includes: obtaining the charging power curve, discharge power curve and distribution network load curve of the electric vehicle within a set time period; constructing a first objective function based on the charging power curve and the discharge power curve; the first objective function represents the minimization of the charging cost corresponding to the charging power curve absorbed by the distributed power source within the set time period; constructing a second objective function based on the distribution network load curve and the distribution network historical load average curve; the second objective function represents the minimization of the deviation between the load of the distribution network and the historical load average of the distribution network within the set time period; solving the first objective function and the second objective function, and performing charging optimization control based on the solution results of the first objective function and the second objective function.

[0053] On the other hand, the present invention also provides a machine-readable storage medium having a computer program stored thereon, which is implemented when the processor executes the electric vehicle charging optimization method, the method comprising: obtaining the charging power curve, the discharging power curve and the distribution network load curve of the electric vehicle within a set time period; constructing a first objective function based on the charging power curve and the discharging power curve; the first objective function represents the minimization of the charging cost corresponding to the charging power curve absorbed by the distributed power source within the set time period; constructing a second objective function based on the distribution network load curve and the distribution network historical load average curve; the second objective function represents the minimization of the deviation between the load of the distribution network and the historical load average of the distribution network within the set time period; solving the first objective function and the second objective function, and performing charging optimization control based on the solution results of the first objective function and the second objective function.

[0054] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0055] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0056] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for optimizing electric vehicle charging, characterized in that: include: Obtaining the charging power curve, discharging power curve and distribution network load curve of the electric vehicle within a set time period; A first objective function is constructed based on the charging power curve and the discharging power curve; the first objective function represents the minimization of charging costs corresponding to the dissipation of the charging power curve by the distributed power source within the set time period; Constructing a second objective function based on the distribution network load curve and the distribution network historical load average curve; the second objective function represents minimizing the deviation between the distribution network load and the distribution network historical load average within the set time period; The first objective function and the second objective function are solved, and charging optimization control is performed based on the solution results of the first objective function and the solution results of the second objective function.

2. The electric vehicle charging optimization method according to claim 1, characterized in that: The distributed power source includes wind power generation, photovoltaic power generation, and gas turbine power generation; and constructing a first objective function based on the charging power curve and the discharging power curve includes: determining the wind power generation power, photovoltaic power generation power, and gas turbine power generation power at each moment in a set time period based on the charging power at each moment in the charging power curve; wherein the charging power at each moment in the charging power curve is the same as the total power generation power of the distributed power source; and the total power generation power of the distributed power source is equal to the sum of the wind power generation power, the photovoltaic power generation power, and the gas turbine power generation power; Calculating the charging cost of the electric vehicle based on the charging power curve, the discharging power curve, the charging electricity price curve within the set time period, and the discharging electricity price curve within the set time period; Calculating the wind power generation cost based on the wind power generation power, the wind turbine unit operation and maintenance coefficient, and the wind turbine unit management cost coefficient; Calculating the photovoltaic power generation cost based on the photovoltaic power generation power, the photovoltaic generator unit operation and maintenance coefficient, and the photovoltaic generator unit management cost coefficient; Calculating the power generation cost of the gas turbine based on the power generation capacity of the gas turbine, the power generation cost coefficient of the gas turbine, and the unit operation and maintenance coefficient of the gas turbine; A first objective function is constructed based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost.

3. The electric vehicle charging optimization method according to claim 2, characterized in that: The constructing of a first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost includes: constructing a first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, the gas turbine power generation cost, a first weight coefficient, a second weight coefficient, and a third weight coefficient; The first weight coefficient represents the weight coefficient of the wind power generation cost; the second weight coefficient represents the weight coefficient of the photovoltaic power generation cost; and the third weight coefficient represents the weight coefficient of the gas turbine power generation cost.

4. The electric vehicle charging optimization method according to claim 1, characterized in that: The constructing of a second objective function based on the distribution network load curve and the distribution network historical load average value curve includes: Calculating the distribution network load standard deviation within the set time period based on the distribution network load curve and the distribution network historical load average curve; Minimizing the standard deviation of the distribution network load to construct the second objective function; The distribution network historical load average value curve includes the distribution network historical load average value at each moment within the set time period.

5. An electric vehicle charging optimization device, characterized in that: include: An acquisition module is used to obtain the charging power curve, discharging power curve and distribution network load curve of the electric vehicle within a set time period; A first construction module is configured to construct a first objective function based on the charging power curve and the discharging power curve; the first objective function represents the minimum charging cost corresponding to the charging power curve being absorbed by the distributed power source within the set time period; A second construction module is configured to construct a second objective function based on the distribution network load curve and the distribution network historical load average curve; the second objective function represents minimizing the deviation between the load of the distribution network and the historical load average of the distribution network within the set time period; A control module is configured to solve the first objective function and the second objective function, and perform charging optimization control based on the solution results of the first objective function and the solution results of the second objective function.

6. The electric vehicle charging optimization device according to claim 5, characterized in that: The distributed power source includes wind power generation, photovoltaic power generation, and gas turbine power generation; and constructing a first objective function based on the charging power curve and the discharging power curve includes: determining the wind power generation power, photovoltaic power generation power, and gas turbine power generation power at each moment in a set time period based on the charging power at each moment in the charging power curve; wherein the charging power at each moment in the charging power curve is the same as the total power generation power of the distributed power source; and the total power generation power of the distributed power source is equal to the sum of the wind power generation power, the photovoltaic power generation power, and the gas turbine power generation power; Calculating the charging cost of the electric vehicle based on the charging power curve, the discharging power curve, the charging electricity price curve within the set time period, and the discharging electricity price curve within the set time period; Calculating the wind power generation cost based on the wind power generation power, the wind turbine unit operation and maintenance coefficient, and the wind turbine unit management cost coefficient; Calculating the photovoltaic power generation cost based on the photovoltaic power generation power, the photovoltaic generator unit operation and maintenance coefficient, and the photovoltaic generator unit management cost coefficient; Calculating the power generation cost of the gas turbine based on the power generation capacity of the gas turbine, the power generation cost coefficient of the gas turbine, and the unit operation and maintenance coefficient of the gas turbine; A first objective function is constructed based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost.

7. The electric vehicle charging optimization device according to claim 6, characterized in that: The constructing of a first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost includes: constructing a first objective function based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, the gas turbine power generation cost, a first weight coefficient, a second weight coefficient, and a third weight coefficient; The first weight coefficient represents the weight coefficient of the wind power generation cost; the second weight coefficient represents the weight coefficient of the photovoltaic power generation cost; and the third weight coefficient represents the weight coefficient of the gas turbine power generation cost.

8. The electric vehicle charging optimization device according to claim 5, characterized in that: The constructing of a second objective function based on the distribution network load curve and the distribution network historical load average value curve includes: Calculating the distribution network load standard deviation within the set time period based on the distribution network load curve and the distribution network historical load average curve; Minimizing the standard deviation of the distribution network load to construct the second objective function; The distribution network historical load average value curve includes the distribution network historical load average value at each moment within the set time period.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the electric vehicle charging optimization method according to any one of claims 1 to 4 is implemented.

10. A machine-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the electric vehicle charging optimization method according to any one of claims 1 to 4 is implemented.

11. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the electric vehicle charging optimization method according to any one of claims 1 to 4 is implemented.

Images